diff --git a/ref_band b/ref_band new file mode 100644 index 0000000..35ff19f --- /dev/null +++ b/ref_band @@ -0,0 +1 @@ +1367â„« diff --git a/run_analysis.sh b/run_analysis.sh index 7435b6f..4ab90b5 100755 --- a/run_analysis.sh +++ b/run_analysis.sh @@ -2,4 +2,32 @@ # This script is meant to encapsulate all functions in proper order to produce # the analyses. Parameters that this script should control: Δt, σ type, input -# dataset(lightcurve directory), more? \ No newline at end of file +# dataset(lightcurve directory), more? + +mkdir -p analyses/tables + +#for analysis in analyses/* +for echo_curve in $lc_dir/* +do + # Grab and determine labels of analyses, skip if over the same band. + ref_band=$(basename $analysis|sed 's|\([^≺]*\)[_ ]≺[_ ][^≺_ ]*[_ ]{[^_ ]*}|\1|') + echo_band=$(basename $analysis|sed 's|[^≺]*[_ ]≺[_ ]\([^≺_ ]*\)[_ ]{[^_ ]*}|\1|') + if [[ $ref_band == $echo_band ]]; then continue; fi + err_str=$(basename $analysis|sed 's|[^≺]*[_ ]≺[_ ][^≺_ ]*[_ ]{[^_ ]*;\(σ∊[CLM][MFC]\)}|\1|') + +echo "Tabling PSD and time lags referred to ${ref_band} for $echo_band{${err_str}}." + + # Propagate tables into analyses/tables + echoPSD_tabfile=analyses/tables/PSD_${echo_band}_\{${err_str}\}.tab + refPSD_tabfile=analyses/tables/PSD_${ref_band}_\{${err_str}\}.tab + timelag_tabfile=analyses/tables/timelag_${ref_band}_≺_${echo_band}_\{${err_str}\}.tab + + # Output curves to temporary files using perl script, move tables to + # permanent location. This just assumes there are no conflicts. + scripts/extract_tables.pl $analysis > /dev/null + mv tmp.echoPSD $echoPSD_tabfile + mv tmp.refPSD $refPSD_tabfile + mv tmp.timelag $timelag_tabfile +done + +rm tmp.* \ No newline at end of file diff --git a/scripts/analyze_lightcurve.py b/scripts/analyze_lightcurve.py index 9934cce..56f67ed 100644 --- a/scripts/analyze_lightcurve.py +++ b/scripts/analyze_lightcurve.py @@ -7,10 +7,6 @@ import getopt sys.path.insert(1,"/usr/local/science/clag/") import clag -# psd1_output = open("psd1.tmp") -# psd2_output = open("psd2.tmp") -# lag_output = open("lag.tmp") - # For jupyter notebook # %pylab inline @@ -76,7 +72,7 @@ P1.logLikelihood(inpars) ## Now do the fitting and find the best fit psd values at the given frequency bins ## -psd1, psd1e = clag.optimize(P1, inpars) +ref_psd, ref_psd_err = clag.optimize(P1, inpars) @@ -85,7 +81,7 @@ psd1, psd1e = clag.optimize(P1, inpars) fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.) #loglog(fqd, 0.1*fqd**(-1.5), label='input psd') -#errorbar(fqd[1:-1], psd1[1:-1], yerr=psd1e[1:-1], fmt='o', ms=10, label='fit') +#errorbar(fqd[1:-1], ref_psd[1:-1], yerr=ref_psd_err[1:-1], fmt='o', ms=10, label='fit') # load second lightcurve @@ -110,7 +106,7 @@ lc2_strength_err = [lc2_table[i, 2] for i in index] P2 = clag.clag('psd', lc2_time, lc2_strength, lc2_strength_err, dt, fqL) -psd2, psd2e = clag.optimize(P2, inpars) +echo_psd, echo_psd_err = clag.optimize(P2, inpars) @@ -123,26 +119,18 @@ Cx = clag.clag('cxd', [list(i) for i in zip(lc1_time,lc1_time)], [list(i) for i in zip(lc1_strength,lc2_strength)], [list(i) for i in zip(lc1_strength_err,lc2_strength_err)], - dt, fqL, psd1, psd2) + dt, fqL, ref_psd, echo_psd) -inpars = np.concatenate( (0.3*(psd1*psd2)**0.5, psd1*0+1.) ) +inpars = np.concatenate( (0.3*(ref_psd*echo_psd)**0.5, ref_psd*0+1.) ) p, pe = clag.optimize(Cx, inpars) phi, phie = p[nfq:], pe[nfq:] lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd) cx, cxe = p[:nfq], pe[:nfq] -np.savetxt("lag.out",lag) +np.savetxt("freq.out",fqL.reshape((-1,len(fqL)))) +np.savetxt("ref_psd.out",[ref_psd,ref_psd_err]) +np.savetxt("echo_psd.out",[echo_psd,echo_psd_err]) +np.savetxt("crsspctrm.out",[cx,cxe]) +np.savetxt("timelag.out",[lag,lage]) - - - - - - -## plot ## - -#semilogx(fqd, fqd*0+1.0, label='input phase lag') -#ylim([0.8, 1.2]) -#errorbar(fqd[1:-1], phi[1:-1], yerr=phie[1:-1], fmt='o', ms=10, label='fit') - diff --git a/scripts/analyze_lightcurves.sh b/scripts/analyze_lightcurves.sh index 76a4316..e15bea0 100755 --- a/scripts/analyze_lightcurves.sh +++ b/scripts/analyze_lightcurves.sh @@ -152,7 +152,8 @@ do sed "s@%ROOTDIR%@$(pwd)@g"| sed "s@%OUTPUTFILE%@${outputfile_noUTF}@g" > $submitscript cd thor - qsub $(basename $submitscript) >> submissions + # qsub $(basename $submitscript) >> submissions + echo CURRENTLY NOT IMPLEMENTED!!! cd .. fi else @@ -160,7 +161,10 @@ do then echo -n " Analysis already exists; skipping." else - time bin/psdlag tmp.psdlagargs > $outputfile + #time bin/psdlag tmp.psdlagargs > $outputfile + time python scripts/analyze_lightcurve.py "$ref_curve $echo_curve" + scripts/extract_tables.pl + fi fi echo "" diff --git a/scripts/extract_tables.pl b/scripts/extract_tables.pl index e353577..68c07f8 100755 --- a/scripts/extract_tables.pl +++ b/scripts/extract_tables.pl @@ -23,54 +23,40 @@ if (!${^UTF8LOCALE}) { say encode($charset,"You are not using UTF-8 encoding. :("); } my $charset=$ENV{LANG}; -our $outputfilename=decode($charset,$ARGV[0]); -open $outputfile,'<',$outputfilename or die $!; -my $star_linenum_1=0; -my $star_linenum_2=0; -my $star_bytenum_1=0; -my $star_bytenum_2=0; -my $linenum=0; -while(<$outputfile>) { - $linenum++; - if ($_ =~ /^\*+$/) { - $star_linenum_1=$star_linenum_2; - $star_linenum_2 = $linenum; - $star_bytenum_1=$star_bytenum_2; - #$star_bytenum_2 = $outputfile.tell(); - $star_bytenum_2 = tell($outputfile); - } -} -if ($debug) { - say encode($charset,"Final set found between lines "), - $star_linenum_1, - " and ", - $star_linenum_2; +# This program attempts to fill function_bin with the tabulated PSDs +# and time lags. +our %function_bin = (); + +open $freq_file,'<',"freq.out" or die $!; +open $ref_psd_file,'<',"ref_psd.out" or die $!; +open $echo_psd_file,'<',"echo_psd.out" or die $!; +open $crsspctrm_file,'<',"crsspctrm.out" or die $!; +open $timelag_file,'<',"timelag.out" or die $!; + +=pod + + This section collects the various quantities. + +=cut + +@bin_bounds = split /\s/,<$freq_file>; +@ref_psd = split /\s/,<$ref_psd_file>; +@ref_psd_σ = split /\s/,<$ref_psd_file>; +@echo_psd = split /\s/,<$echo_psd_file>; +@echo_psd_σ = split /\s/,<$echo_psd_file>; +@crsspctrm_psd = split /\s/,<$crsspctrm_file>; +@crsspctrm_psd_σ = split /\s/,<$crsspctrm_file>; +@timelag = split /\s/,<$timelag_file>; +@timelag_σ = split /\s/,<$timelag_file>; - say encode($charset,"Final set found between bytes "), - $star_bytenum_1, - " and ", - $star_bytenum_2; -} -seek($outputfile,$star_bytenum_1,0); -<$outputfile>; -$bin_bounds_line = <$outputfile>; -#say $bin_bounds_line; -$bin_bounds_line =~ s/^#\s*(.*)$/$1/; -#$line =~ / -@bin_bounds = split /\s/,$bin_bounds_line; if ($debug) { print encode($charset,"Found bin boundaries: "); foreach (@bin_bounds) {print encode($charset,"$_ ");} say encode($charset," "); } - - - -# This captures the frequency bins. -our %function_bin = (); - +my $count = 0; my $upper_bound = 0; my $lower_bound = 0; foreach (@bin_bounds) { @@ -81,10 +67,29 @@ foreach (@bin_bounds) { my $Δ = ($upper_bound - $lower_bound)/2; #say ($μ,":",$Δ); # push(@freq_coords_mean,$μ); - # push(@freq_coords_err,$Δ); + # push(@freq_coords_σ,$Δ); $function_bin{$μ} = {"Δ" => $Δ}; + $function_bin{$μ}{"ref_PSD_μ"} = $ref_psd[$count]; + $function_bin{$μ}{"ref_PSD_σ"} = $ref_psd_σ[$count]; + $function_bin{$μ}{"echo_PSD_μ"} = $echo_psd[$count]; + $function_bin{$μ}{"echo_PSD_σ"} = $echo_psd_σ[$count]; + $function_bin{$μ}{"crsspctrm_μ"} = $crosssp_psd[$count]; + $function_bin{$μ}{"crsspctrm_σ"} = $crosssp_psd_σ[$count]; + # $function_bin{$μ}{"φdiff_μ"} = $μ; + # $function_bin{$μ}{"φdiff_σ"} = $σ; + # $μ = $μ/(2*PI*$μ); + # $σ = $σ/(2*PI*$μ); + $function_bin{$μ}{"timelag_μ"} = $timelag[$count]; + $function_bin{$μ}{"timelag_σ"} = $timelag_σ[$count]; + $count = $count + 1; } +close $freq_file; +close $ref_psd_file; +close $echo_psd_file; +close $crsspctrm_file; +close $timelag_file; + $numbins = keys %function_bin; say encode($charset,"$numbins frequency bins captured in output."); @@ -97,140 +102,6 @@ if($verbose) { } - - -=pod - - This section collects the various quantities. The mode counter -increments to designate the data being captured. - -=cut - -if ($verbose) { - say encode($charset,""); - say encode($charset, - " New Curve -- Reference Curve PSD"); - say encode($charset, - "──────────────────────────────────────────────────"); -} - -foreach ( sort { $a <=> $b } keys %function_bin ) { - my $μ = my $σ = <$outputfile>; - $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/; - $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/; -# if ($μ<0) { -# $μ = abs($μ); -# $function_bin{$_}{"φdiff_μ"} = PI; -# } - $function_bin{$_}{"ref_PSD_μ"} = $μ; - $function_bin{$_}{"ref_PSD_σ"} = $σ; - if ($verbose) { - say encode($charset, - "freq = " . - sprintf('%.3f',$_) . - ": μ = " . - sprintf('%10.3e',$μ) . - "; σ = " . - sprintf('%10.3e',$σ)); - } -} - -if ($verbose) { - say encode($charset,""); - say encode($charset, - " New Curve -- Reverberating Curve PSD"); - say encode($charset, - "──────────────────────────────────────────────────"); -} -foreach ( sort { $a <=> $b } keys %function_bin ) { - my $μ = my $σ = <$outputfile>; - $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/; - $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/; -# if ($μ<0) { -# $μ = abs($μ); -# $function_bin{$_}{"φdiff_μ"} = PI; -# } - $function_bin{$_}{"echo_PSD_μ"} = $μ; - $function_bin{$_}{"echo_PSD_σ"} = $σ; - if ($verbose) { - say encode($charset, - "freq = " . - sprintf('%.3f',$_) . - ": μ = " . - sprintf('%10.3e',$μ) . - "; σ = " . - sprintf('%10.3e',$σ)); - } -} - - -if ($verbose) { - say encode($charset,""); - say encode($charset, - " New Curve -- Cross Spectrum PSD"); - say encode($charset, - "──────────────────────────────────────────────────"); -} -foreach ( sort { $a <=> $b } keys %function_bin ) { - my $μ = my $σ = <$outputfile>; - $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/; - $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/; -# if ($μ<0) { -# $μ = abs($μ); -# $function_bin{$_}{"φdiff_μ"} = PI; -# } - $function_bin{$_}{"crsspctrm_PSD_μ"} = $μ; - $function_bin{$_}{"crsspctrm_PSD_σ"} = $σ; - if ($verbose) { - say encode($charset, - "freq = " . - sprintf('%.3f',$_) . - ": μ = " . - sprintf('%10.3e',$μ) . - "; σ = " . - sprintf('%10.3e',$σ)); - } -} - -if ($verbose) { - say encode($charset,""); - say encode($charset, - " New Curve -- Phase Difference φ"); - say encode($charset, - "──────────────────────────────────────────────────"); -} -foreach ( sort { $a <=> $b } keys %function_bin ) { - my $μ = my $σ = <$outputfile>; - $μ =~ s/^([\-\+e0-9\.]+)+\s+[\-\+e0-9\.]+\s*$/$1/; - $σ =~ s/^[\-\+e0-9\.]+\s+([\-\+e0-9\.]+)\s*$/$1/; - if (exists $function_bin{$_}{"φdiff_μ"}) { - $μ = $function_bin{$_}{"φdiff_μ"} + $μ; - } - $function_bin{$_}{"φdiff_μ"} = $μ; - $function_bin{$_}{"φdiff_σ"} = $σ; - if ($verbose) { - say encode($charset, - "freq = " . - sprintf('%.3f',$_) . - ": μ = " . - sprintf('%10.3e',$μ) . - "; σ = " . - sprintf('%10.3e',$σ)); - } - $μ = $μ/(2*PI*$_); - $σ = $σ/(2*PI*$_); - $function_bin{$_}{"timelag_μ"} = $μ; - $function_bin{$_}{"timelag_σ"} = $σ; - if ($verbose) { - say encode($charset, - " timelag: μ = " . - sprintf('%10.3e',$μ) . - "; σ = " . - sprintf('%10.3e',$σ)) . - "\n"; - } -} - say encode($charset,""); if($debug) { diff --git a/src/inc/alglib/alglibinternal.cpp b/src/inc/alglib/alglibinternal.cpp deleted file mode 100644 index 4e4bbf7..0000000 --- a/src/inc/alglib/alglibinternal.cpp +++ /dev/null @@ -1,15919 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "alglibinternal.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - - - - -static void tsort_tagsortfastirec(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Integer */ ae_vector* bufb, - ae_int_t i1, - ae_int_t i2, - ae_state *_state); -static void tsort_tagsortfastrrec(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Real */ ae_vector* bufb, - ae_int_t i1, - ae_int_t i2, - ae_state *_state); -static void tsort_tagsortfastrec(/* Real */ ae_vector* a, - /* Real */ ae_vector* bufa, - ae_int_t i1, - ae_int_t i2, - ae_state *_state); - - - - - - - - - - - - - - - - - - - - -static void hsschur_internalauxschur(ae_bool wantt, - ae_bool wantz, - ae_int_t n, - ae_int_t ilo, - ae_int_t ihi, - /* Real */ ae_matrix* h, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - ae_int_t iloz, - ae_int_t ihiz, - /* Real */ ae_matrix* z, - /* Real */ ae_vector* work, - /* Real */ ae_vector* workv3, - /* Real */ ae_vector* workc1, - /* Real */ ae_vector* works1, - ae_int_t* info, - ae_state *_state); -static void hsschur_aux2x2schur(double* a, - double* b, - double* c, - double* d, - double* rt1r, - double* rt1i, - double* rt2r, - double* rt2i, - double* cs, - double* sn, - ae_state *_state); -static double hsschur_extschursign(double a, double b, ae_state *_state); -static ae_int_t hsschur_extschursigntoone(double b, ae_state *_state); - - - - -static ae_bool safesolve_cbasicsolveandupdate(ae_complex alpha, - ae_complex beta, - double lnmax, - double bnorm, - double maxgrowth, - double* xnorm, - ae_complex* x, - ae_state *_state); - - -static ae_bool hpccores_hpcpreparechunkedgradientx(/* Real */ ae_vector* weights, - ae_int_t wcount, - /* Real */ ae_vector* hpcbuf, - ae_state *_state); -static ae_bool hpccores_hpcfinalizechunkedgradientx(/* Real */ ae_vector* buf, - ae_int_t wcount, - /* Real */ ae_vector* grad, - ae_state *_state); - - -static void xblas_xsum(/* Real */ ae_vector* w, - double mx, - ae_int_t n, - double* r, - double* rerr, - ae_state *_state); -static double xblas_xfastpow(double r, ae_int_t n, ae_state *_state); - - -static double linmin_ftol = 0.001; -static double linmin_xtol = 100*ae_machineepsilon; -static ae_int_t linmin_maxfev = 20; -static double linmin_stpmin = 1.0E-50; -static double linmin_defstpmax = 1.0E+50; -static double linmin_armijofactor = 1.3; -static void linmin_mcstep(double* stx, - double* fx, - double* dx, - double* sty, - double* fy, - double* dy, - double* stp, - double fp, - double dp, - ae_bool* brackt, - double stmin, - double stmax, - ae_int_t* info, - ae_state *_state); - - -static ae_bool ntheory_isprime(ae_int_t n, ae_state *_state); -static ae_int_t ntheory_modmul(ae_int_t a, - ae_int_t b, - ae_int_t n, - ae_state *_state); -static ae_int_t ntheory_modexp(ae_int_t a, - ae_int_t b, - ae_int_t n, - ae_state *_state); - - -static ae_int_t ftbase_coltype = 0; -static ae_int_t ftbase_coloperandscnt = 1; -static ae_int_t ftbase_coloperandsize = 2; -static ae_int_t ftbase_colmicrovectorsize = 3; -static ae_int_t ftbase_colparam0 = 4; -static ae_int_t ftbase_colparam1 = 5; -static ae_int_t ftbase_colparam2 = 6; -static ae_int_t ftbase_colparam3 = 7; -static ae_int_t ftbase_colscnt = 8; -static ae_int_t ftbase_opend = 0; -static ae_int_t ftbase_opcomplexreffft = 1; -static ae_int_t ftbase_opbluesteinsfft = 2; -static ae_int_t ftbase_opcomplexcodeletfft = 3; -static ae_int_t ftbase_opcomplexcodelettwfft = 4; -static ae_int_t ftbase_opradersfft = 5; -static ae_int_t ftbase_opcomplextranspose = -1; -static ae_int_t ftbase_opcomplexfftfactors = -2; -static ae_int_t ftbase_opstart = -3; -static ae_int_t ftbase_opjmp = -4; -static ae_int_t ftbase_opparallelcall = -5; -static ae_int_t ftbase_maxradix = 6; -static ae_int_t ftbase_updatetw = 16; -static ae_int_t ftbase_recursivethreshold = 1024; -static ae_int_t ftbase_raderthreshold = 19; -static ae_int_t ftbase_ftbasecodeletrecommended = 5; -static double ftbase_ftbaseinefficiencyfactor = 1.3; -static ae_int_t ftbase_ftbasemaxsmoothfactor = 5; -static void ftbase_ftdeterminespacerequirements(ae_int_t n, - ae_int_t* precrsize, - ae_int_t* precisize, - ae_state *_state); -static void ftbase_ftcomplexfftplanrec(ae_int_t n, - ae_int_t k, - ae_bool childplan, - ae_bool topmostplan, - ae_int_t* rowptr, - ae_int_t* bluesteinsize, - ae_int_t* precrptr, - ae_int_t* preciptr, - fasttransformplan* plan, - ae_state *_state); -static void ftbase_ftpushentry(fasttransformplan* plan, - ae_int_t* rowptr, - ae_int_t etype, - ae_int_t eopcnt, - ae_int_t eopsize, - ae_int_t emcvsize, - ae_int_t eparam0, - ae_state *_state); -static void ftbase_ftpushentry2(fasttransformplan* plan, - ae_int_t* rowptr, - ae_int_t etype, - ae_int_t eopcnt, - ae_int_t eopsize, - ae_int_t emcvsize, - ae_int_t eparam0, - ae_int_t eparam1, - ae_state *_state); -static void ftbase_ftpushentry4(fasttransformplan* plan, - ae_int_t* rowptr, - ae_int_t etype, - ae_int_t eopcnt, - ae_int_t eopsize, - ae_int_t emcvsize, - ae_int_t eparam0, - ae_int_t eparam1, - ae_int_t eparam2, - ae_int_t eparam3, - ae_state *_state); -static void ftbase_ftapplysubplan(fasttransformplan* plan, - ae_int_t subplan, - /* Real */ ae_vector* a, - ae_int_t abase, - ae_int_t aoffset, - /* Real */ ae_vector* buf, - ae_int_t repcnt, - ae_state *_state); -static void ftbase_ftapplycomplexreffft(/* Real */ ae_vector* a, - ae_int_t offs, - ae_int_t operandscnt, - ae_int_t operandsize, - ae_int_t microvectorsize, - /* Real */ ae_vector* buf, - ae_state *_state); -static void ftbase_ftapplycomplexcodeletfft(/* Real */ ae_vector* a, - ae_int_t offs, - ae_int_t operandscnt, - ae_int_t operandsize, - ae_int_t microvectorsize, - ae_state *_state); -static void ftbase_ftapplycomplexcodelettwfft(/* Real */ ae_vector* a, - ae_int_t offs, - ae_int_t operandscnt, - ae_int_t operandsize, - ae_int_t microvectorsize, - ae_state *_state); -static void ftbase_ftprecomputebluesteinsfft(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* precr, - ae_int_t offs, - ae_state *_state); -static void ftbase_ftbluesteinsfft(fasttransformplan* plan, - /* Real */ ae_vector* a, - ae_int_t abase, - ae_int_t aoffset, - ae_int_t operandscnt, - ae_int_t n, - ae_int_t m, - ae_int_t precoffs, - ae_int_t subplan, - /* Real */ ae_vector* bufa, - /* Real */ ae_vector* bufb, - /* Real */ ae_vector* bufc, - /* Real */ ae_vector* bufd, - ae_state *_state); -static void ftbase_ftprecomputeradersfft(ae_int_t n, - ae_int_t rq, - ae_int_t riq, - /* Real */ ae_vector* precr, - ae_int_t offs, - ae_state *_state); -static void ftbase_ftradersfft(fasttransformplan* plan, - /* Real */ ae_vector* a, - ae_int_t abase, - ae_int_t aoffset, - ae_int_t operandscnt, - ae_int_t n, - ae_int_t subplan, - ae_int_t rq, - ae_int_t riq, - ae_int_t precoffs, - /* Real */ ae_vector* buf, - ae_state *_state); -static void ftbase_ftfactorize(ae_int_t n, - ae_bool isroot, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state); -static ae_int_t ftbase_ftoptimisticestimate(ae_int_t n, ae_state *_state); -static void ftbase_ffttwcalc(/* Real */ ae_vector* a, - ae_int_t aoffset, - ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static void ftbase_internalcomplexlintranspose(/* Real */ ae_vector* a, - ae_int_t m, - ae_int_t n, - ae_int_t astart, - /* Real */ ae_vector* buf, - ae_state *_state); -static void ftbase_ffticltrec(/* Real */ ae_vector* a, - ae_int_t astart, - ae_int_t astride, - /* Real */ ae_vector* b, - ae_int_t bstart, - ae_int_t bstride, - ae_int_t m, - ae_int_t n, - ae_state *_state); -static void ftbase_fftirltrec(/* Real */ ae_vector* a, - ae_int_t astart, - ae_int_t astride, - /* Real */ ae_vector* b, - ae_int_t bstart, - ae_int_t bstride, - ae_int_t m, - ae_int_t n, - ae_state *_state); -static void ftbase_ftbasefindsmoothrec(ae_int_t n, - ae_int_t seed, - ae_int_t leastfactor, - ae_int_t* best, - ae_state *_state); - - - - - - - - - -/************************************************************************* -This function is used to set error flags during unit tests. When COND -parameter is True, FLAG variable is set to True. When COND is False, -FLAG is unchanged. - -The purpose of this function is to have single point where failures of -unit tests can be detected. - -This function returns value of COND. -*************************************************************************/ -ae_bool seterrorflag(ae_bool* flag, ae_bool cond, ae_state *_state) -{ - ae_bool result; - - - if( cond ) - { - *flag = ae_true; - } - result = cond; - return result; -} - - -/************************************************************************* -Internally calls SetErrorFlag() with condition: - - Abs(Val-RefVal)>Tol*Max(Abs(RefVal),S) - -This function is used to test relative error in Val against RefVal, with -relative error being replaced by absolute when scale of RefVal is less -than S. - -This function returns value of COND. -*************************************************************************/ -ae_bool seterrorflagdiff(ae_bool* flag, - double val, - double refval, - double tol, - double s, - ae_state *_state) -{ - ae_bool result; - - - result = seterrorflag(flag, ae_fp_greater(ae_fabs(val-refval, _state),tol*ae_maxreal(ae_fabs(refval, _state), s, _state)), _state); - return result; -} - - -/************************************************************************* -The function "touches" integer - it is used to avoid compiler messages -about unused variables (in rare cases when we do NOT want to remove these -variables). - - -- ALGLIB -- - Copyright 17.09.2012 by Bochkanov Sergey -*************************************************************************/ -void touchint(ae_int_t* a, ae_state *_state) -{ - - -} - - -/************************************************************************* -The function "touches" real - it is used to avoid compiler messages -about unused variables (in rare cases when we do NOT want to remove these -variables). - - -- ALGLIB -- - Copyright 17.09.2012 by Bochkanov Sergey -*************************************************************************/ -void touchreal(double* a, ae_state *_state) -{ - - -} - - -/************************************************************************* -The function convert integer value to real value. - - -- ALGLIB -- - Copyright 17.09.2012 by Bochkanov Sergey -*************************************************************************/ -double inttoreal(ae_int_t a, ae_state *_state) -{ - double result; - - - result = a; - return result; -} - - -/************************************************************************* -The function calculates binary logarithm. - -NOTE: it costs twice as much as Ln(x) - - -- ALGLIB -- - Copyright 17.09.2012 by Bochkanov Sergey -*************************************************************************/ -double log2(double x, ae_state *_state) -{ - double result; - - - result = ae_log(x, _state)/ae_log(2, _state); - return result; -} - - -/************************************************************************* -This function compares two numbers for approximate equality, with tolerance -to errors as large as max(|a|,|b|)*tol. - - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool approxequalrel(double a, double b, double tol, ae_state *_state) -{ - ae_bool result; - - - result = ae_fp_less_eq(ae_fabs(a-b, _state),ae_maxreal(ae_fabs(a, _state), ae_fabs(b, _state), _state)*tol); - return result; -} - - -/************************************************************************* -This function generates 1-dimensional general interpolation task with -moderate Lipshitz constant (close to 1.0) - -If N=1 then suborutine generates only one point at the middle of [A,B] - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void taskgenint1d(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - double h; - - ae_vector_clear(x); - ae_vector_clear(y); - - ae_assert(n>=1, "TaskGenInterpolationEqdist1D: N<1!", _state); - ae_vector_set_length(x, n, _state); - ae_vector_set_length(y, n, _state); - if( n>1 ) - { - x->ptr.p_double[0] = a; - y->ptr.p_double[0] = 2*ae_randomreal(_state)-1; - h = (b-a)/(n-1); - for(i=1; i<=n-1; i++) - { - if( i!=n-1 ) - { - x->ptr.p_double[i] = a+(i+0.2*(2*ae_randomreal(_state)-1))*h; - } - else - { - x->ptr.p_double[i] = b; - } - y->ptr.p_double[i] = y->ptr.p_double[i-1]+(2*ae_randomreal(_state)-1)*(x->ptr.p_double[i]-x->ptr.p_double[i-1]); - } - } - else - { - x->ptr.p_double[0] = 0.5*(a+b); - y->ptr.p_double[0] = 2*ae_randomreal(_state)-1; - } -} - - -/************************************************************************* -This function generates 1-dimensional equidistant interpolation task with -moderate Lipshitz constant (close to 1.0) - -If N=1 then suborutine generates only one point at the middle of [A,B] - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void taskgenint1dequidist(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - double h; - - ae_vector_clear(x); - ae_vector_clear(y); - - ae_assert(n>=1, "TaskGenInterpolationEqdist1D: N<1!", _state); - ae_vector_set_length(x, n, _state); - ae_vector_set_length(y, n, _state); - if( n>1 ) - { - x->ptr.p_double[0] = a; - y->ptr.p_double[0] = 2*ae_randomreal(_state)-1; - h = (b-a)/(n-1); - for(i=1; i<=n-1; i++) - { - x->ptr.p_double[i] = a+i*h; - y->ptr.p_double[i] = y->ptr.p_double[i-1]+(2*ae_randomreal(_state)-1)*h; - } - } - else - { - x->ptr.p_double[0] = 0.5*(a+b); - y->ptr.p_double[0] = 2*ae_randomreal(_state)-1; - } -} - - -/************************************************************************* -This function generates 1-dimensional Chebyshev-1 interpolation task with -moderate Lipshitz constant (close to 1.0) - -If N=1 then suborutine generates only one point at the middle of [A,B] - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void taskgenint1dcheb1(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(x); - ae_vector_clear(y); - - ae_assert(n>=1, "TaskGenInterpolation1DCheb1: N<1!", _state); - ae_vector_set_length(x, n, _state); - ae_vector_set_length(y, n, _state); - if( n>1 ) - { - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = 0.5*(b+a)+0.5*(b-a)*ae_cos(ae_pi*(2*i+1)/(2*n), _state); - if( i==0 ) - { - y->ptr.p_double[i] = 2*ae_randomreal(_state)-1; - } - else - { - y->ptr.p_double[i] = y->ptr.p_double[i-1]+(2*ae_randomreal(_state)-1)*(x->ptr.p_double[i]-x->ptr.p_double[i-1]); - } - } - } - else - { - x->ptr.p_double[0] = 0.5*(a+b); - y->ptr.p_double[0] = 2*ae_randomreal(_state)-1; - } -} - - -/************************************************************************* -This function generates 1-dimensional Chebyshev-2 interpolation task with -moderate Lipshitz constant (close to 1.0) - -If N=1 then suborutine generates only one point at the middle of [A,B] - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void taskgenint1dcheb2(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(x); - ae_vector_clear(y); - - ae_assert(n>=1, "TaskGenInterpolation1DCheb2: N<1!", _state); - ae_vector_set_length(x, n, _state); - ae_vector_set_length(y, n, _state); - if( n>1 ) - { - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = 0.5*(b+a)+0.5*(b-a)*ae_cos(ae_pi*i/(n-1), _state); - if( i==0 ) - { - y->ptr.p_double[i] = 2*ae_randomreal(_state)-1; - } - else - { - y->ptr.p_double[i] = y->ptr.p_double[i-1]+(2*ae_randomreal(_state)-1)*(x->ptr.p_double[i]-x->ptr.p_double[i-1]); - } - } - } - else - { - x->ptr.p_double[0] = 0.5*(a+b); - y->ptr.p_double[0] = 2*ae_randomreal(_state)-1; - } -} - - -/************************************************************************* -This function checks that all values from X[] are distinct. It does more -than just usual floating point comparison: -* first, it calculates max(X) and min(X) -* second, it maps X[] from [min,max] to [1,2] -* only at this stage actual comparison is done - -The meaning of such check is to ensure that all values are "distinct enough" -and will not cause interpolation subroutine to fail. - -NOTE: - X[] must be sorted by ascending (subroutine ASSERT's it) - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool aredistinct(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double a; - double b; - ae_int_t i; - ae_bool nonsorted; - ae_bool result; - - - ae_assert(n>=1, "APSERVAreDistinct: internal error (N<1)", _state); - if( n==1 ) - { - - /* - * everything is alright, it is up to caller to decide whether it - * can interpolate something with just one point - */ - result = ae_true; - return result; - } - a = x->ptr.p_double[0]; - b = x->ptr.p_double[0]; - nonsorted = ae_false; - for(i=1; i<=n-1; i++) - { - a = ae_minreal(a, x->ptr.p_double[i], _state); - b = ae_maxreal(b, x->ptr.p_double[i], _state); - nonsorted = nonsorted||ae_fp_greater_eq(x->ptr.p_double[i-1],x->ptr.p_double[i]); - } - ae_assert(!nonsorted, "APSERVAreDistinct: internal error (not sorted)", _state); - for(i=1; i<=n-1; i++) - { - if( ae_fp_eq((x->ptr.p_double[i]-a)/(b-a)+1,(x->ptr.p_double[i-1]-a)/(b-a)+1) ) - { - result = ae_false; - return result; - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that two boolean values are the same (both are True -or both are False). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool aresameboolean(ae_bool v1, ae_bool v2, ae_state *_state) -{ - ae_bool result; - - - result = (v1&&v2)||(!v1&&!v2); - return result; -} - - -/************************************************************************* -If Length(X)cntcntcnt0&&n>0 ) - { - if( x->rowscolsrows; - n2 = x->cols; - ae_swap_matrices(x, &oldx); - ae_matrix_set_length(x, m, n, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( iptr.pp_double[i][j] = oldx.ptr.pp_double[i][j]; - } - else - { - x->ptr.pp_double[i][j] = 0.0; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Resizes X and: -* preserves old contents of X -* fills new elements by zeros - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void imatrixresize(/* Integer */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix oldx; - ae_int_t i; - ae_int_t j; - ae_int_t m2; - ae_int_t n2; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init(&oldx, 0, 0, DT_INT, _state, ae_true); - - m2 = x->rows; - n2 = x->cols; - ae_swap_matrices(x, &oldx); - ae_matrix_set_length(x, m, n, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( iptr.pp_int[i][j] = oldx.ptr.pp_int[i][j]; - } - else - { - x->ptr.pp_int[i][j] = 0; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function checks that length(X) is at least N and first N values from -X[] are finite - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool isfinitevector(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteVector: internal error (N<0)", _state); - if( n==0 ) - { - result = ae_true; - return result; - } - if( x->cntptr.p_double[i], _state) ) - { - result = ae_false; - return result; - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that first N values from X[] are finite - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool isfinitecvector(/* Complex */ ae_vector* z, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteCVector: internal error (N<0)", _state); - for(i=0; i<=n-1; i++) - { - if( !ae_isfinite(z->ptr.p_complex[i].x, _state)||!ae_isfinite(z->ptr.p_complex[i].y, _state) ) - { - result = ae_false; - return result; - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that size of X is at least MxN and values from -X[0..M-1,0..N-1] are finite. - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool apservisfinitematrix(/* Real */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteMatrix: internal error (N<0)", _state); - ae_assert(m>=0, "APSERVIsFiniteMatrix: internal error (M<0)", _state); - if( m==0||n==0 ) - { - result = ae_true; - return result; - } - if( x->rowscolsptr.pp_double[i][j], _state) ) - { - result = ae_false; - return result; - } - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that all values from X[0..M-1,0..N-1] are finite - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool apservisfinitecmatrix(/* Complex */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteCMatrix: internal error (N<0)", _state); - ae_assert(m>=0, "APSERVIsFiniteCMatrix: internal error (M<0)", _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( !ae_isfinite(x->ptr.pp_complex[i][j].x, _state)||!ae_isfinite(x->ptr.pp_complex[i][j].y, _state) ) - { - result = ae_false; - return result; - } - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that size of X is at least NxN and all values from -upper/lower triangle of X[0..N-1,0..N-1] are finite - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool isfinitertrmatrix(/* Real */ ae_matrix* x, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j1; - ae_int_t j2; - ae_int_t j; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteRTRMatrix: internal error (N<0)", _state); - if( n==0 ) - { - result = ae_true; - return result; - } - if( x->rowscolsptr.pp_double[i][j], _state) ) - { - result = ae_false; - return result; - } - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that all values from upper/lower triangle of -X[0..N-1,0..N-1] are finite - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool apservisfinitectrmatrix(/* Complex */ ae_matrix* x, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j1; - ae_int_t j2; - ae_int_t j; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteCTRMatrix: internal error (N<0)", _state); - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - if( !ae_isfinite(x->ptr.pp_complex[i][j].x, _state)||!ae_isfinite(x->ptr.pp_complex[i][j].y, _state) ) - { - result = ae_false; - return result; - } - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function checks that all values from X[0..M-1,0..N-1] are finite or -NaN's. - - -- ALGLIB -- - Copyright 18.06.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool apservisfiniteornanmatrix(/* Real */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_bool result; - - - ae_assert(n>=0, "APSERVIsFiniteOrNaNMatrix: internal error (N<0)", _state); - ae_assert(m>=0, "APSERVIsFiniteOrNaNMatrix: internal error (M<0)", _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( !(ae_isfinite(x->ptr.pp_double[i][j], _state)||ae_isnan(x->ptr.pp_double[i][j], _state)) ) - { - result = ae_false; - return result; - } - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -Safe sqrt(x^2+y^2) - - -- ALGLIB -- - Copyright by Bochkanov Sergey -*************************************************************************/ -double safepythag2(double x, double y, ae_state *_state) -{ - double w; - double xabs; - double yabs; - double z; - double result; - - - xabs = ae_fabs(x, _state); - yabs = ae_fabs(y, _state); - w = ae_maxreal(xabs, yabs, _state); - z = ae_minreal(xabs, yabs, _state); - if( ae_fp_eq(z,0) ) - { - result = w; - } - else - { - result = w*ae_sqrt(1+ae_sqr(z/w, _state), _state); - } - return result; -} - - -/************************************************************************* -Safe sqrt(x^2+y^2) - - -- ALGLIB -- - Copyright by Bochkanov Sergey -*************************************************************************/ -double safepythag3(double x, double y, double z, ae_state *_state) -{ - double w; - double result; - - - w = ae_maxreal(ae_fabs(x, _state), ae_maxreal(ae_fabs(y, _state), ae_fabs(z, _state), _state), _state); - if( ae_fp_eq(w,0) ) - { - result = 0; - return result; - } - x = x/w; - y = y/w; - z = z/w; - result = w*ae_sqrt(ae_sqr(x, _state)+ae_sqr(y, _state)+ae_sqr(z, _state), _state); - return result; -} - - -/************************************************************************* -Safe division. - -This function attempts to calculate R=X/Y without overflow. - -It returns: -* +1, if abs(X/Y)>=MaxRealNumber or undefined - overflow-like situation - (no overlfow is generated, R is either NAN, PosINF, NegINF) -* 0, if MinRealNumber0 - (R contains result, may be zero) -* -1, if 00 - */ - if( ae_fp_eq(y,0) ) - { - result = 1; - if( ae_fp_eq(x,0) ) - { - *r = _state->v_nan; - } - if( ae_fp_greater(x,0) ) - { - *r = _state->v_posinf; - } - if( ae_fp_less(x,0) ) - { - *r = _state->v_neginf; - } - return result; - } - if( ae_fp_eq(x,0) ) - { - *r = 0; - result = 0; - return result; - } - - /* - * make Y>0 - */ - if( ae_fp_less(y,0) ) - { - x = -x; - y = -y; - } - - /* - * - */ - if( ae_fp_greater_eq(y,1) ) - { - *r = x/y; - if( ae_fp_less_eq(ae_fabs(*r, _state),ae_minrealnumber) ) - { - result = -1; - *r = 0; - } - else - { - result = 0; - } - } - else - { - if( ae_fp_greater_eq(ae_fabs(x, _state),ae_maxrealnumber*y) ) - { - if( ae_fp_greater(x,0) ) - { - *r = _state->v_posinf; - } - else - { - *r = _state->v_neginf; - } - result = 1; - } - else - { - *r = x/y; - result = 0; - } - } - return result; -} - - -/************************************************************************* -This function calculates "safe" min(X/Y,V) for positive finite X, Y, V. -No overflow is generated in any case. - - -- ALGLIB -- - Copyright by Bochkanov Sergey -*************************************************************************/ -double safeminposrv(double x, double y, double v, ae_state *_state) -{ - double r; - double result; - - - if( ae_fp_greater_eq(y,1) ) - { - - /* - * Y>=1, we can safely divide by Y - */ - r = x/y; - result = v; - if( ae_fp_greater(v,r) ) - { - result = r; - } - else - { - result = v; - } - } - else - { - - /* - * Y<1, we can safely multiply by Y - */ - if( ae_fp_less(x,v*y) ) - { - result = x/y; - } - else - { - result = v; - } - } - return result; -} - - -/************************************************************************* -This function makes periodic mapping of X to [A,B]. - -It accepts X, A, B (A>B). It returns T which lies in [A,B] and integer K, -such that X = T + K*(B-A). - -NOTES: -* K is represented as real value, although actually it is integer -* T is guaranteed to be in [A,B] -* T replaces X - - -- ALGLIB -- - Copyright by Bochkanov Sergey -*************************************************************************/ -void apperiodicmap(double* x, - double a, - double b, - double* k, - ae_state *_state) -{ - - *k = 0; - - ae_assert(ae_fp_less(a,b), "APPeriodicMap: internal error!", _state); - *k = ae_ifloor((*x-a)/(b-a), _state); - *x = *x-*k*(b-a); - while(ae_fp_less(*x,a)) - { - *x = *x+(b-a); - *k = *k-1; - } - while(ae_fp_greater(*x,b)) - { - *x = *x-(b-a); - *k = *k+1; - } - *x = ae_maxreal(*x, a, _state); - *x = ae_minreal(*x, b, _state); -} - - -/************************************************************************* -Returns random normal number using low-quality system-provided generator - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -double randomnormal(ae_state *_state) -{ - double u; - double v; - double s; - double result; - - - for(;;) - { - u = 2*ae_randomreal(_state)-1; - v = 2*ae_randomreal(_state)-1; - s = ae_sqr(u, _state)+ae_sqr(v, _state); - if( ae_fp_greater(s,0)&&ae_fp_less(s,1) ) - { - - /* - * two Sqrt's instead of one to - * avoid overflow when S is too small - */ - s = ae_sqrt(-2*ae_log(s, _state), _state)/ae_sqrt(s, _state); - result = u*s; - return result; - } - } - return result; -} - - -/************************************************************************* -Generates random unit vector using low-quality system-provided generator. -Reallocates array if its size is too short. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void randomunit(ae_int_t n, /* Real */ ae_vector* x, ae_state *_state) -{ - ae_int_t i; - double v; - double vv; - - - ae_assert(n>0, "RandomUnit: N<=0", _state); - if( x->cntptr.p_double[i] = vv; - v = v+vv*vv; - } - } - while(ae_fp_less_eq(v,0)); - v = 1/ae_sqrt(v, _state); - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = x->ptr.p_double[i]*v; - } -} - - -/************************************************************************* -This function is used to increment value of integer variable -*************************************************************************/ -void inc(ae_int_t* v, ae_state *_state) -{ - - - *v = *v+1; -} - - -/************************************************************************* -This function is used to decrement value of integer variable -*************************************************************************/ -void dec(ae_int_t* v, ae_state *_state) -{ - - - *v = *v-1; -} - - -/************************************************************************* -This function performs two operations: -1. decrements value of integer variable, if it is positive -2. explicitly sets variable to zero if it is non-positive -It is used by some algorithms to decrease value of internal counters. -*************************************************************************/ -void countdown(ae_int_t* v, ae_state *_state) -{ - - - if( *v>0 ) - { - *v = *v-1; - } - else - { - *v = 0; - } -} - - -/************************************************************************* -'bounds' value: maps X to [B1,B2] - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -double boundval(double x, double b1, double b2, ae_state *_state) -{ - double result; - - - if( ae_fp_less_eq(x,b1) ) - { - result = b1; - return result; - } - if( ae_fp_greater_eq(x,b2) ) - { - result = b2; - return result; - } - result = x; - return result; -} - - -/************************************************************************* -Allocation of serializer: complex value -*************************************************************************/ -void alloccomplex(ae_serializer* s, ae_complex v, ae_state *_state) -{ - - - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); -} - - -/************************************************************************* -Serialization: complex value -*************************************************************************/ -void serializecomplex(ae_serializer* s, ae_complex v, ae_state *_state) -{ - - - ae_serializer_serialize_double(s, v.x, _state); - ae_serializer_serialize_double(s, v.y, _state); -} - - -/************************************************************************* -Unserialization: complex value -*************************************************************************/ -ae_complex unserializecomplex(ae_serializer* s, ae_state *_state) -{ - ae_complex result; - - - ae_serializer_unserialize_double(s, &result.x, _state); - ae_serializer_unserialize_double(s, &result.y, _state); - return result; -} - - -/************************************************************************* -Allocation of serializer: real array -*************************************************************************/ -void allocrealarray(ae_serializer* s, - /* Real */ ae_vector* v, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - - - if( n<0 ) - { - n = v->cnt; - } - ae_serializer_alloc_entry(s); - for(i=0; i<=n-1; i++) - { - ae_serializer_alloc_entry(s); - } -} - - -/************************************************************************* -Serialization: complex value -*************************************************************************/ -void serializerealarray(ae_serializer* s, - /* Real */ ae_vector* v, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - - - if( n<0 ) - { - n = v->cnt; - } - ae_serializer_serialize_int(s, n, _state); - for(i=0; i<=n-1; i++) - { - ae_serializer_serialize_double(s, v->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -Unserialization: complex value -*************************************************************************/ -void unserializerealarray(ae_serializer* s, - /* Real */ ae_vector* v, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double t; - - ae_vector_clear(v); - - ae_serializer_unserialize_int(s, &n, _state); - if( n==0 ) - { - return; - } - ae_vector_set_length(v, n, _state); - for(i=0; i<=n-1; i++) - { - ae_serializer_unserialize_double(s, &t, _state); - v->ptr.p_double[i] = t; - } -} - - -/************************************************************************* -Allocation of serializer: Integer array -*************************************************************************/ -void allocintegerarray(ae_serializer* s, - /* Integer */ ae_vector* v, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - - - if( n<0 ) - { - n = v->cnt; - } - ae_serializer_alloc_entry(s); - for(i=0; i<=n-1; i++) - { - ae_serializer_alloc_entry(s); - } -} - - -/************************************************************************* -Serialization: Integer array -*************************************************************************/ -void serializeintegerarray(ae_serializer* s, - /* Integer */ ae_vector* v, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - - - if( n<0 ) - { - n = v->cnt; - } - ae_serializer_serialize_int(s, n, _state); - for(i=0; i<=n-1; i++) - { - ae_serializer_serialize_int(s, v->ptr.p_int[i], _state); - } -} - - -/************************************************************************* -Unserialization: complex value -*************************************************************************/ -void unserializeintegerarray(ae_serializer* s, - /* Integer */ ae_vector* v, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t t; - - ae_vector_clear(v); - - ae_serializer_unserialize_int(s, &n, _state); - if( n==0 ) - { - return; - } - ae_vector_set_length(v, n, _state); - for(i=0; i<=n-1; i++) - { - ae_serializer_unserialize_int(s, &t, _state); - v->ptr.p_int[i] = t; - } -} - - -/************************************************************************* -Allocation of serializer: real matrix -*************************************************************************/ -void allocrealmatrix(ae_serializer* s, - /* Real */ ae_matrix* v, - ae_int_t n0, - ae_int_t n1, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - if( n0<0 ) - { - n0 = v->rows; - } - if( n1<0 ) - { - n1 = v->cols; - } - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - for(i=0; i<=n0-1; i++) - { - for(j=0; j<=n1-1; j++) - { - ae_serializer_alloc_entry(s); - } - } -} - - -/************************************************************************* -Serialization: complex value -*************************************************************************/ -void serializerealmatrix(ae_serializer* s, - /* Real */ ae_matrix* v, - ae_int_t n0, - ae_int_t n1, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - if( n0<0 ) - { - n0 = v->rows; - } - if( n1<0 ) - { - n1 = v->cols; - } - ae_serializer_serialize_int(s, n0, _state); - ae_serializer_serialize_int(s, n1, _state); - for(i=0; i<=n0-1; i++) - { - for(j=0; j<=n1-1; j++) - { - ae_serializer_serialize_double(s, v->ptr.pp_double[i][j], _state); - } - } -} - - -/************************************************************************* -Unserialization: complex value -*************************************************************************/ -void unserializerealmatrix(ae_serializer* s, - /* Real */ ae_matrix* v, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n0; - ae_int_t n1; - double t; - - ae_matrix_clear(v); - - ae_serializer_unserialize_int(s, &n0, _state); - ae_serializer_unserialize_int(s, &n1, _state); - if( n0==0||n1==0 ) - { - return; - } - ae_matrix_set_length(v, n0, n1, _state); - for(i=0; i<=n0-1; i++) - { - for(j=0; j<=n1-1; j++) - { - ae_serializer_unserialize_double(s, &t, _state); - v->ptr.pp_double[i][j] = t; - } - } -} - - -/************************************************************************* -Copy integer array -*************************************************************************/ -void copyintegerarray(/* Integer */ ae_vector* src, - /* Integer */ ae_vector* dst, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(dst); - - if( src->cnt>0 ) - { - ae_vector_set_length(dst, src->cnt, _state); - for(i=0; i<=src->cnt-1; i++) - { - dst->ptr.p_int[i] = src->ptr.p_int[i]; - } - } -} - - -/************************************************************************* -Copy real array -*************************************************************************/ -void copyrealarray(/* Real */ ae_vector* src, - /* Real */ ae_vector* dst, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(dst); - - if( src->cnt>0 ) - { - ae_vector_set_length(dst, src->cnt, _state); - for(i=0; i<=src->cnt-1; i++) - { - dst->ptr.p_double[i] = src->ptr.p_double[i]; - } - } -} - - -/************************************************************************* -Copy real matrix -*************************************************************************/ -void copyrealmatrix(/* Real */ ae_matrix* src, - /* Real */ ae_matrix* dst, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(dst); - - if( src->rows>0&&src->cols>0 ) - { - ae_matrix_set_length(dst, src->rows, src->cols, _state); - for(i=0; i<=src->rows-1; i++) - { - for(j=0; j<=src->cols-1; j++) - { - dst->ptr.pp_double[i][j] = src->ptr.pp_double[i][j]; - } - } - } -} - - -/************************************************************************* -This function searches integer array. Elements in this array are actually -records, each NRec elements wide. Each record has unique header - NHeader -integer values, which identify it. Records are lexicographically sorted by -header. - -Records are identified by their index, not offset (offset = NRec*index). - -This function searches A (records with indices [I0,I1)) for a record with -header B. It returns index of this record (not offset!), or -1 on failure. - - -- ALGLIB -- - Copyright 28.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t recsearch(/* Integer */ ae_vector* a, - ae_int_t nrec, - ae_int_t nheader, - ae_int_t i0, - ae_int_t i1, - /* Integer */ ae_vector* b, - ae_state *_state) -{ - ae_int_t mididx; - ae_int_t cflag; - ae_int_t k; - ae_int_t offs; - ae_int_t result; - - - result = -1; - for(;;) - { - if( i0>=i1 ) - { - break; - } - mididx = (i0+i1)/2; - offs = nrec*mididx; - cflag = 0; - for(k=0; k<=nheader-1; k++) - { - if( a->ptr.p_int[offs+k]ptr.p_int[k] ) - { - cflag = -1; - break; - } - if( a->ptr.p_int[offs+k]>b->ptr.p_int[k] ) - { - cflag = 1; - break; - } - } - if( cflag==0 ) - { - result = mididx; - return result; - } - if( cflag<0 ) - { - i0 = mididx+1; - } - else - { - i1 = mididx; - } - } - return result; -} - - -/************************************************************************* -This function is used in parallel functions for recurrent division of large -task into two smaller tasks. - -It has following properties: -* it works only for TaskSize>=2 (assertion is thrown otherwise) -* for TaskSize=2, it returns Task0=1, Task1=1 -* in case TaskSize is odd, Task0=TaskSize-1, Task1=1 -* in case TaskSize is even, Task0 and Task1 are approximately TaskSize/2 - and both Task0 and Task1 are even, Task0>=Task1 - - -- ALGLIB -- - Copyright 07.04.2013 by Bochkanov Sergey -*************************************************************************/ -void splitlengtheven(ae_int_t tasksize, - ae_int_t* task0, - ae_int_t* task1, - ae_state *_state) -{ - - *task0 = 0; - *task1 = 0; - - ae_assert(tasksize>=2, "SplitLengthEven: TaskSize<2", _state); - if( tasksize==2 ) - { - *task0 = 1; - *task1 = 1; - return; - } - if( tasksize%2==0 ) - { - - /* - * Even division - */ - *task0 = tasksize/2; - *task1 = tasksize/2; - if( *task0%2!=0 ) - { - *task0 = *task0+1; - *task1 = *task1-1; - } - } - else - { - - /* - * Odd task size, split trailing odd part from it. - */ - *task0 = tasksize-1; - *task1 = 1; - } - ae_assert(*task0>=1, "SplitLengthEven: internal error", _state); - ae_assert(*task1>=1, "SplitLengthEven: internal error", _state); -} - - -/************************************************************************* -This function is used in parallel functions for recurrent division of large -task into two smaller tasks. - -It has following properties: -* it works only for TaskSize>=2 and ChunkSize>=2 - (assertion is thrown otherwise) -* Task0+Task1=TaskSize, Task0>0, Task1>0 -* Task0 and Task1 are close to each other -* in case TaskSize>ChunkSize, Task0 is always divisible by ChunkSize - - -- ALGLIB -- - Copyright 07.04.2013 by Bochkanov Sergey -*************************************************************************/ -void splitlength(ae_int_t tasksize, - ae_int_t chunksize, - ae_int_t* task0, - ae_int_t* task1, - ae_state *_state) -{ - - *task0 = 0; - *task1 = 0; - - ae_assert(chunksize>=2, "SplitLength: ChunkSize<2", _state); - ae_assert(tasksize>=2, "SplitLength: TaskSize<2", _state); - *task0 = tasksize/2; - if( *task0>chunksize&&*task0%chunksize!=0 ) - { - *task0 = *task0-*task0%chunksize; - } - *task1 = tasksize-(*task0); - ae_assert(*task0>=1, "SplitLength: internal error", _state); - ae_assert(*task1>=1, "SplitLength: internal error", _state); -} - - -ae_bool _apbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - apbuffers *p = (apbuffers*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->ia0, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ia1, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ia2, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ia3, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ra0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ra1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ra2, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ra3, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _apbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - apbuffers *dst = (apbuffers*)_dst; - apbuffers *src = (apbuffers*)_src; - if( !ae_vector_init_copy(&dst->ia0, &src->ia0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ia1, &src->ia1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ia2, &src->ia2, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ia3, &src->ia3, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ra0, &src->ra0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ra1, &src->ra1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ra2, &src->ra2, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ra3, &src->ra3, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _apbuffers_clear(void* _p) -{ - apbuffers *p = (apbuffers*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->ia0); - ae_vector_clear(&p->ia1); - ae_vector_clear(&p->ia2); - ae_vector_clear(&p->ia3); - ae_vector_clear(&p->ra0); - ae_vector_clear(&p->ra1); - ae_vector_clear(&p->ra2); - ae_vector_clear(&p->ra3); -} - - -void _apbuffers_destroy(void* _p) -{ - apbuffers *p = (apbuffers*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->ia0); - ae_vector_destroy(&p->ia1); - ae_vector_destroy(&p->ia2); - ae_vector_destroy(&p->ia3); - ae_vector_destroy(&p->ra0); - ae_vector_destroy(&p->ra1); - ae_vector_destroy(&p->ra2); - ae_vector_destroy(&p->ra3); -} - - -ae_bool _sboolean_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sboolean *p = (sboolean*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _sboolean_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sboolean *dst = (sboolean*)_dst; - sboolean *src = (sboolean*)_src; - dst->val = src->val; - return ae_true; -} - - -void _sboolean_clear(void* _p) -{ - sboolean *p = (sboolean*)_p; - ae_touch_ptr((void*)p); -} - - -void _sboolean_destroy(void* _p) -{ - sboolean *p = (sboolean*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _sbooleanarray_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sbooleanarray *p = (sbooleanarray*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->val, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _sbooleanarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sbooleanarray *dst = (sbooleanarray*)_dst; - sbooleanarray *src = (sbooleanarray*)_src; - if( !ae_vector_init_copy(&dst->val, &src->val, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _sbooleanarray_clear(void* _p) -{ - sbooleanarray *p = (sbooleanarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->val); -} - - -void _sbooleanarray_destroy(void* _p) -{ - sbooleanarray *p = (sbooleanarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->val); -} - - -ae_bool _sinteger_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sinteger *p = (sinteger*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _sinteger_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sinteger *dst = (sinteger*)_dst; - sinteger *src = (sinteger*)_src; - dst->val = src->val; - return ae_true; -} - - -void _sinteger_clear(void* _p) -{ - sinteger *p = (sinteger*)_p; - ae_touch_ptr((void*)p); -} - - -void _sinteger_destroy(void* _p) -{ - sinteger *p = (sinteger*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _sintegerarray_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sintegerarray *p = (sintegerarray*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->val, 0, DT_INT, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _sintegerarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sintegerarray *dst = (sintegerarray*)_dst; - sintegerarray *src = (sintegerarray*)_src; - if( !ae_vector_init_copy(&dst->val, &src->val, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _sintegerarray_clear(void* _p) -{ - sintegerarray *p = (sintegerarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->val); -} - - -void _sintegerarray_destroy(void* _p) -{ - sintegerarray *p = (sintegerarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->val); -} - - -ae_bool _sreal_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sreal *p = (sreal*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _sreal_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sreal *dst = (sreal*)_dst; - sreal *src = (sreal*)_src; - dst->val = src->val; - return ae_true; -} - - -void _sreal_clear(void* _p) -{ - sreal *p = (sreal*)_p; - ae_touch_ptr((void*)p); -} - - -void _sreal_destroy(void* _p) -{ - sreal *p = (sreal*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _srealarray_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - srealarray *p = (srealarray*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->val, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _srealarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - srealarray *dst = (srealarray*)_dst; - srealarray *src = (srealarray*)_src; - if( !ae_vector_init_copy(&dst->val, &src->val, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _srealarray_clear(void* _p) -{ - srealarray *p = (srealarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->val); -} - - -void _srealarray_destroy(void* _p) -{ - srealarray *p = (srealarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->val); -} - - -ae_bool _scomplex_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - scomplex *p = (scomplex*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _scomplex_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - scomplex *dst = (scomplex*)_dst; - scomplex *src = (scomplex*)_src; - dst->val = src->val; - return ae_true; -} - - -void _scomplex_clear(void* _p) -{ - scomplex *p = (scomplex*)_p; - ae_touch_ptr((void*)p); -} - - -void _scomplex_destroy(void* _p) -{ - scomplex *p = (scomplex*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _scomplexarray_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - scomplexarray *p = (scomplexarray*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->val, 0, DT_COMPLEX, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _scomplexarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - scomplexarray *dst = (scomplexarray*)_dst; - scomplexarray *src = (scomplexarray*)_src; - if( !ae_vector_init_copy(&dst->val, &src->val, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _scomplexarray_clear(void* _p) -{ - scomplexarray *p = (scomplexarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->val); -} - - -void _scomplexarray_destroy(void* _p) -{ - scomplexarray *p = (scomplexarray*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->val); -} - - - - -ae_int_t getrdfserializationcode(ae_state *_state) -{ - ae_int_t result; - - - result = 1; - return result; -} - - -ae_int_t getkdtreeserializationcode(ae_state *_state) -{ - ae_int_t result; - - - result = 2; - return result; -} - - -ae_int_t getmlpserializationcode(ae_state *_state) -{ - ae_int_t result; - - - result = 3; - return result; -} - - -ae_int_t getmlpeserializationcode(ae_state *_state) -{ - ae_int_t result; - - - result = 4; - return result; -} - - -ae_int_t getrbfserializationcode(ae_state *_state) -{ - ae_int_t result; - - - result = 5; - return result; -} - - - - -/************************************************************************* -This function sorts array of real keys by ascending. - -Its results are: -* sorted array A -* permutation tables P1, P2 - -Algorithm outputs permutation tables using two formats: -* as usual permutation of [0..N-1]. If P1[i]=j, then sorted A[i] contains - value which was moved there from J-th position. -* as a sequence of pairwise permutations. Sorted A[] may be obtained by - swaping A[i] and A[P2[i]] for all i from 0 to N-1. - -INPUT PARAMETERS: - A - unsorted array - N - array size - -OUPUT PARAMETERS: - A - sorted array - P1, P2 - permutation tables, array[N] - -NOTES: - this function assumes that A[] is finite; it doesn't checks that - condition. All other conditions (size of input arrays, etc.) are not - checked too. - - -- ALGLIB -- - Copyright 14.05.2008 by Bochkanov Sergey -*************************************************************************/ -void tagsort(/* Real */ ae_vector* a, - ae_int_t n, - /* Integer */ ae_vector* p1, - /* Integer */ ae_vector* p2, - ae_state *_state) -{ - ae_frame _frame_block; - apbuffers buf; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(p1); - ae_vector_clear(p2); - _apbuffers_init(&buf, _state, ae_true); - - tagsortbuf(a, n, p1, p2, &buf, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Buffered variant of TagSort, which accepts preallocated output arrays as -well as special structure for buffered allocations. If arrays are too -short, they are reallocated. If they are large enough, no memory -allocation is done. - -It is intended to be used in the performance-critical parts of code, where -additional allocations can lead to severe performance degradation - - -- ALGLIB -- - Copyright 14.05.2008 by Bochkanov Sergey -*************************************************************************/ -void tagsortbuf(/* Real */ ae_vector* a, - ae_int_t n, - /* Integer */ ae_vector* p1, - /* Integer */ ae_vector* p2, - apbuffers* buf, - ae_state *_state) -{ - ae_int_t i; - ae_int_t lv; - ae_int_t lp; - ae_int_t rv; - ae_int_t rp; - - - - /* - * Special cases - */ - if( n<=0 ) - { - return; - } - if( n==1 ) - { - ivectorsetlengthatleast(p1, 1, _state); - ivectorsetlengthatleast(p2, 1, _state); - p1->ptr.p_int[0] = 0; - p2->ptr.p_int[0] = 0; - return; - } - - /* - * General case, N>1: prepare permutations table P1 - */ - ivectorsetlengthatleast(p1, n, _state); - for(i=0; i<=n-1; i++) - { - p1->ptr.p_int[i] = i; - } - - /* - * General case, N>1: sort, update P1 - */ - rvectorsetlengthatleast(&buf->ra0, n, _state); - ivectorsetlengthatleast(&buf->ia0, n, _state); - tagsortfasti(a, p1, &buf->ra0, &buf->ia0, n, _state); - - /* - * General case, N>1: fill permutations table P2 - * - * To fill P2 we maintain two arrays: - * * PV (Buf.IA0), Position(Value). PV[i] contains position of I-th key at the moment - * * VP (Buf.IA1), Value(Position). VP[i] contains key which has position I at the moment - * - * At each step we making permutation of two items: - * Left, which is given by position/value pair LP/LV - * and Right, which is given by RP/RV - * and updating PV[] and VP[] correspondingly. - */ - ivectorsetlengthatleast(&buf->ia0, n, _state); - ivectorsetlengthatleast(&buf->ia1, n, _state); - ivectorsetlengthatleast(p2, n, _state); - for(i=0; i<=n-1; i++) - { - buf->ia0.ptr.p_int[i] = i; - buf->ia1.ptr.p_int[i] = i; - } - for(i=0; i<=n-1; i++) - { - - /* - * calculate LP, LV, RP, RV - */ - lp = i; - lv = buf->ia1.ptr.p_int[lp]; - rv = p1->ptr.p_int[i]; - rp = buf->ia0.ptr.p_int[rv]; - - /* - * Fill P2 - */ - p2->ptr.p_int[i] = rp; - - /* - * update PV and VP - */ - buf->ia1.ptr.p_int[lp] = rv; - buf->ia1.ptr.p_int[rp] = lv; - buf->ia0.ptr.p_int[lv] = rp; - buf->ia0.ptr.p_int[rv] = lp; - } -} - - -/************************************************************************* -Same as TagSort, but optimized for real keys and integer labels. - -A is sorted, and same permutations are applied to B. - -NOTES: -1. this function assumes that A[] is finite; it doesn't checks that - condition. All other conditions (size of input arrays, etc.) are not - checked too. -2. this function uses two buffers, BufA and BufB, each is N elements large. - They may be preallocated (which will save some time) or not, in which - case function will automatically allocate memory. - - -- ALGLIB -- - Copyright 11.12.2008 by Bochkanov Sergey -*************************************************************************/ -void tagsortfasti(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Integer */ ae_vector* bufb, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_bool isascending; - ae_bool isdescending; - double tmpr; - ae_int_t tmpi; - - - - /* - * Special case - */ - if( n<=1 ) - { - return; - } - - /* - * Test for already sorted set - */ - isascending = ae_true; - isdescending = ae_true; - for(i=1; i<=n-1; i++) - { - isascending = isascending&&a->ptr.p_double[i]>=a->ptr.p_double[i-1]; - isdescending = isdescending&&a->ptr.p_double[i]<=a->ptr.p_double[i-1]; - } - if( isascending ) - { - return; - } - if( isdescending ) - { - for(i=0; i<=n-1; i++) - { - j = n-1-i; - if( j<=i ) - { - break; - } - tmpr = a->ptr.p_double[i]; - a->ptr.p_double[i] = a->ptr.p_double[j]; - a->ptr.p_double[j] = tmpr; - tmpi = b->ptr.p_int[i]; - b->ptr.p_int[i] = b->ptr.p_int[j]; - b->ptr.p_int[j] = tmpi; - } - return; - } - - /* - * General case - */ - if( bufa->cntcntptr.p_double[i]>=a->ptr.p_double[i-1]; - isdescending = isdescending&&a->ptr.p_double[i]<=a->ptr.p_double[i-1]; - } - if( isascending ) - { - return; - } - if( isdescending ) - { - for(i=0; i<=n-1; i++) - { - j = n-1-i; - if( j<=i ) - { - break; - } - tmpr = a->ptr.p_double[i]; - a->ptr.p_double[i] = a->ptr.p_double[j]; - a->ptr.p_double[j] = tmpr; - tmpr = b->ptr.p_double[i]; - b->ptr.p_double[i] = b->ptr.p_double[j]; - b->ptr.p_double[j] = tmpr; - } - return; - } - - /* - * General case - */ - if( bufa->cntcntptr.p_double[i]>=a->ptr.p_double[i-1]; - isdescending = isdescending&&a->ptr.p_double[i]<=a->ptr.p_double[i-1]; - } - if( isascending ) - { - return; - } - if( isdescending ) - { - for(i=0; i<=n-1; i++) - { - j = n-1-i; - if( j<=i ) - { - break; - } - tmpr = a->ptr.p_double[i]; - a->ptr.p_double[i] = a->ptr.p_double[j]; - a->ptr.p_double[j] = tmpr; - } - return; - } - - /* - * General case - */ - if( bufa->cnt1: sort, update B - */ - i = 2; - do - { - t = i; - while(t!=1) - { - k = t/2; - if( a->ptr.p_int[offset+k-1]>=a->ptr.p_int[offset+t-1] ) - { - t = 1; - } - else - { - tmp = a->ptr.p_int[offset+k-1]; - a->ptr.p_int[offset+k-1] = a->ptr.p_int[offset+t-1]; - a->ptr.p_int[offset+t-1] = tmp; - tmpr = b->ptr.p_double[offset+k-1]; - b->ptr.p_double[offset+k-1] = b->ptr.p_double[offset+t-1]; - b->ptr.p_double[offset+t-1] = tmpr; - t = k; - } - } - i = i+1; - } - while(i<=n); - i = n-1; - do - { - tmp = a->ptr.p_int[offset+i]; - a->ptr.p_int[offset+i] = a->ptr.p_int[offset+0]; - a->ptr.p_int[offset+0] = tmp; - tmpr = b->ptr.p_double[offset+i]; - b->ptr.p_double[offset+i] = b->ptr.p_double[offset+0]; - b->ptr.p_double[offset+0] = tmpr; - t = 1; - while(t!=0) - { - k = 2*t; - if( k>i ) - { - t = 0; - } - else - { - if( kptr.p_int[offset+k]>a->ptr.p_int[offset+k-1] ) - { - k = k+1; - } - } - if( a->ptr.p_int[offset+t-1]>=a->ptr.p_int[offset+k-1] ) - { - t = 0; - } - else - { - tmp = a->ptr.p_int[offset+k-1]; - a->ptr.p_int[offset+k-1] = a->ptr.p_int[offset+t-1]; - a->ptr.p_int[offset+t-1] = tmp; - tmpr = b->ptr.p_double[offset+k-1]; - b->ptr.p_double[offset+k-1] = b->ptr.p_double[offset+t-1]; - b->ptr.p_double[offset+t-1] = tmpr; - t = k; - } - } - } - i = i-1; - } - while(i>=1); -} - - -/************************************************************************* -Heap operations: adds element to the heap - -PARAMETERS: - A - heap itself, must be at least array[0..N] - B - array of integer tags, which are updated according to - permutations in the heap - N - size of the heap (without new element). - updated on output - VA - value of the element being added - VB - value of the tag - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void tagheappushi(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t* n, - double va, - ae_int_t vb, - ae_state *_state) -{ - ae_int_t j; - ae_int_t k; - double v; - - - if( *n<0 ) - { - return; - } - - /* - * N=0 is a special case - */ - if( *n==0 ) - { - a->ptr.p_double[0] = va; - b->ptr.p_int[0] = vb; - *n = *n+1; - return; - } - - /* - * add current point to the heap - * (add to the bottom, then move up) - * - * we don't write point to the heap - * until its final position is determined - * (it allow us to reduce number of array access operations) - */ - j = *n; - *n = *n+1; - while(j>0) - { - k = (j-1)/2; - v = a->ptr.p_double[k]; - if( ae_fp_less(v,va) ) - { - - /* - * swap with higher element - */ - a->ptr.p_double[j] = v; - b->ptr.p_int[j] = b->ptr.p_int[k]; - j = k; - } - else - { - - /* - * element in its place. terminate. - */ - break; - } - } - a->ptr.p_double[j] = va; - b->ptr.p_int[j] = vb; -} - - -/************************************************************************* -Heap operations: replaces top element with new element -(which is moved down) - -PARAMETERS: - A - heap itself, must be at least array[0..N-1] - B - array of integer tags, which are updated according to - permutations in the heap - N - size of the heap - VA - value of the element which replaces top element - VB - value of the tag - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void tagheapreplacetopi(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t n, - double va, - ae_int_t vb, - ae_state *_state) -{ - ae_int_t j; - ae_int_t k1; - ae_int_t k2; - double v; - double v1; - double v2; - - - if( n<1 ) - { - return; - } - - /* - * N=1 is a special case - */ - if( n==1 ) - { - a->ptr.p_double[0] = va; - b->ptr.p_int[0] = vb; - return; - } - - /* - * move down through heap: - * * J - current element - * * K1 - first child (always exists) - * * K2 - second child (may not exists) - * - * we don't write point to the heap - * until its final position is determined - * (it allow us to reduce number of array access operations) - */ - j = 0; - k1 = 1; - k2 = 2; - while(k1=n ) - { - - /* - * only one child. - * - * swap and terminate (because this child - * have no siblings due to heap structure) - */ - v = a->ptr.p_double[k1]; - if( ae_fp_greater(v,va) ) - { - a->ptr.p_double[j] = v; - b->ptr.p_int[j] = b->ptr.p_int[k1]; - j = k1; - } - break; - } - else - { - - /* - * two childs - */ - v1 = a->ptr.p_double[k1]; - v2 = a->ptr.p_double[k2]; - if( ae_fp_greater(v1,v2) ) - { - if( ae_fp_less(va,v1) ) - { - a->ptr.p_double[j] = v1; - b->ptr.p_int[j] = b->ptr.p_int[k1]; - j = k1; - } - else - { - break; - } - } - else - { - if( ae_fp_less(va,v2) ) - { - a->ptr.p_double[j] = v2; - b->ptr.p_int[j] = b->ptr.p_int[k2]; - j = k2; - } - else - { - break; - } - } - k1 = 2*j+1; - k2 = 2*j+2; - } - } - a->ptr.p_double[j] = va; - b->ptr.p_int[j] = vb; -} - - -/************************************************************************* -Heap operations: pops top element from the heap - -PARAMETERS: - A - heap itself, must be at least array[0..N-1] - B - array of integer tags, which are updated according to - permutations in the heap - N - size of the heap, N>=1 - -On output top element is moved to A[N-1], B[N-1], heap is reordered, N is -decreased by 1. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void tagheappopi(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t* n, - ae_state *_state) -{ - double va; - ae_int_t vb; - - - if( *n<1 ) - { - return; - } - - /* - * N=1 is a special case - */ - if( *n==1 ) - { - *n = 0; - return; - } - - /* - * swap top element and last element, - * then reorder heap - */ - va = a->ptr.p_double[*n-1]; - vb = b->ptr.p_int[*n-1]; - a->ptr.p_double[*n-1] = a->ptr.p_double[0]; - b->ptr.p_int[*n-1] = b->ptr.p_int[0]; - *n = *n-1; - tagheapreplacetopi(a, b, *n, va, vb, _state); -} - - -/************************************************************************* -Search first element less than T in sorted array. - -PARAMETERS: - A - sorted array by ascending from 0 to N-1 - N - number of elements in array - T - the desired element - -RESULT: - The very first element's index, which isn't less than T. -In the case when there aren't such elements, returns N. -*************************************************************************/ -ae_int_t lowerbound(/* Real */ ae_vector* a, - ae_int_t n, - double t, - ae_state *_state) -{ - ae_int_t l; - ae_int_t half; - ae_int_t first; - ae_int_t middle; - ae_int_t result; - - - l = n; - first = 0; - while(l>0) - { - half = l/2; - middle = first+half; - if( ae_fp_less(a->ptr.p_double[middle],t) ) - { - first = middle+1; - l = l-half-1; - } - else - { - l = half; - } - } - result = first; - return result; -} - - -/************************************************************************* -Search first element more than T in sorted array. - -PARAMETERS: - A - sorted array by ascending from 0 to N-1 - N - number of elements in array - T - the desired element - - RESULT: - The very first element's index, which more than T. -In the case when there aren't such elements, returns N. -*************************************************************************/ -ae_int_t upperbound(/* Real */ ae_vector* a, - ae_int_t n, - double t, - ae_state *_state) -{ - ae_int_t l; - ae_int_t half; - ae_int_t first; - ae_int_t middle; - ae_int_t result; - - - l = n; - first = 0; - while(l>0) - { - half = l/2; - middle = first+half; - if( ae_fp_less(t,a->ptr.p_double[middle]) ) - { - l = half; - } - else - { - first = middle+1; - l = l-half-1; - } - } - result = first; - return result; -} - - -/************************************************************************* -Internal TagSortFastI: sorts A[I1...I2] (both bounds are included), -applies same permutations to B. - - -- ALGLIB -- - Copyright 06.09.2010 by Bochkanov Sergey -*************************************************************************/ -static void tsort_tagsortfastirec(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Integer */ ae_vector* bufb, - ae_int_t i1, - ae_int_t i2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t cntless; - ae_int_t cnteq; - ae_int_t cntgreater; - double tmpr; - ae_int_t tmpi; - double v0; - double v1; - double v2; - double vp; - - - - /* - * Fast exit - */ - if( i2<=i1 ) - { - return; - } - - /* - * Non-recursive sort for small arrays - */ - if( i2-i1<=16 ) - { - for(j=i1+1; j<=i2; j++) - { - - /* - * Search elements [I1..J-1] for place to insert Jth element. - * - * This code stops immediately if we can leave A[J] at J-th position - * (all elements have same value of A[J] larger than any of them) - */ - tmpr = a->ptr.p_double[j]; - tmpi = j; - for(k=j-1; k>=i1; k--) - { - if( a->ptr.p_double[k]<=tmpr ) - { - break; - } - tmpi = k; - } - k = tmpi; - - /* - * Insert Jth element into Kth position - */ - if( k!=j ) - { - tmpr = a->ptr.p_double[j]; - tmpi = b->ptr.p_int[j]; - for(i=j-1; i>=k; i--) - { - a->ptr.p_double[i+1] = a->ptr.p_double[i]; - b->ptr.p_int[i+1] = b->ptr.p_int[i]; - } - a->ptr.p_double[k] = tmpr; - b->ptr.p_int[k] = tmpi; - } - } - return; - } - - /* - * Quicksort: choose pivot - * Here we assume that I2-I1>=2 - */ - v0 = a->ptr.p_double[i1]; - v1 = a->ptr.p_double[i1+(i2-i1)/2]; - v2 = a->ptr.p_double[i2]; - if( v0>v1 ) - { - tmpr = v1; - v1 = v0; - v0 = tmpr; - } - if( v1>v2 ) - { - tmpr = v2; - v2 = v1; - v1 = tmpr; - } - if( v0>v1 ) - { - tmpr = v1; - v1 = v0; - v0 = tmpr; - } - vp = v1; - - /* - * now pass through A/B and: - * * move elements that are LESS than VP to the left of A/B - * * move elements that are EQUAL to VP to the right of BufA/BufB (in the reverse order) - * * move elements that are GREATER than VP to the left of BufA/BufB (in the normal order - * * move elements from the tail of BufA/BufB to the middle of A/B (restoring normal order) - * * move elements from the left of BufA/BufB to the end of A/B - */ - cntless = 0; - cnteq = 0; - cntgreater = 0; - for(i=i1; i<=i2; i++) - { - v0 = a->ptr.p_double[i]; - if( v0ptr.p_double[k] = v0; - b->ptr.p_int[k] = b->ptr.p_int[i]; - } - cntless = cntless+1; - continue; - } - if( v0==vp ) - { - - /* - * EQUAL - */ - k = i2-cnteq; - bufa->ptr.p_double[k] = v0; - bufb->ptr.p_int[k] = b->ptr.p_int[i]; - cnteq = cnteq+1; - continue; - } - - /* - * GREATER - */ - k = i1+cntgreater; - bufa->ptr.p_double[k] = v0; - bufb->ptr.p_int[k] = b->ptr.p_int[i]; - cntgreater = cntgreater+1; - } - for(i=0; i<=cnteq-1; i++) - { - j = i1+cntless+cnteq-1-i; - k = i2+i-(cnteq-1); - a->ptr.p_double[j] = bufa->ptr.p_double[k]; - b->ptr.p_int[j] = bufb->ptr.p_int[k]; - } - for(i=0; i<=cntgreater-1; i++) - { - j = i1+cntless+cnteq+i; - k = i1+i; - a->ptr.p_double[j] = bufa->ptr.p_double[k]; - b->ptr.p_int[j] = bufb->ptr.p_int[k]; - } - - /* - * Sort left and right parts of the array (ignoring middle part) - */ - tsort_tagsortfastirec(a, b, bufa, bufb, i1, i1+cntless-1, _state); - tsort_tagsortfastirec(a, b, bufa, bufb, i1+cntless+cnteq, i2, _state); -} - - -/************************************************************************* -Internal TagSortFastR: sorts A[I1...I2] (both bounds are included), -applies same permutations to B. - - -- ALGLIB -- - Copyright 06.09.2010 by Bochkanov Sergey -*************************************************************************/ -static void tsort_tagsortfastrrec(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Real */ ae_vector* bufb, - ae_int_t i1, - ae_int_t i2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - double tmpr; - double tmpr2; - ae_int_t tmpi; - ae_int_t cntless; - ae_int_t cnteq; - ae_int_t cntgreater; - double v0; - double v1; - double v2; - double vp; - - - - /* - * Fast exit - */ - if( i2<=i1 ) - { - return; - } - - /* - * Non-recursive sort for small arrays - */ - if( i2-i1<=16 ) - { - for(j=i1+1; j<=i2; j++) - { - - /* - * Search elements [I1..J-1] for place to insert Jth element. - * - * This code stops immediatly if we can leave A[J] at J-th position - * (all elements have same value of A[J] larger than any of them) - */ - tmpr = a->ptr.p_double[j]; - tmpi = j; - for(k=j-1; k>=i1; k--) - { - if( a->ptr.p_double[k]<=tmpr ) - { - break; - } - tmpi = k; - } - k = tmpi; - - /* - * Insert Jth element into Kth position - */ - if( k!=j ) - { - tmpr = a->ptr.p_double[j]; - tmpr2 = b->ptr.p_double[j]; - for(i=j-1; i>=k; i--) - { - a->ptr.p_double[i+1] = a->ptr.p_double[i]; - b->ptr.p_double[i+1] = b->ptr.p_double[i]; - } - a->ptr.p_double[k] = tmpr; - b->ptr.p_double[k] = tmpr2; - } - } - return; - } - - /* - * Quicksort: choose pivot - * Here we assume that I2-I1>=16 - */ - v0 = a->ptr.p_double[i1]; - v1 = a->ptr.p_double[i1+(i2-i1)/2]; - v2 = a->ptr.p_double[i2]; - if( v0>v1 ) - { - tmpr = v1; - v1 = v0; - v0 = tmpr; - } - if( v1>v2 ) - { - tmpr = v2; - v2 = v1; - v1 = tmpr; - } - if( v0>v1 ) - { - tmpr = v1; - v1 = v0; - v0 = tmpr; - } - vp = v1; - - /* - * now pass through A/B and: - * * move elements that are LESS than VP to the left of A/B - * * move elements that are EQUAL to VP to the right of BufA/BufB (in the reverse order) - * * move elements that are GREATER than VP to the left of BufA/BufB (in the normal order - * * move elements from the tail of BufA/BufB to the middle of A/B (restoring normal order) - * * move elements from the left of BufA/BufB to the end of A/B - */ - cntless = 0; - cnteq = 0; - cntgreater = 0; - for(i=i1; i<=i2; i++) - { - v0 = a->ptr.p_double[i]; - if( v0ptr.p_double[k] = v0; - b->ptr.p_double[k] = b->ptr.p_double[i]; - } - cntless = cntless+1; - continue; - } - if( v0==vp ) - { - - /* - * EQUAL - */ - k = i2-cnteq; - bufa->ptr.p_double[k] = v0; - bufb->ptr.p_double[k] = b->ptr.p_double[i]; - cnteq = cnteq+1; - continue; - } - - /* - * GREATER - */ - k = i1+cntgreater; - bufa->ptr.p_double[k] = v0; - bufb->ptr.p_double[k] = b->ptr.p_double[i]; - cntgreater = cntgreater+1; - } - for(i=0; i<=cnteq-1; i++) - { - j = i1+cntless+cnteq-1-i; - k = i2+i-(cnteq-1); - a->ptr.p_double[j] = bufa->ptr.p_double[k]; - b->ptr.p_double[j] = bufb->ptr.p_double[k]; - } - for(i=0; i<=cntgreater-1; i++) - { - j = i1+cntless+cnteq+i; - k = i1+i; - a->ptr.p_double[j] = bufa->ptr.p_double[k]; - b->ptr.p_double[j] = bufb->ptr.p_double[k]; - } - - /* - * Sort left and right parts of the array (ignoring middle part) - */ - tsort_tagsortfastrrec(a, b, bufa, bufb, i1, i1+cntless-1, _state); - tsort_tagsortfastrrec(a, b, bufa, bufb, i1+cntless+cnteq, i2, _state); -} - - -/************************************************************************* -Internal TagSortFastI: sorts A[I1...I2] (both bounds are included), -applies same permutations to B. - - -- ALGLIB -- - Copyright 06.09.2010 by Bochkanov Sergey -*************************************************************************/ -static void tsort_tagsortfastrec(/* Real */ ae_vector* a, - /* Real */ ae_vector* bufa, - ae_int_t i1, - ae_int_t i2, - ae_state *_state) -{ - ae_int_t cntless; - ae_int_t cnteq; - ae_int_t cntgreater; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double tmpr; - ae_int_t tmpi; - double v0; - double v1; - double v2; - double vp; - - - - /* - * Fast exit - */ - if( i2<=i1 ) - { - return; - } - - /* - * Non-recursive sort for small arrays - */ - if( i2-i1<=16 ) - { - for(j=i1+1; j<=i2; j++) - { - - /* - * Search elements [I1..J-1] for place to insert Jth element. - * - * This code stops immediatly if we can leave A[J] at J-th position - * (all elements have same value of A[J] larger than any of them) - */ - tmpr = a->ptr.p_double[j]; - tmpi = j; - for(k=j-1; k>=i1; k--) - { - if( a->ptr.p_double[k]<=tmpr ) - { - break; - } - tmpi = k; - } - k = tmpi; - - /* - * Insert Jth element into Kth position - */ - if( k!=j ) - { - tmpr = a->ptr.p_double[j]; - for(i=j-1; i>=k; i--) - { - a->ptr.p_double[i+1] = a->ptr.p_double[i]; - } - a->ptr.p_double[k] = tmpr; - } - } - return; - } - - /* - * Quicksort: choose pivot - * Here we assume that I2-I1>=16 - */ - v0 = a->ptr.p_double[i1]; - v1 = a->ptr.p_double[i1+(i2-i1)/2]; - v2 = a->ptr.p_double[i2]; - if( v0>v1 ) - { - tmpr = v1; - v1 = v0; - v0 = tmpr; - } - if( v1>v2 ) - { - tmpr = v2; - v2 = v1; - v1 = tmpr; - } - if( v0>v1 ) - { - tmpr = v1; - v1 = v0; - v0 = tmpr; - } - vp = v1; - - /* - * now pass through A/B and: - * * move elements that are LESS than VP to the left of A/B - * * move elements that are EQUAL to VP to the right of BufA/BufB (in the reverse order) - * * move elements that are GREATER than VP to the left of BufA/BufB (in the normal order - * * move elements from the tail of BufA/BufB to the middle of A/B (restoring normal order) - * * move elements from the left of BufA/BufB to the end of A/B - */ - cntless = 0; - cnteq = 0; - cntgreater = 0; - for(i=i1; i<=i2; i++) - { - v0 = a->ptr.p_double[i]; - if( v0ptr.p_double[k] = v0; - } - cntless = cntless+1; - continue; - } - if( v0==vp ) - { - - /* - * EQUAL - */ - k = i2-cnteq; - bufa->ptr.p_double[k] = v0; - cnteq = cnteq+1; - continue; - } - - /* - * GREATER - */ - k = i1+cntgreater; - bufa->ptr.p_double[k] = v0; - cntgreater = cntgreater+1; - } - for(i=0; i<=cnteq-1; i++) - { - j = i1+cntless+cnteq-1-i; - k = i2+i-(cnteq-1); - a->ptr.p_double[j] = bufa->ptr.p_double[k]; - } - for(i=0; i<=cntgreater-1; i++) - { - j = i1+cntless+cnteq+i; - k = i1+i; - a->ptr.p_double[j] = bufa->ptr.p_double[k]; - } - - /* - * Sort left and right parts of the array (ignoring middle part) - */ - tsort_tagsortfastrec(a, bufa, i1, i1+cntless-1, _state); - tsort_tagsortfastrec(a, bufa, i1+cntless+cnteq, i2, _state); -} - - - - -/************************************************************************* -Internal ranking subroutine. - -INPUT PARAMETERS: - X - array to rank - N - array size - IsCentered- whether ranks are centered or not: - * True - ranks are centered in such way that their - sum is zero - * False - ranks are not centered - Buf - temporary buffers - -NOTE: when IsCentered is True and all X[] are equal, this function fills - X by zeros (exact zeros are used, not sum which is only approximately - equal to zero). -*************************************************************************/ -void rankx(/* Real */ ae_vector* x, - ae_int_t n, - ae_bool iscentered, - apbuffers* buf, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - double tmp; - double voffs; - - - - /* - * Prepare - */ - if( n<1 ) - { - return; - } - if( n==1 ) - { - x->ptr.p_double[0] = 0; - return; - } - if( buf->ra1.cntra1, n, _state); - } - if( buf->ia1.cntia1, n, _state); - } - for(i=0; i<=n-1; i++) - { - buf->ra1.ptr.p_double[i] = x->ptr.p_double[i]; - buf->ia1.ptr.p_int[i] = i; - } - tagsortfasti(&buf->ra1, &buf->ia1, &buf->ra2, &buf->ia2, n, _state); - - /* - * Special test for all values being equal - */ - if( ae_fp_eq(buf->ra1.ptr.p_double[0],buf->ra1.ptr.p_double[n-1]) ) - { - if( iscentered ) - { - tmp = 0.0; - } - else - { - tmp = (double)(n-1)/(double)2; - } - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = tmp; - } - return; - } - - /* - * compute tied ranks - */ - i = 0; - while(i<=n-1) - { - j = i+1; - while(j<=n-1) - { - if( ae_fp_neq(buf->ra1.ptr.p_double[j],buf->ra1.ptr.p_double[i]) ) - { - break; - } - j = j+1; - } - for(k=i; k<=j-1; k++) - { - buf->ra1.ptr.p_double[k] = (double)(i+j-1)/(double)2; - } - i = j; - } - - /* - * back to x - */ - if( iscentered ) - { - voffs = (double)(n-1)/(double)2; - } - else - { - voffs = 0.0; - } - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[buf->ia1.ptr.p_int[i]] = buf->ra1.ptr.p_double[i]-voffs; - } -} - - - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixrank1f(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_vector* u, - ae_int_t iu, - /* Complex */ ae_vector* v, - ae_int_t iv, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_cmatrixrank1f(m, n, a, ia, ja, u, iu, v, iv); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixrank1f(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_vector* u, - ae_int_t iu, - /* Real */ ae_vector* v, - ae_int_t iv, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixrank1f(m, n, a, ia, ja, u, iu, v, iv); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixmvf(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Complex */ ae_vector* x, - ae_int_t ix, - /* Complex */ ae_vector* y, - ae_int_t iy, - ae_state *_state) -{ - ae_bool result; - - - result = ae_false; - return result; -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixmvf(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Real */ ae_vector* x, - ae_int_t ix, - /* Real */ ae_vector* y, - ae_int_t iy, - ae_state *_state) -{ - ae_bool result; - - - result = ae_false; - return result; -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_cmatrixrighttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_cmatrixlefttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixrighttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixlefttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_cmatrixsyrkf(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixsyrkf(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixgemmf(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc); -#endif -} - - -/************************************************************************* -Fast kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_ABLAS - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_cmatrixgemmf(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc); -#endif -} - - -/************************************************************************* -CMatrixGEMM kernel, basecase code for CMatrixGEMM. - -This subroutine calculates C = alpha*op1(A)*op2(B) +beta*C where: -* C is MxN general matrix -* op1(A) is MxK matrix -* op2(B) is KxN matrix -* "op" may be identity transformation, transposition, conjugate transposition - -Additional info: -* multiplication result replaces C. If Beta=0, C elements are not used in - calculations (not multiplied by zero - just not referenced) -* if Alpha=0, A is not used (not multiplied by zero - just not referenced) -* if both Beta and Alpha are zero, C is filled by zeros. - -IMPORTANT: - -This function does NOT preallocate output matrix C, it MUST be preallocated -by caller prior to calling this function. In case C does not have enough -space to store result, exception will be generated. - -INPUT PARAMETERS - M - matrix size, M>0 - N - matrix size, N>0 - K - matrix size, K>0 - Alpha - coefficient - A - matrix - IA - submatrix offset - JA - submatrix offset - OpTypeA - transformation type: - * 0 - no transformation - * 1 - transposition - * 2 - conjugate transposition - B - matrix - IB - submatrix offset - JB - submatrix offset - OpTypeB - transformation type: - * 0 - no transformation - * 1 - transposition - * 2 - conjugate transposition - Beta - coefficient - C - PREALLOCATED output matrix - IC - submatrix offset - JC - submatrix offset - - -- ALGLIB routine -- - 27.03.2013 - Bochkanov Sergey -*************************************************************************/ -void cmatrixgemmk(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_complex v; - ae_complex v00; - ae_complex v01; - ae_complex v10; - ae_complex v11; - double v00x; - double v00y; - double v01x; - double v01y; - double v10x; - double v10y; - double v11x; - double v11y; - double a0x; - double a0y; - double a1x; - double a1y; - double b0x; - double b0y; - double b1x; - double b1y; - ae_int_t idxa0; - ae_int_t idxa1; - ae_int_t idxb0; - ae_int_t idxb1; - ae_int_t i0; - ae_int_t i1; - ae_int_t ik; - ae_int_t j0; - ae_int_t j1; - ae_int_t jk; - ae_int_t t; - ae_int_t offsa; - ae_int_t offsb; - - - - /* - * if matrix size is zero - */ - if( m==0||n==0 ) - { - return; - } - - /* - * Try optimized code - */ - if( cmatrixgemmf(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state) ) - { - return; - } - - /* - * if K=0, then C=Beta*C - */ - if( k==0 ) - { - if( ae_c_neq_d(beta,1) ) - { - if( ae_c_neq_d(beta,0) ) - { - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - c->ptr.pp_complex[ic+i][jc+j] = ae_c_mul(beta,c->ptr.pp_complex[ic+i][jc+j]); - } - } - } - else - { - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - c->ptr.pp_complex[ic+i][jc+j] = ae_complex_from_d(0); - } - } - } - } - return; - } - - /* - * This phase is not really necessary, but compiler complains - * about "possibly uninitialized variables" - */ - a0x = 0; - a0y = 0; - a1x = 0; - a1y = 0; - b0x = 0; - b0y = 0; - b1x = 0; - b1y = 0; - - /* - * General case - */ - i = 0; - while(iptr.pp_complex[idxa0][offsa].x; - a0y = a->ptr.pp_complex[idxa0][offsa].y; - a1x = a->ptr.pp_complex[idxa1][offsa].x; - a1y = a->ptr.pp_complex[idxa1][offsa].y; - } - if( optypea==1 ) - { - a0x = a->ptr.pp_complex[offsa][idxa0].x; - a0y = a->ptr.pp_complex[offsa][idxa0].y; - a1x = a->ptr.pp_complex[offsa][idxa1].x; - a1y = a->ptr.pp_complex[offsa][idxa1].y; - } - if( optypea==2 ) - { - a0x = a->ptr.pp_complex[offsa][idxa0].x; - a0y = -a->ptr.pp_complex[offsa][idxa0].y; - a1x = a->ptr.pp_complex[offsa][idxa1].x; - a1y = -a->ptr.pp_complex[offsa][idxa1].y; - } - if( optypeb==0 ) - { - b0x = b->ptr.pp_complex[offsb][idxb0].x; - b0y = b->ptr.pp_complex[offsb][idxb0].y; - b1x = b->ptr.pp_complex[offsb][idxb1].x; - b1y = b->ptr.pp_complex[offsb][idxb1].y; - } - if( optypeb==1 ) - { - b0x = b->ptr.pp_complex[idxb0][offsb].x; - b0y = b->ptr.pp_complex[idxb0][offsb].y; - b1x = b->ptr.pp_complex[idxb1][offsb].x; - b1y = b->ptr.pp_complex[idxb1][offsb].y; - } - if( optypeb==2 ) - { - b0x = b->ptr.pp_complex[idxb0][offsb].x; - b0y = -b->ptr.pp_complex[idxb0][offsb].y; - b1x = b->ptr.pp_complex[idxb1][offsb].x; - b1y = -b->ptr.pp_complex[idxb1][offsb].y; - } - v00x = v00x+a0x*b0x-a0y*b0y; - v00y = v00y+a0x*b0y+a0y*b0x; - v01x = v01x+a0x*b1x-a0y*b1y; - v01y = v01y+a0x*b1y+a0y*b1x; - v10x = v10x+a1x*b0x-a1y*b0y; - v10y = v10y+a1x*b0y+a1y*b0x; - v11x = v11x+a1x*b1x-a1y*b1y; - v11y = v11y+a1x*b1y+a1y*b1x; - offsa = offsa+1; - offsb = offsb+1; - } - v00.x = v00x; - v00.y = v00y; - v10.x = v10x; - v10.y = v10y; - v01.x = v01x; - v01.y = v01y; - v11.x = v11x; - v11.y = v11y; - if( ae_c_eq_d(beta,0) ) - { - c->ptr.pp_complex[ic+i+0][jc+j+0] = ae_c_mul(alpha,v00); - c->ptr.pp_complex[ic+i+0][jc+j+1] = ae_c_mul(alpha,v01); - c->ptr.pp_complex[ic+i+1][jc+j+0] = ae_c_mul(alpha,v10); - c->ptr.pp_complex[ic+i+1][jc+j+1] = ae_c_mul(alpha,v11); - } - else - { - c->ptr.pp_complex[ic+i+0][jc+j+0] = ae_c_add(ae_c_mul(beta,c->ptr.pp_complex[ic+i+0][jc+j+0]),ae_c_mul(alpha,v00)); - c->ptr.pp_complex[ic+i+0][jc+j+1] = ae_c_add(ae_c_mul(beta,c->ptr.pp_complex[ic+i+0][jc+j+1]),ae_c_mul(alpha,v01)); - c->ptr.pp_complex[ic+i+1][jc+j+0] = ae_c_add(ae_c_mul(beta,c->ptr.pp_complex[ic+i+1][jc+j+0]),ae_c_mul(alpha,v10)); - c->ptr.pp_complex[ic+i+1][jc+j+1] = ae_c_add(ae_c_mul(beta,c->ptr.pp_complex[ic+i+1][jc+j+1]),ae_c_mul(alpha,v11)); - } - } - else - { - - /* - * Determine submatrix [I0..I1]x[J0..J1] to process - */ - i0 = i; - i1 = ae_minint(i+1, m-1, _state); - j0 = j; - j1 = ae_minint(j+1, n-1, _state); - - /* - * Process submatrix - */ - for(ik=i0; ik<=i1; ik++) - { - for(jk=j0; jk<=j1; jk++) - { - if( k==0||ae_c_eq_d(alpha,0) ) - { - v = ae_complex_from_d(0); - } - else - { - v = ae_complex_from_d(0.0); - if( optypea==0&&optypeb==0 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia+ik][ja], 1, "N", &b->ptr.pp_complex[ib][jb+jk], b->stride, "N", ae_v_len(ja,ja+k-1)); - } - if( optypea==0&&optypeb==1 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia+ik][ja], 1, "N", &b->ptr.pp_complex[ib+jk][jb], 1, "N", ae_v_len(ja,ja+k-1)); - } - if( optypea==0&&optypeb==2 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia+ik][ja], 1, "N", &b->ptr.pp_complex[ib+jk][jb], 1, "Conj", ae_v_len(ja,ja+k-1)); - } - if( optypea==1&&optypeb==0 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia][ja+ik], a->stride, "N", &b->ptr.pp_complex[ib][jb+jk], b->stride, "N", ae_v_len(ia,ia+k-1)); - } - if( optypea==1&&optypeb==1 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia][ja+ik], a->stride, "N", &b->ptr.pp_complex[ib+jk][jb], 1, "N", ae_v_len(ia,ia+k-1)); - } - if( optypea==1&&optypeb==2 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia][ja+ik], a->stride, "N", &b->ptr.pp_complex[ib+jk][jb], 1, "Conj", ae_v_len(ia,ia+k-1)); - } - if( optypea==2&&optypeb==0 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia][ja+ik], a->stride, "Conj", &b->ptr.pp_complex[ib][jb+jk], b->stride, "N", ae_v_len(ia,ia+k-1)); - } - if( optypea==2&&optypeb==1 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia][ja+ik], a->stride, "Conj", &b->ptr.pp_complex[ib+jk][jb], 1, "N", ae_v_len(ia,ia+k-1)); - } - if( optypea==2&&optypeb==2 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia][ja+ik], a->stride, "Conj", &b->ptr.pp_complex[ib+jk][jb], 1, "Conj", ae_v_len(ia,ia+k-1)); - } - } - if( ae_c_eq_d(beta,0) ) - { - c->ptr.pp_complex[ic+ik][jc+jk] = ae_c_mul(alpha,v); - } - else - { - c->ptr.pp_complex[ic+ik][jc+jk] = ae_c_add(ae_c_mul(beta,c->ptr.pp_complex[ic+ik][jc+jk]),ae_c_mul(alpha,v)); - } - } - } - } - j = j+2; - } - i = i+2; - } -} - - -/************************************************************************* -RMatrixGEMM kernel, basecase code for RMatrixGEMM. - -This subroutine calculates C = alpha*op1(A)*op2(B) +beta*C where: -* C is MxN general matrix -* op1(A) is MxK matrix -* op2(B) is KxN matrix -* "op" may be identity transformation, transposition - -Additional info: -* multiplication result replaces C. If Beta=0, C elements are not used in - calculations (not multiplied by zero - just not referenced) -* if Alpha=0, A is not used (not multiplied by zero - just not referenced) -* if both Beta and Alpha are zero, C is filled by zeros. - -IMPORTANT: - -This function does NOT preallocate output matrix C, it MUST be preallocated -by caller prior to calling this function. In case C does not have enough -space to store result, exception will be generated. - -INPUT PARAMETERS - M - matrix size, M>0 - N - matrix size, N>0 - K - matrix size, K>0 - Alpha - coefficient - A - matrix - IA - submatrix offset - JA - submatrix offset - OpTypeA - transformation type: - * 0 - no transformation - * 1 - transposition - B - matrix - IB - submatrix offset - JB - submatrix offset - OpTypeB - transformation type: - * 0 - no transformation - * 1 - transposition - Beta - coefficient - C - PREALLOCATED output matrix - IC - submatrix offset - JC - submatrix offset - - -- ALGLIB routine -- - 27.03.2013 - Bochkanov Sergey -*************************************************************************/ -void rmatrixgemmk(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - - /* - * if matrix size is zero - */ - if( m==0||n==0 ) - { - return; - } - - /* - * Try optimized code - */ - if( rmatrixgemmf(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state) ) - { - return; - } - - /* - * if K=0, then C=Beta*C - */ - if( k==0||ae_fp_eq(alpha,0) ) - { - if( ae_fp_neq(beta,1) ) - { - if( ae_fp_neq(beta,0) ) - { - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - c->ptr.pp_double[ic+i][jc+j] = beta*c->ptr.pp_double[ic+i][jc+j]; - } - } - } - else - { - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - c->ptr.pp_double[ic+i][jc+j] = 0; - } - } - } - } - return; - } - - /* - * Call specialized code. - * - * NOTE: specialized code was moved to separate function because of strange - * issues with instructions cache on some systems; Having too long - * functions significantly slows down internal loop of the algorithm. - */ - if( optypea==0&&optypeb==0 ) - { - rmatrixgemmk44v00(m, n, k, alpha, a, ia, ja, b, ib, jb, beta, c, ic, jc, _state); - } - if( optypea==0&&optypeb!=0 ) - { - rmatrixgemmk44v01(m, n, k, alpha, a, ia, ja, b, ib, jb, beta, c, ic, jc, _state); - } - if( optypea!=0&&optypeb==0 ) - { - rmatrixgemmk44v10(m, n, k, alpha, a, ia, ja, b, ib, jb, beta, c, ic, jc, _state); - } - if( optypea!=0&&optypeb!=0 ) - { - rmatrixgemmk44v11(m, n, k, alpha, a, ia, ja, b, ib, jb, beta, c, ic, jc, _state); - } -} - - -/************************************************************************* -RMatrixGEMM kernel, basecase code for RMatrixGEMM, specialized for sitation -with OpTypeA=0 and OpTypeB=0. - -Additional info: -* this function requires that Alpha<>0 (assertion is thrown otherwise) - -INPUT PARAMETERS - M - matrix size, M>0 - N - matrix size, N>0 - K - matrix size, K>0 - Alpha - coefficient - A - matrix - IA - submatrix offset - JA - submatrix offset - B - matrix - IB - submatrix offset - JB - submatrix offset - Beta - coefficient - C - PREALLOCATED output matrix - IC - submatrix offset - JC - submatrix offset - - -- ALGLIB routine -- - 27.03.2013 - Bochkanov Sergey -*************************************************************************/ -void rmatrixgemmk44v00(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - double v00; - double v01; - double v02; - double v03; - double v10; - double v11; - double v12; - double v13; - double v20; - double v21; - double v22; - double v23; - double v30; - double v31; - double v32; - double v33; - double a0; - double a1; - double a2; - double a3; - double b0; - double b1; - double b2; - double b3; - ae_int_t idxa0; - ae_int_t idxa1; - ae_int_t idxa2; - ae_int_t idxa3; - ae_int_t idxb0; - ae_int_t idxb1; - ae_int_t idxb2; - ae_int_t idxb3; - ae_int_t i0; - ae_int_t i1; - ae_int_t ik; - ae_int_t j0; - ae_int_t j1; - ae_int_t jk; - ae_int_t t; - ae_int_t offsa; - ae_int_t offsb; - - - ae_assert(ae_fp_neq(alpha,0), "RMatrixGEMMK44V00: internal error (Alpha=0)", _state); - - /* - * if matrix size is zero - */ - if( m==0||n==0 ) - { - return; - } - - /* - * A*B - */ - i = 0; - while(iptr.pp_double[idxa0][offsa]; - a1 = a->ptr.pp_double[idxa1][offsa]; - b0 = b->ptr.pp_double[offsb][idxb0]; - b1 = b->ptr.pp_double[offsb][idxb1]; - v00 = v00+a0*b0; - v01 = v01+a0*b1; - v10 = v10+a1*b0; - v11 = v11+a1*b1; - a2 = a->ptr.pp_double[idxa2][offsa]; - a3 = a->ptr.pp_double[idxa3][offsa]; - v20 = v20+a2*b0; - v21 = v21+a2*b1; - v30 = v30+a3*b0; - v31 = v31+a3*b1; - b2 = b->ptr.pp_double[offsb][idxb2]; - b3 = b->ptr.pp_double[offsb][idxb3]; - v22 = v22+a2*b2; - v23 = v23+a2*b3; - v32 = v32+a3*b2; - v33 = v33+a3*b3; - v02 = v02+a0*b2; - v03 = v03+a0*b3; - v12 = v12+a1*b2; - v13 = v13+a1*b3; - offsa = offsa+1; - offsb = offsb+1; - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+i+0][jc+j+0] = alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = alpha*v33; - } - else - { - c->ptr.pp_double[ic+i+0][jc+j+0] = beta*c->ptr.pp_double[ic+i+0][jc+j+0]+alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = beta*c->ptr.pp_double[ic+i+0][jc+j+1]+alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = beta*c->ptr.pp_double[ic+i+0][jc+j+2]+alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = beta*c->ptr.pp_double[ic+i+0][jc+j+3]+alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = beta*c->ptr.pp_double[ic+i+1][jc+j+0]+alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = beta*c->ptr.pp_double[ic+i+1][jc+j+1]+alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = beta*c->ptr.pp_double[ic+i+1][jc+j+2]+alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = beta*c->ptr.pp_double[ic+i+1][jc+j+3]+alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = beta*c->ptr.pp_double[ic+i+2][jc+j+0]+alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = beta*c->ptr.pp_double[ic+i+2][jc+j+1]+alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = beta*c->ptr.pp_double[ic+i+2][jc+j+2]+alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = beta*c->ptr.pp_double[ic+i+2][jc+j+3]+alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = beta*c->ptr.pp_double[ic+i+3][jc+j+0]+alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = beta*c->ptr.pp_double[ic+i+3][jc+j+1]+alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = beta*c->ptr.pp_double[ic+i+3][jc+j+2]+alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = beta*c->ptr.pp_double[ic+i+3][jc+j+3]+alpha*v33; - } - } - else - { - - /* - * Determine submatrix [I0..I1]x[J0..J1] to process - */ - i0 = i; - i1 = ae_minint(i+3, m-1, _state); - j0 = j; - j1 = ae_minint(j+3, n-1, _state); - - /* - * Process submatrix - */ - for(ik=i0; ik<=i1; ik++) - { - for(jk=j0; jk<=j1; jk++) - { - if( k==0||ae_fp_eq(alpha,0) ) - { - v = 0; - } - else - { - v = ae_v_dotproduct(&a->ptr.pp_double[ia+ik][ja], 1, &b->ptr.pp_double[ib][jb+jk], b->stride, ae_v_len(ja,ja+k-1)); - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+ik][jc+jk] = alpha*v; - } - else - { - c->ptr.pp_double[ic+ik][jc+jk] = beta*c->ptr.pp_double[ic+ik][jc+jk]+alpha*v; - } - } - } - } - j = j+4; - } - i = i+4; - } -} - - -/************************************************************************* -RMatrixGEMM kernel, basecase code for RMatrixGEMM, specialized for sitation -with OpTypeA=0 and OpTypeB=1. - -Additional info: -* this function requires that Alpha<>0 (assertion is thrown otherwise) - -INPUT PARAMETERS - M - matrix size, M>0 - N - matrix size, N>0 - K - matrix size, K>0 - Alpha - coefficient - A - matrix - IA - submatrix offset - JA - submatrix offset - B - matrix - IB - submatrix offset - JB - submatrix offset - Beta - coefficient - C - PREALLOCATED output matrix - IC - submatrix offset - JC - submatrix offset - - -- ALGLIB routine -- - 27.03.2013 - Bochkanov Sergey -*************************************************************************/ -void rmatrixgemmk44v01(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - double v00; - double v01; - double v02; - double v03; - double v10; - double v11; - double v12; - double v13; - double v20; - double v21; - double v22; - double v23; - double v30; - double v31; - double v32; - double v33; - double a0; - double a1; - double a2; - double a3; - double b0; - double b1; - double b2; - double b3; - ae_int_t idxa0; - ae_int_t idxa1; - ae_int_t idxa2; - ae_int_t idxa3; - ae_int_t idxb0; - ae_int_t idxb1; - ae_int_t idxb2; - ae_int_t idxb3; - ae_int_t i0; - ae_int_t i1; - ae_int_t ik; - ae_int_t j0; - ae_int_t j1; - ae_int_t jk; - ae_int_t t; - ae_int_t offsa; - ae_int_t offsb; - - - ae_assert(ae_fp_neq(alpha,0), "RMatrixGEMMK44V00: internal error (Alpha=0)", _state); - - /* - * if matrix size is zero - */ - if( m==0||n==0 ) - { - return; - } - - /* - * A*B' - */ - i = 0; - while(iptr.pp_double[idxa0][offsa]; - a1 = a->ptr.pp_double[idxa1][offsa]; - b0 = b->ptr.pp_double[idxb0][offsb]; - b1 = b->ptr.pp_double[idxb1][offsb]; - v00 = v00+a0*b0; - v01 = v01+a0*b1; - v10 = v10+a1*b0; - v11 = v11+a1*b1; - a2 = a->ptr.pp_double[idxa2][offsa]; - a3 = a->ptr.pp_double[idxa3][offsa]; - v20 = v20+a2*b0; - v21 = v21+a2*b1; - v30 = v30+a3*b0; - v31 = v31+a3*b1; - b2 = b->ptr.pp_double[idxb2][offsb]; - b3 = b->ptr.pp_double[idxb3][offsb]; - v22 = v22+a2*b2; - v23 = v23+a2*b3; - v32 = v32+a3*b2; - v33 = v33+a3*b3; - v02 = v02+a0*b2; - v03 = v03+a0*b3; - v12 = v12+a1*b2; - v13 = v13+a1*b3; - offsa = offsa+1; - offsb = offsb+1; - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+i+0][jc+j+0] = alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = alpha*v33; - } - else - { - c->ptr.pp_double[ic+i+0][jc+j+0] = beta*c->ptr.pp_double[ic+i+0][jc+j+0]+alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = beta*c->ptr.pp_double[ic+i+0][jc+j+1]+alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = beta*c->ptr.pp_double[ic+i+0][jc+j+2]+alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = beta*c->ptr.pp_double[ic+i+0][jc+j+3]+alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = beta*c->ptr.pp_double[ic+i+1][jc+j+0]+alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = beta*c->ptr.pp_double[ic+i+1][jc+j+1]+alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = beta*c->ptr.pp_double[ic+i+1][jc+j+2]+alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = beta*c->ptr.pp_double[ic+i+1][jc+j+3]+alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = beta*c->ptr.pp_double[ic+i+2][jc+j+0]+alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = beta*c->ptr.pp_double[ic+i+2][jc+j+1]+alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = beta*c->ptr.pp_double[ic+i+2][jc+j+2]+alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = beta*c->ptr.pp_double[ic+i+2][jc+j+3]+alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = beta*c->ptr.pp_double[ic+i+3][jc+j+0]+alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = beta*c->ptr.pp_double[ic+i+3][jc+j+1]+alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = beta*c->ptr.pp_double[ic+i+3][jc+j+2]+alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = beta*c->ptr.pp_double[ic+i+3][jc+j+3]+alpha*v33; - } - } - else - { - - /* - * Determine submatrix [I0..I1]x[J0..J1] to process - */ - i0 = i; - i1 = ae_minint(i+3, m-1, _state); - j0 = j; - j1 = ae_minint(j+3, n-1, _state); - - /* - * Process submatrix - */ - for(ik=i0; ik<=i1; ik++) - { - for(jk=j0; jk<=j1; jk++) - { - if( k==0||ae_fp_eq(alpha,0) ) - { - v = 0; - } - else - { - v = ae_v_dotproduct(&a->ptr.pp_double[ia+ik][ja], 1, &b->ptr.pp_double[ib+jk][jb], 1, ae_v_len(ja,ja+k-1)); - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+ik][jc+jk] = alpha*v; - } - else - { - c->ptr.pp_double[ic+ik][jc+jk] = beta*c->ptr.pp_double[ic+ik][jc+jk]+alpha*v; - } - } - } - } - j = j+4; - } - i = i+4; - } -} - - -/************************************************************************* -RMatrixGEMM kernel, basecase code for RMatrixGEMM, specialized for sitation -with OpTypeA=1 and OpTypeB=0. - -Additional info: -* this function requires that Alpha<>0 (assertion is thrown otherwise) - -INPUT PARAMETERS - M - matrix size, M>0 - N - matrix size, N>0 - K - matrix size, K>0 - Alpha - coefficient - A - matrix - IA - submatrix offset - JA - submatrix offset - B - matrix - IB - submatrix offset - JB - submatrix offset - Beta - coefficient - C - PREALLOCATED output matrix - IC - submatrix offset - JC - submatrix offset - - -- ALGLIB routine -- - 27.03.2013 - Bochkanov Sergey -*************************************************************************/ -void rmatrixgemmk44v10(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - double v00; - double v01; - double v02; - double v03; - double v10; - double v11; - double v12; - double v13; - double v20; - double v21; - double v22; - double v23; - double v30; - double v31; - double v32; - double v33; - double a0; - double a1; - double a2; - double a3; - double b0; - double b1; - double b2; - double b3; - ae_int_t idxa0; - ae_int_t idxa1; - ae_int_t idxa2; - ae_int_t idxa3; - ae_int_t idxb0; - ae_int_t idxb1; - ae_int_t idxb2; - ae_int_t idxb3; - ae_int_t i0; - ae_int_t i1; - ae_int_t ik; - ae_int_t j0; - ae_int_t j1; - ae_int_t jk; - ae_int_t t; - ae_int_t offsa; - ae_int_t offsb; - - - ae_assert(ae_fp_neq(alpha,0), "RMatrixGEMMK44V00: internal error (Alpha=0)", _state); - - /* - * if matrix size is zero - */ - if( m==0||n==0 ) - { - return; - } - - /* - * A'*B - */ - i = 0; - while(iptr.pp_double[offsa][idxa0]; - a1 = a->ptr.pp_double[offsa][idxa1]; - b0 = b->ptr.pp_double[offsb][idxb0]; - b1 = b->ptr.pp_double[offsb][idxb1]; - v00 = v00+a0*b0; - v01 = v01+a0*b1; - v10 = v10+a1*b0; - v11 = v11+a1*b1; - a2 = a->ptr.pp_double[offsa][idxa2]; - a3 = a->ptr.pp_double[offsa][idxa3]; - v20 = v20+a2*b0; - v21 = v21+a2*b1; - v30 = v30+a3*b0; - v31 = v31+a3*b1; - b2 = b->ptr.pp_double[offsb][idxb2]; - b3 = b->ptr.pp_double[offsb][idxb3]; - v22 = v22+a2*b2; - v23 = v23+a2*b3; - v32 = v32+a3*b2; - v33 = v33+a3*b3; - v02 = v02+a0*b2; - v03 = v03+a0*b3; - v12 = v12+a1*b2; - v13 = v13+a1*b3; - offsa = offsa+1; - offsb = offsb+1; - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+i+0][jc+j+0] = alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = alpha*v33; - } - else - { - c->ptr.pp_double[ic+i+0][jc+j+0] = beta*c->ptr.pp_double[ic+i+0][jc+j+0]+alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = beta*c->ptr.pp_double[ic+i+0][jc+j+1]+alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = beta*c->ptr.pp_double[ic+i+0][jc+j+2]+alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = beta*c->ptr.pp_double[ic+i+0][jc+j+3]+alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = beta*c->ptr.pp_double[ic+i+1][jc+j+0]+alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = beta*c->ptr.pp_double[ic+i+1][jc+j+1]+alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = beta*c->ptr.pp_double[ic+i+1][jc+j+2]+alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = beta*c->ptr.pp_double[ic+i+1][jc+j+3]+alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = beta*c->ptr.pp_double[ic+i+2][jc+j+0]+alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = beta*c->ptr.pp_double[ic+i+2][jc+j+1]+alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = beta*c->ptr.pp_double[ic+i+2][jc+j+2]+alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = beta*c->ptr.pp_double[ic+i+2][jc+j+3]+alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = beta*c->ptr.pp_double[ic+i+3][jc+j+0]+alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = beta*c->ptr.pp_double[ic+i+3][jc+j+1]+alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = beta*c->ptr.pp_double[ic+i+3][jc+j+2]+alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = beta*c->ptr.pp_double[ic+i+3][jc+j+3]+alpha*v33; - } - } - else - { - - /* - * Determine submatrix [I0..I1]x[J0..J1] to process - */ - i0 = i; - i1 = ae_minint(i+3, m-1, _state); - j0 = j; - j1 = ae_minint(j+3, n-1, _state); - - /* - * Process submatrix - */ - for(ik=i0; ik<=i1; ik++) - { - for(jk=j0; jk<=j1; jk++) - { - if( k==0||ae_fp_eq(alpha,0) ) - { - v = 0; - } - else - { - v = 0.0; - v = ae_v_dotproduct(&a->ptr.pp_double[ia][ja+ik], a->stride, &b->ptr.pp_double[ib][jb+jk], b->stride, ae_v_len(ia,ia+k-1)); - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+ik][jc+jk] = alpha*v; - } - else - { - c->ptr.pp_double[ic+ik][jc+jk] = beta*c->ptr.pp_double[ic+ik][jc+jk]+alpha*v; - } - } - } - } - j = j+4; - } - i = i+4; - } -} - - -/************************************************************************* -RMatrixGEMM kernel, basecase code for RMatrixGEMM, specialized for sitation -with OpTypeA=1 and OpTypeB=1. - -Additional info: -* this function requires that Alpha<>0 (assertion is thrown otherwise) - -INPUT PARAMETERS - M - matrix size, M>0 - N - matrix size, N>0 - K - matrix size, K>0 - Alpha - coefficient - A - matrix - IA - submatrix offset - JA - submatrix offset - B - matrix - IB - submatrix offset - JB - submatrix offset - Beta - coefficient - C - PREALLOCATED output matrix - IC - submatrix offset - JC - submatrix offset - - -- ALGLIB routine -- - 27.03.2013 - Bochkanov Sergey -*************************************************************************/ -void rmatrixgemmk44v11(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - double v00; - double v01; - double v02; - double v03; - double v10; - double v11; - double v12; - double v13; - double v20; - double v21; - double v22; - double v23; - double v30; - double v31; - double v32; - double v33; - double a0; - double a1; - double a2; - double a3; - double b0; - double b1; - double b2; - double b3; - ae_int_t idxa0; - ae_int_t idxa1; - ae_int_t idxa2; - ae_int_t idxa3; - ae_int_t idxb0; - ae_int_t idxb1; - ae_int_t idxb2; - ae_int_t idxb3; - ae_int_t i0; - ae_int_t i1; - ae_int_t ik; - ae_int_t j0; - ae_int_t j1; - ae_int_t jk; - ae_int_t t; - ae_int_t offsa; - ae_int_t offsb; - - - ae_assert(ae_fp_neq(alpha,0), "RMatrixGEMMK44V00: internal error (Alpha=0)", _state); - - /* - * if matrix size is zero - */ - if( m==0||n==0 ) - { - return; - } - - /* - * A'*B' - */ - i = 0; - while(iptr.pp_double[offsa][idxa0]; - a1 = a->ptr.pp_double[offsa][idxa1]; - b0 = b->ptr.pp_double[idxb0][offsb]; - b1 = b->ptr.pp_double[idxb1][offsb]; - v00 = v00+a0*b0; - v01 = v01+a0*b1; - v10 = v10+a1*b0; - v11 = v11+a1*b1; - a2 = a->ptr.pp_double[offsa][idxa2]; - a3 = a->ptr.pp_double[offsa][idxa3]; - v20 = v20+a2*b0; - v21 = v21+a2*b1; - v30 = v30+a3*b0; - v31 = v31+a3*b1; - b2 = b->ptr.pp_double[idxb2][offsb]; - b3 = b->ptr.pp_double[idxb3][offsb]; - v22 = v22+a2*b2; - v23 = v23+a2*b3; - v32 = v32+a3*b2; - v33 = v33+a3*b3; - v02 = v02+a0*b2; - v03 = v03+a0*b3; - v12 = v12+a1*b2; - v13 = v13+a1*b3; - offsa = offsa+1; - offsb = offsb+1; - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+i+0][jc+j+0] = alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = alpha*v33; - } - else - { - c->ptr.pp_double[ic+i+0][jc+j+0] = beta*c->ptr.pp_double[ic+i+0][jc+j+0]+alpha*v00; - c->ptr.pp_double[ic+i+0][jc+j+1] = beta*c->ptr.pp_double[ic+i+0][jc+j+1]+alpha*v01; - c->ptr.pp_double[ic+i+0][jc+j+2] = beta*c->ptr.pp_double[ic+i+0][jc+j+2]+alpha*v02; - c->ptr.pp_double[ic+i+0][jc+j+3] = beta*c->ptr.pp_double[ic+i+0][jc+j+3]+alpha*v03; - c->ptr.pp_double[ic+i+1][jc+j+0] = beta*c->ptr.pp_double[ic+i+1][jc+j+0]+alpha*v10; - c->ptr.pp_double[ic+i+1][jc+j+1] = beta*c->ptr.pp_double[ic+i+1][jc+j+1]+alpha*v11; - c->ptr.pp_double[ic+i+1][jc+j+2] = beta*c->ptr.pp_double[ic+i+1][jc+j+2]+alpha*v12; - c->ptr.pp_double[ic+i+1][jc+j+3] = beta*c->ptr.pp_double[ic+i+1][jc+j+3]+alpha*v13; - c->ptr.pp_double[ic+i+2][jc+j+0] = beta*c->ptr.pp_double[ic+i+2][jc+j+0]+alpha*v20; - c->ptr.pp_double[ic+i+2][jc+j+1] = beta*c->ptr.pp_double[ic+i+2][jc+j+1]+alpha*v21; - c->ptr.pp_double[ic+i+2][jc+j+2] = beta*c->ptr.pp_double[ic+i+2][jc+j+2]+alpha*v22; - c->ptr.pp_double[ic+i+2][jc+j+3] = beta*c->ptr.pp_double[ic+i+2][jc+j+3]+alpha*v23; - c->ptr.pp_double[ic+i+3][jc+j+0] = beta*c->ptr.pp_double[ic+i+3][jc+j+0]+alpha*v30; - c->ptr.pp_double[ic+i+3][jc+j+1] = beta*c->ptr.pp_double[ic+i+3][jc+j+1]+alpha*v31; - c->ptr.pp_double[ic+i+3][jc+j+2] = beta*c->ptr.pp_double[ic+i+3][jc+j+2]+alpha*v32; - c->ptr.pp_double[ic+i+3][jc+j+3] = beta*c->ptr.pp_double[ic+i+3][jc+j+3]+alpha*v33; - } - } - else - { - - /* - * Determine submatrix [I0..I1]x[J0..J1] to process - */ - i0 = i; - i1 = ae_minint(i+3, m-1, _state); - j0 = j; - j1 = ae_minint(j+3, n-1, _state); - - /* - * Process submatrix - */ - for(ik=i0; ik<=i1; ik++) - { - for(jk=j0; jk<=j1; jk++) - { - if( k==0||ae_fp_eq(alpha,0) ) - { - v = 0; - } - else - { - v = 0.0; - v = ae_v_dotproduct(&a->ptr.pp_double[ia][ja+ik], a->stride, &b->ptr.pp_double[ib+jk][jb], 1, ae_v_len(ia,ia+k-1)); - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+ik][jc+jk] = alpha*v; - } - else - { - c->ptr.pp_double[ic+ik][jc+jk] = beta*c->ptr.pp_double[ic+ik][jc+jk]+alpha*v; - } - } - } - } - j = j+4; - } - i = i+4; - } -} - - - - -/************************************************************************* -MKL-based kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixsyrkmkl(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_MKL - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixsyrkmkl(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper); -#endif -} - - -/************************************************************************* -MKL-based kernel - - -- ALGLIB routine -- - 19.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixgemmmkl(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_MKL - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_rmatrixgemmmkl(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc); -#endif -} - - - - -double vectornorm2(/* Real */ ae_vector* x, - ae_int_t i1, - ae_int_t i2, - ae_state *_state) -{ - ae_int_t n; - ae_int_t ix; - double absxi; - double scl; - double ssq; - double result; - - - n = i2-i1+1; - if( n<1 ) - { - result = 0; - return result; - } - if( n==1 ) - { - result = ae_fabs(x->ptr.p_double[i1], _state); - return result; - } - scl = 0; - ssq = 1; - for(ix=i1; ix<=i2; ix++) - { - if( ae_fp_neq(x->ptr.p_double[ix],0) ) - { - absxi = ae_fabs(x->ptr.p_double[ix], _state); - if( ae_fp_less(scl,absxi) ) - { - ssq = 1+ssq*ae_sqr(scl/absxi, _state); - scl = absxi; - } - else - { - ssq = ssq+ae_sqr(absxi/scl, _state); - } - } - } - result = scl*ae_sqrt(ssq, _state); - return result; -} - - -ae_int_t vectoridxabsmax(/* Real */ ae_vector* x, - ae_int_t i1, - ae_int_t i2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t result; - - - result = i1; - for(i=i1+1; i<=i2; i++) - { - if( ae_fp_greater(ae_fabs(x->ptr.p_double[i], _state),ae_fabs(x->ptr.p_double[result], _state)) ) - { - result = i; - } - } - return result; -} - - -ae_int_t columnidxabsmax(/* Real */ ae_matrix* x, - ae_int_t i1, - ae_int_t i2, - ae_int_t j, - ae_state *_state) -{ - ae_int_t i; - ae_int_t result; - - - result = i1; - for(i=i1+1; i<=i2; i++) - { - if( ae_fp_greater(ae_fabs(x->ptr.pp_double[i][j], _state),ae_fabs(x->ptr.pp_double[result][j], _state)) ) - { - result = i; - } - } - return result; -} - - -ae_int_t rowidxabsmax(/* Real */ ae_matrix* x, - ae_int_t j1, - ae_int_t j2, - ae_int_t i, - ae_state *_state) -{ - ae_int_t j; - ae_int_t result; - - - result = j1; - for(j=j1+1; j<=j2; j++) - { - if( ae_fp_greater(ae_fabs(x->ptr.pp_double[i][j], _state),ae_fabs(x->ptr.pp_double[i][result], _state)) ) - { - result = j; - } - } - return result; -} - - -double upperhessenberg1norm(/* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t i2, - ae_int_t j1, - ae_int_t j2, - /* Real */ ae_vector* work, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double result; - - - ae_assert(i2-i1==j2-j1, "UpperHessenberg1Norm: I2-I1<>J2-J1!", _state); - for(j=j1; j<=j2; j++) - { - work->ptr.p_double[j] = 0; - } - for(i=i1; i<=i2; i++) - { - for(j=ae_maxint(j1, j1+i-i1-1, _state); j<=j2; j++) - { - work->ptr.p_double[j] = work->ptr.p_double[j]+ae_fabs(a->ptr.pp_double[i][j], _state); - } - } - result = 0; - for(j=j1; j<=j2; j++) - { - result = ae_maxreal(result, work->ptr.p_double[j], _state); - } - return result; -} - - -void copymatrix(/* Real */ ae_matrix* a, - ae_int_t is1, - ae_int_t is2, - ae_int_t js1, - ae_int_t js2, - /* Real */ ae_matrix* b, - ae_int_t id1, - ae_int_t id2, - ae_int_t jd1, - ae_int_t jd2, - ae_state *_state) -{ - ae_int_t isrc; - ae_int_t idst; - - - if( is1>is2||js1>js2 ) - { - return; - } - ae_assert(is2-is1==id2-id1, "CopyMatrix: different sizes!", _state); - ae_assert(js2-js1==jd2-jd1, "CopyMatrix: different sizes!", _state); - for(isrc=is1; isrc<=is2; isrc++) - { - idst = isrc-is1+id1; - ae_v_move(&b->ptr.pp_double[idst][jd1], 1, &a->ptr.pp_double[isrc][js1], 1, ae_v_len(jd1,jd2)); - } -} - - -void inplacetranspose(/* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t i2, - ae_int_t j1, - ae_int_t j2, - /* Real */ ae_vector* work, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t ips; - ae_int_t jps; - ae_int_t l; - - - if( i1>i2||j1>j2 ) - { - return; - } - ae_assert(i1-i2==j1-j2, "InplaceTranspose error: incorrect array size!", _state); - for(i=i1; i<=i2-1; i++) - { - j = j1+i-i1; - ips = i+1; - jps = j1+ips-i1; - l = i2-i; - ae_v_move(&work->ptr.p_double[1], 1, &a->ptr.pp_double[ips][j], a->stride, ae_v_len(1,l)); - ae_v_move(&a->ptr.pp_double[ips][j], a->stride, &a->ptr.pp_double[i][jps], 1, ae_v_len(ips,i2)); - ae_v_move(&a->ptr.pp_double[i][jps], 1, &work->ptr.p_double[1], 1, ae_v_len(jps,j2)); - } -} - - -void copyandtranspose(/* Real */ ae_matrix* a, - ae_int_t is1, - ae_int_t is2, - ae_int_t js1, - ae_int_t js2, - /* Real */ ae_matrix* b, - ae_int_t id1, - ae_int_t id2, - ae_int_t jd1, - ae_int_t jd2, - ae_state *_state) -{ - ae_int_t isrc; - ae_int_t jdst; - - - if( is1>is2||js1>js2 ) - { - return; - } - ae_assert(is2-is1==jd2-jd1, "CopyAndTranspose: different sizes!", _state); - ae_assert(js2-js1==id2-id1, "CopyAndTranspose: different sizes!", _state); - for(isrc=is1; isrc<=is2; isrc++) - { - jdst = isrc-is1+jd1; - ae_v_move(&b->ptr.pp_double[id1][jdst], b->stride, &a->ptr.pp_double[isrc][js1], 1, ae_v_len(id1,id2)); - } -} - - -void matrixvectormultiply(/* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t i2, - ae_int_t j1, - ae_int_t j2, - ae_bool trans, - /* Real */ ae_vector* x, - ae_int_t ix1, - ae_int_t ix2, - double alpha, - /* Real */ ae_vector* y, - ae_int_t iy1, - ae_int_t iy2, - double beta, - ae_state *_state) -{ - ae_int_t i; - double v; - - - if( !trans ) - { - - /* - * y := alpha*A*x + beta*y; - */ - if( i1>i2||j1>j2 ) - { - return; - } - ae_assert(j2-j1==ix2-ix1, "MatrixVectorMultiply: A and X dont match!", _state); - ae_assert(i2-i1==iy2-iy1, "MatrixVectorMultiply: A and Y dont match!", _state); - - /* - * beta*y - */ - if( ae_fp_eq(beta,0) ) - { - for(i=iy1; i<=iy2; i++) - { - y->ptr.p_double[i] = 0; - } - } - else - { - ae_v_muld(&y->ptr.p_double[iy1], 1, ae_v_len(iy1,iy2), beta); - } - - /* - * alpha*A*x - */ - for(i=i1; i<=i2; i++) - { - v = ae_v_dotproduct(&a->ptr.pp_double[i][j1], 1, &x->ptr.p_double[ix1], 1, ae_v_len(j1,j2)); - y->ptr.p_double[iy1+i-i1] = y->ptr.p_double[iy1+i-i1]+alpha*v; - } - } - else - { - - /* - * y := alpha*A'*x + beta*y; - */ - if( i1>i2||j1>j2 ) - { - return; - } - ae_assert(i2-i1==ix2-ix1, "MatrixVectorMultiply: A and X dont match!", _state); - ae_assert(j2-j1==iy2-iy1, "MatrixVectorMultiply: A and Y dont match!", _state); - - /* - * beta*y - */ - if( ae_fp_eq(beta,0) ) - { - for(i=iy1; i<=iy2; i++) - { - y->ptr.p_double[i] = 0; - } - } - else - { - ae_v_muld(&y->ptr.p_double[iy1], 1, ae_v_len(iy1,iy2), beta); - } - - /* - * alpha*A'*x - */ - for(i=i1; i<=i2; i++) - { - v = alpha*x->ptr.p_double[ix1+i-i1]; - ae_v_addd(&y->ptr.p_double[iy1], 1, &a->ptr.pp_double[i][j1], 1, ae_v_len(iy1,iy2), v); - } - } -} - - -double pythag2(double x, double y, ae_state *_state) -{ - double w; - double xabs; - double yabs; - double z; - double result; - - - xabs = ae_fabs(x, _state); - yabs = ae_fabs(y, _state); - w = ae_maxreal(xabs, yabs, _state); - z = ae_minreal(xabs, yabs, _state); - if( ae_fp_eq(z,0) ) - { - result = w; - } - else - { - result = w*ae_sqrt(1+ae_sqr(z/w, _state), _state); - } - return result; -} - - -void matrixmatrixmultiply(/* Real */ ae_matrix* a, - ae_int_t ai1, - ae_int_t ai2, - ae_int_t aj1, - ae_int_t aj2, - ae_bool transa, - /* Real */ ae_matrix* b, - ae_int_t bi1, - ae_int_t bi2, - ae_int_t bj1, - ae_int_t bj2, - ae_bool transb, - double alpha, - /* Real */ ae_matrix* c, - ae_int_t ci1, - ae_int_t ci2, - ae_int_t cj1, - ae_int_t cj2, - double beta, - /* Real */ ae_vector* work, - ae_state *_state) -{ - ae_int_t arows; - ae_int_t acols; - ae_int_t brows; - ae_int_t bcols; - ae_int_t crows; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t l; - ae_int_t r; - double v; - - - - /* - * Setup - */ - if( !transa ) - { - arows = ai2-ai1+1; - acols = aj2-aj1+1; - } - else - { - arows = aj2-aj1+1; - acols = ai2-ai1+1; - } - if( !transb ) - { - brows = bi2-bi1+1; - bcols = bj2-bj1+1; - } - else - { - brows = bj2-bj1+1; - bcols = bi2-bi1+1; - } - ae_assert(acols==brows, "MatrixMatrixMultiply: incorrect matrix sizes!", _state); - if( ((arows<=0||acols<=0)||brows<=0)||bcols<=0 ) - { - return; - } - crows = arows; - - /* - * Test WORK - */ - i = ae_maxint(arows, acols, _state); - i = ae_maxint(brows, i, _state); - i = ae_maxint(i, bcols, _state); - work->ptr.p_double[1] = 0; - work->ptr.p_double[i] = 0; - - /* - * Prepare C - */ - if( ae_fp_eq(beta,0) ) - { - for(i=ci1; i<=ci2; i++) - { - for(j=cj1; j<=cj2; j++) - { - c->ptr.pp_double[i][j] = 0; - } - } - } - else - { - for(i=ci1; i<=ci2; i++) - { - ae_v_muld(&c->ptr.pp_double[i][cj1], 1, ae_v_len(cj1,cj2), beta); - } - } - - /* - * A*B - */ - if( !transa&&!transb ) - { - for(l=ai1; l<=ai2; l++) - { - for(r=bi1; r<=bi2; r++) - { - v = alpha*a->ptr.pp_double[l][aj1+r-bi1]; - k = ci1+l-ai1; - ae_v_addd(&c->ptr.pp_double[k][cj1], 1, &b->ptr.pp_double[r][bj1], 1, ae_v_len(cj1,cj2), v); - } - } - return; - } - - /* - * A*B' - */ - if( !transa&&transb ) - { - if( arows*acolsptr.pp_double[l][aj1], 1, &b->ptr.pp_double[r][bj1], 1, ae_v_len(aj1,aj2)); - c->ptr.pp_double[ci1+l-ai1][cj1+r-bi1] = c->ptr.pp_double[ci1+l-ai1][cj1+r-bi1]+alpha*v; - } - } - return; - } - else - { - for(l=ai1; l<=ai2; l++) - { - for(r=bi1; r<=bi2; r++) - { - v = ae_v_dotproduct(&a->ptr.pp_double[l][aj1], 1, &b->ptr.pp_double[r][bj1], 1, ae_v_len(aj1,aj2)); - c->ptr.pp_double[ci1+l-ai1][cj1+r-bi1] = c->ptr.pp_double[ci1+l-ai1][cj1+r-bi1]+alpha*v; - } - } - return; - } - } - - /* - * A'*B - */ - if( transa&&!transb ) - { - for(l=aj1; l<=aj2; l++) - { - for(r=bi1; r<=bi2; r++) - { - v = alpha*a->ptr.pp_double[ai1+r-bi1][l]; - k = ci1+l-aj1; - ae_v_addd(&c->ptr.pp_double[k][cj1], 1, &b->ptr.pp_double[r][bj1], 1, ae_v_len(cj1,cj2), v); - } - } - return; - } - - /* - * A'*B' - */ - if( transa&&transb ) - { - if( arows*acolsptr.p_double[i] = 0.0; - } - for(l=ai1; l<=ai2; l++) - { - v = alpha*b->ptr.pp_double[r][bj1+l-ai1]; - ae_v_addd(&work->ptr.p_double[1], 1, &a->ptr.pp_double[l][aj1], 1, ae_v_len(1,crows), v); - } - ae_v_add(&c->ptr.pp_double[ci1][k], c->stride, &work->ptr.p_double[1], 1, ae_v_len(ci1,ci2)); - } - return; - } - else - { - for(l=aj1; l<=aj2; l++) - { - k = ai2-ai1+1; - ae_v_move(&work->ptr.p_double[1], 1, &a->ptr.pp_double[ai1][l], a->stride, ae_v_len(1,k)); - for(r=bi1; r<=bi2; r++) - { - v = ae_v_dotproduct(&work->ptr.p_double[1], 1, &b->ptr.pp_double[r][bj1], 1, ae_v_len(1,k)); - c->ptr.pp_double[ci1+l-aj1][cj1+r-bi1] = c->ptr.pp_double[ci1+l-aj1][cj1+r-bi1]+alpha*v; - } - } - return; - } - } -} - - - - -void hermitianmatrixvectormultiply(/* Complex */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Complex */ ae_vector* x, - ae_complex alpha, - /* Complex */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ba1; - ae_int_t by1; - ae_int_t by2; - ae_int_t bx1; - ae_int_t bx2; - ae_int_t n; - ae_complex v; - - - n = i2-i1+1; - if( n<=0 ) - { - return; - } - - /* - * Let A = L + D + U, where - * L is strictly lower triangular (main diagonal is zero) - * D is diagonal - * U is strictly upper triangular (main diagonal is zero) - * - * A*x = L*x + D*x + U*x - * - * Calculate D*x first - */ - for(i=i1; i<=i2; i++) - { - y->ptr.p_complex[i-i1+1] = ae_c_mul(a->ptr.pp_complex[i][i],x->ptr.p_complex[i-i1+1]); - } - - /* - * Add L*x + U*x - */ - if( isupper ) - { - for(i=i1; i<=i2-1; i++) - { - - /* - * Add L*x to the result - */ - v = x->ptr.p_complex[i-i1+1]; - by1 = i-i1+2; - by2 = n; - ba1 = i+1; - ae_v_caddc(&y->ptr.p_complex[by1], 1, &a->ptr.pp_complex[i][ba1], 1, "Conj", ae_v_len(by1,by2), v); - - /* - * Add U*x to the result - */ - bx1 = i-i1+2; - bx2 = n; - ba1 = i+1; - v = ae_v_cdotproduct(&x->ptr.p_complex[bx1], 1, "N", &a->ptr.pp_complex[i][ba1], 1, "N", ae_v_len(bx1,bx2)); - y->ptr.p_complex[i-i1+1] = ae_c_add(y->ptr.p_complex[i-i1+1],v); - } - } - else - { - for(i=i1+1; i<=i2; i++) - { - - /* - * Add L*x to the result - */ - bx1 = 1; - bx2 = i-i1; - ba1 = i1; - v = ae_v_cdotproduct(&x->ptr.p_complex[bx1], 1, "N", &a->ptr.pp_complex[i][ba1], 1, "N", ae_v_len(bx1,bx2)); - y->ptr.p_complex[i-i1+1] = ae_c_add(y->ptr.p_complex[i-i1+1],v); - - /* - * Add U*x to the result - */ - v = x->ptr.p_complex[i-i1+1]; - by1 = 1; - by2 = i-i1; - ba1 = i1; - ae_v_caddc(&y->ptr.p_complex[by1], 1, &a->ptr.pp_complex[i][ba1], 1, "Conj", ae_v_len(by1,by2), v); - } - } - ae_v_cmulc(&y->ptr.p_complex[1], 1, ae_v_len(1,n), alpha); -} - - -void hermitianrank2update(/* Complex */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Complex */ ae_vector* x, - /* Complex */ ae_vector* y, - /* Complex */ ae_vector* t, - ae_complex alpha, - ae_state *_state) -{ - ae_int_t i; - ae_int_t tp1; - ae_int_t tp2; - ae_complex v; - - - if( isupper ) - { - for(i=i1; i<=i2; i++) - { - tp1 = i+1-i1; - tp2 = i2-i1+1; - v = ae_c_mul(alpha,x->ptr.p_complex[i+1-i1]); - ae_v_cmovec(&t->ptr.p_complex[tp1], 1, &y->ptr.p_complex[tp1], 1, "Conj", ae_v_len(tp1,tp2), v); - v = ae_c_mul(ae_c_conj(alpha, _state),y->ptr.p_complex[i+1-i1]); - ae_v_caddc(&t->ptr.p_complex[tp1], 1, &x->ptr.p_complex[tp1], 1, "Conj", ae_v_len(tp1,tp2), v); - ae_v_cadd(&a->ptr.pp_complex[i][i], 1, &t->ptr.p_complex[tp1], 1, "N", ae_v_len(i,i2)); - } - } - else - { - for(i=i1; i<=i2; i++) - { - tp1 = 1; - tp2 = i+1-i1; - v = ae_c_mul(alpha,x->ptr.p_complex[i+1-i1]); - ae_v_cmovec(&t->ptr.p_complex[tp1], 1, &y->ptr.p_complex[tp1], 1, "Conj", ae_v_len(tp1,tp2), v); - v = ae_c_mul(ae_c_conj(alpha, _state),y->ptr.p_complex[i+1-i1]); - ae_v_caddc(&t->ptr.p_complex[tp1], 1, &x->ptr.p_complex[tp1], 1, "Conj", ae_v_len(tp1,tp2), v); - ae_v_cadd(&a->ptr.pp_complex[i][i1], 1, &t->ptr.p_complex[tp1], 1, "N", ae_v_len(i1,i)); - } - } -} - - - - -/************************************************************************* -Generation of an elementary reflection transformation - -The subroutine generates elementary reflection H of order N, so that, for -a given X, the following equality holds true: - - ( X(1) ) ( Beta ) -H * ( .. ) = ( 0 ) - ( X(n) ) ( 0 ) - -where - ( V(1) ) -H = 1 - Tau * ( .. ) * ( V(1), ..., V(n) ) - ( V(n) ) - -where the first component of vector V equals 1. - -Input parameters: - X - vector. Array whose index ranges within [1..N]. - N - reflection order. - -Output parameters: - X - components from 2 to N are replaced with vector V. - The first component is replaced with parameter Beta. - Tau - scalar value Tau. If X is a null vector, Tau equals 0, - otherwise 1 <= Tau <= 2. - -This subroutine is the modification of the DLARFG subroutines from -the LAPACK library. - -MODIFICATIONS: - 24.12.2005 sign(Alpha) was replaced with an analogous to the Fortran SIGN code. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void generatereflection(/* Real */ ae_vector* x, - ae_int_t n, - double* tau, - ae_state *_state) -{ - ae_int_t j; - double alpha; - double xnorm; - double v; - double beta; - double mx; - double s; - - *tau = 0; - - if( n<=1 ) - { - *tau = 0; - return; - } - - /* - * Scale if needed (to avoid overflow/underflow during intermediate - * calculations). - */ - mx = 0; - for(j=1; j<=n; j++) - { - mx = ae_maxreal(ae_fabs(x->ptr.p_double[j], _state), mx, _state); - } - s = 1; - if( ae_fp_neq(mx,0) ) - { - if( ae_fp_less_eq(mx,ae_minrealnumber/ae_machineepsilon) ) - { - s = ae_minrealnumber/ae_machineepsilon; - v = 1/s; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), v); - mx = mx*v; - } - else - { - if( ae_fp_greater_eq(mx,ae_maxrealnumber*ae_machineepsilon) ) - { - s = ae_maxrealnumber*ae_machineepsilon; - v = 1/s; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), v); - mx = mx*v; - } - } - } - - /* - * XNORM = DNRM2( N-1, X, INCX ) - */ - alpha = x->ptr.p_double[1]; - xnorm = 0; - if( ae_fp_neq(mx,0) ) - { - for(j=2; j<=n; j++) - { - xnorm = xnorm+ae_sqr(x->ptr.p_double[j]/mx, _state); - } - xnorm = ae_sqrt(xnorm, _state)*mx; - } - if( ae_fp_eq(xnorm,0) ) - { - - /* - * H = I - */ - *tau = 0; - x->ptr.p_double[1] = x->ptr.p_double[1]*s; - return; - } - - /* - * general case - */ - mx = ae_maxreal(ae_fabs(alpha, _state), ae_fabs(xnorm, _state), _state); - beta = -mx*ae_sqrt(ae_sqr(alpha/mx, _state)+ae_sqr(xnorm/mx, _state), _state); - if( ae_fp_less(alpha,0) ) - { - beta = -beta; - } - *tau = (beta-alpha)/beta; - v = 1/(alpha-beta); - ae_v_muld(&x->ptr.p_double[2], 1, ae_v_len(2,n), v); - x->ptr.p_double[1] = beta; - - /* - * Scale back outputs - */ - x->ptr.p_double[1] = x->ptr.p_double[1]*s; -} - - -/************************************************************************* -Application of an elementary reflection to a rectangular matrix of size MxN - -The algorithm pre-multiplies the matrix by an elementary reflection transformation -which is given by column V and scalar Tau (see the description of the -GenerateReflection procedure). Not the whole matrix but only a part of it -is transformed (rows from M1 to M2, columns from N1 to N2). Only the elements -of this submatrix are changed. - -Input parameters: - C - matrix to be transformed. - Tau - scalar defining the transformation. - V - column defining the transformation. - Array whose index ranges within [1..M2-M1+1]. - M1, M2 - range of rows to be transformed. - N1, N2 - range of columns to be transformed. - WORK - working array whose indexes goes from N1 to N2. - -Output parameters: - C - the result of multiplying the input matrix C by the - transformation matrix which is given by Tau and V. - If N1>N2 or M1>M2, C is not modified. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void applyreflectionfromtheleft(/* Real */ ae_matrix* c, - double tau, - /* Real */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* work, - ae_state *_state) -{ - double t; - ae_int_t i; - - - if( (ae_fp_eq(tau,0)||n1>n2)||m1>m2 ) - { - return; - } - - /* - * w := C' * v - */ - for(i=n1; i<=n2; i++) - { - work->ptr.p_double[i] = 0; - } - for(i=m1; i<=m2; i++) - { - t = v->ptr.p_double[i+1-m1]; - ae_v_addd(&work->ptr.p_double[n1], 1, &c->ptr.pp_double[i][n1], 1, ae_v_len(n1,n2), t); - } - - /* - * C := C - tau * v * w' - */ - for(i=m1; i<=m2; i++) - { - t = v->ptr.p_double[i-m1+1]*tau; - ae_v_subd(&c->ptr.pp_double[i][n1], 1, &work->ptr.p_double[n1], 1, ae_v_len(n1,n2), t); - } -} - - -/************************************************************************* -Application of an elementary reflection to a rectangular matrix of size MxN - -The algorithm post-multiplies the matrix by an elementary reflection transformation -which is given by column V and scalar Tau (see the description of the -GenerateReflection procedure). Not the whole matrix but only a part of it -is transformed (rows from M1 to M2, columns from N1 to N2). Only the -elements of this submatrix are changed. - -Input parameters: - C - matrix to be transformed. - Tau - scalar defining the transformation. - V - column defining the transformation. - Array whose index ranges within [1..N2-N1+1]. - M1, M2 - range of rows to be transformed. - N1, N2 - range of columns to be transformed. - WORK - working array whose indexes goes from M1 to M2. - -Output parameters: - C - the result of multiplying the input matrix C by the - transformation matrix which is given by Tau and V. - If N1>N2 or M1>M2, C is not modified. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void applyreflectionfromtheright(/* Real */ ae_matrix* c, - double tau, - /* Real */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* work, - ae_state *_state) -{ - double t; - ae_int_t i; - ae_int_t vm; - - - if( (ae_fp_eq(tau,0)||n1>n2)||m1>m2 ) - { - return; - } - vm = n2-n1+1; - for(i=m1; i<=m2; i++) - { - t = ae_v_dotproduct(&c->ptr.pp_double[i][n1], 1, &v->ptr.p_double[1], 1, ae_v_len(n1,n2)); - t = t*tau; - ae_v_subd(&c->ptr.pp_double[i][n1], 1, &v->ptr.p_double[1], 1, ae_v_len(n1,n2), t); - } - - /* - * This line is necessary to avoid spurious compiler warnings - */ - touchint(&vm, _state); -} - - - - -/************************************************************************* -Generation of an elementary complex reflection transformation - -The subroutine generates elementary complex reflection H of order N, so -that, for a given X, the following equality holds true: - - ( X(1) ) ( Beta ) -H' * ( .. ) = ( 0 ), H'*H = I, Beta is a real number - ( X(n) ) ( 0 ) - -where - - ( V(1) ) -H = 1 - Tau * ( .. ) * ( conj(V(1)), ..., conj(V(n)) ) - ( V(n) ) - -where the first component of vector V equals 1. - -Input parameters: - X - vector. Array with elements [1..N]. - N - reflection order. - -Output parameters: - X - components from 2 to N are replaced by vector V. - The first component is replaced with parameter Beta. - Tau - scalar value Tau. - -This subroutine is the modification of CLARFG subroutines from the LAPACK -library. It has similar functionality except for the fact that it doesn’t -handle errors when intermediate results cause an overflow. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void complexgeneratereflection(/* Complex */ ae_vector* x, - ae_int_t n, - ae_complex* tau, - ae_state *_state) -{ - ae_int_t j; - ae_complex alpha; - double alphi; - double alphr; - double beta; - double xnorm; - double mx; - ae_complex t; - double s; - ae_complex v; - - tau->x = 0; - tau->y = 0; - - if( n<=0 ) - { - *tau = ae_complex_from_d(0); - return; - } - - /* - * Scale if needed (to avoid overflow/underflow during intermediate - * calculations). - */ - mx = 0; - for(j=1; j<=n; j++) - { - mx = ae_maxreal(ae_c_abs(x->ptr.p_complex[j], _state), mx, _state); - } - s = 1; - if( ae_fp_neq(mx,0) ) - { - if( ae_fp_less(mx,1) ) - { - s = ae_sqrt(ae_minrealnumber, _state); - v = ae_complex_from_d(1/s); - ae_v_cmulc(&x->ptr.p_complex[1], 1, ae_v_len(1,n), v); - } - else - { - s = ae_sqrt(ae_maxrealnumber, _state); - v = ae_complex_from_d(1/s); - ae_v_cmulc(&x->ptr.p_complex[1], 1, ae_v_len(1,n), v); - } - } - - /* - * calculate - */ - alpha = x->ptr.p_complex[1]; - mx = 0; - for(j=2; j<=n; j++) - { - mx = ae_maxreal(ae_c_abs(x->ptr.p_complex[j], _state), mx, _state); - } - xnorm = 0; - if( ae_fp_neq(mx,0) ) - { - for(j=2; j<=n; j++) - { - t = ae_c_div_d(x->ptr.p_complex[j],mx); - xnorm = xnorm+ae_c_mul(t,ae_c_conj(t, _state)).x; - } - xnorm = ae_sqrt(xnorm, _state)*mx; - } - alphr = alpha.x; - alphi = alpha.y; - if( ae_fp_eq(xnorm,0)&&ae_fp_eq(alphi,0) ) - { - *tau = ae_complex_from_d(0); - x->ptr.p_complex[1] = ae_c_mul_d(x->ptr.p_complex[1],s); - return; - } - mx = ae_maxreal(ae_fabs(alphr, _state), ae_fabs(alphi, _state), _state); - mx = ae_maxreal(mx, ae_fabs(xnorm, _state), _state); - beta = -mx*ae_sqrt(ae_sqr(alphr/mx, _state)+ae_sqr(alphi/mx, _state)+ae_sqr(xnorm/mx, _state), _state); - if( ae_fp_less(alphr,0) ) - { - beta = -beta; - } - tau->x = (beta-alphr)/beta; - tau->y = -alphi/beta; - alpha = ae_c_d_div(1,ae_c_sub_d(alpha,beta)); - if( n>1 ) - { - ae_v_cmulc(&x->ptr.p_complex[2], 1, ae_v_len(2,n), alpha); - } - alpha = ae_complex_from_d(beta); - x->ptr.p_complex[1] = alpha; - - /* - * Scale back - */ - x->ptr.p_complex[1] = ae_c_mul_d(x->ptr.p_complex[1],s); -} - - -/************************************************************************* -Application of an elementary reflection to a rectangular matrix of size MxN - -The algorithm pre-multiplies the matrix by an elementary reflection -transformation which is given by column V and scalar Tau (see the -description of the GenerateReflection). Not the whole matrix but only a -part of it is transformed (rows from M1 to M2, columns from N1 to N2). Only -the elements of this submatrix are changed. - -Note: the matrix is multiplied by H, not by H'. If it is required to -multiply the matrix by H', it is necessary to pass Conj(Tau) instead of Tau. - -Input parameters: - C - matrix to be transformed. - Tau - scalar defining transformation. - V - column defining transformation. - Array whose index ranges within [1..M2-M1+1] - M1, M2 - range of rows to be transformed. - N1, N2 - range of columns to be transformed. - WORK - working array whose index goes from N1 to N2. - -Output parameters: - C - the result of multiplying the input matrix C by the - transformation matrix which is given by Tau and V. - If N1>N2 or M1>M2, C is not modified. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void complexapplyreflectionfromtheleft(/* Complex */ ae_matrix* c, - ae_complex tau, - /* Complex */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Complex */ ae_vector* work, - ae_state *_state) -{ - ae_complex t; - ae_int_t i; - - - if( (ae_c_eq_d(tau,0)||n1>n2)||m1>m2 ) - { - return; - } - - /* - * w := C^T * conj(v) - */ - for(i=n1; i<=n2; i++) - { - work->ptr.p_complex[i] = ae_complex_from_d(0); - } - for(i=m1; i<=m2; i++) - { - t = ae_c_conj(v->ptr.p_complex[i+1-m1], _state); - ae_v_caddc(&work->ptr.p_complex[n1], 1, &c->ptr.pp_complex[i][n1], 1, "N", ae_v_len(n1,n2), t); - } - - /* - * C := C - tau * v * w^T - */ - for(i=m1; i<=m2; i++) - { - t = ae_c_mul(v->ptr.p_complex[i-m1+1],tau); - ae_v_csubc(&c->ptr.pp_complex[i][n1], 1, &work->ptr.p_complex[n1], 1, "N", ae_v_len(n1,n2), t); - } -} - - -/************************************************************************* -Application of an elementary reflection to a rectangular matrix of size MxN - -The algorithm post-multiplies the matrix by an elementary reflection -transformation which is given by column V and scalar Tau (see the -description of the GenerateReflection). Not the whole matrix but only a -part of it is transformed (rows from M1 to M2, columns from N1 to N2). -Only the elements of this submatrix are changed. - -Input parameters: - C - matrix to be transformed. - Tau - scalar defining transformation. - V - column defining transformation. - Array whose index ranges within [1..N2-N1+1] - M1, M2 - range of rows to be transformed. - N1, N2 - range of columns to be transformed. - WORK - working array whose index goes from M1 to M2. - -Output parameters: - C - the result of multiplying the input matrix C by the - transformation matrix which is given by Tau and V. - If N1>N2 or M1>M2, C is not modified. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void complexapplyreflectionfromtheright(/* Complex */ ae_matrix* c, - ae_complex tau, - /* Complex */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Complex */ ae_vector* work, - ae_state *_state) -{ - ae_complex t; - ae_int_t i; - ae_int_t vm; - - - if( (ae_c_eq_d(tau,0)||n1>n2)||m1>m2 ) - { - return; - } - - /* - * w := C * v - */ - vm = n2-n1+1; - for(i=m1; i<=m2; i++) - { - t = ae_v_cdotproduct(&c->ptr.pp_complex[i][n1], 1, "N", &v->ptr.p_complex[1], 1, "N", ae_v_len(n1,n2)); - work->ptr.p_complex[i] = t; - } - - /* - * C := C - w * conj(v^T) - */ - ae_v_cmove(&v->ptr.p_complex[1], 1, &v->ptr.p_complex[1], 1, "Conj", ae_v_len(1,vm)); - for(i=m1; i<=m2; i++) - { - t = ae_c_mul(work->ptr.p_complex[i],tau); - ae_v_csubc(&c->ptr.pp_complex[i][n1], 1, &v->ptr.p_complex[1], 1, "N", ae_v_len(n1,n2), t); - } - ae_v_cmove(&v->ptr.p_complex[1], 1, &v->ptr.p_complex[1], 1, "Conj", ae_v_len(1,vm)); -} - - - - -void symmetricmatrixvectormultiply(/* Real */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* x, - double alpha, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ba1; - ae_int_t ba2; - ae_int_t by1; - ae_int_t by2; - ae_int_t bx1; - ae_int_t bx2; - ae_int_t n; - double v; - - - n = i2-i1+1; - if( n<=0 ) - { - return; - } - - /* - * Let A = L + D + U, where - * L is strictly lower triangular (main diagonal is zero) - * D is diagonal - * U is strictly upper triangular (main diagonal is zero) - * - * A*x = L*x + D*x + U*x - * - * Calculate D*x first - */ - for(i=i1; i<=i2; i++) - { - y->ptr.p_double[i-i1+1] = a->ptr.pp_double[i][i]*x->ptr.p_double[i-i1+1]; - } - - /* - * Add L*x + U*x - */ - if( isupper ) - { - for(i=i1; i<=i2-1; i++) - { - - /* - * Add L*x to the result - */ - v = x->ptr.p_double[i-i1+1]; - by1 = i-i1+2; - by2 = n; - ba1 = i+1; - ba2 = i2; - ae_v_addd(&y->ptr.p_double[by1], 1, &a->ptr.pp_double[i][ba1], 1, ae_v_len(by1,by2), v); - - /* - * Add U*x to the result - */ - bx1 = i-i1+2; - bx2 = n; - ba1 = i+1; - ba2 = i2; - v = ae_v_dotproduct(&x->ptr.p_double[bx1], 1, &a->ptr.pp_double[i][ba1], 1, ae_v_len(bx1,bx2)); - y->ptr.p_double[i-i1+1] = y->ptr.p_double[i-i1+1]+v; - } - } - else - { - for(i=i1+1; i<=i2; i++) - { - - /* - * Add L*x to the result - */ - bx1 = 1; - bx2 = i-i1; - ba1 = i1; - ba2 = i-1; - v = ae_v_dotproduct(&x->ptr.p_double[bx1], 1, &a->ptr.pp_double[i][ba1], 1, ae_v_len(bx1,bx2)); - y->ptr.p_double[i-i1+1] = y->ptr.p_double[i-i1+1]+v; - - /* - * Add U*x to the result - */ - v = x->ptr.p_double[i-i1+1]; - by1 = 1; - by2 = i-i1; - ba1 = i1; - ba2 = i-1; - ae_v_addd(&y->ptr.p_double[by1], 1, &a->ptr.pp_double[i][ba1], 1, ae_v_len(by1,by2), v); - } - } - ae_v_muld(&y->ptr.p_double[1], 1, ae_v_len(1,n), alpha); - touchint(&ba2, _state); -} - - -void symmetricrank2update(/* Real */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* t, - double alpha, - ae_state *_state) -{ - ae_int_t i; - ae_int_t tp1; - ae_int_t tp2; - double v; - - - if( isupper ) - { - for(i=i1; i<=i2; i++) - { - tp1 = i+1-i1; - tp2 = i2-i1+1; - v = x->ptr.p_double[i+1-i1]; - ae_v_moved(&t->ptr.p_double[tp1], 1, &y->ptr.p_double[tp1], 1, ae_v_len(tp1,tp2), v); - v = y->ptr.p_double[i+1-i1]; - ae_v_addd(&t->ptr.p_double[tp1], 1, &x->ptr.p_double[tp1], 1, ae_v_len(tp1,tp2), v); - ae_v_muld(&t->ptr.p_double[tp1], 1, ae_v_len(tp1,tp2), alpha); - ae_v_add(&a->ptr.pp_double[i][i], 1, &t->ptr.p_double[tp1], 1, ae_v_len(i,i2)); - } - } - else - { - for(i=i1; i<=i2; i++) - { - tp1 = 1; - tp2 = i+1-i1; - v = x->ptr.p_double[i+1-i1]; - ae_v_moved(&t->ptr.p_double[tp1], 1, &y->ptr.p_double[tp1], 1, ae_v_len(tp1,tp2), v); - v = y->ptr.p_double[i+1-i1]; - ae_v_addd(&t->ptr.p_double[tp1], 1, &x->ptr.p_double[tp1], 1, ae_v_len(tp1,tp2), v); - ae_v_muld(&t->ptr.p_double[tp1], 1, ae_v_len(tp1,tp2), alpha); - ae_v_add(&a->ptr.pp_double[i][i1], 1, &t->ptr.p_double[tp1], 1, ae_v_len(i1,i)); - } - } -} - - - - -/************************************************************************* -Application of a sequence of elementary rotations to a matrix - -The algorithm pre-multiplies the matrix by a sequence of rotation -transformations which is given by arrays C and S. Depending on the value -of the IsForward parameter either 1 and 2, 3 and 4 and so on (if IsForward=true) -rows are rotated, or the rows N and N-1, N-2 and N-3 and so on, are rotated. - -Not the whole matrix but only a part of it is transformed (rows from M1 to -M2, columns from N1 to N2). Only the elements of this submatrix are changed. - -Input parameters: - IsForward - the sequence of the rotation application. - M1,M2 - the range of rows to be transformed. - N1, N2 - the range of columns to be transformed. - C,S - transformation coefficients. - Array whose index ranges within [1..M2-M1]. - A - processed matrix. - WORK - working array whose index ranges within [N1..N2]. - -Output parameters: - A - transformed matrix. - -Utility subroutine. -*************************************************************************/ -void applyrotationsfromtheleft(ae_bool isforward, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* c, - /* Real */ ae_vector* s, - /* Real */ ae_matrix* a, - /* Real */ ae_vector* work, - ae_state *_state) -{ - ae_int_t j; - ae_int_t jp1; - double ctemp; - double stemp; - double temp; - - - if( m1>m2||n1>n2 ) - { - return; - } - - /* - * Form P * A - */ - if( isforward ) - { - if( n1!=n2 ) - { - - /* - * Common case: N1<>N2 - */ - for(j=m1; j<=m2-1; j++) - { - ctemp = c->ptr.p_double[j-m1+1]; - stemp = s->ptr.p_double[j-m1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - jp1 = j+1; - ae_v_moved(&work->ptr.p_double[n1], 1, &a->ptr.pp_double[jp1][n1], 1, ae_v_len(n1,n2), ctemp); - ae_v_subd(&work->ptr.p_double[n1], 1, &a->ptr.pp_double[j][n1], 1, ae_v_len(n1,n2), stemp); - ae_v_muld(&a->ptr.pp_double[j][n1], 1, ae_v_len(n1,n2), ctemp); - ae_v_addd(&a->ptr.pp_double[j][n1], 1, &a->ptr.pp_double[jp1][n1], 1, ae_v_len(n1,n2), stemp); - ae_v_move(&a->ptr.pp_double[jp1][n1], 1, &work->ptr.p_double[n1], 1, ae_v_len(n1,n2)); - } - } - } - else - { - - /* - * Special case: N1=N2 - */ - for(j=m1; j<=m2-1; j++) - { - ctemp = c->ptr.p_double[j-m1+1]; - stemp = s->ptr.p_double[j-m1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - temp = a->ptr.pp_double[j+1][n1]; - a->ptr.pp_double[j+1][n1] = ctemp*temp-stemp*a->ptr.pp_double[j][n1]; - a->ptr.pp_double[j][n1] = stemp*temp+ctemp*a->ptr.pp_double[j][n1]; - } - } - } - } - else - { - if( n1!=n2 ) - { - - /* - * Common case: N1<>N2 - */ - for(j=m2-1; j>=m1; j--) - { - ctemp = c->ptr.p_double[j-m1+1]; - stemp = s->ptr.p_double[j-m1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - jp1 = j+1; - ae_v_moved(&work->ptr.p_double[n1], 1, &a->ptr.pp_double[jp1][n1], 1, ae_v_len(n1,n2), ctemp); - ae_v_subd(&work->ptr.p_double[n1], 1, &a->ptr.pp_double[j][n1], 1, ae_v_len(n1,n2), stemp); - ae_v_muld(&a->ptr.pp_double[j][n1], 1, ae_v_len(n1,n2), ctemp); - ae_v_addd(&a->ptr.pp_double[j][n1], 1, &a->ptr.pp_double[jp1][n1], 1, ae_v_len(n1,n2), stemp); - ae_v_move(&a->ptr.pp_double[jp1][n1], 1, &work->ptr.p_double[n1], 1, ae_v_len(n1,n2)); - } - } - } - else - { - - /* - * Special case: N1=N2 - */ - for(j=m2-1; j>=m1; j--) - { - ctemp = c->ptr.p_double[j-m1+1]; - stemp = s->ptr.p_double[j-m1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - temp = a->ptr.pp_double[j+1][n1]; - a->ptr.pp_double[j+1][n1] = ctemp*temp-stemp*a->ptr.pp_double[j][n1]; - a->ptr.pp_double[j][n1] = stemp*temp+ctemp*a->ptr.pp_double[j][n1]; - } - } - } - } -} - - -/************************************************************************* -Application of a sequence of elementary rotations to a matrix - -The algorithm post-multiplies the matrix by a sequence of rotation -transformations which is given by arrays C and S. Depending on the value -of the IsForward parameter either 1 and 2, 3 and 4 and so on (if IsForward=true) -rows are rotated, or the rows N and N-1, N-2 and N-3 and so on are rotated. - -Not the whole matrix but only a part of it is transformed (rows from M1 -to M2, columns from N1 to N2). Only the elements of this submatrix are changed. - -Input parameters: - IsForward - the sequence of the rotation application. - M1,M2 - the range of rows to be transformed. - N1, N2 - the range of columns to be transformed. - C,S - transformation coefficients. - Array whose index ranges within [1..N2-N1]. - A - processed matrix. - WORK - working array whose index ranges within [M1..M2]. - -Output parameters: - A - transformed matrix. - -Utility subroutine. -*************************************************************************/ -void applyrotationsfromtheright(ae_bool isforward, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* c, - /* Real */ ae_vector* s, - /* Real */ ae_matrix* a, - /* Real */ ae_vector* work, - ae_state *_state) -{ - ae_int_t j; - ae_int_t jp1; - double ctemp; - double stemp; - double temp; - - - - /* - * Form A * P' - */ - if( isforward ) - { - if( m1!=m2 ) - { - - /* - * Common case: M1<>M2 - */ - for(j=n1; j<=n2-1; j++) - { - ctemp = c->ptr.p_double[j-n1+1]; - stemp = s->ptr.p_double[j-n1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - jp1 = j+1; - ae_v_moved(&work->ptr.p_double[m1], 1, &a->ptr.pp_double[m1][jp1], a->stride, ae_v_len(m1,m2), ctemp); - ae_v_subd(&work->ptr.p_double[m1], 1, &a->ptr.pp_double[m1][j], a->stride, ae_v_len(m1,m2), stemp); - ae_v_muld(&a->ptr.pp_double[m1][j], a->stride, ae_v_len(m1,m2), ctemp); - ae_v_addd(&a->ptr.pp_double[m1][j], a->stride, &a->ptr.pp_double[m1][jp1], a->stride, ae_v_len(m1,m2), stemp); - ae_v_move(&a->ptr.pp_double[m1][jp1], a->stride, &work->ptr.p_double[m1], 1, ae_v_len(m1,m2)); - } - } - } - else - { - - /* - * Special case: M1=M2 - */ - for(j=n1; j<=n2-1; j++) - { - ctemp = c->ptr.p_double[j-n1+1]; - stemp = s->ptr.p_double[j-n1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - temp = a->ptr.pp_double[m1][j+1]; - a->ptr.pp_double[m1][j+1] = ctemp*temp-stemp*a->ptr.pp_double[m1][j]; - a->ptr.pp_double[m1][j] = stemp*temp+ctemp*a->ptr.pp_double[m1][j]; - } - } - } - } - else - { - if( m1!=m2 ) - { - - /* - * Common case: M1<>M2 - */ - for(j=n2-1; j>=n1; j--) - { - ctemp = c->ptr.p_double[j-n1+1]; - stemp = s->ptr.p_double[j-n1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - jp1 = j+1; - ae_v_moved(&work->ptr.p_double[m1], 1, &a->ptr.pp_double[m1][jp1], a->stride, ae_v_len(m1,m2), ctemp); - ae_v_subd(&work->ptr.p_double[m1], 1, &a->ptr.pp_double[m1][j], a->stride, ae_v_len(m1,m2), stemp); - ae_v_muld(&a->ptr.pp_double[m1][j], a->stride, ae_v_len(m1,m2), ctemp); - ae_v_addd(&a->ptr.pp_double[m1][j], a->stride, &a->ptr.pp_double[m1][jp1], a->stride, ae_v_len(m1,m2), stemp); - ae_v_move(&a->ptr.pp_double[m1][jp1], a->stride, &work->ptr.p_double[m1], 1, ae_v_len(m1,m2)); - } - } - } - else - { - - /* - * Special case: M1=M2 - */ - for(j=n2-1; j>=n1; j--) - { - ctemp = c->ptr.p_double[j-n1+1]; - stemp = s->ptr.p_double[j-n1+1]; - if( ae_fp_neq(ctemp,1)||ae_fp_neq(stemp,0) ) - { - temp = a->ptr.pp_double[m1][j+1]; - a->ptr.pp_double[m1][j+1] = ctemp*temp-stemp*a->ptr.pp_double[m1][j]; - a->ptr.pp_double[m1][j] = stemp*temp+ctemp*a->ptr.pp_double[m1][j]; - } - } - } - } -} - - -/************************************************************************* -The subroutine generates the elementary rotation, so that: - -[ CS SN ] . [ F ] = [ R ] -[ -SN CS ] [ G ] [ 0 ] - -CS**2 + SN**2 = 1 -*************************************************************************/ -void generaterotation(double f, - double g, - double* cs, - double* sn, - double* r, - ae_state *_state) -{ - double f1; - double g1; - - *cs = 0; - *sn = 0; - *r = 0; - - if( ae_fp_eq(g,0) ) - { - *cs = 1; - *sn = 0; - *r = f; - } - else - { - if( ae_fp_eq(f,0) ) - { - *cs = 0; - *sn = 1; - *r = g; - } - else - { - f1 = f; - g1 = g; - if( ae_fp_greater(ae_fabs(f1, _state),ae_fabs(g1, _state)) ) - { - *r = ae_fabs(f1, _state)*ae_sqrt(1+ae_sqr(g1/f1, _state), _state); - } - else - { - *r = ae_fabs(g1, _state)*ae_sqrt(1+ae_sqr(f1/g1, _state), _state); - } - *cs = f1/(*r); - *sn = g1/(*r); - if( ae_fp_greater(ae_fabs(f, _state),ae_fabs(g, _state))&&ae_fp_less(*cs,0) ) - { - *cs = -*cs; - *sn = -*sn; - *r = -*r; - } - } - } -} - - - - -/************************************************************************* -Subroutine performing the Schur decomposition of a matrix in upper -Hessenberg form using the QR algorithm with multiple shifts. - -The source matrix H is represented as S'*H*S = T, where H - matrix in -upper Hessenberg form, S - orthogonal matrix (Schur vectors), T - upper -quasi-triangular matrix (with blocks of sizes 1x1 and 2x2 on the main -diagonal). - -Input parameters: - H - matrix to be decomposed. - Array whose indexes range within [1..N, 1..N]. - N - size of H, N>=0. - - -Output parameters: - H – contains the matrix T. - Array whose indexes range within [1..N, 1..N]. - All elements below the blocks on the main diagonal are equal - to 0. - S - contains Schur vectors. - Array whose indexes range within [1..N, 1..N]. - -Note 1: - The block structure of matrix T could be easily recognized: since all - the elements below the blocks are zeros, the elements a[i+1,i] which - are equal to 0 show the block border. - -Note 2: - the algorithm performance depends on the value of the internal - parameter NS of InternalSchurDecomposition subroutine which defines - the number of shifts in the QR algorithm (analog of the block width - in block matrix algorithms in linear algebra). If you require maximum - performance on your machine, it is recommended to adjust this - parameter manually. - -Result: - True, if the algorithm has converged and the parameters H and S contain - the result. - False, if the algorithm has not converged. - -Algorithm implemented on the basis of subroutine DHSEQR (LAPACK 3.0 library). -*************************************************************************/ -ae_bool upperhessenbergschurdecomposition(/* Real */ ae_matrix* h, - ae_int_t n, - /* Real */ ae_matrix* s, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector wi; - ae_vector wr; - ae_int_t info; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(s); - ae_vector_init(&wi, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wr, 0, DT_REAL, _state, ae_true); - - internalschurdecomposition(h, n, 1, 2, &wr, &wi, s, &info, _state); - result = info==0; - ae_frame_leave(_state); - return result; -} - - -void internalschurdecomposition(/* Real */ ae_matrix* h, - ae_int_t n, - ae_int_t tneeded, - ae_int_t zneeded, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - /* Real */ ae_matrix* z, - ae_int_t* info, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_int_t i; - ae_int_t i1; - ae_int_t i2; - ae_int_t ierr; - ae_int_t ii; - ae_int_t itemp; - ae_int_t itn; - ae_int_t its; - ae_int_t j; - ae_int_t k; - ae_int_t l; - ae_int_t maxb; - ae_int_t nr; - ae_int_t ns; - ae_int_t nv; - double absw; - double smlnum; - double tau; - double temp; - double tst1; - double ulp; - double unfl; - ae_matrix s; - ae_vector v; - ae_vector vv; - ae_vector workc1; - ae_vector works1; - ae_vector workv3; - ae_vector tmpwr; - ae_vector tmpwi; - ae_bool initz; - ae_bool wantt; - ae_bool wantz; - double cnst; - ae_bool failflag; - ae_int_t p1; - ae_int_t p2; - double vt; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(wr); - ae_vector_clear(wi); - *info = 0; - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&s, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&vv, 0, DT_REAL, _state, ae_true); - ae_vector_init(&workc1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&works1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&workv3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpwr, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpwi, 0, DT_REAL, _state, ae_true); - - - /* - * Set the order of the multi-shift QR algorithm to be used. - * If you want to tune algorithm, change this values - */ - ns = 12; - maxb = 50; - - /* - * Now 2 < NS <= MAXB < NH. - */ - maxb = ae_maxint(3, maxb, _state); - ns = ae_minint(maxb, ns, _state); - - /* - * Initialize - */ - cnst = 1.5; - ae_vector_set_length(&work, ae_maxint(n, 1, _state)+1, _state); - ae_matrix_set_length(&s, ns+1, ns+1, _state); - ae_vector_set_length(&v, ns+1+1, _state); - ae_vector_set_length(&vv, ns+1+1, _state); - ae_vector_set_length(wr, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(wi, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(&workc1, 1+1, _state); - ae_vector_set_length(&works1, 1+1, _state); - ae_vector_set_length(&workv3, 3+1, _state); - ae_vector_set_length(&tmpwr, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(&tmpwi, ae_maxint(n, 1, _state)+1, _state); - ae_assert(n>=0, "InternalSchurDecomposition: incorrect N!", _state); - ae_assert(tneeded==0||tneeded==1, "InternalSchurDecomposition: incorrect TNeeded!", _state); - ae_assert((zneeded==0||zneeded==1)||zneeded==2, "InternalSchurDecomposition: incorrect ZNeeded!", _state); - wantt = tneeded==1; - initz = zneeded==2; - wantz = zneeded!=0; - *info = 0; - - /* - * Initialize Z, if necessary - */ - if( initz ) - { - ae_matrix_set_length(z, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - for(j=1; j<=n; j++) - { - if( i==j ) - { - z->ptr.pp_double[i][j] = 1; - } - else - { - z->ptr.pp_double[i][j] = 0; - } - } - } - } - - /* - * Quick return if possible - */ - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - if( n==1 ) - { - wr->ptr.p_double[1] = h->ptr.pp_double[1][1]; - wi->ptr.p_double[1] = 0; - ae_frame_leave(_state); - return; - } - - /* - * Set rows and columns 1 to N to zero below the first - * subdiagonal. - */ - for(j=1; j<=n-2; j++) - { - for(i=j+2; i<=n; i++) - { - h->ptr.pp_double[i][j] = 0; - } - } - - /* - * Test if N is sufficiently small - */ - if( (ns<=2||ns>n)||maxb>=n ) - { - - /* - * Use the standard double-shift algorithm - */ - hsschur_internalauxschur(wantt, wantz, n, 1, n, h, wr, wi, 1, n, z, &work, &workv3, &workc1, &works1, info, _state); - - /* - * fill entries under diagonal blocks of T with zeros - */ - if( wantt ) - { - j = 1; - while(j<=n) - { - if( ae_fp_eq(wi->ptr.p_double[j],0) ) - { - for(i=j+1; i<=n; i++) - { - h->ptr.pp_double[i][j] = 0; - } - j = j+1; - } - else - { - for(i=j+2; i<=n; i++) - { - h->ptr.pp_double[i][j] = 0; - h->ptr.pp_double[i][j+1] = 0; - } - j = j+2; - } - } - } - ae_frame_leave(_state); - return; - } - unfl = ae_minrealnumber; - ulp = 2*ae_machineepsilon; - smlnum = unfl*(n/ulp); - - /* - * I1 and I2 are the indices of the first row and last column of H - * to which transformations must be applied. If eigenvalues only are - * being computed, I1 and I2 are set inside the main loop. - */ - i1 = 1; - i2 = n; - - /* - * ITN is the total number of multiple-shift QR iterations allowed. - */ - itn = 30*n; - - /* - * The main loop begins here. I is the loop index and decreases from - * IHI to ILO in steps of at most MAXB. Each iteration of the loop - * works with the active submatrix in rows and columns L to I. - * Eigenvalues I+1 to IHI have already converged. Either L = ILO or - * H(L,L-1) is negligible so that the matrix splits. - */ - i = n; - for(;;) - { - l = 1; - if( i<1 ) - { - - /* - * fill entries under diagonal blocks of T with zeros - */ - if( wantt ) - { - j = 1; - while(j<=n) - { - if( ae_fp_eq(wi->ptr.p_double[j],0) ) - { - for(i=j+1; i<=n; i++) - { - h->ptr.pp_double[i][j] = 0; - } - j = j+1; - } - else - { - for(i=j+2; i<=n; i++) - { - h->ptr.pp_double[i][j] = 0; - h->ptr.pp_double[i][j+1] = 0; - } - j = j+2; - } - } - } - - /* - * Exit - */ - ae_frame_leave(_state); - return; - } - - /* - * Perform multiple-shift QR iterations on rows and columns ILO to I - * until a submatrix of order at most MAXB splits off at the bottom - * because a subdiagonal element has become negligible. - */ - failflag = ae_true; - for(its=0; its<=itn; its++) - { - - /* - * Look for a single small subdiagonal element. - */ - for(k=i; k>=l+1; k--) - { - tst1 = ae_fabs(h->ptr.pp_double[k-1][k-1], _state)+ae_fabs(h->ptr.pp_double[k][k], _state); - if( ae_fp_eq(tst1,0) ) - { - tst1 = upperhessenberg1norm(h, l, i, l, i, &work, _state); - } - if( ae_fp_less_eq(ae_fabs(h->ptr.pp_double[k][k-1], _state),ae_maxreal(ulp*tst1, smlnum, _state)) ) - { - break; - } - } - l = k; - if( l>1 ) - { - - /* - * H(L,L-1) is negligible. - */ - h->ptr.pp_double[l][l-1] = 0; - } - - /* - * Exit from loop if a submatrix of order <= MAXB has split off. - */ - if( l>=i-maxb+1 ) - { - failflag = ae_false; - break; - } - - /* - * Now the active submatrix is in rows and columns L to I. If - * eigenvalues only are being computed, only the active submatrix - * need be transformed. - */ - if( its==20||its==30 ) - { - - /* - * Exceptional shifts. - */ - for(ii=i-ns+1; ii<=i; ii++) - { - wr->ptr.p_double[ii] = cnst*(ae_fabs(h->ptr.pp_double[ii][ii-1], _state)+ae_fabs(h->ptr.pp_double[ii][ii], _state)); - wi->ptr.p_double[ii] = 0; - } - } - else - { - - /* - * Use eigenvalues of trailing submatrix of order NS as shifts. - */ - copymatrix(h, i-ns+1, i, i-ns+1, i, &s, 1, ns, 1, ns, _state); - hsschur_internalauxschur(ae_false, ae_false, ns, 1, ns, &s, &tmpwr, &tmpwi, 1, ns, z, &work, &workv3, &workc1, &works1, &ierr, _state); - for(p1=1; p1<=ns; p1++) - { - wr->ptr.p_double[i-ns+p1] = tmpwr.ptr.p_double[p1]; - wi->ptr.p_double[i-ns+p1] = tmpwi.ptr.p_double[p1]; - } - if( ierr>0 ) - { - - /* - * If DLAHQR failed to compute all NS eigenvalues, use the - * unconverged diagonal elements as the remaining shifts. - */ - for(ii=1; ii<=ierr; ii++) - { - wr->ptr.p_double[i-ns+ii] = s.ptr.pp_double[ii][ii]; - wi->ptr.p_double[i-ns+ii] = 0; - } - } - } - - /* - * Form the first column of (G-w(1)) (G-w(2)) . . . (G-w(ns)) - * where G is the Hessenberg submatrix H(L:I,L:I) and w is - * the vector of shifts (stored in WR and WI). The result is - * stored in the local array V. - */ - v.ptr.p_double[1] = 1; - for(ii=2; ii<=ns+1; ii++) - { - v.ptr.p_double[ii] = 0; - } - nv = 1; - for(j=i-ns+1; j<=i; j++) - { - if( ae_fp_greater_eq(wi->ptr.p_double[j],0) ) - { - if( ae_fp_eq(wi->ptr.p_double[j],0) ) - { - - /* - * real shift - */ - p1 = nv+1; - ae_v_move(&vv.ptr.p_double[1], 1, &v.ptr.p_double[1], 1, ae_v_len(1,p1)); - matrixvectormultiply(h, l, l+nv, l, l+nv-1, ae_false, &vv, 1, nv, 1.0, &v, 1, nv+1, -wr->ptr.p_double[j], _state); - nv = nv+1; - } - else - { - if( ae_fp_greater(wi->ptr.p_double[j],0) ) - { - - /* - * complex conjugate pair of shifts - */ - p1 = nv+1; - ae_v_move(&vv.ptr.p_double[1], 1, &v.ptr.p_double[1], 1, ae_v_len(1,p1)); - matrixvectormultiply(h, l, l+nv, l, l+nv-1, ae_false, &v, 1, nv, 1.0, &vv, 1, nv+1, -2*wr->ptr.p_double[j], _state); - itemp = vectoridxabsmax(&vv, 1, nv+1, _state); - temp = 1/ae_maxreal(ae_fabs(vv.ptr.p_double[itemp], _state), smlnum, _state); - p1 = nv+1; - ae_v_muld(&vv.ptr.p_double[1], 1, ae_v_len(1,p1), temp); - absw = pythag2(wr->ptr.p_double[j], wi->ptr.p_double[j], _state); - temp = temp*absw*absw; - matrixvectormultiply(h, l, l+nv+1, l, l+nv, ae_false, &vv, 1, nv+1, 1.0, &v, 1, nv+2, temp, _state); - nv = nv+2; - } - } - - /* - * Scale V(1:NV) so that max(abs(V(i))) = 1. If V is zero, - * reset it to the unit vector. - */ - itemp = vectoridxabsmax(&v, 1, nv, _state); - temp = ae_fabs(v.ptr.p_double[itemp], _state); - if( ae_fp_eq(temp,0) ) - { - v.ptr.p_double[1] = 1; - for(ii=2; ii<=nv; ii++) - { - v.ptr.p_double[ii] = 0; - } - } - else - { - temp = ae_maxreal(temp, smlnum, _state); - vt = 1/temp; - ae_v_muld(&v.ptr.p_double[1], 1, ae_v_len(1,nv), vt); - } - } - } - - /* - * Multiple-shift QR step - */ - for(k=l; k<=i-1; k++) - { - - /* - * The first iteration of this loop determines a reflection G - * from the vector V and applies it from left and right to H, - * thus creating a nonzero bulge below the subdiagonal. - * - * Each subsequent iteration determines a reflection G to - * restore the Hessenberg form in the (K-1)th column, and thus - * chases the bulge one step toward the bottom of the active - * submatrix. NR is the order of G. - */ - nr = ae_minint(ns+1, i-k+1, _state); - if( k>l ) - { - p1 = k-1; - p2 = k+nr-1; - ae_v_move(&v.ptr.p_double[1], 1, &h->ptr.pp_double[k][p1], h->stride, ae_v_len(1,nr)); - touchint(&p2, _state); - } - generatereflection(&v, nr, &tau, _state); - if( k>l ) - { - h->ptr.pp_double[k][k-1] = v.ptr.p_double[1]; - for(ii=k+1; ii<=i; ii++) - { - h->ptr.pp_double[ii][k-1] = 0; - } - } - v.ptr.p_double[1] = 1; - - /* - * Apply G from the left to transform the rows of the matrix in - * columns K to I2. - */ - applyreflectionfromtheleft(h, tau, &v, k, k+nr-1, k, i2, &work, _state); - - /* - * Apply G from the right to transform the columns of the - * matrix in rows I1 to min(K+NR,I). - */ - applyreflectionfromtheright(h, tau, &v, i1, ae_minint(k+nr, i, _state), k, k+nr-1, &work, _state); - if( wantz ) - { - - /* - * Accumulate transformations in the matrix Z - */ - applyreflectionfromtheright(z, tau, &v, 1, n, k, k+nr-1, &work, _state); - } - } - } - - /* - * Failure to converge in remaining number of iterations - */ - if( failflag ) - { - *info = i; - ae_frame_leave(_state); - return; - } - - /* - * A submatrix of order <= MAXB in rows and columns L to I has split - * off. Use the double-shift QR algorithm to handle it. - */ - hsschur_internalauxschur(wantt, wantz, n, l, i, h, wr, wi, 1, n, z, &work, &workv3, &workc1, &works1, info, _state); - if( *info>0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Decrement number of remaining iterations, and return to start of - * the main loop with a new value of I. - */ - itn = itn-its; - i = l-1; - } - ae_frame_leave(_state); -} - - -static void hsschur_internalauxschur(ae_bool wantt, - ae_bool wantz, - ae_int_t n, - ae_int_t ilo, - ae_int_t ihi, - /* Real */ ae_matrix* h, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - ae_int_t iloz, - ae_int_t ihiz, - /* Real */ ae_matrix* z, - /* Real */ ae_vector* work, - /* Real */ ae_vector* workv3, - /* Real */ ae_vector* workc1, - /* Real */ ae_vector* works1, - ae_int_t* info, - ae_state *_state) -{ - ae_int_t i; - ae_int_t i1; - ae_int_t i2; - ae_int_t itn; - ae_int_t its; - ae_int_t j; - ae_int_t k; - ae_int_t l; - ae_int_t m; - ae_int_t nh; - ae_int_t nr; - ae_int_t nz; - double ave; - double cs; - double disc; - double h00; - double h10; - double h11; - double h12; - double h21; - double h22; - double h33; - double h33s; - double h43h34; - double h44; - double h44s; - double s; - double smlnum; - double sn; - double sum; - double t1; - double t2; - double t3; - double tst1; - double unfl; - double v1; - double v2; - double v3; - ae_bool failflag; - double dat1; - double dat2; - ae_int_t p1; - double him1im1; - double him1i; - double hiim1; - double hii; - double wrim1; - double wri; - double wiim1; - double wii; - double ulp; - - *info = 0; - - *info = 0; - dat1 = 0.75; - dat2 = -0.4375; - ulp = ae_machineepsilon; - - /* - * Quick return if possible - */ - if( n==0 ) - { - return; - } - if( ilo==ihi ) - { - wr->ptr.p_double[ilo] = h->ptr.pp_double[ilo][ilo]; - wi->ptr.p_double[ilo] = 0; - return; - } - nh = ihi-ilo+1; - nz = ihiz-iloz+1; - - /* - * Set machine-dependent constants for the stopping criterion. - * If norm(H) <= sqrt(MaxRealNumber), overflow should not occur. - */ - unfl = ae_minrealnumber; - smlnum = unfl*(nh/ulp); - - /* - * I1 and I2 are the indices of the first row and last column of H - * to which transformations must be applied. If eigenvalues only are - * being computed, I1 and I2 are set inside the main loop. - */ - i1 = 1; - i2 = n; - - /* - * ITN is the total number of QR iterations allowed. - */ - itn = 30*nh; - - /* - * The main loop begins here. I is the loop index and decreases from - * IHI to ILO in steps of 1 or 2. Each iteration of the loop works - * with the active submatrix in rows and columns L to I. - * Eigenvalues I+1 to IHI have already converged. Either L = ILO or - * H(L,L-1) is negligible so that the matrix splits. - */ - i = ihi; - for(;;) - { - l = ilo; - if( i=l+1; k--) - { - tst1 = ae_fabs(h->ptr.pp_double[k-1][k-1], _state)+ae_fabs(h->ptr.pp_double[k][k], _state); - if( ae_fp_eq(tst1,0) ) - { - tst1 = upperhessenberg1norm(h, l, i, l, i, work, _state); - } - if( ae_fp_less_eq(ae_fabs(h->ptr.pp_double[k][k-1], _state),ae_maxreal(ulp*tst1, smlnum, _state)) ) - { - break; - } - } - l = k; - if( l>ilo ) - { - - /* - * H(L,L-1) is negligible - */ - h->ptr.pp_double[l][l-1] = 0; - } - - /* - * Exit from loop if a submatrix of order 1 or 2 has split off. - */ - if( l>=i-1 ) - { - failflag = ae_false; - break; - } - - /* - * Now the active submatrix is in rows and columns L to I. If - * eigenvalues only are being computed, only the active submatrix - * need be transformed. - */ - if( its==10||its==20 ) - { - - /* - * Exceptional shift. - */ - s = ae_fabs(h->ptr.pp_double[i][i-1], _state)+ae_fabs(h->ptr.pp_double[i-1][i-2], _state); - h44 = dat1*s+h->ptr.pp_double[i][i]; - h33 = h44; - h43h34 = dat2*s*s; - } - else - { - - /* - * Prepare to use Francis' double shift - * (i.e. 2nd degree generalized Rayleigh quotient) - */ - h44 = h->ptr.pp_double[i][i]; - h33 = h->ptr.pp_double[i-1][i-1]; - h43h34 = h->ptr.pp_double[i][i-1]*h->ptr.pp_double[i-1][i]; - s = h->ptr.pp_double[i-1][i-2]*h->ptr.pp_double[i-1][i-2]; - disc = (h33-h44)*0.5; - disc = disc*disc+h43h34; - if( ae_fp_greater(disc,0) ) - { - - /* - * Real roots: use Wilkinson's shift twice - */ - disc = ae_sqrt(disc, _state); - ave = 0.5*(h33+h44); - if( ae_fp_greater(ae_fabs(h33, _state)-ae_fabs(h44, _state),0) ) - { - h33 = h33*h44-h43h34; - h44 = h33/(hsschur_extschursign(disc, ave, _state)+ave); - } - else - { - h44 = hsschur_extschursign(disc, ave, _state)+ave; - } - h33 = h44; - h43h34 = 0; - } - } - - /* - * Look for two consecutive small subdiagonal elements. - */ - for(m=i-2; m>=l; m--) - { - - /* - * Determine the effect of starting the double-shift QR - * iteration at row M, and see if this would make H(M,M-1) - * negligible. - */ - h11 = h->ptr.pp_double[m][m]; - h22 = h->ptr.pp_double[m+1][m+1]; - h21 = h->ptr.pp_double[m+1][m]; - h12 = h->ptr.pp_double[m][m+1]; - h44s = h44-h11; - h33s = h33-h11; - v1 = (h33s*h44s-h43h34)/h21+h12; - v2 = h22-h11-h33s-h44s; - v3 = h->ptr.pp_double[m+2][m+1]; - s = ae_fabs(v1, _state)+ae_fabs(v2, _state)+ae_fabs(v3, _state); - v1 = v1/s; - v2 = v2/s; - v3 = v3/s; - workv3->ptr.p_double[1] = v1; - workv3->ptr.p_double[2] = v2; - workv3->ptr.p_double[3] = v3; - if( m==l ) - { - break; - } - h00 = h->ptr.pp_double[m-1][m-1]; - h10 = h->ptr.pp_double[m][m-1]; - tst1 = ae_fabs(v1, _state)*(ae_fabs(h00, _state)+ae_fabs(h11, _state)+ae_fabs(h22, _state)); - if( ae_fp_less_eq(ae_fabs(h10, _state)*(ae_fabs(v2, _state)+ae_fabs(v3, _state)),ulp*tst1) ) - { - break; - } - } - - /* - * Double-shift QR step - */ - for(k=m; k<=i-1; k++) - { - - /* - * The first iteration of this loop determines a reflection G - * from the vector V and applies it from left and right to H, - * thus creating a nonzero bulge below the subdiagonal. - * - * Each subsequent iteration determines a reflection G to - * restore the Hessenberg form in the (K-1)th column, and thus - * chases the bulge one step toward the bottom of the active - * submatrix. NR is the order of G. - */ - nr = ae_minint(3, i-k+1, _state); - if( k>m ) - { - for(p1=1; p1<=nr; p1++) - { - workv3->ptr.p_double[p1] = h->ptr.pp_double[k+p1-1][k-1]; - } - } - generatereflection(workv3, nr, &t1, _state); - if( k>m ) - { - h->ptr.pp_double[k][k-1] = workv3->ptr.p_double[1]; - h->ptr.pp_double[k+1][k-1] = 0; - if( kptr.pp_double[k+2][k-1] = 0; - } - } - else - { - if( m>l ) - { - h->ptr.pp_double[k][k-1] = -h->ptr.pp_double[k][k-1]; - } - } - v2 = workv3->ptr.p_double[2]; - t2 = t1*v2; - if( nr==3 ) - { - v3 = workv3->ptr.p_double[3]; - t3 = t1*v3; - - /* - * Apply G from the left to transform the rows of the matrix - * in columns K to I2. - */ - for(j=k; j<=i2; j++) - { - sum = h->ptr.pp_double[k][j]+v2*h->ptr.pp_double[k+1][j]+v3*h->ptr.pp_double[k+2][j]; - h->ptr.pp_double[k][j] = h->ptr.pp_double[k][j]-sum*t1; - h->ptr.pp_double[k+1][j] = h->ptr.pp_double[k+1][j]-sum*t2; - h->ptr.pp_double[k+2][j] = h->ptr.pp_double[k+2][j]-sum*t3; - } - - /* - * Apply G from the right to transform the columns of the - * matrix in rows I1 to min(K+3,I). - */ - for(j=i1; j<=ae_minint(k+3, i, _state); j++) - { - sum = h->ptr.pp_double[j][k]+v2*h->ptr.pp_double[j][k+1]+v3*h->ptr.pp_double[j][k+2]; - h->ptr.pp_double[j][k] = h->ptr.pp_double[j][k]-sum*t1; - h->ptr.pp_double[j][k+1] = h->ptr.pp_double[j][k+1]-sum*t2; - h->ptr.pp_double[j][k+2] = h->ptr.pp_double[j][k+2]-sum*t3; - } - if( wantz ) - { - - /* - * Accumulate transformations in the matrix Z - */ - for(j=iloz; j<=ihiz; j++) - { - sum = z->ptr.pp_double[j][k]+v2*z->ptr.pp_double[j][k+1]+v3*z->ptr.pp_double[j][k+2]; - z->ptr.pp_double[j][k] = z->ptr.pp_double[j][k]-sum*t1; - z->ptr.pp_double[j][k+1] = z->ptr.pp_double[j][k+1]-sum*t2; - z->ptr.pp_double[j][k+2] = z->ptr.pp_double[j][k+2]-sum*t3; - } - } - } - else - { - if( nr==2 ) - { - - /* - * Apply G from the left to transform the rows of the matrix - * in columns K to I2. - */ - for(j=k; j<=i2; j++) - { - sum = h->ptr.pp_double[k][j]+v2*h->ptr.pp_double[k+1][j]; - h->ptr.pp_double[k][j] = h->ptr.pp_double[k][j]-sum*t1; - h->ptr.pp_double[k+1][j] = h->ptr.pp_double[k+1][j]-sum*t2; - } - - /* - * Apply G from the right to transform the columns of the - * matrix in rows I1 to min(K+3,I). - */ - for(j=i1; j<=i; j++) - { - sum = h->ptr.pp_double[j][k]+v2*h->ptr.pp_double[j][k+1]; - h->ptr.pp_double[j][k] = h->ptr.pp_double[j][k]-sum*t1; - h->ptr.pp_double[j][k+1] = h->ptr.pp_double[j][k+1]-sum*t2; - } - if( wantz ) - { - - /* - * Accumulate transformations in the matrix Z - */ - for(j=iloz; j<=ihiz; j++) - { - sum = z->ptr.pp_double[j][k]+v2*z->ptr.pp_double[j][k+1]; - z->ptr.pp_double[j][k] = z->ptr.pp_double[j][k]-sum*t1; - z->ptr.pp_double[j][k+1] = z->ptr.pp_double[j][k+1]-sum*t2; - } - } - } - } - } - } - if( failflag ) - { - - /* - * Failure to converge in remaining number of iterations - */ - *info = i; - return; - } - if( l==i ) - { - - /* - * H(I,I-1) is negligible: one eigenvalue has converged. - */ - wr->ptr.p_double[i] = h->ptr.pp_double[i][i]; - wi->ptr.p_double[i] = 0; - } - else - { - if( l==i-1 ) - { - - /* - * H(I-1,I-2) is negligible: a pair of eigenvalues have converged. - * - * Transform the 2-by-2 submatrix to standard Schur form, - * and compute and store the eigenvalues. - */ - him1im1 = h->ptr.pp_double[i-1][i-1]; - him1i = h->ptr.pp_double[i-1][i]; - hiim1 = h->ptr.pp_double[i][i-1]; - hii = h->ptr.pp_double[i][i]; - hsschur_aux2x2schur(&him1im1, &him1i, &hiim1, &hii, &wrim1, &wiim1, &wri, &wii, &cs, &sn, _state); - wr->ptr.p_double[i-1] = wrim1; - wi->ptr.p_double[i-1] = wiim1; - wr->ptr.p_double[i] = wri; - wi->ptr.p_double[i] = wii; - h->ptr.pp_double[i-1][i-1] = him1im1; - h->ptr.pp_double[i-1][i] = him1i; - h->ptr.pp_double[i][i-1] = hiim1; - h->ptr.pp_double[i][i] = hii; - if( wantt ) - { - - /* - * Apply the transformation to the rest of H. - */ - if( i2>i ) - { - workc1->ptr.p_double[1] = cs; - works1->ptr.p_double[1] = sn; - applyrotationsfromtheleft(ae_true, i-1, i, i+1, i2, workc1, works1, h, work, _state); - } - workc1->ptr.p_double[1] = cs; - works1->ptr.p_double[1] = sn; - applyrotationsfromtheright(ae_true, i1, i-2, i-1, i, workc1, works1, h, work, _state); - } - if( wantz ) - { - - /* - * Apply the transformation to Z. - */ - workc1->ptr.p_double[1] = cs; - works1->ptr.p_double[1] = sn; - applyrotationsfromtheright(ae_true, iloz, iloz+nz-1, i-1, i, workc1, works1, z, work, _state); - } - } - } - - /* - * Decrement number of remaining iterations, and return to start of - * the main loop with new value of I. - */ - itn = itn-its; - i = l-1; - } -} - - -static void hsschur_aux2x2schur(double* a, - double* b, - double* c, - double* d, - double* rt1r, - double* rt1i, - double* rt2r, - double* rt2i, - double* cs, - double* sn, - ae_state *_state) -{ - double multpl; - double aa; - double bb; - double bcmax; - double bcmis; - double cc; - double cs1; - double dd; - double eps; - double p; - double sab; - double sac; - double scl; - double sigma; - double sn1; - double tau; - double temp; - double z; - - *rt1r = 0; - *rt1i = 0; - *rt2r = 0; - *rt2i = 0; - *cs = 0; - *sn = 0; - - multpl = 4.0; - eps = ae_machineepsilon; - if( ae_fp_eq(*c,0) ) - { - *cs = 1; - *sn = 0; - } - else - { - if( ae_fp_eq(*b,0) ) - { - - /* - * Swap rows and columns - */ - *cs = 0; - *sn = 1; - temp = *d; - *d = *a; - *a = temp; - *b = -*c; - *c = 0; - } - else - { - if( ae_fp_eq(*a-(*d),0)&&hsschur_extschursigntoone(*b, _state)!=hsschur_extschursigntoone(*c, _state) ) - { - *cs = 1; - *sn = 0; - } - else - { - temp = *a-(*d); - p = 0.5*temp; - bcmax = ae_maxreal(ae_fabs(*b, _state), ae_fabs(*c, _state), _state); - bcmis = ae_minreal(ae_fabs(*b, _state), ae_fabs(*c, _state), _state)*hsschur_extschursigntoone(*b, _state)*hsschur_extschursigntoone(*c, _state); - scl = ae_maxreal(ae_fabs(p, _state), bcmax, _state); - z = p/scl*p+bcmax/scl*bcmis; - - /* - * If Z is of the order of the machine accuracy, postpone the - * decision on the nature of eigenvalues - */ - if( ae_fp_greater_eq(z,multpl*eps) ) - { - - /* - * Real eigenvalues. Compute A and D. - */ - z = p+hsschur_extschursign(ae_sqrt(scl, _state)*ae_sqrt(z, _state), p, _state); - *a = *d+z; - *d = *d-bcmax/z*bcmis; - - /* - * Compute B and the rotation matrix - */ - tau = pythag2(*c, z, _state); - *cs = z/tau; - *sn = *c/tau; - *b = *b-(*c); - *c = 0; - } - else - { - - /* - * Complex eigenvalues, or real (almost) equal eigenvalues. - * Make diagonal elements equal. - */ - sigma = *b+(*c); - tau = pythag2(sigma, temp, _state); - *cs = ae_sqrt(0.5*(1+ae_fabs(sigma, _state)/tau), _state); - *sn = -p/(tau*(*cs))*hsschur_extschursign(1, sigma, _state); - - /* - * Compute [ AA BB ] = [ A B ] [ CS -SN ] - * [ CC DD ] [ C D ] [ SN CS ] - */ - aa = *a*(*cs)+*b*(*sn); - bb = -*a*(*sn)+*b*(*cs); - cc = *c*(*cs)+*d*(*sn); - dd = -*c*(*sn)+*d*(*cs); - - /* - * Compute [ A B ] = [ CS SN ] [ AA BB ] - * [ C D ] [-SN CS ] [ CC DD ] - */ - *a = aa*(*cs)+cc*(*sn); - *b = bb*(*cs)+dd*(*sn); - *c = -aa*(*sn)+cc*(*cs); - *d = -bb*(*sn)+dd*(*cs); - temp = 0.5*(*a+(*d)); - *a = temp; - *d = temp; - if( ae_fp_neq(*c,0) ) - { - if( ae_fp_neq(*b,0) ) - { - if( hsschur_extschursigntoone(*b, _state)==hsschur_extschursigntoone(*c, _state) ) - { - - /* - * Real eigenvalues: reduce to upper triangular form - */ - sab = ae_sqrt(ae_fabs(*b, _state), _state); - sac = ae_sqrt(ae_fabs(*c, _state), _state); - p = hsschur_extschursign(sab*sac, *c, _state); - tau = 1/ae_sqrt(ae_fabs(*b+(*c), _state), _state); - *a = temp+p; - *d = temp-p; - *b = *b-(*c); - *c = 0; - cs1 = sab*tau; - sn1 = sac*tau; - temp = *cs*cs1-*sn*sn1; - *sn = *cs*sn1+*sn*cs1; - *cs = temp; - } - } - else - { - *b = -*c; - *c = 0; - temp = *cs; - *cs = -*sn; - *sn = temp; - } - } - } - } - } - } - - /* - * Store eigenvalues in (RT1R,RT1I) and (RT2R,RT2I). - */ - *rt1r = *a; - *rt2r = *d; - if( ae_fp_eq(*c,0) ) - { - *rt1i = 0; - *rt2i = 0; - } - else - { - *rt1i = ae_sqrt(ae_fabs(*b, _state), _state)*ae_sqrt(ae_fabs(*c, _state), _state); - *rt2i = -*rt1i; - } -} - - -static double hsschur_extschursign(double a, double b, ae_state *_state) -{ - double result; - - - if( ae_fp_greater_eq(b,0) ) - { - result = ae_fabs(a, _state); - } - else - { - result = -ae_fabs(a, _state); - } - return result; -} - - -static ae_int_t hsschur_extschursigntoone(double b, ae_state *_state) -{ - ae_int_t result; - - - if( ae_fp_greater_eq(b,0) ) - { - result = 1; - } - else - { - result = -1; - } - return result; -} - - - - -/************************************************************************* -Utility subroutine performing the "safe" solution of system of linear -equations with triangular coefficient matrices. - -The subroutine uses scaling and solves the scaled system A*x=s*b (where s -is a scalar value) instead of A*x=b, choosing s so that x can be -represented by a floating-point number. The closer the system gets to a -singular, the less s is. If the system is singular, s=0 and x contains the -non-trivial solution of equation A*x=0. - -The feature of an algorithm is that it could not cause an overflow or a -division by zero regardless of the matrix used as the input. - -The algorithm can solve systems of equations with upper/lower triangular -matrices, with/without unit diagonal, and systems of type A*x=b or A'*x=b -(where A' is a transposed matrix A). - -Input parameters: - A - system matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - X - right-hand member of a system. - Array whose index ranges within [0..N-1]. - IsUpper - matrix type. If it is True, the system matrix is the upper - triangular and is located in the corresponding part of - matrix A. - Trans - problem type. If it is True, the problem to be solved is - A'*x=b, otherwise it is A*x=b. - Isunit - matrix type. If it is True, the system matrix has a unit - diagonal (the elements on the main diagonal are not used - in the calculation process), otherwise the matrix is considered - to be a general triangular matrix. - -Output parameters: - X - solution. Array whose index ranges within [0..N-1]. - S - scaling factor. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - June 30, 1992 -*************************************************************************/ -void rmatrixtrsafesolve(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* x, - double* s, - ae_bool isupper, - ae_bool istrans, - ae_bool isunit, - ae_state *_state) -{ - ae_frame _frame_block; - ae_bool normin; - ae_vector cnorm; - ae_matrix a1; - ae_vector x1; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *s = 0; - ae_vector_init(&cnorm, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a1, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x1, 0, DT_REAL, _state, ae_true); - - - /* - * From 0-based to 1-based - */ - normin = ae_false; - ae_matrix_set_length(&a1, n+1, n+1, _state); - ae_vector_set_length(&x1, n+1, _state); - for(i=1; i<=n; i++) - { - ae_v_move(&a1.ptr.pp_double[i][1], 1, &a->ptr.pp_double[i-1][0], 1, ae_v_len(1,n)); - } - ae_v_move(&x1.ptr.p_double[1], 1, &x->ptr.p_double[0], 1, ae_v_len(1,n)); - - /* - * Solve 1-based - */ - safesolvetriangular(&a1, n, &x1, s, isupper, istrans, isunit, normin, &cnorm, _state); - - /* - * From 1-based to 0-based - */ - ae_v_move(&x->ptr.p_double[0], 1, &x1.ptr.p_double[1], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Obsolete 1-based subroutine. -See RMatrixTRSafeSolve for 0-based replacement. -*************************************************************************/ -void safesolvetriangular(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* x, - double* s, - ae_bool isupper, - ae_bool istrans, - ae_bool isunit, - ae_bool normin, - /* Real */ ae_vector* cnorm, - ae_state *_state) -{ - ae_int_t i; - ae_int_t imax; - ae_int_t j; - ae_int_t jfirst; - ae_int_t jinc; - ae_int_t jlast; - ae_int_t jm1; - ae_int_t jp1; - ae_int_t ip1; - ae_int_t im1; - ae_int_t k; - ae_int_t flg; - double v; - double vd; - double bignum; - double grow; - double rec; - double smlnum; - double sumj; - double tjj; - double tjjs; - double tmax; - double tscal; - double uscal; - double xbnd; - double xj; - double xmax; - ae_bool notran; - ae_bool upper; - ae_bool nounit; - - *s = 0; - - upper = isupper; - notran = !istrans; - nounit = !isunit; - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - tjjs = 0; - - /* - * Quick return if possible - */ - if( n==0 ) - { - return; - } - - /* - * Determine machine dependent parameters to control overflow. - */ - smlnum = ae_minrealnumber/(ae_machineepsilon*2); - bignum = 1/smlnum; - *s = 1; - if( !normin ) - { - ae_vector_set_length(cnorm, n+1, _state); - - /* - * Compute the 1-norm of each column, not including the diagonal. - */ - if( upper ) - { - - /* - * A is upper triangular. - */ - for(j=1; j<=n; j++) - { - v = 0; - for(k=1; k<=j-1; k++) - { - v = v+ae_fabs(a->ptr.pp_double[k][j], _state); - } - cnorm->ptr.p_double[j] = v; - } - } - else - { - - /* - * A is lower triangular. - */ - for(j=1; j<=n-1; j++) - { - v = 0; - for(k=j+1; k<=n; k++) - { - v = v+ae_fabs(a->ptr.pp_double[k][j], _state); - } - cnorm->ptr.p_double[j] = v; - } - cnorm->ptr.p_double[n] = 0; - } - } - - /* - * Scale the column norms by TSCAL if the maximum element in CNORM is - * greater than BIGNUM. - */ - imax = 1; - for(k=2; k<=n; k++) - { - if( ae_fp_greater(cnorm->ptr.p_double[k],cnorm->ptr.p_double[imax]) ) - { - imax = k; - } - } - tmax = cnorm->ptr.p_double[imax]; - if( ae_fp_less_eq(tmax,bignum) ) - { - tscal = 1; - } - else - { - tscal = 1/(smlnum*tmax); - ae_v_muld(&cnorm->ptr.p_double[1], 1, ae_v_len(1,n), tscal); - } - - /* - * Compute a bound on the computed solution vector to see if the - * Level 2 BLAS routine DTRSV can be used. - */ - j = 1; - for(k=2; k<=n; k++) - { - if( ae_fp_greater(ae_fabs(x->ptr.p_double[k], _state),ae_fabs(x->ptr.p_double[j], _state)) ) - { - j = k; - } - } - xmax = ae_fabs(x->ptr.p_double[j], _state); - xbnd = xmax; - if( notran ) - { - - /* - * Compute the growth in A * x = b. - */ - if( upper ) - { - jfirst = n; - jlast = 1; - jinc = -1; - } - else - { - jfirst = 1; - jlast = n; - jinc = 1; - } - if( ae_fp_neq(tscal,1) ) - { - grow = 0; - } - else - { - if( nounit ) - { - - /* - * A is non-unit triangular. - * - * Compute GROW = 1/G(j) and XBND = 1/M(j). - * Initially, G(0) = max{x(i), i=1,...,n}. - */ - grow = 1/ae_maxreal(xbnd, smlnum, _state); - xbnd = grow; - j = jfirst; - while((jinc>0&&j<=jlast)||(jinc<0&&j>=jlast)) - { - - /* - * Exit the loop if the growth factor is too small. - */ - if( ae_fp_less_eq(grow,smlnum) ) - { - break; - } - - /* - * M(j) = G(j-1) / abs(A(j,j)) - */ - tjj = ae_fabs(a->ptr.pp_double[j][j], _state); - xbnd = ae_minreal(xbnd, ae_minreal(1, tjj, _state)*grow, _state); - if( ae_fp_greater_eq(tjj+cnorm->ptr.p_double[j],smlnum) ) - { - - /* - * G(j) = G(j-1)*( 1 + CNORM(j) / abs(A(j,j)) ) - */ - grow = grow*(tjj/(tjj+cnorm->ptr.p_double[j])); - } - else - { - - /* - * G(j) could overflow, set GROW to 0. - */ - grow = 0; - } - if( j==jlast ) - { - grow = xbnd; - } - j = j+jinc; - } - } - else - { - - /* - * A is unit triangular. - * - * Compute GROW = 1/G(j), where G(0) = max{x(i), i=1,...,n}. - */ - grow = ae_minreal(1, 1/ae_maxreal(xbnd, smlnum, _state), _state); - j = jfirst; - while((jinc>0&&j<=jlast)||(jinc<0&&j>=jlast)) - { - - /* - * Exit the loop if the growth factor is too small. - */ - if( ae_fp_less_eq(grow,smlnum) ) - { - break; - } - - /* - * G(j) = G(j-1)*( 1 + CNORM(j) ) - */ - grow = grow*(1/(1+cnorm->ptr.p_double[j])); - j = j+jinc; - } - } - } - } - else - { - - /* - * Compute the growth in A' * x = b. - */ - if( upper ) - { - jfirst = 1; - jlast = n; - jinc = 1; - } - else - { - jfirst = n; - jlast = 1; - jinc = -1; - } - if( ae_fp_neq(tscal,1) ) - { - grow = 0; - } - else - { - if( nounit ) - { - - /* - * A is non-unit triangular. - * - * Compute GROW = 1/G(j) and XBND = 1/M(j). - * Initially, M(0) = max{x(i), i=1,...,n}. - */ - grow = 1/ae_maxreal(xbnd, smlnum, _state); - xbnd = grow; - j = jfirst; - while((jinc>0&&j<=jlast)||(jinc<0&&j>=jlast)) - { - - /* - * Exit the loop if the growth factor is too small. - */ - if( ae_fp_less_eq(grow,smlnum) ) - { - break; - } - - /* - * G(j) = max( G(j-1), M(j-1)*( 1 + CNORM(j) ) ) - */ - xj = 1+cnorm->ptr.p_double[j]; - grow = ae_minreal(grow, xbnd/xj, _state); - - /* - * M(j) = M(j-1)*( 1 + CNORM(j) ) / abs(A(j,j)) - */ - tjj = ae_fabs(a->ptr.pp_double[j][j], _state); - if( ae_fp_greater(xj,tjj) ) - { - xbnd = xbnd*(tjj/xj); - } - if( j==jlast ) - { - grow = ae_minreal(grow, xbnd, _state); - } - j = j+jinc; - } - } - else - { - - /* - * A is unit triangular. - * - * Compute GROW = 1/G(j), where G(0) = max{x(i), i=1,...,n}. - */ - grow = ae_minreal(1, 1/ae_maxreal(xbnd, smlnum, _state), _state); - j = jfirst; - while((jinc>0&&j<=jlast)||(jinc<0&&j>=jlast)) - { - - /* - * Exit the loop if the growth factor is too small. - */ - if( ae_fp_less_eq(grow,smlnum) ) - { - break; - } - - /* - * G(j) = ( 1 + CNORM(j) )*G(j-1) - */ - xj = 1+cnorm->ptr.p_double[j]; - grow = grow/xj; - j = j+jinc; - } - } - } - } - if( ae_fp_greater(grow*tscal,smlnum) ) - { - - /* - * Use the Level 2 BLAS solve if the reciprocal of the bound on - * elements of X is not too small. - */ - if( (upper&¬ran)||(!upper&&!notran) ) - { - if( nounit ) - { - vd = a->ptr.pp_double[n][n]; - } - else - { - vd = 1; - } - x->ptr.p_double[n] = x->ptr.p_double[n]/vd; - for(i=n-1; i>=1; i--) - { - ip1 = i+1; - if( upper ) - { - v = ae_v_dotproduct(&a->ptr.pp_double[i][ip1], 1, &x->ptr.p_double[ip1], 1, ae_v_len(ip1,n)); - } - else - { - v = ae_v_dotproduct(&a->ptr.pp_double[ip1][i], a->stride, &x->ptr.p_double[ip1], 1, ae_v_len(ip1,n)); - } - if( nounit ) - { - vd = a->ptr.pp_double[i][i]; - } - else - { - vd = 1; - } - x->ptr.p_double[i] = (x->ptr.p_double[i]-v)/vd; - } - } - else - { - if( nounit ) - { - vd = a->ptr.pp_double[1][1]; - } - else - { - vd = 1; - } - x->ptr.p_double[1] = x->ptr.p_double[1]/vd; - for(i=2; i<=n; i++) - { - im1 = i-1; - if( upper ) - { - v = ae_v_dotproduct(&a->ptr.pp_double[1][i], a->stride, &x->ptr.p_double[1], 1, ae_v_len(1,im1)); - } - else - { - v = ae_v_dotproduct(&a->ptr.pp_double[i][1], 1, &x->ptr.p_double[1], 1, ae_v_len(1,im1)); - } - if( nounit ) - { - vd = a->ptr.pp_double[i][i]; - } - else - { - vd = 1; - } - x->ptr.p_double[i] = (x->ptr.p_double[i]-v)/vd; - } - } - } - else - { - - /* - * Use a Level 1 BLAS solve, scaling intermediate results. - */ - if( ae_fp_greater(xmax,bignum) ) - { - - /* - * Scale X so that its components are less than or equal to - * BIGNUM in absolute value. - */ - *s = bignum/xmax; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), *s); - xmax = bignum; - } - if( notran ) - { - - /* - * Solve A * x = b - */ - j = jfirst; - while((jinc>0&&j<=jlast)||(jinc<0&&j>=jlast)) - { - - /* - * Compute x(j) = b(j) / A(j,j), scaling x if necessary. - */ - xj = ae_fabs(x->ptr.p_double[j], _state); - flg = 0; - if( nounit ) - { - tjjs = a->ptr.pp_double[j][j]*tscal; - } - else - { - tjjs = tscal; - if( ae_fp_eq(tscal,1) ) - { - flg = 100; - } - } - if( flg!=100 ) - { - tjj = ae_fabs(tjjs, _state); - if( ae_fp_greater(tjj,smlnum) ) - { - - /* - * abs(A(j,j)) > SMLNUM: - */ - if( ae_fp_less(tjj,1) ) - { - if( ae_fp_greater(xj,tjj*bignum) ) - { - - /* - * Scale x by 1/b(j). - */ - rec = 1/xj; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), rec); - *s = *s*rec; - xmax = xmax*rec; - } - } - x->ptr.p_double[j] = x->ptr.p_double[j]/tjjs; - xj = ae_fabs(x->ptr.p_double[j], _state); - } - else - { - if( ae_fp_greater(tjj,0) ) - { - - /* - * 0 < abs(A(j,j)) <= SMLNUM: - */ - if( ae_fp_greater(xj,tjj*bignum) ) - { - - /* - * Scale x by (1/abs(x(j)))*abs(A(j,j))*BIGNUM - * to avoid overflow when dividing by A(j,j). - */ - rec = tjj*bignum/xj; - if( ae_fp_greater(cnorm->ptr.p_double[j],1) ) - { - - /* - * Scale by 1/CNORM(j) to avoid overflow when - * multiplying x(j) times column j. - */ - rec = rec/cnorm->ptr.p_double[j]; - } - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), rec); - *s = *s*rec; - xmax = xmax*rec; - } - x->ptr.p_double[j] = x->ptr.p_double[j]/tjjs; - xj = ae_fabs(x->ptr.p_double[j], _state); - } - else - { - - /* - * A(j,j) = 0: Set x(1:n) = 0, x(j) = 1, and - * scale = 0, and compute a solution to A*x = 0. - */ - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = 0; - } - x->ptr.p_double[j] = 1; - xj = 1; - *s = 0; - xmax = 0; - } - } - } - - /* - * Scale x if necessary to avoid overflow when adding a - * multiple of column j of A. - */ - if( ae_fp_greater(xj,1) ) - { - rec = 1/xj; - if( ae_fp_greater(cnorm->ptr.p_double[j],(bignum-xmax)*rec) ) - { - - /* - * Scale x by 1/(2*abs(x(j))). - */ - rec = rec*0.5; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), rec); - *s = *s*rec; - } - } - else - { - if( ae_fp_greater(xj*cnorm->ptr.p_double[j],bignum-xmax) ) - { - - /* - * Scale x by 1/2. - */ - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), 0.5); - *s = *s*0.5; - } - } - if( upper ) - { - if( j>1 ) - { - - /* - * Compute the update - * x(1:j-1) := x(1:j-1) - x(j) * A(1:j-1,j) - */ - v = x->ptr.p_double[j]*tscal; - jm1 = j-1; - ae_v_subd(&x->ptr.p_double[1], 1, &a->ptr.pp_double[1][j], a->stride, ae_v_len(1,jm1), v); - i = 1; - for(k=2; k<=j-1; k++) - { - if( ae_fp_greater(ae_fabs(x->ptr.p_double[k], _state),ae_fabs(x->ptr.p_double[i], _state)) ) - { - i = k; - } - } - xmax = ae_fabs(x->ptr.p_double[i], _state); - } - } - else - { - if( jptr.p_double[j]*tscal; - ae_v_subd(&x->ptr.p_double[jp1], 1, &a->ptr.pp_double[jp1][j], a->stride, ae_v_len(jp1,n), v); - i = j+1; - for(k=j+2; k<=n; k++) - { - if( ae_fp_greater(ae_fabs(x->ptr.p_double[k], _state),ae_fabs(x->ptr.p_double[i], _state)) ) - { - i = k; - } - } - xmax = ae_fabs(x->ptr.p_double[i], _state); - } - } - j = j+jinc; - } - } - else - { - - /* - * Solve A' * x = b - */ - j = jfirst; - while((jinc>0&&j<=jlast)||(jinc<0&&j>=jlast)) - { - - /* - * Compute x(j) = b(j) - sum A(k,j)*x(k). - * k<>j - */ - xj = ae_fabs(x->ptr.p_double[j], _state); - uscal = tscal; - rec = 1/ae_maxreal(xmax, 1, _state); - if( ae_fp_greater(cnorm->ptr.p_double[j],(bignum-xj)*rec) ) - { - - /* - * If x(j) could overflow, scale x by 1/(2*XMAX). - */ - rec = rec*0.5; - if( nounit ) - { - tjjs = a->ptr.pp_double[j][j]*tscal; - } - else - { - tjjs = tscal; - } - tjj = ae_fabs(tjjs, _state); - if( ae_fp_greater(tjj,1) ) - { - - /* - * Divide by A(j,j) when scaling x if A(j,j) > 1. - */ - rec = ae_minreal(1, rec*tjj, _state); - uscal = uscal/tjjs; - } - if( ae_fp_less(rec,1) ) - { - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), rec); - *s = *s*rec; - xmax = xmax*rec; - } - } - sumj = 0; - if( ae_fp_eq(uscal,1) ) - { - - /* - * If the scaling needed for A in the dot product is 1, - * call DDOT to perform the dot product. - */ - if( upper ) - { - if( j>1 ) - { - jm1 = j-1; - sumj = ae_v_dotproduct(&a->ptr.pp_double[1][j], a->stride, &x->ptr.p_double[1], 1, ae_v_len(1,jm1)); - } - else - { - sumj = 0; - } - } - else - { - if( jptr.pp_double[jp1][j], a->stride, &x->ptr.p_double[jp1], 1, ae_v_len(jp1,n)); - } - } - } - else - { - - /* - * Otherwise, use in-line code for the dot product. - */ - if( upper ) - { - for(i=1; i<=j-1; i++) - { - v = a->ptr.pp_double[i][j]*uscal; - sumj = sumj+v*x->ptr.p_double[i]; - } - } - else - { - if( jptr.pp_double[i][j]*uscal; - sumj = sumj+v*x->ptr.p_double[i]; - } - } - } - } - if( ae_fp_eq(uscal,tscal) ) - { - - /* - * Compute x(j) := ( x(j) - sumj ) / A(j,j) if 1/A(j,j) - * was not used to scale the dotproduct. - */ - x->ptr.p_double[j] = x->ptr.p_double[j]-sumj; - xj = ae_fabs(x->ptr.p_double[j], _state); - flg = 0; - if( nounit ) - { - tjjs = a->ptr.pp_double[j][j]*tscal; - } - else - { - tjjs = tscal; - if( ae_fp_eq(tscal,1) ) - { - flg = 150; - } - } - - /* - * Compute x(j) = x(j) / A(j,j), scaling if necessary. - */ - if( flg!=150 ) - { - tjj = ae_fabs(tjjs, _state); - if( ae_fp_greater(tjj,smlnum) ) - { - - /* - * abs(A(j,j)) > SMLNUM: - */ - if( ae_fp_less(tjj,1) ) - { - if( ae_fp_greater(xj,tjj*bignum) ) - { - - /* - * Scale X by 1/abs(x(j)). - */ - rec = 1/xj; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), rec); - *s = *s*rec; - xmax = xmax*rec; - } - } - x->ptr.p_double[j] = x->ptr.p_double[j]/tjjs; - } - else - { - if( ae_fp_greater(tjj,0) ) - { - - /* - * 0 < abs(A(j,j)) <= SMLNUM: - */ - if( ae_fp_greater(xj,tjj*bignum) ) - { - - /* - * Scale x by (1/abs(x(j)))*abs(A(j,j))*BIGNUM. - */ - rec = tjj*bignum/xj; - ae_v_muld(&x->ptr.p_double[1], 1, ae_v_len(1,n), rec); - *s = *s*rec; - xmax = xmax*rec; - } - x->ptr.p_double[j] = x->ptr.p_double[j]/tjjs; - } - else - { - - /* - * A(j,j) = 0: Set x(1:n) = 0, x(j) = 1, and - * scale = 0, and compute a solution to A'*x = 0. - */ - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = 0; - } - x->ptr.p_double[j] = 1; - *s = 0; - xmax = 0; - } - } - } - } - else - { - - /* - * Compute x(j) := x(j) / A(j,j) - sumj if the dot - * product has already been divided by 1/A(j,j). - */ - x->ptr.p_double[j] = x->ptr.p_double[j]/tjjs-sumj; - } - xmax = ae_maxreal(xmax, ae_fabs(x->ptr.p_double[j], _state), _state); - j = j+jinc; - } - } - *s = *s/tscal; - } - - /* - * Scale the column norms by 1/TSCAL for return. - */ - if( ae_fp_neq(tscal,1) ) - { - v = 1/tscal; - ae_v_muld(&cnorm->ptr.p_double[1], 1, ae_v_len(1,n), v); - } -} - - - - -/************************************************************************* -Real implementation of CMatrixScaledTRSafeSolve - - -- ALGLIB routine -- - 21.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixscaledtrsafesolve(/* Real */ ae_matrix* a, - double sa, - ae_int_t n, - /* Real */ ae_vector* x, - ae_bool isupper, - ae_int_t trans, - ae_bool isunit, - double maxgrowth, - ae_state *_state) -{ - ae_frame _frame_block; - double lnmax; - double nrmb; - double nrmx; - ae_int_t i; - ae_complex alpha; - ae_complex beta; - double vr; - ae_complex cx; - ae_vector tmp; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "RMatrixTRSafeSolve: incorrect N!", _state); - ae_assert(trans==0||trans==1, "RMatrixTRSafeSolve: incorrect Trans!", _state); - result = ae_true; - lnmax = ae_log(ae_maxrealnumber, _state); - - /* - * Quick return if possible - */ - if( n<=0 ) - { - ae_frame_leave(_state); - return result; - } - - /* - * Load norms: right part and X - */ - nrmb = 0; - for(i=0; i<=n-1; i++) - { - nrmb = ae_maxreal(nrmb, ae_fabs(x->ptr.p_double[i], _state), _state); - } - nrmx = 0; - - /* - * Solve - */ - ae_vector_set_length(&tmp, n, _state); - result = ae_true; - if( isupper&&trans==0 ) - { - - /* - * U*x = b - */ - for(i=n-1; i>=0; i--) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_complex_from_d(a->ptr.pp_double[i][i]*sa); - } - if( iptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), sa); - vr = ae_v_dotproduct(&tmp.ptr.p_double[i+1], 1, &x->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1)); - beta = ae_complex_from_d(x->ptr.p_double[i]-vr); - } - else - { - beta = ae_complex_from_d(x->ptr.p_double[i]); - } - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &cx, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_double[i] = cx.x; - } - ae_frame_leave(_state); - return result; - } - if( !isupper&&trans==0 ) - { - - /* - * L*x = b - */ - for(i=0; i<=n-1; i++) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_complex_from_d(a->ptr.pp_double[i][i]*sa); - } - if( i>0 ) - { - ae_v_moved(&tmp.ptr.p_double[0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), sa); - vr = ae_v_dotproduct(&tmp.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,i-1)); - beta = ae_complex_from_d(x->ptr.p_double[i]-vr); - } - else - { - beta = ae_complex_from_d(x->ptr.p_double[i]); - } - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &cx, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_double[i] = cx.x; - } - ae_frame_leave(_state); - return result; - } - if( isupper&&trans==1 ) - { - - /* - * U^T*x = b - */ - for(i=0; i<=n-1; i++) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_complex_from_d(a->ptr.pp_double[i][i]*sa); - } - beta = ae_complex_from_d(x->ptr.p_double[i]); - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &cx, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_double[i] = cx.x; - - /* - * update the rest of right part - */ - if( iptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), sa); - ae_v_subd(&x->ptr.p_double[i+1], 1, &tmp.ptr.p_double[i+1], 1, ae_v_len(i+1,n-1), vr); - } - } - ae_frame_leave(_state); - return result; - } - if( !isupper&&trans==1 ) - { - - /* - * L^T*x = b - */ - for(i=n-1; i>=0; i--) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_complex_from_d(a->ptr.pp_double[i][i]*sa); - } - beta = ae_complex_from_d(x->ptr.p_double[i]); - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &cx, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_double[i] = cx.x; - - /* - * update the rest of right part - */ - if( i>0 ) - { - vr = cx.x; - ae_v_moved(&tmp.ptr.p_double[0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), sa); - ae_v_subd(&x->ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,i-1), vr); - } - } - ae_frame_leave(_state); - return result; - } - result = ae_false; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Internal subroutine for safe solution of - - SA*op(A)=b - -where A is NxN upper/lower triangular/unitriangular matrix, op(A) is -either identity transform, transposition or Hermitian transposition, SA is -a scaling factor such that max(|SA*A[i,j]|) is close to 1.0 in magnutude. - -This subroutine limits relative growth of solution (in inf-norm) by -MaxGrowth, returning False if growth exceeds MaxGrowth. Degenerate or -near-degenerate matrices are handled correctly (False is returned) as long -as MaxGrowth is significantly less than MaxRealNumber/norm(b). - - -- ALGLIB routine -- - 21.01.2010 - Bochkanov Sergey -*************************************************************************/ -ae_bool cmatrixscaledtrsafesolve(/* Complex */ ae_matrix* a, - double sa, - ae_int_t n, - /* Complex */ ae_vector* x, - ae_bool isupper, - ae_int_t trans, - ae_bool isunit, - double maxgrowth, - ae_state *_state) -{ - ae_frame _frame_block; - double lnmax; - double nrmb; - double nrmx; - ae_int_t i; - ae_complex alpha; - ae_complex beta; - ae_complex vc; - ae_vector tmp; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tmp, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0, "CMatrixTRSafeSolve: incorrect N!", _state); - ae_assert((trans==0||trans==1)||trans==2, "CMatrixTRSafeSolve: incorrect Trans!", _state); - result = ae_true; - lnmax = ae_log(ae_maxrealnumber, _state); - - /* - * Quick return if possible - */ - if( n<=0 ) - { - ae_frame_leave(_state); - return result; - } - - /* - * Load norms: right part and X - */ - nrmb = 0; - for(i=0; i<=n-1; i++) - { - nrmb = ae_maxreal(nrmb, ae_c_abs(x->ptr.p_complex[i], _state), _state); - } - nrmx = 0; - - /* - * Solve - */ - ae_vector_set_length(&tmp, n, _state); - result = ae_true; - if( isupper&&trans==0 ) - { - - /* - * U*x = b - */ - for(i=n-1; i>=0; i--) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_c_mul_d(a->ptr.pp_complex[i][i],sa); - } - if( iptr.pp_complex[i][i+1], 1, "N", ae_v_len(i+1,n-1), sa); - vc = ae_v_cdotproduct(&tmp.ptr.p_complex[i+1], 1, "N", &x->ptr.p_complex[i+1], 1, "N", ae_v_len(i+1,n-1)); - beta = ae_c_sub(x->ptr.p_complex[i],vc); - } - else - { - beta = x->ptr.p_complex[i]; - } - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &vc, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_complex[i] = vc; - } - ae_frame_leave(_state); - return result; - } - if( !isupper&&trans==0 ) - { - - /* - * L*x = b - */ - for(i=0; i<=n-1; i++) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_c_mul_d(a->ptr.pp_complex[i][i],sa); - } - if( i>0 ) - { - ae_v_cmoved(&tmp.ptr.p_complex[0], 1, &a->ptr.pp_complex[i][0], 1, "N", ae_v_len(0,i-1), sa); - vc = ae_v_cdotproduct(&tmp.ptr.p_complex[0], 1, "N", &x->ptr.p_complex[0], 1, "N", ae_v_len(0,i-1)); - beta = ae_c_sub(x->ptr.p_complex[i],vc); - } - else - { - beta = x->ptr.p_complex[i]; - } - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &vc, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_complex[i] = vc; - } - ae_frame_leave(_state); - return result; - } - if( isupper&&trans==1 ) - { - - /* - * U^T*x = b - */ - for(i=0; i<=n-1; i++) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_c_mul_d(a->ptr.pp_complex[i][i],sa); - } - beta = x->ptr.p_complex[i]; - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &vc, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_complex[i] = vc; - - /* - * update the rest of right part - */ - if( iptr.pp_complex[i][i+1], 1, "N", ae_v_len(i+1,n-1), sa); - ae_v_csubc(&x->ptr.p_complex[i+1], 1, &tmp.ptr.p_complex[i+1], 1, "N", ae_v_len(i+1,n-1), vc); - } - } - ae_frame_leave(_state); - return result; - } - if( !isupper&&trans==1 ) - { - - /* - * L^T*x = b - */ - for(i=n-1; i>=0; i--) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_c_mul_d(a->ptr.pp_complex[i][i],sa); - } - beta = x->ptr.p_complex[i]; - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &vc, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_complex[i] = vc; - - /* - * update the rest of right part - */ - if( i>0 ) - { - ae_v_cmoved(&tmp.ptr.p_complex[0], 1, &a->ptr.pp_complex[i][0], 1, "N", ae_v_len(0,i-1), sa); - ae_v_csubc(&x->ptr.p_complex[0], 1, &tmp.ptr.p_complex[0], 1, "N", ae_v_len(0,i-1), vc); - } - } - ae_frame_leave(_state); - return result; - } - if( isupper&&trans==2 ) - { - - /* - * U^H*x = b - */ - for(i=0; i<=n-1; i++) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_c_mul_d(ae_c_conj(a->ptr.pp_complex[i][i], _state),sa); - } - beta = x->ptr.p_complex[i]; - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &vc, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_complex[i] = vc; - - /* - * update the rest of right part - */ - if( iptr.pp_complex[i][i+1], 1, "Conj", ae_v_len(i+1,n-1), sa); - ae_v_csubc(&x->ptr.p_complex[i+1], 1, &tmp.ptr.p_complex[i+1], 1, "N", ae_v_len(i+1,n-1), vc); - } - } - ae_frame_leave(_state); - return result; - } - if( !isupper&&trans==2 ) - { - - /* - * L^T*x = b - */ - for(i=n-1; i>=0; i--) - { - - /* - * Task is reduced to alpha*x[i] = beta - */ - if( isunit ) - { - alpha = ae_complex_from_d(sa); - } - else - { - alpha = ae_c_mul_d(ae_c_conj(a->ptr.pp_complex[i][i], _state),sa); - } - beta = x->ptr.p_complex[i]; - - /* - * solve alpha*x[i] = beta - */ - result = safesolve_cbasicsolveandupdate(alpha, beta, lnmax, nrmb, maxgrowth, &nrmx, &vc, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - x->ptr.p_complex[i] = vc; - - /* - * update the rest of right part - */ - if( i>0 ) - { - ae_v_cmoved(&tmp.ptr.p_complex[0], 1, &a->ptr.pp_complex[i][0], 1, "Conj", ae_v_len(0,i-1), sa); - ae_v_csubc(&x->ptr.p_complex[0], 1, &tmp.ptr.p_complex[0], 1, "N", ae_v_len(0,i-1), vc); - } - } - ae_frame_leave(_state); - return result; - } - result = ae_false; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -complex basic solver-updater for reduced linear system - - alpha*x[i] = beta - -solves this equation and updates it in overlfow-safe manner (keeping track -of relative growth of solution). - -Parameters: - Alpha - alpha - Beta - beta - LnMax - precomputed Ln(MaxRealNumber) - BNorm - inf-norm of b (right part of original system) - MaxGrowth- maximum growth of norm(x) relative to norm(b) - XNorm - inf-norm of other components of X (which are already processed) - it is updated by CBasicSolveAndUpdate. - X - solution - - -- ALGLIB routine -- - 26.01.2009 - Bochkanov Sergey -*************************************************************************/ -static ae_bool safesolve_cbasicsolveandupdate(ae_complex alpha, - ae_complex beta, - double lnmax, - double bnorm, - double maxgrowth, - double* xnorm, - ae_complex* x, - ae_state *_state) -{ - double v; - ae_bool result; - - x->x = 0; - x->y = 0; - - result = ae_false; - if( ae_c_eq_d(alpha,0) ) - { - return result; - } - if( ae_c_neq_d(beta,0) ) - { - - /* - * alpha*x[i]=beta - */ - v = ae_log(ae_c_abs(beta, _state), _state)-ae_log(ae_c_abs(alpha, _state), _state); - if( ae_fp_greater(v,lnmax) ) - { - return result; - } - *x = ae_c_div(beta,alpha); - } - else - { - - /* - * alpha*x[i]=0 - */ - *x = ae_complex_from_d(0); - } - - /* - * update NrmX, test growth limit - */ - *xnorm = ae_maxreal(*xnorm, ae_c_abs(*x, _state), _state); - if( ae_fp_greater(*xnorm,maxgrowth*bnorm) ) - { - return result; - } - result = ae_true; - return result; -} - - - - -/************************************************************************* -Prepares HPC compuations of chunked gradient with HPCChunkedGradient(). -You have to call this function before calling HPCChunkedGradient() for -a new set of weights. You have to call it only once, see example below: - -HOW TO PROCESS DATASET WITH THIS FUNCTION: - Grad:=0 - HPCPrepareChunkedGradient(Weights, WCount, NTotal, NOut, Buf) - foreach chunk-of-dataset do - HPCChunkedGradient(...) - HPCFinalizeChunkedGradient(Buf, Grad) - -*************************************************************************/ -void hpcpreparechunkedgradient(/* Real */ ae_vector* weights, - ae_int_t wcount, - ae_int_t ntotal, - ae_int_t nin, - ae_int_t nout, - mlpbuffers* buf, - ae_state *_state) -{ - ae_int_t i; - ae_int_t batch4size; - ae_int_t chunksize; - - - chunksize = 4; - batch4size = 3*chunksize*ntotal+chunksize*(2*nout+1); - if( buf->xy.rowsxy.colsxy, chunksize, nin+nout, _state); - } - if( buf->xy2.rowsxy2.colsxy2, chunksize, nin+nout, _state); - } - if( buf->xyrow.cntxyrow, nin+nout, _state); - } - if( buf->x.cntx, nin, _state); - } - if( buf->y.cnty, nout, _state); - } - if( buf->desiredy.cntdesiredy, nout, _state); - } - if( buf->batch4buf.cntbatch4buf, batch4size, _state); - } - if( buf->hpcbuf.cnthpcbuf, wcount, _state); - } - if( buf->g.cntg, wcount, _state); - } - if( !hpccores_hpcpreparechunkedgradientx(weights, wcount, &buf->hpcbuf, _state) ) - { - for(i=0; i<=wcount-1; i++) - { - buf->hpcbuf.ptr.p_double[i] = 0.0; - } - } - buf->wcount = wcount; - buf->ntotal = ntotal; - buf->nin = nin; - buf->nout = nout; - buf->chunksize = chunksize; -} - - -/************************************************************************* -Finalizes HPC compuations of chunked gradient with HPCChunkedGradient(). -You have to call this function after calling HPCChunkedGradient() for -a new set of weights. You have to call it only once, see example below: - -HOW TO PROCESS DATASET WITH THIS FUNCTION: - Grad:=0 - HPCPrepareChunkedGradient(Weights, WCount, NTotal, NOut, Buf) - foreach chunk-of-dataset do - HPCChunkedGradient(...) - HPCFinalizeChunkedGradient(Buf, Grad) - -*************************************************************************/ -void hpcfinalizechunkedgradient(mlpbuffers* buf, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_int_t i; - - - if( !hpccores_hpcfinalizechunkedgradientx(&buf->hpcbuf, buf->wcount, grad, _state) ) - { - for(i=0; i<=buf->wcount-1; i++) - { - grad->ptr.p_double[i] = grad->ptr.p_double[i]+buf->hpcbuf.ptr.p_double[i]; - } - } -} - - -/************************************************************************* -Fast kernel for chunked gradient. - -*************************************************************************/ -ae_bool hpcchunkedgradient(/* Real */ ae_vector* weights, - /* Integer */ ae_vector* structinfo, - /* Real */ ae_vector* columnmeans, - /* Real */ ae_vector* columnsigmas, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - double* e, - ae_bool naturalerrorfunc, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_SSE2 - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_hpcchunkedgradient(weights, structinfo, columnmeans, columnsigmas, xy, cstart, csize, batch4buf, hpcbuf, e, naturalerrorfunc); -#endif -} - - -/************************************************************************* -Fast kernel for chunked processing. - -*************************************************************************/ -ae_bool hpcchunkedprocess(/* Real */ ae_vector* weights, - /* Integer */ ae_vector* structinfo, - /* Real */ ae_vector* columnmeans, - /* Real */ ae_vector* columnsigmas, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_SSE2 - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_hpcchunkedprocess(weights, structinfo, columnmeans, columnsigmas, xy, cstart, csize, batch4buf, hpcbuf); -#endif -} - - -/************************************************************************* -Stub function. - - -- ALGLIB routine -- - 14.06.2013 - Bochkanov Sergey -*************************************************************************/ -static ae_bool hpccores_hpcpreparechunkedgradientx(/* Real */ ae_vector* weights, - ae_int_t wcount, - /* Real */ ae_vector* hpcbuf, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_SSE2 - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_hpcpreparechunkedgradientx(weights, wcount, hpcbuf); -#endif -} - - -/************************************************************************* -Stub function. - - -- ALGLIB routine -- - 14.06.2013 - Bochkanov Sergey -*************************************************************************/ -static ae_bool hpccores_hpcfinalizechunkedgradientx(/* Real */ ae_vector* buf, - ae_int_t wcount, - /* Real */ ae_vector* grad, - ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_SSE2 - ae_bool result; - - - result = ae_false; - return result; -#else - return _ialglib_i_hpcfinalizechunkedgradientx(buf, wcount, grad); -#endif -} - - -ae_bool _mlpbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlpbuffers *p = (mlpbuffers*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->batch4buf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hpcbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->xy, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->xy2, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xyrow, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->desiredy, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mlpbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlpbuffers *dst = (mlpbuffers*)_dst; - mlpbuffers *src = (mlpbuffers*)_src; - dst->chunksize = src->chunksize; - dst->ntotal = src->ntotal; - dst->nin = src->nin; - dst->nout = src->nout; - dst->wcount = src->wcount; - if( !ae_vector_init_copy(&dst->batch4buf, &src->batch4buf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->hpcbuf, &src->hpcbuf, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->xy, &src->xy, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->xy2, &src->xy2, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xyrow, &src->xyrow, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->desiredy, &src->desiredy, _state, make_automatic) ) - return ae_false; - dst->e = src->e; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp0, &src->tmp0, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _mlpbuffers_clear(void* _p) -{ - mlpbuffers *p = (mlpbuffers*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->batch4buf); - ae_vector_clear(&p->hpcbuf); - ae_matrix_clear(&p->xy); - ae_matrix_clear(&p->xy2); - ae_vector_clear(&p->xyrow); - ae_vector_clear(&p->x); - ae_vector_clear(&p->y); - ae_vector_clear(&p->desiredy); - ae_vector_clear(&p->g); - ae_vector_clear(&p->tmp0); -} - - -void _mlpbuffers_destroy(void* _p) -{ - mlpbuffers *p = (mlpbuffers*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->batch4buf); - ae_vector_destroy(&p->hpcbuf); - ae_matrix_destroy(&p->xy); - ae_matrix_destroy(&p->xy2); - ae_vector_destroy(&p->xyrow); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->y); - ae_vector_destroy(&p->desiredy); - ae_vector_destroy(&p->g); - ae_vector_destroy(&p->tmp0); -} - - - - -/************************************************************************* -More precise dot-product. Absolute error of subroutine result is about -1 ulp of max(MX,V), where: - MX = max( |a[i]*b[i]| ) - V = |(a,b)| - -INPUT PARAMETERS - A - array[0..N-1], vector 1 - B - array[0..N-1], vector 2 - N - vectors length, N<2^29. - Temp - array[0..N-1], pre-allocated temporary storage - -OUTPUT PARAMETERS - R - (A,B) - RErr - estimate of error. This estimate accounts for both errors - during calculation of (A,B) and errors introduced by - rounding of A and B to fit in double (about 1 ulp). - - -- ALGLIB -- - Copyright 24.08.2009 by Bochkanov Sergey -*************************************************************************/ -void xdot(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* temp, - double* r, - double* rerr, - ae_state *_state) -{ - ae_int_t i; - double mx; - double v; - - *r = 0; - *rerr = 0; - - - /* - * special cases: - * * N=0 - */ - if( n==0 ) - { - *r = 0; - *rerr = 0; - return; - } - mx = 0; - for(i=0; i<=n-1; i++) - { - v = a->ptr.p_double[i]*b->ptr.p_double[i]; - temp->ptr.p_double[i] = v; - mx = ae_maxreal(mx, ae_fabs(v, _state), _state); - } - if( ae_fp_eq(mx,0) ) - { - *r = 0; - *rerr = 0; - return; - } - xblas_xsum(temp, mx, n, r, rerr, _state); -} - - -/************************************************************************* -More precise complex dot-product. Absolute error of subroutine result is -about 1 ulp of max(MX,V), where: - MX = max( |a[i]*b[i]| ) - V = |(a,b)| - -INPUT PARAMETERS - A - array[0..N-1], vector 1 - B - array[0..N-1], vector 2 - N - vectors length, N<2^29. - Temp - array[0..2*N-1], pre-allocated temporary storage - -OUTPUT PARAMETERS - R - (A,B) - RErr - estimate of error. This estimate accounts for both errors - during calculation of (A,B) and errors introduced by - rounding of A and B to fit in double (about 1 ulp). - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void xcdot(/* Complex */ ae_vector* a, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* temp, - ae_complex* r, - double* rerr, - ae_state *_state) -{ - ae_int_t i; - double mx; - double v; - double rerrx; - double rerry; - - r->x = 0; - r->y = 0; - *rerr = 0; - - - /* - * special cases: - * * N=0 - */ - if( n==0 ) - { - *r = ae_complex_from_d(0); - *rerr = 0; - return; - } - - /* - * calculate real part - */ - mx = 0; - for(i=0; i<=n-1; i++) - { - v = a->ptr.p_complex[i].x*b->ptr.p_complex[i].x; - temp->ptr.p_double[2*i+0] = v; - mx = ae_maxreal(mx, ae_fabs(v, _state), _state); - v = -a->ptr.p_complex[i].y*b->ptr.p_complex[i].y; - temp->ptr.p_double[2*i+1] = v; - mx = ae_maxreal(mx, ae_fabs(v, _state), _state); - } - if( ae_fp_eq(mx,0) ) - { - r->x = 0; - rerrx = 0; - } - else - { - xblas_xsum(temp, mx, 2*n, &r->x, &rerrx, _state); - } - - /* - * calculate imaginary part - */ - mx = 0; - for(i=0; i<=n-1; i++) - { - v = a->ptr.p_complex[i].x*b->ptr.p_complex[i].y; - temp->ptr.p_double[2*i+0] = v; - mx = ae_maxreal(mx, ae_fabs(v, _state), _state); - v = a->ptr.p_complex[i].y*b->ptr.p_complex[i].x; - temp->ptr.p_double[2*i+1] = v; - mx = ae_maxreal(mx, ae_fabs(v, _state), _state); - } - if( ae_fp_eq(mx,0) ) - { - r->y = 0; - rerry = 0; - } - else - { - xblas_xsum(temp, mx, 2*n, &r->y, &rerry, _state); - } - - /* - * total error - */ - if( ae_fp_eq(rerrx,0)&&ae_fp_eq(rerry,0) ) - { - *rerr = 0; - } - else - { - *rerr = ae_maxreal(rerrx, rerry, _state)*ae_sqrt(1+ae_sqr(ae_minreal(rerrx, rerry, _state)/ae_maxreal(rerrx, rerry, _state), _state), _state); - } -} - - -/************************************************************************* -Internal subroutine for extra-precise calculation of SUM(w[i]). - -INPUT PARAMETERS: - W - array[0..N-1], values to be added - W is modified during calculations. - MX - max(W[i]) - N - array size - -OUTPUT PARAMETERS: - R - SUM(w[i]) - RErr- error estimate for R - - -- ALGLIB -- - Copyright 24.08.2009 by Bochkanov Sergey -*************************************************************************/ -static void xblas_xsum(/* Real */ ae_vector* w, - double mx, - ae_int_t n, - double* r, - double* rerr, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t ks; - double v; - double s; - double ln2; - double chunk; - double invchunk; - ae_bool allzeros; - - *r = 0; - *rerr = 0; - - - /* - * special cases: - * * N=0 - * * N is too large to use integer arithmetics - */ - if( n==0 ) - { - *r = 0; - *rerr = 0; - return; - } - if( ae_fp_eq(mx,0) ) - { - *r = 0; - *rerr = 0; - return; - } - ae_assert(n<536870912, "XDot: N is too large!", _state); - - /* - * Prepare - */ - ln2 = ae_log(2, _state); - *rerr = mx*ae_machineepsilon; - - /* - * 1. find S such that 0.5<=S*MX<1 - * 2. multiply W by S, so task is normalized in some sense - * 3. S:=1/S so we can obtain original vector multiplying by S - */ - k = ae_round(ae_log(mx, _state)/ln2, _state); - s = xblas_xfastpow(2, -k, _state); - while(ae_fp_greater_eq(s*mx,1)) - { - s = 0.5*s; - } - while(ae_fp_less(s*mx,0.5)) - { - s = 2*s; - } - ae_v_muld(&w->ptr.p_double[0], 1, ae_v_len(0,n-1), s); - s = 1/s; - - /* - * find Chunk=2^M such that N*Chunk<2^29 - * - * we have chosen upper limit (2^29) with enough space left - * to tolerate possible problems with rounding and N's close - * to the limit, so we don't want to be very strict here. - */ - k = ae_trunc(ae_log((double)536870912/(double)n, _state)/ln2, _state); - chunk = xblas_xfastpow(2, k, _state); - if( ae_fp_less(chunk,2) ) - { - chunk = 2; - } - invchunk = 1/chunk; - - /* - * calculate result - */ - *r = 0; - ae_v_muld(&w->ptr.p_double[0], 1, ae_v_len(0,n-1), chunk); - for(;;) - { - s = s*invchunk; - allzeros = ae_true; - ks = 0; - for(i=0; i<=n-1; i++) - { - v = w->ptr.p_double[i]; - k = ae_trunc(v, _state); - if( ae_fp_neq(v,k) ) - { - allzeros = ae_false; - } - w->ptr.p_double[i] = chunk*(v-k); - ks = ks+k; - } - *r = *r+s*ks; - v = ae_fabs(*r, _state); - if( allzeros||ae_fp_eq(s*n+mx,mx) ) - { - break; - } - } - - /* - * correct error - */ - *rerr = ae_maxreal(*rerr, ae_fabs(*r, _state)*ae_machineepsilon, _state); -} - - -/************************************************************************* -Fast Pow - - -- ALGLIB -- - Copyright 24.08.2009 by Bochkanov Sergey -*************************************************************************/ -static double xblas_xfastpow(double r, ae_int_t n, ae_state *_state) -{ - double result; - - - result = 0; - if( n>0 ) - { - if( n%2==0 ) - { - result = ae_sqr(xblas_xfastpow(r, n/2, _state), _state); - } - else - { - result = r*xblas_xfastpow(r, n-1, _state); - } - return result; - } - if( n==0 ) - { - result = 1; - } - if( n<0 ) - { - result = xblas_xfastpow(1/r, -n, _state); - } - return result; -} - - - - -/************************************************************************* -Normalizes direction/step pair: makes |D|=1, scales Stp. -If |D|=0, it returns, leavind D/Stp unchanged. - - -- ALGLIB -- - Copyright 01.04.2010 by Bochkanov Sergey -*************************************************************************/ -void linminnormalized(/* Real */ ae_vector* d, - double* stp, - ae_int_t n, - ae_state *_state) -{ - double mx; - double s; - ae_int_t i; - - - - /* - * first, scale D to avoid underflow/overflow durng squaring - */ - mx = 0; - for(i=0; i<=n-1; i++) - { - mx = ae_maxreal(mx, ae_fabs(d->ptr.p_double[i], _state), _state); - } - if( ae_fp_eq(mx,0) ) - { - return; - } - s = 1/mx; - ae_v_muld(&d->ptr.p_double[0], 1, ae_v_len(0,n-1), s); - *stp = *stp/s; - - /* - * normalize D - */ - s = ae_v_dotproduct(&d->ptr.p_double[0], 1, &d->ptr.p_double[0], 1, ae_v_len(0,n-1)); - s = 1/ae_sqrt(s, _state); - ae_v_muld(&d->ptr.p_double[0], 1, ae_v_len(0,n-1), s); - *stp = *stp/s; -} - - -/************************************************************************* -THE PURPOSE OF MCSRCH IS TO FIND A STEP WHICH SATISFIES A SUFFICIENT -DECREASE CONDITION AND A CURVATURE CONDITION. - -AT EACH STAGE THE SUBROUTINE UPDATES AN INTERVAL OF UNCERTAINTY WITH -ENDPOINTS STX AND STY. THE INTERVAL OF UNCERTAINTY IS INITIALLY CHOSEN -SO THAT IT CONTAINS A MINIMIZER OF THE MODIFIED FUNCTION - - F(X+STP*S) - F(X) - FTOL*STP*(GRADF(X)'S). - -IF A STEP IS OBTAINED FOR WHICH THE MODIFIED FUNCTION HAS A NONPOSITIVE -FUNCTION VALUE AND NONNEGATIVE DERIVATIVE, THEN THE INTERVAL OF -UNCERTAINTY IS CHOSEN SO THAT IT CONTAINS A MINIMIZER OF F(X+STP*S). - -THE ALGORITHM IS DESIGNED TO FIND A STEP WHICH SATISFIES THE SUFFICIENT -DECREASE CONDITION - - F(X+STP*S) .LE. F(X) + FTOL*STP*(GRADF(X)'S), - -AND THE CURVATURE CONDITION - - ABS(GRADF(X+STP*S)'S)) .LE. GTOL*ABS(GRADF(X)'S). - -IF FTOL IS LESS THAN GTOL AND IF, FOR EXAMPLE, THE FUNCTION IS BOUNDED -BELOW, THEN THERE IS ALWAYS A STEP WHICH SATISFIES BOTH CONDITIONS. -IF NO STEP CAN BE FOUND WHICH SATISFIES BOTH CONDITIONS, THEN THE -ALGORITHM USUALLY STOPS WHEN ROUNDING ERRORS PREVENT FURTHER PROGRESS. -IN THIS CASE STP ONLY SATISFIES THE SUFFICIENT DECREASE CONDITION. - - -:::::::::::::IMPORTANT NOTES::::::::::::: - -NOTE 1: - -This routine guarantees that it will stop at the last point where function -value was calculated. It won't make several additional function evaluations -after finding good point. So if you store function evaluations requested by -this routine, you can be sure that last one is the point where we've stopped. - -NOTE 2: - -when 0xtrapf = 4.0; - zero = 0; - if( ae_fp_eq(stpmax,0) ) - { - stpmax = linmin_defstpmax; - } - if( ae_fp_less(*stp,linmin_stpmin) ) - { - *stp = linmin_stpmin; - } - if( ae_fp_greater(*stp,stpmax) ) - { - *stp = stpmax; - } - - /* - * Main cycle - */ - for(;;) - { - if( *stage==0 ) - { - - /* - * NEXT - */ - *stage = 2; - continue; - } - if( *stage==2 ) - { - state->infoc = 1; - *info = 0; - - /* - * CHECK THE INPUT PARAMETERS FOR ERRORS. - */ - if( ae_fp_less(stpmax,linmin_stpmin)&&ae_fp_greater(stpmax,0) ) - { - *info = 5; - *stp = 0.0; - return; - } - if( ((((((n<=0||ae_fp_less_eq(*stp,0))||ae_fp_less(linmin_ftol,0))||ae_fp_less(gtol,zero))||ae_fp_less(linmin_xtol,zero))||ae_fp_less(linmin_stpmin,zero))||ae_fp_less(stpmax,linmin_stpmin))||linmin_maxfev<=0 ) - { - *stage = 0; - return; - } - - /* - * COMPUTE THE INITIAL GRADIENT IN THE SEARCH DIRECTION - * AND CHECK THAT S IS A DESCENT DIRECTION. - */ - v = ae_v_dotproduct(&g->ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->dginit = v; - if( ae_fp_greater_eq(state->dginit,0) ) - { - *stage = 0; - return; - } - - /* - * INITIALIZE LOCAL VARIABLES. - */ - state->brackt = ae_false; - state->stage1 = ae_true; - *nfev = 0; - state->finit = *f; - state->dgtest = linmin_ftol*state->dginit; - state->width = stpmax-linmin_stpmin; - state->width1 = state->width/p5; - ae_v_move(&wa->ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * THE VARIABLES STX, FX, DGX CONTAIN THE VALUES OF THE STEP, - * FUNCTION, AND DIRECTIONAL DERIVATIVE AT THE BEST STEP. - * THE VARIABLES STY, FY, DGY CONTAIN THE VALUE OF THE STEP, - * FUNCTION, AND DERIVATIVE AT THE OTHER ENDPOINT OF - * THE INTERVAL OF UNCERTAINTY. - * THE VARIABLES STP, F, DG CONTAIN THE VALUES OF THE STEP, - * FUNCTION, AND DERIVATIVE AT THE CURRENT STEP. - */ - state->stx = 0; - state->fx = state->finit; - state->dgx = state->dginit; - state->sty = 0; - state->fy = state->finit; - state->dgy = state->dginit; - - /* - * NEXT - */ - *stage = 3; - continue; - } - if( *stage==3 ) - { - - /* - * START OF ITERATION. - * - * SET THE MINIMUM AND MAXIMUM STEPS TO CORRESPOND - * TO THE PRESENT INTERVAL OF UNCERTAINTY. - */ - if( state->brackt ) - { - if( ae_fp_less(state->stx,state->sty) ) - { - state->stmin = state->stx; - state->stmax = state->sty; - } - else - { - state->stmin = state->sty; - state->stmax = state->stx; - } - } - else - { - state->stmin = state->stx; - state->stmax = *stp+state->xtrapf*(*stp-state->stx); - } - - /* - * FORCE THE STEP TO BE WITHIN THE BOUNDS STPMAX AND STPMIN. - */ - if( ae_fp_greater(*stp,stpmax) ) - { - *stp = stpmax; - } - if( ae_fp_less(*stp,linmin_stpmin) ) - { - *stp = linmin_stpmin; - } - - /* - * IF AN UNUSUAL TERMINATION IS TO OCCUR THEN LET - * STP BE THE LOWEST POINT OBTAINED SO FAR. - */ - if( (((state->brackt&&(ae_fp_less_eq(*stp,state->stmin)||ae_fp_greater_eq(*stp,state->stmax)))||*nfev>=linmin_maxfev-1)||state->infoc==0)||(state->brackt&&ae_fp_less_eq(state->stmax-state->stmin,linmin_xtol*state->stmax)) ) - { - *stp = state->stx; - } - - /* - * EVALUATE THE FUNCTION AND GRADIENT AT STP - * AND COMPUTE THE DIRECTIONAL DERIVATIVE. - */ - ae_v_move(&x->ptr.p_double[0], 1, &wa->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&x->ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1), *stp); - - /* - * NEXT - */ - *stage = 4; - return; - } - if( *stage==4 ) - { - *info = 0; - *nfev = *nfev+1; - v = ae_v_dotproduct(&g->ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->dg = v; - state->ftest1 = state->finit+*stp*state->dgtest; - - /* - * TEST FOR CONVERGENCE. - */ - if( (state->brackt&&(ae_fp_less_eq(*stp,state->stmin)||ae_fp_greater_eq(*stp,state->stmax)))||state->infoc==0 ) - { - *info = 6; - } - if( (ae_fp_eq(*stp,stpmax)&&ae_fp_less_eq(*f,state->ftest1))&&ae_fp_less_eq(state->dg,state->dgtest) ) - { - *info = 5; - } - if( ae_fp_eq(*stp,linmin_stpmin)&&(ae_fp_greater(*f,state->ftest1)||ae_fp_greater_eq(state->dg,state->dgtest)) ) - { - *info = 4; - } - if( *nfev>=linmin_maxfev ) - { - *info = 3; - } - if( state->brackt&&ae_fp_less_eq(state->stmax-state->stmin,linmin_xtol*state->stmax) ) - { - *info = 2; - } - if( ae_fp_less_eq(*f,state->ftest1)&&ae_fp_less_eq(ae_fabs(state->dg, _state),-gtol*state->dginit) ) - { - *info = 1; - } - - /* - * CHECK FOR TERMINATION. - */ - if( *info!=0 ) - { - *stage = 0; - return; - } - - /* - * IN THE FIRST STAGE WE SEEK A STEP FOR WHICH THE MODIFIED - * FUNCTION HAS A NONPOSITIVE VALUE AND NONNEGATIVE DERIVATIVE. - */ - if( (state->stage1&&ae_fp_less_eq(*f,state->ftest1))&&ae_fp_greater_eq(state->dg,ae_minreal(linmin_ftol, gtol, _state)*state->dginit) ) - { - state->stage1 = ae_false; - } - - /* - * A MODIFIED FUNCTION IS USED TO PREDICT THE STEP ONLY IF - * WE HAVE NOT OBTAINED A STEP FOR WHICH THE MODIFIED - * FUNCTION HAS A NONPOSITIVE FUNCTION VALUE AND NONNEGATIVE - * DERIVATIVE, AND IF A LOWER FUNCTION VALUE HAS BEEN - * OBTAINED BUT THE DECREASE IS NOT SUFFICIENT. - */ - if( (state->stage1&&ae_fp_less_eq(*f,state->fx))&&ae_fp_greater(*f,state->ftest1) ) - { - - /* - * DEFINE THE MODIFIED FUNCTION AND DERIVATIVE VALUES. - */ - state->fm = *f-*stp*state->dgtest; - state->fxm = state->fx-state->stx*state->dgtest; - state->fym = state->fy-state->sty*state->dgtest; - state->dgm = state->dg-state->dgtest; - state->dgxm = state->dgx-state->dgtest; - state->dgym = state->dgy-state->dgtest; - - /* - * CALL CSTEP TO UPDATE THE INTERVAL OF UNCERTAINTY - * AND TO COMPUTE THE NEW STEP. - */ - linmin_mcstep(&state->stx, &state->fxm, &state->dgxm, &state->sty, &state->fym, &state->dgym, stp, state->fm, state->dgm, &state->brackt, state->stmin, state->stmax, &state->infoc, _state); - - /* - * RESET THE FUNCTION AND GRADIENT VALUES FOR F. - */ - state->fx = state->fxm+state->stx*state->dgtest; - state->fy = state->fym+state->sty*state->dgtest; - state->dgx = state->dgxm+state->dgtest; - state->dgy = state->dgym+state->dgtest; - } - else - { - - /* - * CALL MCSTEP TO UPDATE THE INTERVAL OF UNCERTAINTY - * AND TO COMPUTE THE NEW STEP. - */ - linmin_mcstep(&state->stx, &state->fx, &state->dgx, &state->sty, &state->fy, &state->dgy, stp, *f, state->dg, &state->brackt, state->stmin, state->stmax, &state->infoc, _state); - } - - /* - * FORCE A SUFFICIENT DECREASE IN THE SIZE OF THE - * INTERVAL OF UNCERTAINTY. - */ - if( state->brackt ) - { - if( ae_fp_greater_eq(ae_fabs(state->sty-state->stx, _state),p66*state->width1) ) - { - *stp = state->stx+p5*(state->sty-state->stx); - } - state->width1 = state->width; - state->width = ae_fabs(state->sty-state->stx, _state); - } - - /* - * NEXT. - */ - *stage = 3; - continue; - } - } -} - - -/************************************************************************* -These functions perform Armijo line search using at most FMAX function -evaluations. It doesn't enforce some kind of " sufficient decrease" -criterion - it just tries different Armijo steps and returns optimum found -so far. - -Optimization is done using F-rcomm interface: -* ArmijoCreate initializes State structure - (reusing previously allocated buffers) -* ArmijoIteration is subsequently called -* ArmijoResults returns results - -INPUT PARAMETERS: - N - problem size - X - array[N], starting point - F - F(X+S*STP) - S - step direction, S>0 - STP - step length - STPMAX - maximum value for STP or zero (if no limit is imposed) - FMAX - maximum number of function evaluations - State - optimization state - - -- ALGLIB -- - Copyright 05.10.2010 by Bochkanov Sergey -*************************************************************************/ -void armijocreate(ae_int_t n, - /* Real */ ae_vector* x, - double f, - /* Real */ ae_vector* s, - double stp, - double stpmax, - ae_int_t fmax, - armijostate* state, - ae_state *_state) -{ - - - if( state->x.cntx, n, _state); - } - if( state->xbase.cntxbase, n, _state); - } - if( state->s.cnts, n, _state); - } - state->stpmax = stpmax; - state->fmax = fmax; - state->stplen = stp; - state->fcur = f; - state->n = n; - ae_v_move(&state->xbase.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->s.ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_vector_set_length(&state->rstate.ia, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 0+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -This is rcomm-based search function - - -- ALGLIB -- - Copyright 05.10.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool armijoiteration(armijostate* state, ae_state *_state) -{ - double v; - ae_int_t n; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - v = state->rstate.ra.ptr.p_double[0]; - } - else - { - n = -983; - v = -989; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - - /* - * Routine body - */ - if( (ae_fp_less_eq(state->stplen,0)||ae_fp_less(state->stpmax,0))||state->fmax<2 ) - { - state->info = 0; - result = ae_false; - return result; - } - if( ae_fp_less_eq(state->stplen,linmin_stpmin) ) - { - state->info = 4; - result = ae_false; - return result; - } - n = state->n; - state->nfev = 0; - - /* - * We always need F - */ - state->needf = ae_true; - - /* - * Bound StpLen - */ - if( ae_fp_greater(state->stplen,state->stpmax)&&ae_fp_neq(state->stpmax,0) ) - { - state->stplen = state->stpmax; - } - - /* - * Increase length - */ - v = state->stplen*linmin_armijofactor; - if( ae_fp_greater(v,state->stpmax)&&ae_fp_neq(state->stpmax,0) ) - { - v = state->stpmax; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->x.ptr.p_double[0], 1, &state->s.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->nfev = state->nfev+1; - if( ae_fp_greater_eq(state->f,state->fcur) ) - { - goto lbl_4; - } - state->stplen = v; - state->fcur = state->f; -lbl_6: - if( ae_false ) - { - goto lbl_7; - } - - /* - * test stopping conditions - */ - if( state->nfev>=state->fmax ) - { - state->info = 3; - result = ae_false; - return result; - } - if( ae_fp_greater_eq(state->stplen,state->stpmax) ) - { - state->info = 5; - result = ae_false; - return result; - } - - /* - * evaluate F - */ - v = state->stplen*linmin_armijofactor; - if( ae_fp_greater(v,state->stpmax)&&ae_fp_neq(state->stpmax,0) ) - { - v = state->stpmax; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->x.ptr.p_double[0], 1, &state->s.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->nfev = state->nfev+1; - - /* - * make decision - */ - if( ae_fp_less(state->f,state->fcur) ) - { - state->stplen = v; - state->fcur = state->f; - } - else - { - state->info = 1; - result = ae_false; - return result; - } - goto lbl_6; -lbl_7: -lbl_4: - - /* - * Decrease length - */ - v = state->stplen/linmin_armijofactor; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->x.ptr.p_double[0], 1, &state->s.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->nfev = state->nfev+1; - if( ae_fp_greater_eq(state->f,state->fcur) ) - { - goto lbl_8; - } - state->stplen = state->stplen/linmin_armijofactor; - state->fcur = state->f; -lbl_10: - if( ae_false ) - { - goto lbl_11; - } - - /* - * test stopping conditions - */ - if( state->nfev>=state->fmax ) - { - state->info = 3; - result = ae_false; - return result; - } - if( ae_fp_less_eq(state->stplen,linmin_stpmin) ) - { - state->info = 4; - result = ae_false; - return result; - } - - /* - * evaluate F - */ - v = state->stplen/linmin_armijofactor; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->x.ptr.p_double[0], 1, &state->s.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->nfev = state->nfev+1; - - /* - * make decision - */ - if( ae_fp_less(state->f,state->fcur) ) - { - state->stplen = state->stplen/linmin_armijofactor; - state->fcur = state->f; - } - else - { - state->info = 1; - result = ae_false; - return result; - } - goto lbl_10; -lbl_11: -lbl_8: - - /* - * Nothing to be done - */ - state->info = 1; - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ra.ptr.p_double[0] = v; - return result; -} - - -/************************************************************************* -Results of Armijo search - -OUTPUT PARAMETERS: - INFO - on output it is set to one of the return codes: - * 0 improper input params - * 1 optimum step is found with at most FMAX evaluations - * 3 FMAX evaluations were used, - X contains optimum found so far - * 4 step is at lower bound STPMIN - * 5 step is at upper bound - STP - step length (in case of failure it is still returned) - F - function value (in case of failure it is still returned) - - -- ALGLIB -- - Copyright 05.10.2010 by Bochkanov Sergey -*************************************************************************/ -void armijoresults(armijostate* state, - ae_int_t* info, - double* stp, - double* f, - ae_state *_state) -{ - - - *info = state->info; - *stp = state->stplen; - *f = state->fcur; -} - - -static void linmin_mcstep(double* stx, - double* fx, - double* dx, - double* sty, - double* fy, - double* dy, - double* stp, - double fp, - double dp, - ae_bool* brackt, - double stmin, - double stmax, - ae_int_t* info, - ae_state *_state) -{ - ae_bool bound; - double gamma; - double p; - double q; - double r; - double s; - double sgnd; - double stpc; - double stpf; - double stpq; - double theta; - - - *info = 0; - - /* - * CHECK THE INPUT PARAMETERS FOR ERRORS. - */ - if( ((*brackt&&(ae_fp_less_eq(*stp,ae_minreal(*stx, *sty, _state))||ae_fp_greater_eq(*stp,ae_maxreal(*stx, *sty, _state))))||ae_fp_greater_eq(*dx*(*stp-(*stx)),0))||ae_fp_less(stmax,stmin) ) - { - return; - } - - /* - * DETERMINE IF THE DERIVATIVES HAVE OPPOSITE SIGN. - */ - sgnd = dp*(*dx/ae_fabs(*dx, _state)); - - /* - * FIRST CASE. A HIGHER FUNCTION VALUE. - * THE MINIMUM IS BRACKETED. IF THE CUBIC STEP IS CLOSER - * TO STX THAN THE QUADRATIC STEP, THE CUBIC STEP IS TAKEN, - * ELSE THE AVERAGE OF THE CUBIC AND QUADRATIC STEPS IS TAKEN. - */ - if( ae_fp_greater(fp,*fx) ) - { - *info = 1; - bound = ae_true; - theta = 3*(*fx-fp)/(*stp-(*stx))+(*dx)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dx, _state), ae_fabs(dp, _state), _state), _state); - gamma = s*ae_sqrt(ae_sqr(theta/s, _state)-*dx/s*(dp/s), _state); - if( ae_fp_less(*stp,*stx) ) - { - gamma = -gamma; - } - p = gamma-(*dx)+theta; - q = gamma-(*dx)+gamma+dp; - r = p/q; - stpc = *stx+r*(*stp-(*stx)); - stpq = *stx+*dx/((*fx-fp)/(*stp-(*stx))+(*dx))/2*(*stp-(*stx)); - if( ae_fp_less(ae_fabs(stpc-(*stx), _state),ae_fabs(stpq-(*stx), _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpc+(stpq-stpc)/2; - } - *brackt = ae_true; - } - else - { - if( ae_fp_less(sgnd,0) ) - { - - /* - * SECOND CASE. A LOWER FUNCTION VALUE AND DERIVATIVES OF - * OPPOSITE SIGN. THE MINIMUM IS BRACKETED. IF THE CUBIC - * STEP IS CLOSER TO STX THAN THE QUADRATIC (SECANT) STEP, - * THE CUBIC STEP IS TAKEN, ELSE THE QUADRATIC STEP IS TAKEN. - */ - *info = 2; - bound = ae_false; - theta = 3*(*fx-fp)/(*stp-(*stx))+(*dx)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dx, _state), ae_fabs(dp, _state), _state), _state); - gamma = s*ae_sqrt(ae_sqr(theta/s, _state)-*dx/s*(dp/s), _state); - if( ae_fp_greater(*stp,*stx) ) - { - gamma = -gamma; - } - p = gamma-dp+theta; - q = gamma-dp+gamma+(*dx); - r = p/q; - stpc = *stp+r*(*stx-(*stp)); - stpq = *stp+dp/(dp-(*dx))*(*stx-(*stp)); - if( ae_fp_greater(ae_fabs(stpc-(*stp), _state),ae_fabs(stpq-(*stp), _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpq; - } - *brackt = ae_true; - } - else - { - if( ae_fp_less(ae_fabs(dp, _state),ae_fabs(*dx, _state)) ) - { - - /* - * THIRD CASE. A LOWER FUNCTION VALUE, DERIVATIVES OF THE - * SAME SIGN, AND THE MAGNITUDE OF THE DERIVATIVE DECREASES. - * THE CUBIC STEP IS ONLY USED IF THE CUBIC TENDS TO INFINITY - * IN THE DIRECTION OF THE STEP OR IF THE MINIMUM OF THE CUBIC - * IS BEYOND STP. OTHERWISE THE CUBIC STEP IS DEFINED TO BE - * EITHER STPMIN OR STPMAX. THE QUADRATIC (SECANT) STEP IS ALSO - * COMPUTED AND IF THE MINIMUM IS BRACKETED THEN THE THE STEP - * CLOSEST TO STX IS TAKEN, ELSE THE STEP FARTHEST AWAY IS TAKEN. - */ - *info = 3; - bound = ae_true; - theta = 3*(*fx-fp)/(*stp-(*stx))+(*dx)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dx, _state), ae_fabs(dp, _state), _state), _state); - - /* - * THE CASE GAMMA = 0 ONLY ARISES IF THE CUBIC DOES NOT TEND - * TO INFINITY IN THE DIRECTION OF THE STEP. - */ - gamma = s*ae_sqrt(ae_maxreal(0, ae_sqr(theta/s, _state)-*dx/s*(dp/s), _state), _state); - if( ae_fp_greater(*stp,*stx) ) - { - gamma = -gamma; - } - p = gamma-dp+theta; - q = gamma+(*dx-dp)+gamma; - r = p/q; - if( ae_fp_less(r,0)&&ae_fp_neq(gamma,0) ) - { - stpc = *stp+r*(*stx-(*stp)); - } - else - { - if( ae_fp_greater(*stp,*stx) ) - { - stpc = stmax; - } - else - { - stpc = stmin; - } - } - stpq = *stp+dp/(dp-(*dx))*(*stx-(*stp)); - if( *brackt ) - { - if( ae_fp_less(ae_fabs(*stp-stpc, _state),ae_fabs(*stp-stpq, _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpq; - } - } - else - { - if( ae_fp_greater(ae_fabs(*stp-stpc, _state),ae_fabs(*stp-stpq, _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpq; - } - } - } - else - { - - /* - * FOURTH CASE. A LOWER FUNCTION VALUE, DERIVATIVES OF THE - * SAME SIGN, AND THE MAGNITUDE OF THE DERIVATIVE DOES - * NOT DECREASE. IF THE MINIMUM IS NOT BRACKETED, THE STEP - * IS EITHER STPMIN OR STPMAX, ELSE THE CUBIC STEP IS TAKEN. - */ - *info = 4; - bound = ae_false; - if( *brackt ) - { - theta = 3*(fp-(*fy))/(*sty-(*stp))+(*dy)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dy, _state), ae_fabs(dp, _state), _state), _state); - gamma = s*ae_sqrt(ae_sqr(theta/s, _state)-*dy/s*(dp/s), _state); - if( ae_fp_greater(*stp,*sty) ) - { - gamma = -gamma; - } - p = gamma-dp+theta; - q = gamma-dp+gamma+(*dy); - r = p/q; - stpc = *stp+r*(*sty-(*stp)); - stpf = stpc; - } - else - { - if( ae_fp_greater(*stp,*stx) ) - { - stpf = stmax; - } - else - { - stpf = stmin; - } - } - } - } - } - - /* - * UPDATE THE INTERVAL OF UNCERTAINTY. THIS UPDATE DOES NOT - * DEPEND ON THE NEW STEP OR THE CASE ANALYSIS ABOVE. - */ - if( ae_fp_greater(fp,*fx) ) - { - *sty = *stp; - *fy = fp; - *dy = dp; - } - else - { - if( ae_fp_less(sgnd,0.0) ) - { - *sty = *stx; - *fy = *fx; - *dy = *dx; - } - *stx = *stp; - *fx = fp; - *dx = dp; - } - - /* - * COMPUTE THE NEW STEP AND SAFEGUARD IT. - */ - stpf = ae_minreal(stmax, stpf, _state); - stpf = ae_maxreal(stmin, stpf, _state); - *stp = stpf; - if( *brackt&&bound ) - { - if( ae_fp_greater(*sty,*stx) ) - { - *stp = ae_minreal(*stx+0.66*(*sty-(*stx)), *stp, _state); - } - else - { - *stp = ae_maxreal(*stx+0.66*(*sty-(*stx)), *stp, _state); - } - } -} - - -ae_bool _linminstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - linminstate *p = (linminstate*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _linminstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - linminstate *dst = (linminstate*)_dst; - linminstate *src = (linminstate*)_src; - dst->brackt = src->brackt; - dst->stage1 = src->stage1; - dst->infoc = src->infoc; - dst->dg = src->dg; - dst->dgm = src->dgm; - dst->dginit = src->dginit; - dst->dgtest = src->dgtest; - dst->dgx = src->dgx; - dst->dgxm = src->dgxm; - dst->dgy = src->dgy; - dst->dgym = src->dgym; - dst->finit = src->finit; - dst->ftest1 = src->ftest1; - dst->fm = src->fm; - dst->fx = src->fx; - dst->fxm = src->fxm; - dst->fy = src->fy; - dst->fym = src->fym; - dst->stx = src->stx; - dst->sty = src->sty; - dst->stmin = src->stmin; - dst->stmax = src->stmax; - dst->width = src->width; - dst->width1 = src->width1; - dst->xtrapf = src->xtrapf; - return ae_true; -} - - -void _linminstate_clear(void* _p) -{ - linminstate *p = (linminstate*)_p; - ae_touch_ptr((void*)p); -} - - -void _linminstate_destroy(void* _p) -{ - linminstate *p = (linminstate*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _armijostate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - armijostate *p = (armijostate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xbase, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _armijostate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - armijostate *dst = (armijostate*)_dst; - armijostate *src = (armijostate*)_src; - dst->needf = src->needf; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - dst->n = src->n; - if( !ae_vector_init_copy(&dst->xbase, &src->xbase, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - dst->stplen = src->stplen; - dst->fcur = src->fcur; - dst->stpmax = src->stpmax; - dst->fmax = src->fmax; - dst->nfev = src->nfev; - dst->info = src->info; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _armijostate_clear(void* _p) -{ - armijostate *p = (armijostate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->xbase); - ae_vector_clear(&p->s); - _rcommstate_clear(&p->rstate); -} - - -void _armijostate_destroy(void* _p) -{ - armijostate *p = (armijostate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->xbase); - ae_vector_destroy(&p->s); - _rcommstate_destroy(&p->rstate); -} - - - - -void findprimitiverootandinverse(ae_int_t n, - ae_int_t* proot, - ae_int_t* invproot, - ae_state *_state) -{ - ae_int_t candroot; - ae_int_t phin; - ae_int_t q; - ae_int_t f; - ae_bool allnonone; - ae_int_t x; - ae_int_t lastx; - ae_int_t y; - ae_int_t lasty; - ae_int_t a; - ae_int_t b; - ae_int_t t; - ae_int_t n2; - - *proot = 0; - *invproot = 0; - - ae_assert(n>=3, "FindPrimitiveRootAndInverse: N<3", _state); - *proot = 0; - *invproot = 0; - - /* - * check that N is prime - */ - ae_assert(ntheory_isprime(n, _state), "FindPrimitiveRoot: N is not prime", _state); - - /* - * Because N is prime, Euler totient function is equal to N-1 - */ - phin = n-1; - - /* - * Test different values of PRoot - from 2 to N-1. - * One of these values MUST be primitive root. - * - * For testing we use algorithm from Wiki (Primitive root modulo n): - * * compute phi(N) - * * determine the different prime factors of phi(N), say p1, ..., pk - * * for every element m of Zn*, compute m^(phi(N)/pi) mod N for i=1..k - * using a fast algorithm for modular exponentiation. - * * a number m for which these k results are all different from 1 is a - * primitive root. - */ - for(candroot=2; candroot<=n-1; candroot++) - { - - /* - * We have current candidate root in CandRoot. - * - * Scan different prime factors of PhiN. Here: - * * F is a current candidate factor - * * Q is a current quotient - amount which was left after dividing PhiN - * by all previous factors - * - * For each factor, perform test mentioned above. - */ - q = phin; - f = 2; - allnonone = ae_true; - while(q>1) - { - if( q%f==0 ) - { - t = ntheory_modexp(candroot, phin/f, n, _state); - if( t==1 ) - { - allnonone = ae_false; - break; - } - while(q%f==0) - { - q = q/f; - } - } - f = f+1; - } - if( allnonone ) - { - *proot = candroot; - break; - } - } - ae_assert(*proot>=2, "FindPrimitiveRoot: internal error (root not found)", _state); - - /* - * Use extended Euclidean algorithm to find multiplicative inverse of primitive root - */ - x = 0; - lastx = 1; - y = 1; - lasty = 0; - a = *proot; - b = n; - while(b!=0) - { - q = a/b; - t = a%b; - a = b; - b = t; - t = lastx-q*x; - lastx = x; - x = t; - t = lasty-q*y; - lasty = y; - y = t; - } - while(lastx<0) - { - lastx = lastx+n; - } - *invproot = lastx; - - /* - * Check that it is safe to perform multiplication modulo N. - * Check results for consistency. - */ - n2 = (n-1)*(n-1); - ae_assert(n2/(n-1)==n-1, "FindPrimitiveRoot: internal error", _state); - ae_assert(*proot*(*invproot)/(*proot)==(*invproot), "FindPrimitiveRoot: internal error", _state); - ae_assert(*proot*(*invproot)/(*invproot)==(*proot), "FindPrimitiveRoot: internal error", _state); - ae_assert(*proot*(*invproot)%n==1, "FindPrimitiveRoot: internal error", _state); -} - - -static ae_bool ntheory_isprime(ae_int_t n, ae_state *_state) -{ - ae_int_t p; - ae_bool result; - - - result = ae_false; - p = 2; - while(p*p<=n) - { - if( n%p==0 ) - { - return result; - } - p = p+1; - } - result = ae_true; - return result; -} - - -static ae_int_t ntheory_modmul(ae_int_t a, - ae_int_t b, - ae_int_t n, - ae_state *_state) -{ - ae_int_t t; - double ra; - double rb; - ae_int_t result; - - - ae_assert(a>=0&&a=N", _state); - ae_assert(b>=0&&b=N", _state); - - /* - * Base cases - */ - ra = a; - rb = b; - if( b==0||a==0 ) - { - result = 0; - return result; - } - if( b==1||a==1 ) - { - result = a*b; - return result; - } - if( ae_fp_eq(ra*rb,a*b) ) - { - result = a*b%n; - return result; - } - - /* - * Non-base cases - */ - if( b%2==0 ) - { - - /* - * A*B = (A*(B/2)) * 2 - * - * Product T=A*(B/2) is calculated recursively, product T*2 is - * calculated as follows: - * * result:=T-N - * * result:=result+T - * * if result<0 then result:=result+N - * - * In case integer result overflows, we generate exception - */ - t = ntheory_modmul(a, b/2, n, _state); - result = t-n; - result = result+t; - if( result<0 ) - { - result = result+n; - } - } - else - { - - /* - * A*B = (A*(B div 2)) * 2 + A - * - * Product T=A*(B/2) is calculated recursively, product T*2 is - * calculated as follows: - * * result:=T-N - * * result:=result+T - * * if result<0 then result:=result+N - * - * In case integer result overflows, we generate exception - */ - t = ntheory_modmul(a, b/2, n, _state); - result = t-n; - result = result+t; - if( result<0 ) - { - result = result+n; - } - result = result-n; - result = result+a; - if( result<0 ) - { - result = result+n; - } - } - return result; -} - - -static ae_int_t ntheory_modexp(ae_int_t a, - ae_int_t b, - ae_int_t n, - ae_state *_state) -{ - ae_int_t t; - ae_int_t result; - - - ae_assert(a>=0&&a=N", _state); - ae_assert(b>=0, "ModExp: B<0", _state); - - /* - * Base cases - */ - if( b==0 ) - { - result = 1; - return result; - } - if( b==1 ) - { - result = a; - return result; - } - - /* - * Non-base cases - */ - if( b%2==0 ) - { - t = ntheory_modmul(a, a, n, _state); - result = ntheory_modexp(t, b/2, n, _state); - } - else - { - t = ntheory_modmul(a, a, n, _state); - result = ntheory_modexp(t, b/2, n, _state); - result = ntheory_modmul(result, a, n, _state); - } - return result; -} - - - - -/************************************************************************* -This subroutine generates FFT plan for K complex FFT's with length N each. - -INPUT PARAMETERS: - N - FFT length (in complex numbers), N>=1 - K - number of repetitions, K>=1 - -OUTPUT PARAMETERS: - Plan - plan - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -void ftcomplexfftplan(ae_int_t n, - ae_int_t k, - fasttransformplan* plan, - ae_state *_state) -{ - ae_frame _frame_block; - srealarray bluesteinbuf; - ae_int_t rowptr; - ae_int_t bluesteinsize; - ae_int_t precrptr; - ae_int_t preciptr; - ae_int_t precrsize; - ae_int_t precisize; - - ae_frame_make(_state, &_frame_block); - _fasttransformplan_clear(plan); - _srealarray_init(&bluesteinbuf, _state, ae_true); - - - /* - * Initial check for parameters - */ - ae_assert(n>0, "FTComplexFFTPlan: N<=0", _state); - ae_assert(k>0, "FTComplexFFTPlan: K<=0", _state); - - /* - * Determine required sizes of precomputed real and integer - * buffers. This stage of code is highly dependent on internals - * of FTComplexFFTPlanRec() and must be kept synchronized with - * possible changes in internals of plan generation function. - * - * Buffer size is determined as follows: - * * N is factorized - * * we factor out anything which is less or equal to MaxRadix - * * prime factor F>RaderThreshold requires 4*FTBaseFindSmooth(2*F-1) - * real entries to store precomputed Quantities for Bluestein's - * transformation - * * prime factor F<=RaderThreshold does NOT require - * precomputed storage - */ - precrsize = 0; - precisize = 0; - ftbase_ftdeterminespacerequirements(n, &precrsize, &precisize, _state); - if( precrsize>0 ) - { - ae_vector_set_length(&plan->precr, precrsize, _state); - } - if( precisize>0 ) - { - ae_vector_set_length(&plan->preci, precisize, _state); - } - - /* - * Generate plan - */ - rowptr = 0; - precrptr = 0; - preciptr = 0; - bluesteinsize = 1; - ae_vector_set_length(&plan->buffer, 2*n*k, _state); - ftbase_ftcomplexfftplanrec(n, k, ae_true, ae_true, &rowptr, &bluesteinsize, &precrptr, &preciptr, plan, _state); - ae_vector_set_length(&bluesteinbuf.val, bluesteinsize, _state); - ae_shared_pool_set_seed(&plan->bluesteinpool, &bluesteinbuf, sizeof(bluesteinbuf), _srealarray_init, _srealarray_init_copy, _srealarray_destroy, _state); - - /* - * Check that actual amount of precomputed space used by transformation - * plan is EXACTLY equal to amount of space allocated by us. - */ - ae_assert(precrptr==precrsize, "FTComplexFFTPlan: internal error (PrecRPtr<>PrecRSize)", _state); - ae_assert(preciptr==precisize, "FTComplexFFTPlan: internal error (PrecRPtr<>PrecRSize)", _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine applies transformation plan to input/output array A. - -INPUT PARAMETERS: - Plan - transformation plan - A - array, must be large enough for plan to work - OffsA - offset of the subarray to process - RepCnt - repetition count (transformation is repeatedly applied - to subsequent subarrays) - -OUTPUT PARAMETERS: - Plan - plan (temporary buffers can be modified, plan itself - is unchanged and can be reused) - A - transformed array - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -void ftapplyplan(fasttransformplan* plan, - /* Real */ ae_vector* a, - ae_int_t offsa, - ae_int_t repcnt, - ae_state *_state) -{ - ae_int_t plansize; - ae_int_t i; - - - plansize = plan->entries.ptr.pp_int[0][ftbase_coloperandscnt]*plan->entries.ptr.pp_int[0][ftbase_coloperandsize]*plan->entries.ptr.pp_int[0][ftbase_colmicrovectorsize]; - for(i=0; i<=repcnt-1; i++) - { - ftbase_ftapplysubplan(plan, 0, a, offsa+plansize*i, 0, &plan->buffer, 1, _state); - } -} - - -/************************************************************************* -Returns good factorization N=N1*N2. - -Usually N1<=N2 (but not always - small N's may be exception). -if N1<>1 then N2<>1. - -Factorization is chosen depending on task type and codelets we have. - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -void ftbasefactorize(ae_int_t n, - ae_int_t tasktype, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state) -{ - ae_int_t j; - - *n1 = 0; - *n2 = 0; - - *n1 = 0; - *n2 = 0; - - /* - * try to find good codelet - */ - if( *n1*(*n2)!=n ) - { - for(j=ftbase_ftbasecodeletrecommended; j>=2; j--) - { - if( n%j==0 ) - { - *n1 = j; - *n2 = n/j; - break; - } - } - } - - /* - * try to factorize N - */ - if( *n1*(*n2)!=n ) - { - for(j=ftbase_ftbasecodeletrecommended+1; j<=n-1; j++) - { - if( n%j==0 ) - { - *n1 = j; - *n2 = n/j; - break; - } - } - } - - /* - * looks like N is prime :( - */ - if( *n1*(*n2)!=n ) - { - *n1 = 1; - *n2 = n; - } - - /* - * normalize - */ - if( *n2==1&&*n1!=1 ) - { - *n2 = *n1; - *n1 = 1; - } -} - - -/************************************************************************* -Is number smooth? - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool ftbaseissmooth(ae_int_t n, ae_state *_state) -{ - ae_int_t i; - ae_bool result; - - - for(i=2; i<=ftbase_ftbasemaxsmoothfactor; i++) - { - while(n%i==0) - { - n = n/i; - } - } - result = n==1; - return result; -} - - -/************************************************************************* -Returns smallest smooth (divisible only by 2, 3, 5) number that is greater -than or equal to max(N,2) - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -ae_int_t ftbasefindsmooth(ae_int_t n, ae_state *_state) -{ - ae_int_t best; - ae_int_t result; - - - best = 2; - while(bestRaderThreshold requires 4*FTBaseFindSmooth(2*F-1) - * real entries to store precomputed Quantities for Bluestein's - * transformation - * * prime factor F<=RaderThreshold requires 2*(F-1)+ESTIMATE(F-1) - * precomputed storage - */ - ncur = n; - for(i=2; i<=ftbase_maxradix; i++) - { - while(ncur%i==0) - { - ncur = ncur/i; - } - } - f = 2; - while(f<=ncur) - { - while(ncur%f==0) - { - if( f>ftbase_raderthreshold ) - { - *precrsize = *precrsize+4*ftbasefindsmooth(2*f-1, _state); - } - else - { - *precrsize = *precrsize+2*(f-1); - ftbase_ftdeterminespacerequirements(f-1, precrsize, precisize, _state); - } - ncur = ncur/f; - } - f = f+1; - } -} - - -/************************************************************************* -Recurrent function called by FTComplexFFTPlan() and other functions. It -recursively builds transformation plan - -INPUT PARAMETERS: - N - FFT length (in complex numbers), N>=1 - K - number of repetitions, K>=1 - ChildPlan - if True, plan generator inserts OpStart/opEnd in the - plan header/footer. - TopmostPlan - if True, plan generator assumes that it is topmost plan: - * it may use global buffer for transpositions - and there is no other plan which executes in parallel - RowPtr - index which points to past-the-last entry generated so far - BluesteinSize- amount of storage (in real numbers) required for Bluestein buffer - PrecRPtr - pointer to unused part of precomputed real buffer (Plan.PrecR): - * when this function stores some data to precomputed buffer, - it advances pointer. - * it is responsibility of the function to assert that - Plan.PrecR has enough space to store data before actually - writing to buffer. - * it is responsibility of the caller to allocate enough - space before calling this function - PrecIPtr - pointer to unused part of precomputed integer buffer (Plan.PrecI): - * when this function stores some data to precomputed buffer, - it advances pointer. - * it is responsibility of the function to assert that - Plan.PrecR has enough space to store data before actually - writing to buffer. - * it is responsibility of the caller to allocate enough - space before calling this function - Plan - plan (generated so far) - -OUTPUT PARAMETERS: - RowPtr - updated pointer (advanced by number of entries generated - by function) - BluesteinSize- updated amount - (may be increased, but may never be decreased) - -NOTE: in case TopmostPlan is True, ChildPlan is also must be True. - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftcomplexfftplanrec(ae_int_t n, - ae_int_t k, - ae_bool childplan, - ae_bool topmostplan, - ae_int_t* rowptr, - ae_int_t* bluesteinsize, - ae_int_t* precrptr, - ae_int_t* preciptr, - fasttransformplan* plan, - ae_state *_state) -{ - ae_frame _frame_block; - srealarray localbuf; - ae_int_t m; - ae_int_t n1; - ae_int_t n2; - ae_int_t gq; - ae_int_t giq; - ae_int_t row0; - ae_int_t row1; - ae_int_t row2; - ae_int_t row3; - - ae_frame_make(_state, &_frame_block); - _srealarray_init(&localbuf, _state, ae_true); - - ae_assert(n>0, "FTComplexFFTPlan: N<=0", _state); - ae_assert(k>0, "FTComplexFFTPlan: K<=0", _state); - ae_assert(!topmostplan||childplan, "FTComplexFFTPlan: ChildPlan is inconsistent with TopmostPlan", _state); - - /* - * Try to generate "topmost" plan - */ - if( topmostplan&&n>ftbase_recursivethreshold ) - { - ftbase_ftfactorize(n, ae_false, &n1, &n2, _state); - if( n1*n2==0 ) - { - - /* - * Handle prime-factor FFT with Bluestein's FFT. - * Determine size of Bluestein's buffer. - */ - m = ftbasefindsmooth(2*n-1, _state); - *bluesteinsize = ae_maxint(2*m, *bluesteinsize, _state); - - /* - * Generate plan - */ - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - ftbase_ftpushentry4(plan, rowptr, ftbase_opbluesteinsfft, k, n, 2, m, 2, *precrptr, 0, _state); - row0 = *rowptr; - ftbase_ftpushentry(plan, rowptr, ftbase_opjmp, 0, 0, 0, 0, _state); - ftbase_ftcomplexfftplanrec(m, 1, ae_true, ae_true, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - row1 = *rowptr; - plan->entries.ptr.pp_int[row0][ftbase_colparam0] = row1-row0; - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - - /* - * Fill precomputed buffer - */ - ftbase_ftprecomputebluesteinsfft(n, m, &plan->precr, *precrptr, _state); - - /* - * Update pointer to the precomputed area - */ - *precrptr = *precrptr+4*m; - } - else - { - - /* - * Handle composite FFT with recursive Cooley-Tukey which - * uses global buffer instead of local one. - */ - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - row0 = *rowptr; - ftbase_ftpushentry2(plan, rowptr, ftbase_opparallelcall, k*n2, n1, 2, 0, ftbase_ftoptimisticestimate(n, _state), _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplexfftfactors, k, n, 2, n1, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n2, _state); - row2 = *rowptr; - ftbase_ftpushentry2(plan, rowptr, ftbase_opparallelcall, k*n1, n2, 2, 0, ftbase_ftoptimisticestimate(n, _state), _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - row1 = *rowptr; - ftbase_ftcomplexfftplanrec(n1, 1, ae_true, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - plan->entries.ptr.pp_int[row0][ftbase_colparam0] = row1-row0; - row3 = *rowptr; - ftbase_ftcomplexfftplanrec(n2, 1, ae_true, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - plan->entries.ptr.pp_int[row2][ftbase_colparam0] = row3-row2; - } - ae_frame_leave(_state); - return; - } - - /* - * Prepare "non-topmost" plan: - * * calculate factorization - * * use local (shared) buffer - * * update buffer size - ANY plan will need at least - * 2*N temporaries, additional requirements can be - * applied later - */ - ftbase_ftfactorize(n, ae_false, &n1, &n2, _state); - - /* - * Handle FFT's with N1*N2=0: either small-N or prime-factor - */ - if( n1*n2==0 ) - { - if( n<=ftbase_maxradix ) - { - - /* - * Small-N FFT - */ - if( childplan ) - { - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - } - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplexcodeletfft, k, n, 2, 0, _state); - if( childplan ) - { - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - } - ae_frame_leave(_state); - return; - } - if( n<=ftbase_raderthreshold ) - { - - /* - * Handle prime-factor FFT's with Rader's FFT - */ - m = n-1; - if( childplan ) - { - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - } - findprimitiverootandinverse(n, &gq, &giq, _state); - ftbase_ftpushentry4(plan, rowptr, ftbase_opradersfft, k, n, 2, 2, gq, giq, *precrptr, _state); - ftbase_ftprecomputeradersfft(n, gq, giq, &plan->precr, *precrptr, _state); - *precrptr = *precrptr+2*(n-1); - row0 = *rowptr; - ftbase_ftpushentry(plan, rowptr, ftbase_opjmp, 0, 0, 0, 0, _state); - ftbase_ftcomplexfftplanrec(m, 1, ae_true, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - row1 = *rowptr; - plan->entries.ptr.pp_int[row0][ftbase_colparam0] = row1-row0; - if( childplan ) - { - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - } - } - else - { - - /* - * Handle prime-factor FFT's with Bluestein's FFT - */ - m = ftbasefindsmooth(2*n-1, _state); - *bluesteinsize = ae_maxint(2*m, *bluesteinsize, _state); - if( childplan ) - { - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - } - ftbase_ftpushentry4(plan, rowptr, ftbase_opbluesteinsfft, k, n, 2, m, 2, *precrptr, 0, _state); - ftbase_ftprecomputebluesteinsfft(n, m, &plan->precr, *precrptr, _state); - *precrptr = *precrptr+4*m; - row0 = *rowptr; - ftbase_ftpushentry(plan, rowptr, ftbase_opjmp, 0, 0, 0, 0, _state); - ftbase_ftcomplexfftplanrec(m, 1, ae_true, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - row1 = *rowptr; - plan->entries.ptr.pp_int[row0][ftbase_colparam0] = row1-row0; - if( childplan ) - { - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - } - } - ae_frame_leave(_state); - return; - } - - /* - * Handle Cooley-Tukey FFT with small N1 - */ - if( n1<=ftbase_maxradix ) - { - - /* - * Specialized transformation for small N1: - * * N2 short inplace FFT's, each N1-point, with integrated twiddle factors - * * N1 long FFT's - * * final transposition - */ - if( childplan ) - { - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - } - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplexcodelettwfft, k, n1, 2*n2, 0, _state); - ftbase_ftcomplexfftplanrec(n2, k*n1, ae_false, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - if( childplan ) - { - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - } - ae_frame_leave(_state); - return; - } - - /* - * Handle general Cooley-Tukey FFT, either "flat" or "recursive" - */ - if( n<=ftbase_recursivethreshold ) - { - - /* - * General code for large N1/N2, "flat" version without explicit recurrence - * (nested subplans are inserted directly into the body of the plan) - */ - if( childplan ) - { - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - } - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - ftbase_ftcomplexfftplanrec(n1, k*n2, ae_false, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplexfftfactors, k, n, 2, n1, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n2, _state); - ftbase_ftcomplexfftplanrec(n2, k*n1, ae_false, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - if( childplan ) - { - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - } - } - else - { - - /* - * General code for large N1/N2, "recursive" version - nested subplans - * are separated from the plan body. - * - * Generate parent plan. - */ - if( childplan ) - { - ftbase_ftpushentry2(plan, rowptr, ftbase_opstart, k, n, 2, -1, ftbase_ftoptimisticestimate(n, _state), _state); - } - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - row0 = *rowptr; - ftbase_ftpushentry2(plan, rowptr, ftbase_opparallelcall, k*n2, n1, 2, 0, ftbase_ftoptimisticestimate(n, _state), _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplexfftfactors, k, n, 2, n1, _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n2, _state); - row2 = *rowptr; - ftbase_ftpushentry2(plan, rowptr, ftbase_opparallelcall, k*n1, n2, 2, 0, ftbase_ftoptimisticestimate(n, _state), _state); - ftbase_ftpushentry(plan, rowptr, ftbase_opcomplextranspose, k, n, 2, n1, _state); - if( childplan ) - { - ftbase_ftpushentry(plan, rowptr, ftbase_opend, k, n, 2, 0, _state); - } - - /* - * Generate child subplans, insert refence to parent plans - */ - row1 = *rowptr; - ftbase_ftcomplexfftplanrec(n1, 1, ae_true, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - plan->entries.ptr.pp_int[row0][ftbase_colparam0] = row1-row0; - row3 = *rowptr; - ftbase_ftcomplexfftplanrec(n2, 1, ae_true, ae_false, rowptr, bluesteinsize, precrptr, preciptr, plan, _state); - plan->entries.ptr.pp_int[row2][ftbase_colparam0] = row3-row2; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function pushes one more entry to the plan. It resizes Entries matrix -if needed. - -INPUT PARAMETERS: - Plan - plan (generated so far) - RowPtr - index which points to past-the-last entry generated so far - EType - entry type - EOpCnt - operands count - EOpSize - operand size - EMcvSize - microvector size - EParam0 - parameter 0 - -OUTPUT PARAMETERS: - Plan - updated plan - RowPtr - updated pointer - -NOTE: Param1 is set to -1. - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftpushentry(fasttransformplan* plan, - ae_int_t* rowptr, - ae_int_t etype, - ae_int_t eopcnt, - ae_int_t eopsize, - ae_int_t emcvsize, - ae_int_t eparam0, - ae_state *_state) -{ - - - ftbase_ftpushentry2(plan, rowptr, etype, eopcnt, eopsize, emcvsize, eparam0, -1, _state); -} - - -/************************************************************************* -Same as FTPushEntry(), but sets Param0 AND Param1. -This function pushes one more entry to the plan. It resized Entries matrix -if needed. - -INPUT PARAMETERS: - Plan - plan (generated so far) - RowPtr - index which points to past-the-last entry generated so far - EType - entry type - EOpCnt - operands count - EOpSize - operand size - EMcvSize - microvector size - EParam0 - parameter 0 - EParam1 - parameter 1 - -OUTPUT PARAMETERS: - Plan - updated plan - RowPtr - updated pointer - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftpushentry2(fasttransformplan* plan, - ae_int_t* rowptr, - ae_int_t etype, - ae_int_t eopcnt, - ae_int_t eopsize, - ae_int_t emcvsize, - ae_int_t eparam0, - ae_int_t eparam1, - ae_state *_state) -{ - - - if( *rowptr>=plan->entries.rows ) - { - imatrixresize(&plan->entries, ae_maxint(2*plan->entries.rows, 1, _state), ftbase_colscnt, _state); - } - plan->entries.ptr.pp_int[*rowptr][ftbase_coltype] = etype; - plan->entries.ptr.pp_int[*rowptr][ftbase_coloperandscnt] = eopcnt; - plan->entries.ptr.pp_int[*rowptr][ftbase_coloperandsize] = eopsize; - plan->entries.ptr.pp_int[*rowptr][ftbase_colmicrovectorsize] = emcvsize; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam0] = eparam0; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam1] = eparam1; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam2] = 0; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam3] = 0; - *rowptr = *rowptr+1; -} - - -/************************************************************************* -Same as FTPushEntry(), but sets Param0, Param1, Param2 and Param3. -This function pushes one more entry to the plan. It resized Entries matrix -if needed. - -INPUT PARAMETERS: - Plan - plan (generated so far) - RowPtr - index which points to past-the-last entry generated so far - EType - entry type - EOpCnt - operands count - EOpSize - operand size - EMcvSize - microvector size - EParam0 - parameter 0 - EParam1 - parameter 1 - EParam2 - parameter 2 - EParam3 - parameter 3 - -OUTPUT PARAMETERS: - Plan - updated plan - RowPtr - updated pointer - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftpushentry4(fasttransformplan* plan, - ae_int_t* rowptr, - ae_int_t etype, - ae_int_t eopcnt, - ae_int_t eopsize, - ae_int_t emcvsize, - ae_int_t eparam0, - ae_int_t eparam1, - ae_int_t eparam2, - ae_int_t eparam3, - ae_state *_state) -{ - - - if( *rowptr>=plan->entries.rows ) - { - imatrixresize(&plan->entries, ae_maxint(2*plan->entries.rows, 1, _state), ftbase_colscnt, _state); - } - plan->entries.ptr.pp_int[*rowptr][ftbase_coltype] = etype; - plan->entries.ptr.pp_int[*rowptr][ftbase_coloperandscnt] = eopcnt; - plan->entries.ptr.pp_int[*rowptr][ftbase_coloperandsize] = eopsize; - plan->entries.ptr.pp_int[*rowptr][ftbase_colmicrovectorsize] = emcvsize; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam0] = eparam0; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam1] = eparam1; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam2] = eparam2; - plan->entries.ptr.pp_int[*rowptr][ftbase_colparam3] = eparam3; - *rowptr = *rowptr+1; -} - - -/************************************************************************* -This subroutine applies subplan to input/output array A. - -INPUT PARAMETERS: - Plan - transformation plan - SubPlan - subplan index - A - array, must be large enough for plan to work - ABase - base offset in array A, this value points to start of - subarray whose length is equal to length of the plan - AOffset - offset with respect to ABase, 0<=AOffsetentries.ptr.pp_int[subplan][ftbase_coltype]==ftbase_opstart, "FTApplySubPlan: incorrect subplan header", _state); - rowidx = subplan+1; - while(plan->entries.ptr.pp_int[rowidx][ftbase_coltype]!=ftbase_opend) - { - operation = plan->entries.ptr.pp_int[rowidx][ftbase_coltype]; - operandscnt = repcnt*plan->entries.ptr.pp_int[rowidx][ftbase_coloperandscnt]; - operandsize = plan->entries.ptr.pp_int[rowidx][ftbase_coloperandsize]; - microvectorsize = plan->entries.ptr.pp_int[rowidx][ftbase_colmicrovectorsize]; - param0 = plan->entries.ptr.pp_int[rowidx][ftbase_colparam0]; - param1 = plan->entries.ptr.pp_int[rowidx][ftbase_colparam1]; - touchint(¶m1, _state); - - /* - * Process "jump" operation - */ - if( operation==ftbase_opjmp ) - { - rowidx = rowidx+plan->entries.ptr.pp_int[rowidx][ftbase_colparam0]; - continue; - } - - /* - * Process "parallel call" operation: - * * we perform initial check for consistency between parent and child plans - * * we call FTSplitAndApplyParallelPlan(), which splits parallel plan into - * several parallel tasks - */ - if( operation==ftbase_opparallelcall ) - { - parentsize = operandsize*microvectorsize; - childsize = plan->entries.ptr.pp_int[rowidx+param0][ftbase_coloperandscnt]*plan->entries.ptr.pp_int[rowidx+param0][ftbase_coloperandsize]*plan->entries.ptr.pp_int[rowidx+param0][ftbase_colmicrovectorsize]; - ae_assert(plan->entries.ptr.pp_int[rowidx+param0][ftbase_coltype]==ftbase_opstart, "FTApplySubPlan: incorrect child subplan header", _state); - ae_assert(parentsize==childsize, "FTApplySubPlan: incorrect child subplan header", _state); - chunksize = ae_maxint(ftbase_recursivethreshold/childsize, 1, _state); - lastchunksize = operandscnt%chunksize; - if( lastchunksize==0 ) - { - lastchunksize = chunksize; - } - i = 0; - while(ibluesteinpool, &_bufa, _state); - ae_shared_pool_retrieve(&plan->bluesteinpool, &_bufb, _state); - ae_shared_pool_retrieve(&plan->bluesteinpool, &_bufc, _state); - ae_shared_pool_retrieve(&plan->bluesteinpool, &_bufd, _state); - ftbase_ftbluesteinsfft(plan, a, abase, aoffset, operandscnt, operandsize, plan->entries.ptr.pp_int[rowidx][ftbase_colparam0], plan->entries.ptr.pp_int[rowidx][ftbase_colparam2], rowidx+plan->entries.ptr.pp_int[rowidx][ftbase_colparam1], &bufa->val, &bufb->val, &bufc->val, &bufd->val, _state); - ae_shared_pool_recycle(&plan->bluesteinpool, &_bufa, _state); - ae_shared_pool_recycle(&plan->bluesteinpool, &_bufb, _state); - ae_shared_pool_recycle(&plan->bluesteinpool, &_bufc, _state); - ae_shared_pool_recycle(&plan->bluesteinpool, &_bufd, _state); - rowidx = rowidx+1; - continue; - } - - /* - * Process Rader's FFT - */ - if( operation==ftbase_opradersfft ) - { - ftbase_ftradersfft(plan, a, abase, aoffset, operandscnt, operandsize, rowidx+plan->entries.ptr.pp_int[rowidx][ftbase_colparam0], plan->entries.ptr.pp_int[rowidx][ftbase_colparam1], plan->entries.ptr.pp_int[rowidx][ftbase_colparam2], plan->entries.ptr.pp_int[rowidx][ftbase_colparam3], buf, _state); - rowidx = rowidx+1; - continue; - } - - /* - * Process "complex twiddle factors" operation - */ - if( operation==ftbase_opcomplexfftfactors ) - { - ae_assert(microvectorsize==2, "FTApplySubPlan: MicrovectorSize<>1", _state); - n1 = plan->entries.ptr.pp_int[rowidx][ftbase_colparam0]; - n2 = operandsize/n1; - for(i=0; i<=operandscnt-1; i++) - { - ftbase_ffttwcalc(a, abase+aoffset+i*operandsize*2, n1, n2, _state); - } - rowidx = rowidx+1; - continue; - } - - /* - * Process "complex transposition" operation - */ - if( operation==ftbase_opcomplextranspose ) - { - ae_assert(microvectorsize==2, "FTApplySubPlan: MicrovectorSize<>1", _state); - n1 = plan->entries.ptr.pp_int[rowidx][ftbase_colparam0]; - n2 = operandsize/n1; - for(i=0; i<=operandscnt-1; i++) - { - ftbase_internalcomplexlintranspose(a, n1, n2, abase+aoffset+i*operandsize*2, buf, _state); - } - rowidx = rowidx+1; - continue; - } - - /* - * Error - */ - ae_assert(ae_false, "FTApplySubPlan: unexpected plan type", _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine applies complex reference FFT to input/output array A. - -VERY SLOW OPERATION, do not use it in real life plans :) - -INPUT PARAMETERS: - A - array, must be large enough for plan to work - Offs - offset of the subarray to process - OperandsCnt - operands count (see description of FastTransformPlan) - OperandSize - operand size (see description of FastTransformPlan) - MicrovectorSize-microvector size (see description of FastTransformPlan) - Buf - temporary array, must be at least OperandsCnt*OperandSize*MicrovectorSize - -OUTPUT PARAMETERS: - A - transformed array - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftapplycomplexreffft(/* Real */ ae_vector* a, - ae_int_t offs, - ae_int_t operandscnt, - ae_int_t operandsize, - ae_int_t microvectorsize, - /* Real */ ae_vector* buf, - ae_state *_state) -{ - ae_int_t opidx; - ae_int_t i; - ae_int_t k; - double hre; - double him; - double c; - double s; - double re; - double im; - ae_int_t n; - - - ae_assert(operandscnt>=1, "FTApplyComplexRefFFT: OperandsCnt<1", _state); - ae_assert(operandsize>=1, "FTApplyComplexRefFFT: OperandSize<1", _state); - ae_assert(microvectorsize==2, "FTApplyComplexRefFFT: MicrovectorSize<>2", _state); - n = operandsize; - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - for(i=0; i<=n-1; i++) - { - hre = 0; - him = 0; - for(k=0; k<=n-1; k++) - { - re = a->ptr.p_double[offs+opidx*operandsize*2+2*k+0]; - im = a->ptr.p_double[offs+opidx*operandsize*2+2*k+1]; - c = ae_cos(-2*ae_pi*k*i/n, _state); - s = ae_sin(-2*ae_pi*k*i/n, _state); - hre = hre+c*re-s*im; - him = him+c*im+s*re; - } - buf->ptr.p_double[2*i+0] = hre; - buf->ptr.p_double[2*i+1] = him; - } - for(i=0; i<=operandsize*2-1; i++) - { - a->ptr.p_double[offs+opidx*operandsize*2+i] = buf->ptr.p_double[i]; - } - } -} - - -/************************************************************************* -This subroutine applies complex codelet FFT to input/output array A. - -INPUT PARAMETERS: - A - array, must be large enough for plan to work - Offs - offset of the subarray to process - OperandsCnt - operands count (see description of FastTransformPlan) - OperandSize - operand size (see description of FastTransformPlan) - MicrovectorSize-microvector size, must be 2 - -OUTPUT PARAMETERS: - A - transformed array - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftapplycomplexcodeletfft(/* Real */ ae_vector* a, - ae_int_t offs, - ae_int_t operandscnt, - ae_int_t operandsize, - ae_int_t microvectorsize, - ae_state *_state) -{ - ae_int_t opidx; - ae_int_t n; - ae_int_t aoffset; - double a0x; - double a0y; - double a1x; - double a1y; - double a2x; - double a2y; - double a3x; - double a3y; - double a4x; - double a4y; - double a5x; - double a5y; - double v0; - double v1; - double v2; - double v3; - double t1x; - double t1y; - double t2x; - double t2y; - double t3x; - double t3y; - double t4x; - double t4y; - double t5x; - double t5y; - double m1x; - double m1y; - double m2x; - double m2y; - double m3x; - double m3y; - double m4x; - double m4y; - double m5x; - double m5y; - double s1x; - double s1y; - double s2x; - double s2y; - double s3x; - double s3y; - double s4x; - double s4y; - double s5x; - double s5y; - double c1; - double c2; - double c3; - double c4; - double c5; - double v; - - - ae_assert(operandscnt>=1, "FTApplyComplexCodeletFFT: OperandsCnt<1", _state); - ae_assert(operandsize>=1, "FTApplyComplexCodeletFFT: OperandSize<1", _state); - ae_assert(microvectorsize==2, "FTApplyComplexCodeletFFT: MicrovectorSize<>2", _state); - n = operandsize; - - /* - * Hard-coded transforms for different N's - */ - ae_assert(n<=ftbase_maxradix, "FTApplyComplexCodeletFFT: N>MaxRadix", _state); - if( n==2 ) - { - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset = offs+opidx*operandsize*2; - a0x = a->ptr.p_double[aoffset+0]; - a0y = a->ptr.p_double[aoffset+1]; - a1x = a->ptr.p_double[aoffset+2]; - a1y = a->ptr.p_double[aoffset+3]; - v0 = a0x+a1x; - v1 = a0y+a1y; - v2 = a0x-a1x; - v3 = a0y-a1y; - a->ptr.p_double[aoffset+0] = v0; - a->ptr.p_double[aoffset+1] = v1; - a->ptr.p_double[aoffset+2] = v2; - a->ptr.p_double[aoffset+3] = v3; - } - return; - } - if( n==3 ) - { - c1 = ae_cos(2*ae_pi/3, _state)-1; - c2 = ae_sin(2*ae_pi/3, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset = offs+opidx*operandsize*2; - a0x = a->ptr.p_double[aoffset+0]; - a0y = a->ptr.p_double[aoffset+1]; - a1x = a->ptr.p_double[aoffset+2]; - a1y = a->ptr.p_double[aoffset+3]; - a2x = a->ptr.p_double[aoffset+4]; - a2y = a->ptr.p_double[aoffset+5]; - t1x = a1x+a2x; - t1y = a1y+a2y; - a0x = a0x+t1x; - a0y = a0y+t1y; - m1x = c1*t1x; - m1y = c1*t1y; - m2x = c2*(a1y-a2y); - m2y = c2*(a2x-a1x); - s1x = a0x+m1x; - s1y = a0y+m1y; - a1x = s1x+m2x; - a1y = s1y+m2y; - a2x = s1x-m2x; - a2y = s1y-m2y; - a->ptr.p_double[aoffset+0] = a0x; - a->ptr.p_double[aoffset+1] = a0y; - a->ptr.p_double[aoffset+2] = a1x; - a->ptr.p_double[aoffset+3] = a1y; - a->ptr.p_double[aoffset+4] = a2x; - a->ptr.p_double[aoffset+5] = a2y; - } - return; - } - if( n==4 ) - { - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset = offs+opidx*operandsize*2; - a0x = a->ptr.p_double[aoffset+0]; - a0y = a->ptr.p_double[aoffset+1]; - a1x = a->ptr.p_double[aoffset+2]; - a1y = a->ptr.p_double[aoffset+3]; - a2x = a->ptr.p_double[aoffset+4]; - a2y = a->ptr.p_double[aoffset+5]; - a3x = a->ptr.p_double[aoffset+6]; - a3y = a->ptr.p_double[aoffset+7]; - t1x = a0x+a2x; - t1y = a0y+a2y; - t2x = a1x+a3x; - t2y = a1y+a3y; - m2x = a0x-a2x; - m2y = a0y-a2y; - m3x = a1y-a3y; - m3y = a3x-a1x; - a->ptr.p_double[aoffset+0] = t1x+t2x; - a->ptr.p_double[aoffset+1] = t1y+t2y; - a->ptr.p_double[aoffset+4] = t1x-t2x; - a->ptr.p_double[aoffset+5] = t1y-t2y; - a->ptr.p_double[aoffset+2] = m2x+m3x; - a->ptr.p_double[aoffset+3] = m2y+m3y; - a->ptr.p_double[aoffset+6] = m2x-m3x; - a->ptr.p_double[aoffset+7] = m2y-m3y; - } - return; - } - if( n==5 ) - { - v = 2*ae_pi/5; - c1 = (ae_cos(v, _state)+ae_cos(2*v, _state))/2-1; - c2 = (ae_cos(v, _state)-ae_cos(2*v, _state))/2; - c3 = -ae_sin(v, _state); - c4 = -(ae_sin(v, _state)+ae_sin(2*v, _state)); - c5 = ae_sin(v, _state)-ae_sin(2*v, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset = offs+opidx*operandsize*2; - t1x = a->ptr.p_double[aoffset+2]+a->ptr.p_double[aoffset+8]; - t1y = a->ptr.p_double[aoffset+3]+a->ptr.p_double[aoffset+9]; - t2x = a->ptr.p_double[aoffset+4]+a->ptr.p_double[aoffset+6]; - t2y = a->ptr.p_double[aoffset+5]+a->ptr.p_double[aoffset+7]; - t3x = a->ptr.p_double[aoffset+2]-a->ptr.p_double[aoffset+8]; - t3y = a->ptr.p_double[aoffset+3]-a->ptr.p_double[aoffset+9]; - t4x = a->ptr.p_double[aoffset+6]-a->ptr.p_double[aoffset+4]; - t4y = a->ptr.p_double[aoffset+7]-a->ptr.p_double[aoffset+5]; - t5x = t1x+t2x; - t5y = t1y+t2y; - a->ptr.p_double[aoffset+0] = a->ptr.p_double[aoffset+0]+t5x; - a->ptr.p_double[aoffset+1] = a->ptr.p_double[aoffset+1]+t5y; - m1x = c1*t5x; - m1y = c1*t5y; - m2x = c2*(t1x-t2x); - m2y = c2*(t1y-t2y); - m3x = -c3*(t3y+t4y); - m3y = c3*(t3x+t4x); - m4x = -c4*t4y; - m4y = c4*t4x; - m5x = -c5*t3y; - m5y = c5*t3x; - s3x = m3x-m4x; - s3y = m3y-m4y; - s5x = m3x+m5x; - s5y = m3y+m5y; - s1x = a->ptr.p_double[aoffset+0]+m1x; - s1y = a->ptr.p_double[aoffset+1]+m1y; - s2x = s1x+m2x; - s2y = s1y+m2y; - s4x = s1x-m2x; - s4y = s1y-m2y; - a->ptr.p_double[aoffset+2] = s2x+s3x; - a->ptr.p_double[aoffset+3] = s2y+s3y; - a->ptr.p_double[aoffset+4] = s4x+s5x; - a->ptr.p_double[aoffset+5] = s4y+s5y; - a->ptr.p_double[aoffset+6] = s4x-s5x; - a->ptr.p_double[aoffset+7] = s4y-s5y; - a->ptr.p_double[aoffset+8] = s2x-s3x; - a->ptr.p_double[aoffset+9] = s2y-s3y; - } - return; - } - if( n==6 ) - { - c1 = ae_cos(2*ae_pi/3, _state)-1; - c2 = ae_sin(2*ae_pi/3, _state); - c3 = ae_cos(-ae_pi/3, _state); - c4 = ae_sin(-ae_pi/3, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset = offs+opidx*operandsize*2; - a0x = a->ptr.p_double[aoffset+0]; - a0y = a->ptr.p_double[aoffset+1]; - a1x = a->ptr.p_double[aoffset+2]; - a1y = a->ptr.p_double[aoffset+3]; - a2x = a->ptr.p_double[aoffset+4]; - a2y = a->ptr.p_double[aoffset+5]; - a3x = a->ptr.p_double[aoffset+6]; - a3y = a->ptr.p_double[aoffset+7]; - a4x = a->ptr.p_double[aoffset+8]; - a4y = a->ptr.p_double[aoffset+9]; - a5x = a->ptr.p_double[aoffset+10]; - a5y = a->ptr.p_double[aoffset+11]; - v0 = a0x; - v1 = a0y; - a0x = a0x+a3x; - a0y = a0y+a3y; - a3x = v0-a3x; - a3y = v1-a3y; - v0 = a1x; - v1 = a1y; - a1x = a1x+a4x; - a1y = a1y+a4y; - a4x = v0-a4x; - a4y = v1-a4y; - v0 = a2x; - v1 = a2y; - a2x = a2x+a5x; - a2y = a2y+a5y; - a5x = v0-a5x; - a5y = v1-a5y; - t4x = a4x*c3-a4y*c4; - t4y = a4x*c4+a4y*c3; - a4x = t4x; - a4y = t4y; - t5x = -a5x*c3-a5y*c4; - t5y = a5x*c4-a5y*c3; - a5x = t5x; - a5y = t5y; - t1x = a1x+a2x; - t1y = a1y+a2y; - a0x = a0x+t1x; - a0y = a0y+t1y; - m1x = c1*t1x; - m1y = c1*t1y; - m2x = c2*(a1y-a2y); - m2y = c2*(a2x-a1x); - s1x = a0x+m1x; - s1y = a0y+m1y; - a1x = s1x+m2x; - a1y = s1y+m2y; - a2x = s1x-m2x; - a2y = s1y-m2y; - t1x = a4x+a5x; - t1y = a4y+a5y; - a3x = a3x+t1x; - a3y = a3y+t1y; - m1x = c1*t1x; - m1y = c1*t1y; - m2x = c2*(a4y-a5y); - m2y = c2*(a5x-a4x); - s1x = a3x+m1x; - s1y = a3y+m1y; - a4x = s1x+m2x; - a4y = s1y+m2y; - a5x = s1x-m2x; - a5y = s1y-m2y; - a->ptr.p_double[aoffset+0] = a0x; - a->ptr.p_double[aoffset+1] = a0y; - a->ptr.p_double[aoffset+2] = a3x; - a->ptr.p_double[aoffset+3] = a3y; - a->ptr.p_double[aoffset+4] = a1x; - a->ptr.p_double[aoffset+5] = a1y; - a->ptr.p_double[aoffset+6] = a4x; - a->ptr.p_double[aoffset+7] = a4y; - a->ptr.p_double[aoffset+8] = a2x; - a->ptr.p_double[aoffset+9] = a2y; - a->ptr.p_double[aoffset+10] = a5x; - a->ptr.p_double[aoffset+11] = a5y; - } - return; - } -} - - -/************************************************************************* -This subroutine applies complex "integrated" codelet FFT to input/output -array A. "Integrated" codelet differs from "normal" one in following ways: -* it can work with MicrovectorSize>1 -* hence, it can be used in Cooley-Tukey FFT without transpositions -* it performs inlined multiplication by twiddle factors of Cooley-Tukey - FFT with N2=MicrovectorSize/2. - -INPUT PARAMETERS: - A - array, must be large enough for plan to work - Offs - offset of the subarray to process - OperandsCnt - operands count (see description of FastTransformPlan) - OperandSize - operand size (see description of FastTransformPlan) - MicrovectorSize-microvector size, must be 1 - -OUTPUT PARAMETERS: - A - transformed array - - -- ALGLIB -- - Copyright 05.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftapplycomplexcodelettwfft(/* Real */ ae_vector* a, - ae_int_t offs, - ae_int_t operandscnt, - ae_int_t operandsize, - ae_int_t microvectorsize, - ae_state *_state) -{ - ae_int_t opidx; - ae_int_t mvidx; - ae_int_t n; - ae_int_t m; - ae_int_t aoffset0; - ae_int_t aoffset2; - ae_int_t aoffset4; - ae_int_t aoffset6; - ae_int_t aoffset8; - ae_int_t aoffset10; - double a0x; - double a0y; - double a1x; - double a1y; - double a2x; - double a2y; - double a3x; - double a3y; - double a4x; - double a4y; - double a5x; - double a5y; - double v0; - double v1; - double v2; - double v3; - double q0x; - double q0y; - double t1x; - double t1y; - double t2x; - double t2y; - double t3x; - double t3y; - double t4x; - double t4y; - double t5x; - double t5y; - double m1x; - double m1y; - double m2x; - double m2y; - double m3x; - double m3y; - double m4x; - double m4y; - double m5x; - double m5y; - double s1x; - double s1y; - double s2x; - double s2y; - double s3x; - double s3y; - double s4x; - double s4y; - double s5x; - double s5y; - double c1; - double c2; - double c3; - double c4; - double c5; - double v; - double tw0; - double tw1; - double twx; - double twxm1; - double twy; - double tw2x; - double tw2y; - double tw3x; - double tw3y; - double tw4x; - double tw4y; - double tw5x; - double tw5y; - - - ae_assert(operandscnt>=1, "FTApplyComplexCodeletFFT: OperandsCnt<1", _state); - ae_assert(operandsize>=1, "FTApplyComplexCodeletFFT: OperandSize<1", _state); - ae_assert(microvectorsize>=1, "FTApplyComplexCodeletFFT: MicrovectorSize<>1", _state); - ae_assert(microvectorsize%2==0, "FTApplyComplexCodeletFFT: MicrovectorSize is not even", _state); - n = operandsize; - m = microvectorsize/2; - - /* - * Hard-coded transforms for different N's - */ - ae_assert(n<=ftbase_maxradix, "FTApplyComplexCodeletTwFFT: N>MaxRadix", _state); - if( n==2 ) - { - v = -2*ae_pi/(n*m); - tw0 = -2*ae_sqr(ae_sin(0.5*v, _state), _state); - tw1 = ae_sin(v, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset0 = offs+opidx*operandsize*microvectorsize; - aoffset2 = aoffset0+microvectorsize; - twxm1 = 0.0; - twy = 0.0; - for(mvidx=0; mvidx<=m-1; mvidx++) - { - a0x = a->ptr.p_double[aoffset0]; - a0y = a->ptr.p_double[aoffset0+1]; - a1x = a->ptr.p_double[aoffset2]; - a1y = a->ptr.p_double[aoffset2+1]; - v0 = a0x+a1x; - v1 = a0y+a1y; - v2 = a0x-a1x; - v3 = a0y-a1y; - a->ptr.p_double[aoffset0] = v0; - a->ptr.p_double[aoffset0+1] = v1; - a->ptr.p_double[aoffset2] = v2*(1+twxm1)-v3*twy; - a->ptr.p_double[aoffset2+1] = v3*(1+twxm1)+v2*twy; - aoffset0 = aoffset0+2; - aoffset2 = aoffset2+2; - if( (mvidx+1)%ftbase_updatetw==0 ) - { - v = -2*ae_pi*(mvidx+1)/(n*m); - twxm1 = ae_sin(0.5*v, _state); - twxm1 = -2*twxm1*twxm1; - twy = ae_sin(v, _state); - } - else - { - v = twxm1+tw0+twxm1*tw0-twy*tw1; - twy = twy+tw1+twxm1*tw1+twy*tw0; - twxm1 = v; - } - } - } - return; - } - if( n==3 ) - { - v = -2*ae_pi/(n*m); - tw0 = -2*ae_sqr(ae_sin(0.5*v, _state), _state); - tw1 = ae_sin(v, _state); - c1 = ae_cos(2*ae_pi/3, _state)-1; - c2 = ae_sin(2*ae_pi/3, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset0 = offs+opidx*operandsize*microvectorsize; - aoffset2 = aoffset0+microvectorsize; - aoffset4 = aoffset2+microvectorsize; - twx = 1.0; - twxm1 = 0.0; - twy = 0.0; - for(mvidx=0; mvidx<=m-1; mvidx++) - { - a0x = a->ptr.p_double[aoffset0]; - a0y = a->ptr.p_double[aoffset0+1]; - a1x = a->ptr.p_double[aoffset2]; - a1y = a->ptr.p_double[aoffset2+1]; - a2x = a->ptr.p_double[aoffset4]; - a2y = a->ptr.p_double[aoffset4+1]; - t1x = a1x+a2x; - t1y = a1y+a2y; - a0x = a0x+t1x; - a0y = a0y+t1y; - m1x = c1*t1x; - m1y = c1*t1y; - m2x = c2*(a1y-a2y); - m2y = c2*(a2x-a1x); - s1x = a0x+m1x; - s1y = a0y+m1y; - a1x = s1x+m2x; - a1y = s1y+m2y; - a2x = s1x-m2x; - a2y = s1y-m2y; - tw2x = twx*twx-twy*twy; - tw2y = 2*twx*twy; - a->ptr.p_double[aoffset0] = a0x; - a->ptr.p_double[aoffset0+1] = a0y; - a->ptr.p_double[aoffset2] = a1x*twx-a1y*twy; - a->ptr.p_double[aoffset2+1] = a1y*twx+a1x*twy; - a->ptr.p_double[aoffset4] = a2x*tw2x-a2y*tw2y; - a->ptr.p_double[aoffset4+1] = a2y*tw2x+a2x*tw2y; - aoffset0 = aoffset0+2; - aoffset2 = aoffset2+2; - aoffset4 = aoffset4+2; - if( (mvidx+1)%ftbase_updatetw==0 ) - { - v = -2*ae_pi*(mvidx+1)/(n*m); - twxm1 = ae_sin(0.5*v, _state); - twxm1 = -2*twxm1*twxm1; - twy = ae_sin(v, _state); - twx = twxm1+1; - } - else - { - v = twxm1+tw0+twxm1*tw0-twy*tw1; - twy = twy+tw1+twxm1*tw1+twy*tw0; - twxm1 = v; - twx = v+1; - } - } - } - return; - } - if( n==4 ) - { - v = -2*ae_pi/(n*m); - tw0 = -2*ae_sqr(ae_sin(0.5*v, _state), _state); - tw1 = ae_sin(v, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset0 = offs+opidx*operandsize*microvectorsize; - aoffset2 = aoffset0+microvectorsize; - aoffset4 = aoffset2+microvectorsize; - aoffset6 = aoffset4+microvectorsize; - twx = 1.0; - twxm1 = 0.0; - twy = 0.0; - for(mvidx=0; mvidx<=m-1; mvidx++) - { - a0x = a->ptr.p_double[aoffset0]; - a0y = a->ptr.p_double[aoffset0+1]; - a1x = a->ptr.p_double[aoffset2]; - a1y = a->ptr.p_double[aoffset2+1]; - a2x = a->ptr.p_double[aoffset4]; - a2y = a->ptr.p_double[aoffset4+1]; - a3x = a->ptr.p_double[aoffset6]; - a3y = a->ptr.p_double[aoffset6+1]; - t1x = a0x+a2x; - t1y = a0y+a2y; - t2x = a1x+a3x; - t2y = a1y+a3y; - m2x = a0x-a2x; - m2y = a0y-a2y; - m3x = a1y-a3y; - m3y = a3x-a1x; - tw2x = twx*twx-twy*twy; - tw2y = 2*twx*twy; - tw3x = twx*tw2x-twy*tw2y; - tw3y = twx*tw2y+twy*tw2x; - a1x = m2x+m3x; - a1y = m2y+m3y; - a2x = t1x-t2x; - a2y = t1y-t2y; - a3x = m2x-m3x; - a3y = m2y-m3y; - a->ptr.p_double[aoffset0] = t1x+t2x; - a->ptr.p_double[aoffset0+1] = t1y+t2y; - a->ptr.p_double[aoffset2] = a1x*twx-a1y*twy; - a->ptr.p_double[aoffset2+1] = a1y*twx+a1x*twy; - a->ptr.p_double[aoffset4] = a2x*tw2x-a2y*tw2y; - a->ptr.p_double[aoffset4+1] = a2y*tw2x+a2x*tw2y; - a->ptr.p_double[aoffset6] = a3x*tw3x-a3y*tw3y; - a->ptr.p_double[aoffset6+1] = a3y*tw3x+a3x*tw3y; - aoffset0 = aoffset0+2; - aoffset2 = aoffset2+2; - aoffset4 = aoffset4+2; - aoffset6 = aoffset6+2; - if( (mvidx+1)%ftbase_updatetw==0 ) - { - v = -2*ae_pi*(mvidx+1)/(n*m); - twxm1 = ae_sin(0.5*v, _state); - twxm1 = -2*twxm1*twxm1; - twy = ae_sin(v, _state); - twx = twxm1+1; - } - else - { - v = twxm1+tw0+twxm1*tw0-twy*tw1; - twy = twy+tw1+twxm1*tw1+twy*tw0; - twxm1 = v; - twx = v+1; - } - } - } - return; - } - if( n==5 ) - { - v = -2*ae_pi/(n*m); - tw0 = -2*ae_sqr(ae_sin(0.5*v, _state), _state); - tw1 = ae_sin(v, _state); - v = 2*ae_pi/5; - c1 = (ae_cos(v, _state)+ae_cos(2*v, _state))/2-1; - c2 = (ae_cos(v, _state)-ae_cos(2*v, _state))/2; - c3 = -ae_sin(v, _state); - c4 = -(ae_sin(v, _state)+ae_sin(2*v, _state)); - c5 = ae_sin(v, _state)-ae_sin(2*v, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset0 = offs+opidx*operandsize*microvectorsize; - aoffset2 = aoffset0+microvectorsize; - aoffset4 = aoffset2+microvectorsize; - aoffset6 = aoffset4+microvectorsize; - aoffset8 = aoffset6+microvectorsize; - twx = 1.0; - twxm1 = 0.0; - twy = 0.0; - for(mvidx=0; mvidx<=m-1; mvidx++) - { - a0x = a->ptr.p_double[aoffset0]; - a0y = a->ptr.p_double[aoffset0+1]; - a1x = a->ptr.p_double[aoffset2]; - a1y = a->ptr.p_double[aoffset2+1]; - a2x = a->ptr.p_double[aoffset4]; - a2y = a->ptr.p_double[aoffset4+1]; - a3x = a->ptr.p_double[aoffset6]; - a3y = a->ptr.p_double[aoffset6+1]; - a4x = a->ptr.p_double[aoffset8]; - a4y = a->ptr.p_double[aoffset8+1]; - t1x = a1x+a4x; - t1y = a1y+a4y; - t2x = a2x+a3x; - t2y = a2y+a3y; - t3x = a1x-a4x; - t3y = a1y-a4y; - t4x = a3x-a2x; - t4y = a3y-a2y; - t5x = t1x+t2x; - t5y = t1y+t2y; - q0x = a0x+t5x; - q0y = a0y+t5y; - m1x = c1*t5x; - m1y = c1*t5y; - m2x = c2*(t1x-t2x); - m2y = c2*(t1y-t2y); - m3x = -c3*(t3y+t4y); - m3y = c3*(t3x+t4x); - m4x = -c4*t4y; - m4y = c4*t4x; - m5x = -c5*t3y; - m5y = c5*t3x; - s3x = m3x-m4x; - s3y = m3y-m4y; - s5x = m3x+m5x; - s5y = m3y+m5y; - s1x = q0x+m1x; - s1y = q0y+m1y; - s2x = s1x+m2x; - s2y = s1y+m2y; - s4x = s1x-m2x; - s4y = s1y-m2y; - tw2x = twx*twx-twy*twy; - tw2y = 2*twx*twy; - tw3x = twx*tw2x-twy*tw2y; - tw3y = twx*tw2y+twy*tw2x; - tw4x = tw2x*tw2x-tw2y*tw2y; - tw4y = tw2x*tw2y+tw2y*tw2x; - a1x = s2x+s3x; - a1y = s2y+s3y; - a2x = s4x+s5x; - a2y = s4y+s5y; - a3x = s4x-s5x; - a3y = s4y-s5y; - a4x = s2x-s3x; - a4y = s2y-s3y; - a->ptr.p_double[aoffset0] = q0x; - a->ptr.p_double[aoffset0+1] = q0y; - a->ptr.p_double[aoffset2] = a1x*twx-a1y*twy; - a->ptr.p_double[aoffset2+1] = a1x*twy+a1y*twx; - a->ptr.p_double[aoffset4] = a2x*tw2x-a2y*tw2y; - a->ptr.p_double[aoffset4+1] = a2x*tw2y+a2y*tw2x; - a->ptr.p_double[aoffset6] = a3x*tw3x-a3y*tw3y; - a->ptr.p_double[aoffset6+1] = a3x*tw3y+a3y*tw3x; - a->ptr.p_double[aoffset8] = a4x*tw4x-a4y*tw4y; - a->ptr.p_double[aoffset8+1] = a4x*tw4y+a4y*tw4x; - aoffset0 = aoffset0+2; - aoffset2 = aoffset2+2; - aoffset4 = aoffset4+2; - aoffset6 = aoffset6+2; - aoffset8 = aoffset8+2; - if( (mvidx+1)%ftbase_updatetw==0 ) - { - v = -2*ae_pi*(mvidx+1)/(n*m); - twxm1 = ae_sin(0.5*v, _state); - twxm1 = -2*twxm1*twxm1; - twy = ae_sin(v, _state); - twx = twxm1+1; - } - else - { - v = twxm1+tw0+twxm1*tw0-twy*tw1; - twy = twy+tw1+twxm1*tw1+twy*tw0; - twxm1 = v; - twx = v+1; - } - } - } - return; - } - if( n==6 ) - { - c1 = ae_cos(2*ae_pi/3, _state)-1; - c2 = ae_sin(2*ae_pi/3, _state); - c3 = ae_cos(-ae_pi/3, _state); - c4 = ae_sin(-ae_pi/3, _state); - v = -2*ae_pi/(n*m); - tw0 = -2*ae_sqr(ae_sin(0.5*v, _state), _state); - tw1 = ae_sin(v, _state); - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - aoffset0 = offs+opidx*operandsize*microvectorsize; - aoffset2 = aoffset0+microvectorsize; - aoffset4 = aoffset2+microvectorsize; - aoffset6 = aoffset4+microvectorsize; - aoffset8 = aoffset6+microvectorsize; - aoffset10 = aoffset8+microvectorsize; - twx = 1.0; - twxm1 = 0.0; - twy = 0.0; - for(mvidx=0; mvidx<=m-1; mvidx++) - { - a0x = a->ptr.p_double[aoffset0+0]; - a0y = a->ptr.p_double[aoffset0+1]; - a1x = a->ptr.p_double[aoffset2+0]; - a1y = a->ptr.p_double[aoffset2+1]; - a2x = a->ptr.p_double[aoffset4+0]; - a2y = a->ptr.p_double[aoffset4+1]; - a3x = a->ptr.p_double[aoffset6+0]; - a3y = a->ptr.p_double[aoffset6+1]; - a4x = a->ptr.p_double[aoffset8+0]; - a4y = a->ptr.p_double[aoffset8+1]; - a5x = a->ptr.p_double[aoffset10+0]; - a5y = a->ptr.p_double[aoffset10+1]; - v0 = a0x; - v1 = a0y; - a0x = a0x+a3x; - a0y = a0y+a3y; - a3x = v0-a3x; - a3y = v1-a3y; - v0 = a1x; - v1 = a1y; - a1x = a1x+a4x; - a1y = a1y+a4y; - a4x = v0-a4x; - a4y = v1-a4y; - v0 = a2x; - v1 = a2y; - a2x = a2x+a5x; - a2y = a2y+a5y; - a5x = v0-a5x; - a5y = v1-a5y; - t4x = a4x*c3-a4y*c4; - t4y = a4x*c4+a4y*c3; - a4x = t4x; - a4y = t4y; - t5x = -a5x*c3-a5y*c4; - t5y = a5x*c4-a5y*c3; - a5x = t5x; - a5y = t5y; - t1x = a1x+a2x; - t1y = a1y+a2y; - a0x = a0x+t1x; - a0y = a0y+t1y; - m1x = c1*t1x; - m1y = c1*t1y; - m2x = c2*(a1y-a2y); - m2y = c2*(a2x-a1x); - s1x = a0x+m1x; - s1y = a0y+m1y; - a1x = s1x+m2x; - a1y = s1y+m2y; - a2x = s1x-m2x; - a2y = s1y-m2y; - t1x = a4x+a5x; - t1y = a4y+a5y; - a3x = a3x+t1x; - a3y = a3y+t1y; - m1x = c1*t1x; - m1y = c1*t1y; - m2x = c2*(a4y-a5y); - m2y = c2*(a5x-a4x); - s1x = a3x+m1x; - s1y = a3y+m1y; - a4x = s1x+m2x; - a4y = s1y+m2y; - a5x = s1x-m2x; - a5y = s1y-m2y; - tw2x = twx*twx-twy*twy; - tw2y = 2*twx*twy; - tw3x = twx*tw2x-twy*tw2y; - tw3y = twx*tw2y+twy*tw2x; - tw4x = tw2x*tw2x-tw2y*tw2y; - tw4y = 2*tw2x*tw2y; - tw5x = tw3x*tw2x-tw3y*tw2y; - tw5y = tw3x*tw2y+tw3y*tw2x; - a->ptr.p_double[aoffset0+0] = a0x; - a->ptr.p_double[aoffset0+1] = a0y; - a->ptr.p_double[aoffset2+0] = a3x*twx-a3y*twy; - a->ptr.p_double[aoffset2+1] = a3y*twx+a3x*twy; - a->ptr.p_double[aoffset4+0] = a1x*tw2x-a1y*tw2y; - a->ptr.p_double[aoffset4+1] = a1y*tw2x+a1x*tw2y; - a->ptr.p_double[aoffset6+0] = a4x*tw3x-a4y*tw3y; - a->ptr.p_double[aoffset6+1] = a4y*tw3x+a4x*tw3y; - a->ptr.p_double[aoffset8+0] = a2x*tw4x-a2y*tw4y; - a->ptr.p_double[aoffset8+1] = a2y*tw4x+a2x*tw4y; - a->ptr.p_double[aoffset10+0] = a5x*tw5x-a5y*tw5y; - a->ptr.p_double[aoffset10+1] = a5y*tw5x+a5x*tw5y; - aoffset0 = aoffset0+2; - aoffset2 = aoffset2+2; - aoffset4 = aoffset4+2; - aoffset6 = aoffset6+2; - aoffset8 = aoffset8+2; - aoffset10 = aoffset10+2; - if( (mvidx+1)%ftbase_updatetw==0 ) - { - v = -2*ae_pi*(mvidx+1)/(n*m); - twxm1 = ae_sin(0.5*v, _state); - twxm1 = -2*twxm1*twxm1; - twy = ae_sin(v, _state); - twx = twxm1+1; - } - else - { - v = twxm1+tw0+twxm1*tw0-twy*tw1; - twy = twy+tw1+twxm1*tw1+twy*tw0; - twxm1 = v; - twx = v+1; - } - } - } - return; - } -} - - -/************************************************************************* -This subroutine precomputes data for complex Bluestein's FFT and writes -them to array PrecR[] at specified offset. It is responsibility of the -caller to make sure that PrecR[] is large enough. - -INPUT PARAMETERS: - N - original size of the transform - M - size of the "padded" Bluestein's transform - PrecR - preallocated array - Offs - offset - -OUTPUT PARAMETERS: - PrecR - data at Offs:Offs+4*M-1 are modified: - * PrecR[Offs:Offs+2*M-1] stores Z[k]=exp(i*pi*k^2/N) - * PrecR[Offs+2*M:Offs+4*M-1] stores FFT of the Z - Other parts of PrecR are unchanged. - -NOTE: this function performs internal M-point FFT. It allocates temporary - plan which is destroyed after leaving this function. - - -- ALGLIB -- - Copyright 08.05.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftprecomputebluesteinsfft(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* precr, - ae_int_t offs, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - double bx; - double by; - fasttransformplan plan; - - ae_frame_make(_state, &_frame_block); - _fasttransformplan_init(&plan, _state, ae_true); - - - /* - * Fill first half of PrecR with b[k] = exp(i*pi*k^2/N) - */ - for(i=0; i<=2*m-1; i++) - { - precr->ptr.p_double[offs+i] = 0; - } - for(i=0; i<=n-1; i++) - { - bx = ae_cos(ae_pi/n*i*i, _state); - by = ae_sin(ae_pi/n*i*i, _state); - precr->ptr.p_double[offs+2*i+0] = bx; - precr->ptr.p_double[offs+2*i+1] = by; - precr->ptr.p_double[offs+2*((m-i)%m)+0] = bx; - precr->ptr.p_double[offs+2*((m-i)%m)+1] = by; - } - - /* - * Precomputed FFT - */ - ftcomplexfftplan(m, 1, &plan, _state); - for(i=0; i<=2*m-1; i++) - { - precr->ptr.p_double[offs+2*m+i] = precr->ptr.p_double[offs+i]; - } - ftbase_ftapplysubplan(&plan, 0, precr, offs+2*m, 0, &plan.buffer, 1, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine applies complex Bluestein's FFT to input/output array A. - -INPUT PARAMETERS: - Plan - transformation plan - A - array, must be large enough for plan to work - ABase - base offset in array A, this value points to start of - subarray whose length is equal to length of the plan - AOffset - offset with respect to ABase, 0<=AOffsetptr.p_double[p0+0]; - y = a->ptr.p_double[p0+1]; - bx = plan->precr.ptr.p_double[p1+0]; - by = -plan->precr.ptr.p_double[p1+1]; - bufa->ptr.p_double[2*i+0] = x*bx-y*by; - bufa->ptr.p_double[2*i+1] = x*by+y*bx; - p0 = p0+2; - p1 = p1+2; - } - for(i=2*n; i<=2*m-1; i++) - { - bufa->ptr.p_double[i] = 0; - } - - /* - * Perform convolution of A and Z (using precomputed - * FFT of Z stored in Plan structure). - */ - ftbase_ftapplysubplan(plan, subplan, bufa, 0, 0, bufc, 1, _state); - p0 = 0; - p1 = precoffs+2*m; - for(i=0; i<=m-1; i++) - { - ax = bufa->ptr.p_double[p0+0]; - ay = bufa->ptr.p_double[p0+1]; - bx = plan->precr.ptr.p_double[p1+0]; - by = plan->precr.ptr.p_double[p1+1]; - bufa->ptr.p_double[p0+0] = ax*bx-ay*by; - bufa->ptr.p_double[p0+1] = -(ax*by+ay*bx); - p0 = p0+2; - p1 = p1+2; - } - ftbase_ftapplysubplan(plan, subplan, bufa, 0, 0, bufc, 1, _state); - - /* - * Post processing: - * A:=conj(Z)*conj(A)/M - * Here conj(A)/M corresponds to last stage of inverse DFT, - * and conj(Z) comes from Bluestein's FFT algorithm. - */ - p0 = precoffs; - p1 = 0; - p2 = abase+aoffset+op*2*n; - for(i=0; i<=n-1; i++) - { - bx = plan->precr.ptr.p_double[p0+0]; - by = plan->precr.ptr.p_double[p0+1]; - rx = bufa->ptr.p_double[p1+0]/m; - ry = -bufa->ptr.p_double[p1+1]/m; - a->ptr.p_double[p2+0] = rx*bx-ry*(-by); - a->ptr.p_double[p2+1] = rx*(-by)+ry*bx; - p0 = p0+2; - p1 = p1+2; - p2 = p2+2; - } - } -} - - -/************************************************************************* -This subroutine precomputes data for complex Rader's FFT and writes them -to array PrecR[] at specified offset. It is responsibility of the caller -to make sure that PrecR[] is large enough. - -INPUT PARAMETERS: - N - original size of the transform (before reduction to N-1) - RQ - primitive root modulo N - RIQ - inverse of primitive root modulo N - PrecR - preallocated array - Offs - offset - -OUTPUT PARAMETERS: - PrecR - data at Offs:Offs+2*(N-1)-1 store FFT of Rader's factors, - other parts of PrecR are unchanged. - -NOTE: this function performs internal (N-1)-point FFT. It allocates temporary - plan which is destroyed after leaving this function. - - -- ALGLIB -- - Copyright 08.05.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftprecomputeradersfft(ae_int_t n, - ae_int_t rq, - ae_int_t riq, - /* Real */ ae_vector* precr, - ae_int_t offs, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t q; - fasttransformplan plan; - ae_int_t kiq; - double v; - - ae_frame_make(_state, &_frame_block); - _fasttransformplan_init(&plan, _state, ae_true); - - - /* - * Fill PrecR with Rader factors, perform FFT - */ - kiq = 1; - for(q=0; q<=n-2; q++) - { - v = -2*ae_pi*kiq/n; - precr->ptr.p_double[offs+2*q+0] = ae_cos(v, _state); - precr->ptr.p_double[offs+2*q+1] = ae_sin(v, _state); - kiq = kiq*riq%n; - } - ftcomplexfftplan(n-1, 1, &plan, _state); - ftbase_ftapplysubplan(&plan, 0, precr, offs, 0, &plan.buffer, 1, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine applies complex Rader's FFT to input/output array A. - -INPUT PARAMETERS: - A - array, must be large enough for plan to work - ABase - base offset in array A, this value points to start of - subarray whose length is equal to length of the plan - AOffset - offset with respect to ABase, 0<=AOffset=1, "FTApplyComplexRefFFT: OperandsCnt<1", _state); - - /* - * Process operands - */ - for(opidx=0; opidx<=operandscnt-1; opidx++) - { - - /* - * fill QA - */ - kq = 1; - p0 = abase+aoffset+opidx*n*2; - p1 = aoffset+opidx*n*2; - rx = a->ptr.p_double[p0+0]; - ry = a->ptr.p_double[p0+1]; - x0 = rx; - y0 = ry; - for(q=0; q<=n-2; q++) - { - ax = a->ptr.p_double[p0+2*kq+0]; - ay = a->ptr.p_double[p0+2*kq+1]; - buf->ptr.p_double[p1+0] = ax; - buf->ptr.p_double[p1+1] = ay; - rx = rx+ax; - ry = ry+ay; - kq = kq*rq%n; - p1 = p1+2; - } - p0 = abase+aoffset+opidx*n*2; - p1 = aoffset+opidx*n*2; - for(q=0; q<=n-2; q++) - { - a->ptr.p_double[p0] = buf->ptr.p_double[p1]; - a->ptr.p_double[p0+1] = buf->ptr.p_double[p1+1]; - p0 = p0+2; - p1 = p1+2; - } - - /* - * Convolution - */ - ftbase_ftapplysubplan(plan, subplan, a, abase, aoffset+opidx*n*2, buf, 1, _state); - p0 = abase+aoffset+opidx*n*2; - p1 = precoffs; - for(i=0; i<=n-2; i++) - { - ax = a->ptr.p_double[p0+0]; - ay = a->ptr.p_double[p0+1]; - bx = plan->precr.ptr.p_double[p1+0]; - by = plan->precr.ptr.p_double[p1+1]; - a->ptr.p_double[p0+0] = ax*bx-ay*by; - a->ptr.p_double[p0+1] = -(ax*by+ay*bx); - p0 = p0+2; - p1 = p1+2; - } - ftbase_ftapplysubplan(plan, subplan, a, abase, aoffset+opidx*n*2, buf, 1, _state); - p0 = abase+aoffset+opidx*n*2; - for(i=0; i<=n-2; i++) - { - a->ptr.p_double[p0+0] = a->ptr.p_double[p0+0]/(n-1); - a->ptr.p_double[p0+1] = -a->ptr.p_double[p0+1]/(n-1); - p0 = p0+2; - } - - /* - * Result - */ - buf->ptr.p_double[aoffset+opidx*n*2+0] = rx; - buf->ptr.p_double[aoffset+opidx*n*2+1] = ry; - kiq = 1; - p0 = aoffset+opidx*n*2; - p1 = abase+aoffset+opidx*n*2; - for(q=0; q<=n-2; q++) - { - buf->ptr.p_double[p0+2*kiq+0] = x0+a->ptr.p_double[p1+0]; - buf->ptr.p_double[p0+2*kiq+1] = y0+a->ptr.p_double[p1+1]; - kiq = kiq*riq%n; - p1 = p1+2; - } - p0 = abase+aoffset+opidx*n*2; - p1 = aoffset+opidx*n*2; - for(q=0; q<=n-1; q++) - { - a->ptr.p_double[p0] = buf->ptr.p_double[p1]; - a->ptr.p_double[p0+1] = buf->ptr.p_double[p1+1]; - p0 = p0+2; - p1 = p1+2; - } - } -} - - -/************************************************************************* -Factorizes task size N into product of two smaller sizes N1 and N2 - -INPUT PARAMETERS: - N - task size, N>0 - IsRoot - whether taks is root task (first one in a sequence) - -OUTPUT PARAMETERS: - N1, N2 - such numbers that: - * for prime N: N1=N2=0 - * for composite N<=MaxRadix: N1=N2=0 - * for composite N>MaxRadix: 1<=N1<=N2, N1*N2=N - - -- ALGLIB -- - Copyright 08.04.2013 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftfactorize(ae_int_t n, - ae_bool isroot, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state) -{ - ae_int_t j; - ae_int_t k; - - *n1 = 0; - *n2 = 0; - - ae_assert(n>0, "FTFactorize: N<=0", _state); - *n1 = 0; - *n2 = 0; - - /* - * Small N - */ - if( n<=ftbase_maxradix ) - { - return; - } - - /* - * Large N, recursive split - */ - if( n>ftbase_recursivethreshold ) - { - k = ae_iceil(ae_sqrt(n, _state), _state)+1; - ae_assert(k*k>=n, "FTFactorize: internal error during recursive factorization", _state); - for(j=k; j>=2; j--) - { - if( n%j==0 ) - { - *n1 = ae_minint(n/j, j, _state); - *n2 = ae_maxint(n/j, j, _state); - return; - } - } - } - - /* - * N>MaxRadix, try to find good codelet - */ - for(j=ftbase_maxradix; j>=2; j--) - { - if( n%j==0 ) - { - *n1 = j; - *n2 = n/j; - break; - } - } - - /* - * In case no good codelet was found, - * try to factorize N into product of ANY primes. - */ - if( *n1*(*n2)!=n ) - { - for(j=2; j<=n-1; j++) - { - if( n%j==0 ) - { - *n1 = j; - *n2 = n/j; - break; - } - if( j*j>n ) - { - break; - } - } - } - - /* - * normalize - */ - if( *n1>(*n2) ) - { - j = *n1; - *n1 = *n2; - *n2 = j; - } -} - - -/************************************************************************* -Returns optimistic estimate of the FFT cost, in UNITs (1 UNIT = 100 KFLOPs) - -INPUT PARAMETERS: - N - task size, N>0 - -RESULU: - cost in UNITs, rounded down to nearest integer - -NOTE: If FFT cost is less than 1 UNIT, it will return 0 as result. - - -- ALGLIB -- - Copyright 08.04.2013 by Bochkanov Sergey -*************************************************************************/ -static ae_int_t ftbase_ftoptimisticestimate(ae_int_t n, ae_state *_state) -{ - ae_int_t result; - - - ae_assert(n>0, "FTOptimisticEstimate: N<=0", _state); - result = ae_ifloor(1.0E-5*5*n*ae_log(n, _state)/ae_log(2, _state), _state); - return result; -} - - -/************************************************************************* -Twiddle factors calculation - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ffttwcalc(/* Real */ ae_vector* a, - ae_int_t aoffset, - ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j2; - ae_int_t n; - ae_int_t halfn1; - ae_int_t offs; - double x; - double y; - double twxm1; - double twy; - double twbasexm1; - double twbasey; - double twrowxm1; - double twrowy; - double tmpx; - double tmpy; - double v; - ae_int_t updatetw2; - - - - /* - * Multiplication by twiddle factors for complex Cooley-Tukey FFT - * with N factorized as N1*N2. - * - * Naive solution to this problem is given below: - * - * > for K:=1 to N2-1 do - * > for J:=1 to N1-1 do - * > begin - * > Idx:=K*N1+J; - * > X:=A[AOffset+2*Idx+0]; - * > Y:=A[AOffset+2*Idx+1]; - * > TwX:=Cos(-2*Pi()*K*J/(N1*N2)); - * > TwY:=Sin(-2*Pi()*K*J/(N1*N2)); - * > A[AOffset+2*Idx+0]:=X*TwX-Y*TwY; - * > A[AOffset+2*Idx+1]:=X*TwY+Y*TwX; - * > end; - * - * However, there are exist more efficient solutions. - * - * Each pass of the inner cycle corresponds to multiplication of one - * entry of A by W[k,j]=exp(-I*2*pi*k*j/N). This factor can be rewritten - * as exp(-I*2*pi*k/N)^j. So we can replace costly exponentiation by - * repeated multiplication: W[k,j+1]=W[k,j]*exp(-I*2*pi*k/N), with - * second factor being computed once in the beginning of the iteration. - * - * Also, exp(-I*2*pi*k/N) can be represented as exp(-I*2*pi/N)^k, i.e. - * we have W[K+1,1]=W[K,1]*W[1,1]. - * - * In our loop we use following variables: - * * [TwBaseXM1,TwBaseY] = [cos(2*pi/N)-1, sin(2*pi/N)] - * * [TwRowXM1, TwRowY] = [cos(2*pi*I/N)-1, sin(2*pi*I/N)] - * * [TwXM1, TwY] = [cos(2*pi*I*J/N)-1, sin(2*pi*I*J/N)] - * - * Meaning of the variables: - * * [TwXM1,TwY] is current twiddle factor W[I,J] - * * [TwRowXM1, TwRowY] is W[I,1] - * * [TwBaseXM1,TwBaseY] is W[1,1] - * - * During inner loop we multiply current twiddle factor by W[I,1], - * during outer loop we update W[I,1]. - * - */ - ae_assert(ftbase_updatetw>=2, "FFTTwCalc: internal error - UpdateTw<2", _state); - updatetw2 = ftbase_updatetw/2; - halfn1 = n1/2; - n = n1*n2; - v = -2*ae_pi/n; - twbasexm1 = -2*ae_sqr(ae_sin(0.5*v, _state), _state); - twbasey = ae_sin(v, _state); - twrowxm1 = 0; - twrowy = 0; - offs = aoffset; - for(i=0; i<=n2-1; i++) - { - - /* - * Initialize twiddle factor for current row - */ - twxm1 = 0; - twy = 0; - - /* - * N1-point block is separated into 2-point chunks and residual 1-point chunk - * (in case N1 is odd). Unrolled loop is several times faster. - */ - for(j2=0; j2<=halfn1-1; j2++) - { - - /* - * Processing: - * * process first element in a chunk. - * * update twiddle factor (unconditional update) - * * process second element - * * conditional update of the twiddle factor - */ - x = a->ptr.p_double[offs+0]; - y = a->ptr.p_double[offs+1]; - tmpx = x*(1+twxm1)-y*twy; - tmpy = x*twy+y*(1+twxm1); - a->ptr.p_double[offs+0] = tmpx; - a->ptr.p_double[offs+1] = tmpy; - tmpx = (1+twxm1)*twrowxm1-twy*twrowy; - twy = twy+(1+twxm1)*twrowy+twy*twrowxm1; - twxm1 = twxm1+tmpx; - x = a->ptr.p_double[offs+2]; - y = a->ptr.p_double[offs+3]; - tmpx = x*(1+twxm1)-y*twy; - tmpy = x*twy+y*(1+twxm1); - a->ptr.p_double[offs+2] = tmpx; - a->ptr.p_double[offs+3] = tmpy; - offs = offs+4; - if( (j2+1)%updatetw2==0&&j2ptr.p_double[offs+0]; - y = a->ptr.p_double[offs+1]; - tmpx = x*(1+twxm1)-y*twy; - tmpy = x*twy+y*(1+twxm1); - a->ptr.p_double[offs+0] = tmpx; - a->ptr.p_double[offs+1] = tmpy; - offs = offs+2; - } - - /* - * update TwRow: TwRow(new) = TwRow(old)*TwBase - */ - if( iptr.p_double[astart], 1, &buf->ptr.p_double[0], 1, ae_v_len(astart,astart+2*m*n-1)); -} - - -/************************************************************************* -Recurrent subroutine for a InternalComplexLinTranspose - -Write A^T to B, where: -* A is m*n complex matrix stored in array A as pairs of real/image values, - beginning from AStart position, with AStride stride -* B is n*m complex matrix stored in array B as pairs of real/image values, - beginning from BStart position, with BStride stride -stride is measured in complex numbers, i.e. in real/image pairs. - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ffticltrec(/* Real */ ae_vector* a, - ae_int_t astart, - ae_int_t astride, - /* Real */ ae_vector* b, - ae_int_t bstart, - ae_int_t bstride, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t idx1; - ae_int_t idx2; - ae_int_t m2; - ae_int_t m1; - ae_int_t n1; - - - if( m==0||n==0 ) - { - return; - } - if( ae_maxint(m, n, _state)<=8 ) - { - m2 = 2*bstride; - for(i=0; i<=m-1; i++) - { - idx1 = bstart+2*i; - idx2 = astart+2*i*astride; - for(j=0; j<=n-1; j++) - { - b->ptr.p_double[idx1+0] = a->ptr.p_double[idx2+0]; - b->ptr.p_double[idx1+1] = a->ptr.p_double[idx2+1]; - idx1 = idx1+m2; - idx2 = idx2+2; - } - } - return; - } - if( n>m ) - { - - /* - * New partition: - * - * "A^T -> B" becomes "(A1 A2)^T -> ( B1 ) - * ( B2 ) - */ - n1 = n/2; - if( n-n1>=8&&n1%8!=0 ) - { - n1 = n1+(8-n1%8); - } - ae_assert(n-n1>0, "Assertion failed", _state); - ftbase_ffticltrec(a, astart, astride, b, bstart, bstride, m, n1, _state); - ftbase_ffticltrec(a, astart+2*n1, astride, b, bstart+2*n1*bstride, bstride, m, n-n1, _state); - } - else - { - - /* - * New partition: - * - * "A^T -> B" becomes "( A1 )^T -> ( B1 B2 ) - * ( A2 ) - */ - m1 = m/2; - if( m-m1>=8&&m1%8!=0 ) - { - m1 = m1+(8-m1%8); - } - ae_assert(m-m1>0, "Assertion failed", _state); - ftbase_ffticltrec(a, astart, astride, b, bstart, bstride, m1, n, _state); - ftbase_ffticltrec(a, astart+2*m1*astride, astride, b, bstart+2*m1, bstride, m-m1, n, _state); - } -} - - -/************************************************************************* -Recurrent subroutine for a InternalRealLinTranspose - - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_fftirltrec(/* Real */ ae_vector* a, - ae_int_t astart, - ae_int_t astride, - /* Real */ ae_vector* b, - ae_int_t bstart, - ae_int_t bstride, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t idx1; - ae_int_t idx2; - ae_int_t m1; - ae_int_t n1; - - - if( m==0||n==0 ) - { - return; - } - if( ae_maxint(m, n, _state)<=8 ) - { - for(i=0; i<=m-1; i++) - { - idx1 = bstart+i; - idx2 = astart+i*astride; - for(j=0; j<=n-1; j++) - { - b->ptr.p_double[idx1] = a->ptr.p_double[idx2]; - idx1 = idx1+bstride; - idx2 = idx2+1; - } - } - return; - } - if( n>m ) - { - - /* - * New partition: - * - * "A^T -> B" becomes "(A1 A2)^T -> ( B1 ) - * ( B2 ) - */ - n1 = n/2; - if( n-n1>=8&&n1%8!=0 ) - { - n1 = n1+(8-n1%8); - } - ae_assert(n-n1>0, "Assertion failed", _state); - ftbase_fftirltrec(a, astart, astride, b, bstart, bstride, m, n1, _state); - ftbase_fftirltrec(a, astart+n1, astride, b, bstart+n1*bstride, bstride, m, n-n1, _state); - } - else - { - - /* - * New partition: - * - * "A^T -> B" becomes "( A1 )^T -> ( B1 B2 ) - * ( A2 ) - */ - m1 = m/2; - if( m-m1>=8&&m1%8!=0 ) - { - m1 = m1+(8-m1%8); - } - ae_assert(m-m1>0, "Assertion failed", _state); - ftbase_fftirltrec(a, astart, astride, b, bstart, bstride, m1, n, _state); - ftbase_fftirltrec(a, astart+m1*astride, astride, b, bstart+m1, bstride, m-m1, n, _state); - } -} - - -/************************************************************************* -recurrent subroutine for FFTFindSmoothRec - - -- ALGLIB -- - Copyright 01.05.2009 by Bochkanov Sergey -*************************************************************************/ -static void ftbase_ftbasefindsmoothrec(ae_int_t n, - ae_int_t seed, - ae_int_t leastfactor, - ae_int_t* best, - ae_state *_state) -{ - - - ae_assert(ftbase_ftbasemaxsmoothfactor<=5, "FTBaseFindSmoothRec: internal error!", _state); - if( seed>=n ) - { - *best = ae_minint(*best, seed, _state); - return; - } - if( leastfactor<=2 ) - { - ftbase_ftbasefindsmoothrec(n, seed*2, 2, best, _state); - } - if( leastfactor<=3 ) - { - ftbase_ftbasefindsmoothrec(n, seed*3, 3, best, _state); - } - if( leastfactor<=5 ) - { - ftbase_ftbasefindsmoothrec(n, seed*5, 5, best, _state); - } -} - - -ae_bool _fasttransformplan_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - fasttransformplan *p = (fasttransformplan*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->entries, 0, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->buffer, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->precr, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->preci, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init(&p->bluesteinpool, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _fasttransformplan_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - fasttransformplan *dst = (fasttransformplan*)_dst; - fasttransformplan *src = (fasttransformplan*)_src; - if( !ae_matrix_init_copy(&dst->entries, &src->entries, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->buffer, &src->buffer, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->precr, &src->precr, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->preci, &src->preci, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init_copy(&dst->bluesteinpool, &src->bluesteinpool, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _fasttransformplan_clear(void* _p) -{ - fasttransformplan *p = (fasttransformplan*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->entries); - ae_vector_clear(&p->buffer); - ae_vector_clear(&p->precr); - ae_vector_clear(&p->preci); - ae_shared_pool_clear(&p->bluesteinpool); -} - - -void _fasttransformplan_destroy(void* _p) -{ - fasttransformplan *p = (fasttransformplan*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->entries); - ae_vector_destroy(&p->buffer); - ae_vector_destroy(&p->precr); - ae_vector_destroy(&p->preci); - ae_shared_pool_destroy(&p->bluesteinpool); -} - - - - -double nulog1p(double x, ae_state *_state) -{ - double z; - double lp; - double lq; - double result; - - - z = 1.0+x; - if( ae_fp_less(z,0.70710678118654752440)||ae_fp_greater(z,1.41421356237309504880) ) - { - result = ae_log(z, _state); - return result; - } - z = x*x; - lp = 4.5270000862445199635215E-5; - lp = lp*x+4.9854102823193375972212E-1; - lp = lp*x+6.5787325942061044846969E0; - lp = lp*x+2.9911919328553073277375E1; - lp = lp*x+6.0949667980987787057556E1; - lp = lp*x+5.7112963590585538103336E1; - lp = lp*x+2.0039553499201281259648E1; - lq = 1.0000000000000000000000E0; - lq = lq*x+1.5062909083469192043167E1; - lq = lq*x+8.3047565967967209469434E1; - lq = lq*x+2.2176239823732856465394E2; - lq = lq*x+3.0909872225312059774938E2; - lq = lq*x+2.1642788614495947685003E2; - lq = lq*x+6.0118660497603843919306E1; - z = -0.5*z+x*(z*lp/lq); - result = x+z; - return result; -} - - -double nuexpm1(double x, ae_state *_state) -{ - double r; - double xx; - double ep; - double eq; - double result; - - - if( ae_fp_less(x,-0.5)||ae_fp_greater(x,0.5) ) - { - result = ae_exp(x, _state)-1.0; - return result; - } - xx = x*x; - ep = 1.2617719307481059087798E-4; - ep = ep*xx+3.0299440770744196129956E-2; - ep = ep*xx+9.9999999999999999991025E-1; - eq = 3.0019850513866445504159E-6; - eq = eq*xx+2.5244834034968410419224E-3; - eq = eq*xx+2.2726554820815502876593E-1; - eq = eq*xx+2.0000000000000000000897E0; - r = x*ep; - r = r/(eq-r); - result = r+r; - return result; -} - - -double nucosm1(double x, ae_state *_state) -{ - double xx; - double c; - double result; - - - if( ae_fp_less(x,-0.25*ae_pi)||ae_fp_greater(x,0.25*ae_pi) ) - { - result = ae_cos(x, _state)-1; - return result; - } - xx = x*x; - c = 4.7377507964246204691685E-14; - c = c*xx-1.1470284843425359765671E-11; - c = c*xx+2.0876754287081521758361E-9; - c = c*xx-2.7557319214999787979814E-7; - c = c*xx+2.4801587301570552304991E-5; - c = c*xx-1.3888888888888872993737E-3; - c = c*xx+4.1666666666666666609054E-2; - result = -0.5*xx+xx*xx*c; - return result; -} - - - - - -} - diff --git a/src/inc/alglib/alglibinternal.h b/src/inc/alglib/alglibinternal.h deleted file mode 100644 index a59bf7e..0000000 --- a/src/inc/alglib/alglibinternal.h +++ /dev/null @@ -1,1074 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _alglibinternal_pkg_h -#define _alglibinternal_pkg_h -#include "ap.h" - - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - ae_vector ia0; - ae_vector ia1; - ae_vector ia2; - ae_vector ia3; - ae_vector ra0; - ae_vector ra1; - ae_vector ra2; - ae_vector ra3; -} apbuffers; -typedef struct -{ - ae_bool val; -} sboolean; -typedef struct -{ - ae_vector val; -} sbooleanarray; -typedef struct -{ - ae_int_t val; -} sinteger; -typedef struct -{ - ae_vector val; -} sintegerarray; -typedef struct -{ - double val; -} sreal; -typedef struct -{ - ae_vector val; -} srealarray; -typedef struct -{ - ae_complex val; -} scomplex; -typedef struct -{ - ae_vector val; -} scomplexarray; -typedef struct -{ - ae_int_t chunksize; - ae_int_t ntotal; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_vector batch4buf; - ae_vector hpcbuf; - ae_matrix xy; - ae_matrix xy2; - ae_vector xyrow; - ae_vector x; - ae_vector y; - ae_vector desiredy; - double e; - ae_vector g; - ae_vector tmp0; -} mlpbuffers; -typedef struct -{ - ae_bool brackt; - ae_bool stage1; - ae_int_t infoc; - double dg; - double dgm; - double dginit; - double dgtest; - double dgx; - double dgxm; - double dgy; - double dgym; - double finit; - double ftest1; - double fm; - double fx; - double fxm; - double fy; - double fym; - double stx; - double sty; - double stmin; - double stmax; - double width; - double width1; - double xtrapf; -} linminstate; -typedef struct -{ - ae_bool needf; - ae_vector x; - double f; - ae_int_t n; - ae_vector xbase; - ae_vector s; - double stplen; - double fcur; - double stpmax; - ae_int_t fmax; - ae_int_t nfev; - ae_int_t info; - rcommstate rstate; -} armijostate; -typedef struct -{ - ae_matrix entries; - ae_vector buffer; - ae_vector precr; - ae_vector preci; - ae_shared_pool bluesteinpool; -} fasttransformplan; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -ae_bool seterrorflag(ae_bool* flag, ae_bool cond, ae_state *_state); -ae_bool seterrorflagdiff(ae_bool* flag, - double val, - double refval, - double tol, - double s, - ae_state *_state); -void touchint(ae_int_t* a, ae_state *_state); -void touchreal(double* a, ae_state *_state); -double inttoreal(ae_int_t a, ae_state *_state); -double log2(double x, ae_state *_state); -ae_bool approxequalrel(double a, double b, double tol, ae_state *_state); -void taskgenint1d(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void taskgenint1dequidist(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void taskgenint1dcheb1(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void taskgenint1dcheb2(double a, - double b, - ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -ae_bool aredistinct(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -ae_bool aresameboolean(ae_bool v1, ae_bool v2, ae_state *_state); -void bvectorsetlengthatleast(/* Boolean */ ae_vector* x, - ae_int_t n, - ae_state *_state); -void ivectorsetlengthatleast(/* Integer */ ae_vector* x, - ae_int_t n, - ae_state *_state); -void rvectorsetlengthatleast(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -void rmatrixsetlengthatleast(/* Real */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state); -void rmatrixresize(/* Real */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state); -void imatrixresize(/* Integer */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state); -ae_bool isfinitevector(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -ae_bool isfinitecvector(/* Complex */ ae_vector* z, - ae_int_t n, - ae_state *_state); -ae_bool apservisfinitematrix(/* Real */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state); -ae_bool apservisfinitecmatrix(/* Complex */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state); -ae_bool isfinitertrmatrix(/* Real */ ae_matrix* x, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -ae_bool apservisfinitectrmatrix(/* Complex */ ae_matrix* x, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -ae_bool apservisfiniteornanmatrix(/* Real */ ae_matrix* x, - ae_int_t m, - ae_int_t n, - ae_state *_state); -double safepythag2(double x, double y, ae_state *_state); -double safepythag3(double x, double y, double z, ae_state *_state); -ae_int_t saferdiv(double x, double y, double* r, ae_state *_state); -double safeminposrv(double x, double y, double v, ae_state *_state); -void apperiodicmap(double* x, - double a, - double b, - double* k, - ae_state *_state); -double randomnormal(ae_state *_state); -void randomunit(ae_int_t n, /* Real */ ae_vector* x, ae_state *_state); -void inc(ae_int_t* v, ae_state *_state); -void dec(ae_int_t* v, ae_state *_state); -void countdown(ae_int_t* v, ae_state *_state); -double boundval(double x, double b1, double b2, ae_state *_state); -void alloccomplex(ae_serializer* s, ae_complex v, ae_state *_state); -void serializecomplex(ae_serializer* s, ae_complex v, ae_state *_state); -ae_complex unserializecomplex(ae_serializer* s, ae_state *_state); -void allocrealarray(ae_serializer* s, - /* Real */ ae_vector* v, - ae_int_t n, - ae_state *_state); -void serializerealarray(ae_serializer* s, - /* Real */ ae_vector* v, - ae_int_t n, - ae_state *_state); -void unserializerealarray(ae_serializer* s, - /* Real */ ae_vector* v, - ae_state *_state); -void allocintegerarray(ae_serializer* s, - /* Integer */ ae_vector* v, - ae_int_t n, - ae_state *_state); -void serializeintegerarray(ae_serializer* s, - /* Integer */ ae_vector* v, - ae_int_t n, - ae_state *_state); -void unserializeintegerarray(ae_serializer* s, - /* Integer */ ae_vector* v, - ae_state *_state); -void allocrealmatrix(ae_serializer* s, - /* Real */ ae_matrix* v, - ae_int_t n0, - ae_int_t n1, - ae_state *_state); -void serializerealmatrix(ae_serializer* s, - /* Real */ ae_matrix* v, - ae_int_t n0, - ae_int_t n1, - ae_state *_state); -void unserializerealmatrix(ae_serializer* s, - /* Real */ ae_matrix* v, - ae_state *_state); -void copyintegerarray(/* Integer */ ae_vector* src, - /* Integer */ ae_vector* dst, - ae_state *_state); -void copyrealarray(/* Real */ ae_vector* src, - /* Real */ ae_vector* dst, - ae_state *_state); -void copyrealmatrix(/* Real */ ae_matrix* src, - /* Real */ ae_matrix* dst, - ae_state *_state); -ae_int_t recsearch(/* Integer */ ae_vector* a, - ae_int_t nrec, - ae_int_t nheader, - ae_int_t i0, - ae_int_t i1, - /* Integer */ ae_vector* b, - ae_state *_state); -void splitlengtheven(ae_int_t tasksize, - ae_int_t* task0, - ae_int_t* task1, - ae_state *_state); -void splitlength(ae_int_t tasksize, - ae_int_t chunksize, - ae_int_t* task0, - ae_int_t* task1, - ae_state *_state); -ae_bool _apbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _apbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _apbuffers_clear(void* _p); -void _apbuffers_destroy(void* _p); -ae_bool _sboolean_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sboolean_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sboolean_clear(void* _p); -void _sboolean_destroy(void* _p); -ae_bool _sbooleanarray_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sbooleanarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sbooleanarray_clear(void* _p); -void _sbooleanarray_destroy(void* _p); -ae_bool _sinteger_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sinteger_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sinteger_clear(void* _p); -void _sinteger_destroy(void* _p); -ae_bool _sintegerarray_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sintegerarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sintegerarray_clear(void* _p); -void _sintegerarray_destroy(void* _p); -ae_bool _sreal_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sreal_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sreal_clear(void* _p); -void _sreal_destroy(void* _p); -ae_bool _srealarray_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _srealarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _srealarray_clear(void* _p); -void _srealarray_destroy(void* _p); -ae_bool _scomplex_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _scomplex_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _scomplex_clear(void* _p); -void _scomplex_destroy(void* _p); -ae_bool _scomplexarray_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _scomplexarray_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _scomplexarray_clear(void* _p); -void _scomplexarray_destroy(void* _p); -ae_int_t getrdfserializationcode(ae_state *_state); -ae_int_t getkdtreeserializationcode(ae_state *_state); -ae_int_t getmlpserializationcode(ae_state *_state); -ae_int_t getmlpeserializationcode(ae_state *_state); -ae_int_t getrbfserializationcode(ae_state *_state); -void tagsort(/* Real */ ae_vector* a, - ae_int_t n, - /* Integer */ ae_vector* p1, - /* Integer */ ae_vector* p2, - ae_state *_state); -void tagsortbuf(/* Real */ ae_vector* a, - ae_int_t n, - /* Integer */ ae_vector* p1, - /* Integer */ ae_vector* p2, - apbuffers* buf, - ae_state *_state); -void tagsortfasti(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Integer */ ae_vector* bufb, - ae_int_t n, - ae_state *_state); -void tagsortfastr(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* bufa, - /* Real */ ae_vector* bufb, - ae_int_t n, - ae_state *_state); -void tagsortfast(/* Real */ ae_vector* a, - /* Real */ ae_vector* bufa, - ae_int_t n, - ae_state *_state); -void tagsortmiddleir(/* Integer */ ae_vector* a, - /* Real */ ae_vector* b, - ae_int_t offset, - ae_int_t n, - ae_state *_state); -void tagheappushi(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t* n, - double va, - ae_int_t vb, - ae_state *_state); -void tagheapreplacetopi(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t n, - double va, - ae_int_t vb, - ae_state *_state); -void tagheappopi(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t* n, - ae_state *_state); -ae_int_t lowerbound(/* Real */ ae_vector* a, - ae_int_t n, - double t, - ae_state *_state); -ae_int_t upperbound(/* Real */ ae_vector* a, - ae_int_t n, - double t, - ae_state *_state); -void rankx(/* Real */ ae_vector* x, - ae_int_t n, - ae_bool iscentered, - apbuffers* buf, - ae_state *_state); -ae_bool cmatrixrank1f(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_vector* u, - ae_int_t iu, - /* Complex */ ae_vector* v, - ae_int_t iv, - ae_state *_state); -ae_bool rmatrixrank1f(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_vector* u, - ae_int_t iu, - /* Real */ ae_vector* v, - ae_int_t iv, - ae_state *_state); -ae_bool cmatrixmvf(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Complex */ ae_vector* x, - ae_int_t ix, - /* Complex */ ae_vector* y, - ae_int_t iy, - ae_state *_state); -ae_bool rmatrixmvf(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Real */ ae_vector* x, - ae_int_t ix, - /* Real */ ae_vector* y, - ae_int_t iy, - ae_state *_state); -ae_bool cmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -ae_bool cmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -ae_bool rmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -ae_bool rmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -ae_bool cmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); -ae_bool rmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); -ae_bool rmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -ae_bool cmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void cmatrixgemmk(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void rmatrixgemmk(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void rmatrixgemmk44v00(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void rmatrixgemmk44v01(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void rmatrixgemmk44v10(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void rmatrixgemmk44v11(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -ae_bool rmatrixsyrkmkl(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); -ae_bool rmatrixgemmmkl(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -double vectornorm2(/* Real */ ae_vector* x, - ae_int_t i1, - ae_int_t i2, - ae_state *_state); -ae_int_t vectoridxabsmax(/* Real */ ae_vector* x, - ae_int_t i1, - ae_int_t i2, - ae_state *_state); -ae_int_t columnidxabsmax(/* Real */ ae_matrix* x, - ae_int_t i1, - ae_int_t i2, - ae_int_t j, - ae_state *_state); -ae_int_t rowidxabsmax(/* Real */ ae_matrix* x, - ae_int_t j1, - ae_int_t j2, - ae_int_t i, - ae_state *_state); -double upperhessenberg1norm(/* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t i2, - ae_int_t j1, - ae_int_t j2, - /* Real */ ae_vector* work, - ae_state *_state); -void copymatrix(/* Real */ ae_matrix* a, - ae_int_t is1, - ae_int_t is2, - ae_int_t js1, - ae_int_t js2, - /* Real */ ae_matrix* b, - ae_int_t id1, - ae_int_t id2, - ae_int_t jd1, - ae_int_t jd2, - ae_state *_state); -void inplacetranspose(/* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t i2, - ae_int_t j1, - ae_int_t j2, - /* Real */ ae_vector* work, - ae_state *_state); -void copyandtranspose(/* Real */ ae_matrix* a, - ae_int_t is1, - ae_int_t is2, - ae_int_t js1, - ae_int_t js2, - /* Real */ ae_matrix* b, - ae_int_t id1, - ae_int_t id2, - ae_int_t jd1, - ae_int_t jd2, - ae_state *_state); -void matrixvectormultiply(/* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t i2, - ae_int_t j1, - ae_int_t j2, - ae_bool trans, - /* Real */ ae_vector* x, - ae_int_t ix1, - ae_int_t ix2, - double alpha, - /* Real */ ae_vector* y, - ae_int_t iy1, - ae_int_t iy2, - double beta, - ae_state *_state); -double pythag2(double x, double y, ae_state *_state); -void matrixmatrixmultiply(/* Real */ ae_matrix* a, - ae_int_t ai1, - ae_int_t ai2, - ae_int_t aj1, - ae_int_t aj2, - ae_bool transa, - /* Real */ ae_matrix* b, - ae_int_t bi1, - ae_int_t bi2, - ae_int_t bj1, - ae_int_t bj2, - ae_bool transb, - double alpha, - /* Real */ ae_matrix* c, - ae_int_t ci1, - ae_int_t ci2, - ae_int_t cj1, - ae_int_t cj2, - double beta, - /* Real */ ae_vector* work, - ae_state *_state); -void hermitianmatrixvectormultiply(/* Complex */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Complex */ ae_vector* x, - ae_complex alpha, - /* Complex */ ae_vector* y, - ae_state *_state); -void hermitianrank2update(/* Complex */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Complex */ ae_vector* x, - /* Complex */ ae_vector* y, - /* Complex */ ae_vector* t, - ae_complex alpha, - ae_state *_state); -void generatereflection(/* Real */ ae_vector* x, - ae_int_t n, - double* tau, - ae_state *_state); -void applyreflectionfromtheleft(/* Real */ ae_matrix* c, - double tau, - /* Real */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* work, - ae_state *_state); -void applyreflectionfromtheright(/* Real */ ae_matrix* c, - double tau, - /* Real */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* work, - ae_state *_state); -void complexgeneratereflection(/* Complex */ ae_vector* x, - ae_int_t n, - ae_complex* tau, - ae_state *_state); -void complexapplyreflectionfromtheleft(/* Complex */ ae_matrix* c, - ae_complex tau, - /* Complex */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Complex */ ae_vector* work, - ae_state *_state); -void complexapplyreflectionfromtheright(/* Complex */ ae_matrix* c, - ae_complex tau, - /* Complex */ ae_vector* v, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Complex */ ae_vector* work, - ae_state *_state); -void symmetricmatrixvectormultiply(/* Real */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* x, - double alpha, - /* Real */ ae_vector* y, - ae_state *_state); -void symmetricrank2update(/* Real */ ae_matrix* a, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* t, - double alpha, - ae_state *_state); -void applyrotationsfromtheleft(ae_bool isforward, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* c, - /* Real */ ae_vector* s, - /* Real */ ae_matrix* a, - /* Real */ ae_vector* work, - ae_state *_state); -void applyrotationsfromtheright(ae_bool isforward, - ae_int_t m1, - ae_int_t m2, - ae_int_t n1, - ae_int_t n2, - /* Real */ ae_vector* c, - /* Real */ ae_vector* s, - /* Real */ ae_matrix* a, - /* Real */ ae_vector* work, - ae_state *_state); -void generaterotation(double f, - double g, - double* cs, - double* sn, - double* r, - ae_state *_state); -ae_bool upperhessenbergschurdecomposition(/* Real */ ae_matrix* h, - ae_int_t n, - /* Real */ ae_matrix* s, - ae_state *_state); -void internalschurdecomposition(/* Real */ ae_matrix* h, - ae_int_t n, - ae_int_t tneeded, - ae_int_t zneeded, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - /* Real */ ae_matrix* z, - ae_int_t* info, - ae_state *_state); -void rmatrixtrsafesolve(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* x, - double* s, - ae_bool isupper, - ae_bool istrans, - ae_bool isunit, - ae_state *_state); -void safesolvetriangular(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* x, - double* s, - ae_bool isupper, - ae_bool istrans, - ae_bool isunit, - ae_bool normin, - /* Real */ ae_vector* cnorm, - ae_state *_state); -ae_bool rmatrixscaledtrsafesolve(/* Real */ ae_matrix* a, - double sa, - ae_int_t n, - /* Real */ ae_vector* x, - ae_bool isupper, - ae_int_t trans, - ae_bool isunit, - double maxgrowth, - ae_state *_state); -ae_bool cmatrixscaledtrsafesolve(/* Complex */ ae_matrix* a, - double sa, - ae_int_t n, - /* Complex */ ae_vector* x, - ae_bool isupper, - ae_int_t trans, - ae_bool isunit, - double maxgrowth, - ae_state *_state); -void hpcpreparechunkedgradient(/* Real */ ae_vector* weights, - ae_int_t wcount, - ae_int_t ntotal, - ae_int_t nin, - ae_int_t nout, - mlpbuffers* buf, - ae_state *_state); -void hpcfinalizechunkedgradient(mlpbuffers* buf, - /* Real */ ae_vector* grad, - ae_state *_state); -ae_bool hpcchunkedgradient(/* Real */ ae_vector* weights, - /* Integer */ ae_vector* structinfo, - /* Real */ ae_vector* columnmeans, - /* Real */ ae_vector* columnsigmas, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - double* e, - ae_bool naturalerrorfunc, - ae_state *_state); -ae_bool hpcchunkedprocess(/* Real */ ae_vector* weights, - /* Integer */ ae_vector* structinfo, - /* Real */ ae_vector* columnmeans, - /* Real */ ae_vector* columnsigmas, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - ae_state *_state); -ae_bool _mlpbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlpbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlpbuffers_clear(void* _p); -void _mlpbuffers_destroy(void* _p); -void xdot(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* temp, - double* r, - double* rerr, - ae_state *_state); -void xcdot(/* Complex */ ae_vector* a, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* temp, - ae_complex* r, - double* rerr, - ae_state *_state); -void linminnormalized(/* Real */ ae_vector* d, - double* stp, - ae_int_t n, - ae_state *_state); -void mcsrch(ae_int_t n, - /* Real */ ae_vector* x, - double* f, - /* Real */ ae_vector* g, - /* Real */ ae_vector* s, - double* stp, - double stpmax, - double gtol, - ae_int_t* info, - ae_int_t* nfev, - /* Real */ ae_vector* wa, - linminstate* state, - ae_int_t* stage, - ae_state *_state); -void armijocreate(ae_int_t n, - /* Real */ ae_vector* x, - double f, - /* Real */ ae_vector* s, - double stp, - double stpmax, - ae_int_t fmax, - armijostate* state, - ae_state *_state); -ae_bool armijoiteration(armijostate* state, ae_state *_state); -void armijoresults(armijostate* state, - ae_int_t* info, - double* stp, - double* f, - ae_state *_state); -ae_bool _linminstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _linminstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _linminstate_clear(void* _p); -void _linminstate_destroy(void* _p); -ae_bool _armijostate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _armijostate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _armijostate_clear(void* _p); -void _armijostate_destroy(void* _p); -void findprimitiverootandinverse(ae_int_t n, - ae_int_t* proot, - ae_int_t* invproot, - ae_state *_state); -void ftcomplexfftplan(ae_int_t n, - ae_int_t k, - fasttransformplan* plan, - ae_state *_state); -void ftapplyplan(fasttransformplan* plan, - /* Real */ ae_vector* a, - ae_int_t offsa, - ae_int_t repcnt, - ae_state *_state); -void ftbasefactorize(ae_int_t n, - ae_int_t tasktype, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state); -ae_bool ftbaseissmooth(ae_int_t n, ae_state *_state); -ae_int_t ftbasefindsmooth(ae_int_t n, ae_state *_state); -ae_int_t ftbasefindsmootheven(ae_int_t n, ae_state *_state); -double ftbasegetflopestimate(ae_int_t n, ae_state *_state); -ae_bool _fasttransformplan_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _fasttransformplan_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _fasttransformplan_clear(void* _p); -void _fasttransformplan_destroy(void* _p); -double nulog1p(double x, ae_state *_state); -double nuexpm1(double x, ae_state *_state); -double nucosm1(double x, ae_state *_state); - -} -#endif - diff --git a/src/inc/alglib/alglibmisc.cpp b/src/inc/alglib/alglibmisc.cpp deleted file mode 100644 index cc4e095..0000000 --- a/src/inc/alglib/alglibmisc.cpp +++ /dev/null @@ -1,3611 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "alglibmisc.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Portable high quality random number generator state. -Initialized with HQRNDRandomize() or HQRNDSeed(). - -Fields: - S1, S2 - seed values - V - precomputed value - MagicV - 'magic' value used to determine whether State structure - was correctly initialized. -*************************************************************************/ -_hqrndstate_owner::_hqrndstate_owner() -{ - p_struct = (alglib_impl::hqrndstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::hqrndstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_hqrndstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_hqrndstate_owner::_hqrndstate_owner(const _hqrndstate_owner &rhs) -{ - p_struct = (alglib_impl::hqrndstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::hqrndstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_hqrndstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_hqrndstate_owner& _hqrndstate_owner::operator=(const _hqrndstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_hqrndstate_clear(p_struct); - if( !alglib_impl::_hqrndstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_hqrndstate_owner::~_hqrndstate_owner() -{ - alglib_impl::_hqrndstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::hqrndstate* _hqrndstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::hqrndstate* _hqrndstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -hqrndstate::hqrndstate() : _hqrndstate_owner() -{ -} - -hqrndstate::hqrndstate(const hqrndstate &rhs):_hqrndstate_owner(rhs) -{ -} - -hqrndstate& hqrndstate::operator=(const hqrndstate &rhs) -{ - if( this==&rhs ) - return *this; - _hqrndstate_owner::operator=(rhs); - return *this; -} - -hqrndstate::~hqrndstate() -{ -} - -/************************************************************************* -HQRNDState initialization with random values which come from standard -RNG. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndrandomize(hqrndstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hqrndrandomize(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -HQRNDState initialization with seed values - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndseed(const ae_int_t s1, const ae_int_t s2, hqrndstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hqrndseed(s1, s2, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function generates random real number in (0,1), -not including interval boundaries - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double hqrnduniformr(const hqrndstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hqrnduniformr(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function generates random integer number in [0, N) - -1. State structure must be initialized with HQRNDRandomize() or HQRNDSeed() -2. N can be any positive number except for very large numbers: - * close to 2^31 on 32-bit systems - * close to 2^62 on 64-bit systems - An exception will be generated if N is too large. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -ae_int_t hqrnduniformi(const hqrndstate &state, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::hqrnduniformi(const_cast(state.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Random number generator: normal numbers - -This function generates one random number from normal distribution. -Its performance is equal to that of HQRNDNormal2() - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double hqrndnormal(const hqrndstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hqrndnormal(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Random number generator: random X and Y such that X^2+Y^2=1 - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndunit2(const hqrndstate &state, double &x, double &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hqrndunit2(const_cast(state.c_ptr()), &x, &y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Random number generator: normal numbers - -This function generates two independent random numbers from normal -distribution. Its performance is equal to that of HQRNDNormal() - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndnormal2(const hqrndstate &state, double &x1, double &x2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hqrndnormal2(const_cast(state.c_ptr()), &x1, &x2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Random number generator: exponential distribution - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 11.08.2007 by Bochkanov Sergey -*************************************************************************/ -double hqrndexponential(const hqrndstate &state, const double lambdav) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hqrndexponential(const_cast(state.c_ptr()), lambdav, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function generates random number from discrete distribution given by -finite sample X. - -INPUT PARAMETERS - State - high quality random number generator, must be - initialized with HQRNDRandomize() or HQRNDSeed(). - X - finite sample - N - number of elements to use, N>=1 - -RESULT - this function returns one of the X[i] for random i=0..N-1 - - -- ALGLIB -- - Copyright 08.11.2011 by Bochkanov Sergey -*************************************************************************/ -double hqrnddiscrete(const hqrndstate &state, const real_1d_array &x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hqrnddiscrete(const_cast(state.c_ptr()), const_cast(x.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function generates random number from continuous distribution given -by finite sample X. - -INPUT PARAMETERS - State - high quality random number generator, must be - initialized with HQRNDRandomize() or HQRNDSeed(). - X - finite sample, array[N] (can be larger, in this case only - leading N elements are used). THIS ARRAY MUST BE SORTED BY - ASCENDING. - N - number of elements to use, N>=1 - -RESULT - this function returns random number from continuous distribution which - tries to approximate X as mush as possible. min(X)<=Result<=max(X). - - -- ALGLIB -- - Copyright 08.11.2011 by Bochkanov Sergey -*************************************************************************/ -double hqrndcontinuous(const hqrndstate &state, const real_1d_array &x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hqrndcontinuous(const_cast(state.c_ptr()), const_cast(x.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_kdtree_owner::_kdtree_owner() -{ - p_struct = (alglib_impl::kdtree*)alglib_impl::ae_malloc(sizeof(alglib_impl::kdtree), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_kdtree_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_kdtree_owner::_kdtree_owner(const _kdtree_owner &rhs) -{ - p_struct = (alglib_impl::kdtree*)alglib_impl::ae_malloc(sizeof(alglib_impl::kdtree), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_kdtree_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_kdtree_owner& _kdtree_owner::operator=(const _kdtree_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_kdtree_clear(p_struct); - if( !alglib_impl::_kdtree_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_kdtree_owner::~_kdtree_owner() -{ - alglib_impl::_kdtree_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::kdtree* _kdtree_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::kdtree* _kdtree_owner::c_ptr() const -{ - return const_cast(p_struct); -} -kdtree::kdtree() : _kdtree_owner() -{ -} - -kdtree::kdtree(const kdtree &rhs):_kdtree_owner(rhs) -{ -} - -kdtree& kdtree::operator=(const kdtree &rhs) -{ - if( this==&rhs ) - return *this; - _kdtree_owner::operator=(rhs); - return *this; -} - -kdtree::~kdtree() -{ -} - - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void kdtreeserialize(kdtree &obj, std::string &s_out) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - alglib_impl::ae_int_t ssize; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_alloc_start(&serializer); - alglib_impl::kdtreealloc(&serializer, obj.c_ptr(), &state); - ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer); - s_out.clear(); - s_out.reserve((size_t)(ssize+1)); - alglib_impl::ae_serializer_sstart_str(&serializer, &s_out); - alglib_impl::kdtreeserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - if( s_out.length()>(size_t)ssize ) - throw ap_error("ALGLIB: serialization integrity error"); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void kdtreeunserialize(std::string &s_in, kdtree &obj) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_ustart_str(&serializer, &s_in); - alglib_impl::kdtreeunserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values and optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - N - number of points, N>=0. - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuild(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreebuild(const_cast(xy.c_ptr()), n, nx, ny, normtype, const_cast(kdt.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values and optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - N - number of points, N>=0. - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuild(const real_2d_array &xy, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = xy.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreebuild(const_cast(xy.c_ptr()), n, nx, ny, normtype, const_cast(kdt.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values, integer tags and -optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - Tags - tags, array[0..N-1], contains integer tags associated - with points. - N - number of points, N>=0 - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuildtagged(const real_2d_array &xy, const integer_1d_array &tags, const ae_int_t n, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreebuildtagged(const_cast(xy.c_ptr()), const_cast(tags.c_ptr()), n, nx, ny, normtype, const_cast(kdt.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values, integer tags and -optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - Tags - tags, array[0..N-1], contains integer tags associated - with points. - N - number of points, N>=0 - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuildtagged(const real_2d_array &xy, const integer_1d_array &tags, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (xy.rows()!=tags.length())) - throw ap_error("Error while calling 'kdtreebuildtagged': looks like one of arguments has wrong size"); - n = xy.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreebuildtagged(const_cast(xy.c_ptr()), const_cast(tags.c_ptr()), n, nx, ny, normtype, const_cast(kdt.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -K-NN query: K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of actual neighbors found (either K or N, if K>N). - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::kdtreequeryknn(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), k, selfmatch, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -K-NN query: K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of actual neighbors found (either K or N, if K>N). - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - bool selfmatch; - - selfmatch = true; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::kdtreequeryknn(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), k, selfmatch, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -R-NN query: all points within R-sphere centered at X - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - R - radius of sphere (in corresponding norm), R>0 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of neighbors found, >=0 - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -actual results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r, const bool selfmatch) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::kdtreequeryrnn(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), r, selfmatch, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -R-NN query: all points within R-sphere centered at X - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - R - radius of sphere (in corresponding norm), R>0 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of neighbors found, >=0 - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -actual results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r) -{ - alglib_impl::ae_state _alglib_env_state; - bool selfmatch; - - selfmatch = true; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::kdtreequeryrnn(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), r, selfmatch, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -K-NN query: approximate K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - Eps - approximation factor, Eps>=0. eps-approximate nearest - neighbor is a neighbor whose distance from X is at - most (1+eps) times distance of true nearest neighbor. - -RESULT - number of actual neighbors found (either K or N, if K>N). - -NOTES - significant performance gain may be achieved only when Eps is is on - the order of magnitude of 1 or larger. - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const double eps) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::kdtreequeryaknn(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), k, selfmatch, eps, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -K-NN query: approximate K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - Eps - approximation factor, Eps>=0. eps-approximate nearest - neighbor is a neighbor whose distance from X is at - most (1+eps) times distance of true nearest neighbor. - -RESULT - number of actual neighbors found (either K or N, if K>N). - -NOTES - significant performance gain may be achieved only when Eps is is on - the order of magnitude of 1 or larger. - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const double eps) -{ - alglib_impl::ae_state _alglib_env_state; - bool selfmatch; - - selfmatch = true; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::kdtreequeryaknn(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), k, selfmatch, eps, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -X-values from last query - -INPUT PARAMETERS - KDT - KD-tree - X - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - X - rows are filled with X-values - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsTags() tag values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsx(const kdtree &kdt, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultsx(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -X- and Y-values from last query - -INPUT PARAMETERS - KDT - KD-tree - XY - possibly pre-allocated buffer. If XY is too small to store - result, it is resized. If size(XY) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - XY - rows are filled with points: first NX columns with - X-values, next NY columns - with Y-values. - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsTags() tag values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxy(const kdtree &kdt, real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultsxy(const_cast(kdt.c_ptr()), const_cast(xy.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Tags from last query - -INPUT PARAMETERS - KDT - KD-tree - Tags - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - Tags - filled with tags associated with points, - or, when no tags were supplied, with zeros - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultstags(const kdtree &kdt, integer_1d_array &tags) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultstags(const_cast(kdt.c_ptr()), const_cast(tags.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Distances from last query - -INPUT PARAMETERS - KDT - KD-tree - R - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - R - filled with distances (in corresponding norm) - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsTags() tag values - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsdistances(const kdtree &kdt, real_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultsdistances(const_cast(kdt.c_ptr()), const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -X-values from last query; 'interactive' variant for languages like Python -which support constructs like "X = KDTreeQueryResultsXI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxi(const kdtree &kdt, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultsxi(const_cast(kdt.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -XY-values from last query; 'interactive' variant for languages like Python -which support constructs like "XY = KDTreeQueryResultsXYI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxyi(const kdtree &kdt, real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultsxyi(const_cast(kdt.c_ptr()), const_cast(xy.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Tags from last query; 'interactive' variant for languages like Python -which support constructs like "Tags = KDTreeQueryResultsTagsI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultstagsi(const kdtree &kdt, integer_1d_array &tags) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultstagsi(const_cast(kdt.c_ptr()), const_cast(tags.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Distances from last query; 'interactive' variant for languages like Python -which support constructs like "R = KDTreeQueryResultsDistancesI(KDT)" -and interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsdistancesi(const kdtree &kdt, real_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kdtreequeryresultsdistancesi(const_cast(kdt.c_ptr()), const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static ae_int_t hqrnd_hqrndmax = 2147483561; -static ae_int_t hqrnd_hqrndm1 = 2147483563; -static ae_int_t hqrnd_hqrndm2 = 2147483399; -static ae_int_t hqrnd_hqrndmagic = 1634357784; -static ae_int_t hqrnd_hqrndintegerbase(hqrndstate* state, - ae_state *_state); - - -static ae_int_t nearestneighbor_splitnodesize = 6; -static ae_int_t nearestneighbor_kdtreefirstversion = 0; -static void nearestneighbor_kdtreesplit(kdtree* kdt, - ae_int_t i1, - ae_int_t i2, - ae_int_t d, - double s, - ae_int_t* i3, - ae_state *_state); -static void nearestneighbor_kdtreegeneratetreerec(kdtree* kdt, - ae_int_t* nodesoffs, - ae_int_t* splitsoffs, - ae_int_t i1, - ae_int_t i2, - ae_int_t maxleafsize, - ae_state *_state); -static void nearestneighbor_kdtreequerynnrec(kdtree* kdt, - ae_int_t offs, - ae_state *_state); -static void nearestneighbor_kdtreeinitbox(kdtree* kdt, - /* Real */ ae_vector* x, - ae_state *_state); -static void nearestneighbor_kdtreeallocdatasetindependent(kdtree* kdt, - ae_int_t nx, - ae_int_t ny, - ae_state *_state); -static void nearestneighbor_kdtreeallocdatasetdependent(kdtree* kdt, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_state *_state); -static void nearestneighbor_kdtreealloctemporaries(kdtree* kdt, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_state *_state); - - - - - -/************************************************************************* -HQRNDState initialization with random values which come from standard -RNG. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndrandomize(hqrndstate* state, ae_state *_state) -{ - ae_int_t s0; - ae_int_t s1; - - _hqrndstate_clear(state); - - s0 = ae_randominteger(hqrnd_hqrndm1, _state); - s1 = ae_randominteger(hqrnd_hqrndm2, _state); - hqrndseed(s0, s1, state, _state); -} - - -/************************************************************************* -HQRNDState initialization with seed values - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndseed(ae_int_t s1, - ae_int_t s2, - hqrndstate* state, - ae_state *_state) -{ - - _hqrndstate_clear(state); - - - /* - * Protection against negative seeds: - * - * SEED := -(SEED+1) - * - * We can use just "-SEED" because there exists such integer number N - * that N<0, -N=N<0 too. (This number is equal to 0x800...000). Need - * to handle such seed correctly forces us to use a bit complicated - * formula. - */ - if( s1<0 ) - { - s1 = -(s1+1); - } - if( s2<0 ) - { - s2 = -(s2+1); - } - state->s1 = s1%(hqrnd_hqrndm1-1)+1; - state->s2 = s2%(hqrnd_hqrndm2-1)+1; - state->magicv = hqrnd_hqrndmagic; -} - - -/************************************************************************* -This function generates random real number in (0,1), -not including interval boundaries - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double hqrnduniformr(hqrndstate* state, ae_state *_state) -{ - double result; - - - result = (double)(hqrnd_hqrndintegerbase(state, _state)+1)/(double)(hqrnd_hqrndmax+2); - return result; -} - - -/************************************************************************* -This function generates random integer number in [0, N) - -1. State structure must be initialized with HQRNDRandomize() or HQRNDSeed() -2. N can be any positive number except for very large numbers: - * close to 2^31 on 32-bit systems - * close to 2^62 on 64-bit systems - An exception will be generated if N is too large. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -ae_int_t hqrnduniformi(hqrndstate* state, ae_int_t n, ae_state *_state) -{ - ae_int_t maxcnt; - ae_int_t mx; - ae_int_t a; - ae_int_t b; - ae_int_t result; - - - ae_assert(n>0, "HQRNDUniformI: N<=0!", _state); - maxcnt = hqrnd_hqrndmax+1; - - /* - * Two branches: one for N<=MaxCnt, another for N>MaxCnt. - */ - if( n>maxcnt ) - { - - /* - * N>=MaxCnt. - * - * We have two options here: - * a) N is exactly divisible by MaxCnt - * b) N is not divisible by MaxCnt - * - * In both cases we reduce problem on interval spanning [0,N) - * to several subproblems on intervals spanning [0,MaxCnt). - */ - if( n%maxcnt==0 ) - { - - /* - * N is exactly divisible by MaxCnt. - * - * [0,N) range is dividided into N/MaxCnt bins, - * each of them having length equal to MaxCnt. - * - * We generate: - * * random bin number B - * * random offset within bin A - * Both random numbers are generated by recursively - * calling HQRNDUniformI(). - * - * Result is equal to A+MaxCnt*B. - */ - ae_assert(n/maxcnt<=maxcnt, "HQRNDUniformI: N is too large", _state); - a = hqrnduniformi(state, maxcnt, _state); - b = hqrnduniformi(state, n/maxcnt, _state); - result = a+maxcnt*b; - } - else - { - - /* - * N is NOT exactly divisible by MaxCnt. - * - * [0,N) range is dividided into Ceil(N/MaxCnt) bins, - * each of them having length equal to MaxCnt. - * - * We generate: - * * random bin number B in [0, Ceil(N/MaxCnt)-1] - * * random offset within bin A - * * if both of what is below is true - * 1) bin number B is that of the last bin - * 2) A >= N mod MaxCnt - * then we repeat generation of A/B. - * This stage is essential in order to avoid bias in the result. - * * otherwise, we return A*MaxCnt+N - */ - ae_assert(n/maxcnt+1<=maxcnt, "HQRNDUniformI: N is too large", _state); - result = -1; - do - { - a = hqrnduniformi(state, maxcnt, _state); - b = hqrnduniformi(state, n/maxcnt+1, _state); - if( b==n/maxcnt&&a>=n%maxcnt ) - { - continue; - } - result = a+maxcnt*b; - } - while(result<0); - } - } - else - { - - /* - * N<=MaxCnt - * - * Code below is a bit complicated because we can not simply - * return "HQRNDIntegerBase() mod N" - it will be skewed for - * large N's in [0.1*HQRNDMax...HQRNDMax]. - */ - mx = maxcnt-maxcnt%n; - do - { - result = hqrnd_hqrndintegerbase(state, _state); - } - while(result>=mx); - result = result%n; - } - return result; -} - - -/************************************************************************* -Random number generator: normal numbers - -This function generates one random number from normal distribution. -Its performance is equal to that of HQRNDNormal2() - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double hqrndnormal(hqrndstate* state, ae_state *_state) -{ - double v1; - double v2; - double result; - - - hqrndnormal2(state, &v1, &v2, _state); - result = v1; - return result; -} - - -/************************************************************************* -Random number generator: random X and Y such that X^2+Y^2=1 - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndunit2(hqrndstate* state, double* x, double* y, ae_state *_state) -{ - double v; - double mx; - double mn; - - *x = 0; - *y = 0; - - do - { - hqrndnormal2(state, x, y, _state); - } - while(!(ae_fp_neq(*x,0)||ae_fp_neq(*y,0))); - mx = ae_maxreal(ae_fabs(*x, _state), ae_fabs(*y, _state), _state); - mn = ae_minreal(ae_fabs(*x, _state), ae_fabs(*y, _state), _state); - v = mx*ae_sqrt(1+ae_sqr(mn/mx, _state), _state); - *x = *x/v; - *y = *y/v; -} - - -/************************************************************************* -Random number generator: normal numbers - -This function generates two independent random numbers from normal -distribution. Its performance is equal to that of HQRNDNormal() - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndnormal2(hqrndstate* state, - double* x1, - double* x2, - ae_state *_state) -{ - double u; - double v; - double s; - - *x1 = 0; - *x2 = 0; - - for(;;) - { - u = 2*hqrnduniformr(state, _state)-1; - v = 2*hqrnduniformr(state, _state)-1; - s = ae_sqr(u, _state)+ae_sqr(v, _state); - if( ae_fp_greater(s,0)&&ae_fp_less(s,1) ) - { - - /* - * two Sqrt's instead of one to - * avoid overflow when S is too small - */ - s = ae_sqrt(-2*ae_log(s, _state), _state)/ae_sqrt(s, _state); - *x1 = u*s; - *x2 = v*s; - return; - } - } -} - - -/************************************************************************* -Random number generator: exponential distribution - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 11.08.2007 by Bochkanov Sergey -*************************************************************************/ -double hqrndexponential(hqrndstate* state, - double lambdav, - ae_state *_state) -{ - double result; - - - ae_assert(ae_fp_greater(lambdav,0), "HQRNDExponential: LambdaV<=0!", _state); - result = -ae_log(hqrnduniformr(state, _state), _state)/lambdav; - return result; -} - - -/************************************************************************* -This function generates random number from discrete distribution given by -finite sample X. - -INPUT PARAMETERS - State - high quality random number generator, must be - initialized with HQRNDRandomize() or HQRNDSeed(). - X - finite sample - N - number of elements to use, N>=1 - -RESULT - this function returns one of the X[i] for random i=0..N-1 - - -- ALGLIB -- - Copyright 08.11.2011 by Bochkanov Sergey -*************************************************************************/ -double hqrnddiscrete(hqrndstate* state, - /* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double result; - - - ae_assert(n>0, "HQRNDDiscrete: N<=0", _state); - ae_assert(n<=x->cnt, "HQRNDDiscrete: Length(X)ptr.p_double[hqrnduniformi(state, n, _state)]; - return result; -} - - -/************************************************************************* -This function generates random number from continuous distribution given -by finite sample X. - -INPUT PARAMETERS - State - high quality random number generator, must be - initialized with HQRNDRandomize() or HQRNDSeed(). - X - finite sample, array[N] (can be larger, in this case only - leading N elements are used). THIS ARRAY MUST BE SORTED BY - ASCENDING. - N - number of elements to use, N>=1 - -RESULT - this function returns random number from continuous distribution which - tries to approximate X as mush as possible. min(X)<=Result<=max(X). - - -- ALGLIB -- - Copyright 08.11.2011 by Bochkanov Sergey -*************************************************************************/ -double hqrndcontinuous(hqrndstate* state, - /* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double mx; - double mn; - ae_int_t i; - double result; - - - ae_assert(n>0, "HQRNDContinuous: N<=0", _state); - ae_assert(n<=x->cnt, "HQRNDContinuous: Length(X)ptr.p_double[0]; - return result; - } - i = hqrnduniformi(state, n-1, _state); - mn = x->ptr.p_double[i]; - mx = x->ptr.p_double[i+1]; - ae_assert(ae_fp_greater_eq(mx,mn), "HQRNDDiscrete: X is not sorted by ascending", _state); - if( ae_fp_neq(mx,mn) ) - { - result = (mx-mn)*hqrnduniformr(state, _state)+mn; - } - else - { - result = mn; - } - return result; -} - - -/************************************************************************* -This function returns random integer in [0,HQRNDMax] - -L'Ecuyer, Efficient and portable combined random number generators -*************************************************************************/ -static ae_int_t hqrnd_hqrndintegerbase(hqrndstate* state, - ae_state *_state) -{ - ae_int_t k; - ae_int_t result; - - - ae_assert(state->magicv==hqrnd_hqrndmagic, "HQRNDIntegerBase: State is not correctly initialized!", _state); - k = state->s1/53668; - state->s1 = 40014*(state->s1-k*53668)-k*12211; - if( state->s1<0 ) - { - state->s1 = state->s1+2147483563; - } - k = state->s2/52774; - state->s2 = 40692*(state->s2-k*52774)-k*3791; - if( state->s2<0 ) - { - state->s2 = state->s2+2147483399; - } - - /* - * Result - */ - result = state->s1-state->s2; - if( result<1 ) - { - result = result+2147483562; - } - result = result-1; - return result; -} - - -ae_bool _hqrndstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - hqrndstate *p = (hqrndstate*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _hqrndstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - hqrndstate *dst = (hqrndstate*)_dst; - hqrndstate *src = (hqrndstate*)_src; - dst->s1 = src->s1; - dst->s2 = src->s2; - dst->magicv = src->magicv; - return ae_true; -} - - -void _hqrndstate_clear(void* _p) -{ - hqrndstate *p = (hqrndstate*)_p; - ae_touch_ptr((void*)p); -} - - -void _hqrndstate_destroy(void* _p) -{ - hqrndstate *p = (hqrndstate*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values and optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - N - number of points, N>=0. - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuild(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_int_t normtype, - kdtree* kdt, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tags; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _kdtree_clear(kdt); - ae_vector_init(&tags, 0, DT_INT, _state, ae_true); - - ae_assert(n>=0, "KDTreeBuild: N<0", _state); - ae_assert(nx>=1, "KDTreeBuild: NX<1", _state); - ae_assert(ny>=0, "KDTreeBuild: NY<0", _state); - ae_assert(normtype>=0&&normtype<=2, "KDTreeBuild: incorrect NormType", _state); - ae_assert(xy->rows>=n, "KDTreeBuild: rows(X)cols>=nx+ny||n==0, "KDTreeBuild: cols(X)0 ) - { - ae_vector_set_length(&tags, n, _state); - for(i=0; i<=n-1; i++) - { - tags.ptr.p_int[i] = 0; - } - } - kdtreebuildtagged(xy, &tags, n, nx, ny, normtype, kdt, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values, integer tags and -optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - Tags - tags, array[0..N-1], contains integer tags associated - with points. - N - number of points, N>=0 - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuildtagged(/* Real */ ae_matrix* xy, - /* Integer */ ae_vector* tags, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_int_t normtype, - kdtree* kdt, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t maxnodes; - ae_int_t nodesoffs; - ae_int_t splitsoffs; - - _kdtree_clear(kdt); - - ae_assert(n>=0, "KDTreeBuildTagged: N<0", _state); - ae_assert(nx>=1, "KDTreeBuildTagged: NX<1", _state); - ae_assert(ny>=0, "KDTreeBuildTagged: NY<0", _state); - ae_assert(normtype>=0&&normtype<=2, "KDTreeBuildTagged: incorrect NormType", _state); - ae_assert(xy->rows>=n, "KDTreeBuildTagged: rows(X)cols>=nx+ny||n==0, "KDTreeBuildTagged: cols(X)n = n; - kdt->nx = nx; - kdt->ny = ny; - kdt->normtype = normtype; - kdt->kcur = 0; - - /* - * N=0 => quick exit - */ - if( n==0 ) - { - return; - } - - /* - * Allocate - */ - nearestneighbor_kdtreeallocdatasetindependent(kdt, nx, ny, _state); - nearestneighbor_kdtreeallocdatasetdependent(kdt, n, nx, ny, _state); - - /* - * Initial fill - */ - for(i=0; i<=n-1; i++) - { - ae_v_move(&kdt->xy.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1)); - ae_v_move(&kdt->xy.ptr.pp_double[i][nx], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(nx,2*nx+ny-1)); - kdt->tags.ptr.p_int[i] = tags->ptr.p_int[i]; - } - - /* - * Determine bounding box - */ - ae_v_move(&kdt->boxmin.ptr.p_double[0], 1, &kdt->xy.ptr.pp_double[0][0], 1, ae_v_len(0,nx-1)); - ae_v_move(&kdt->boxmax.ptr.p_double[0], 1, &kdt->xy.ptr.pp_double[0][0], 1, ae_v_len(0,nx-1)); - for(i=1; i<=n-1; i++) - { - for(j=0; j<=nx-1; j++) - { - kdt->boxmin.ptr.p_double[j] = ae_minreal(kdt->boxmin.ptr.p_double[j], kdt->xy.ptr.pp_double[i][j], _state); - kdt->boxmax.ptr.p_double[j] = ae_maxreal(kdt->boxmax.ptr.p_double[j], kdt->xy.ptr.pp_double[i][j], _state); - } - } - - /* - * prepare tree structure - * * MaxNodes=N because we guarantee no trivial splits, i.e. - * every split will generate two non-empty boxes - */ - maxnodes = n; - ae_vector_set_length(&kdt->nodes, nearestneighbor_splitnodesize*2*maxnodes, _state); - ae_vector_set_length(&kdt->splits, 2*maxnodes, _state); - nodesoffs = 0; - splitsoffs = 0; - ae_v_move(&kdt->curboxmin.ptr.p_double[0], 1, &kdt->boxmin.ptr.p_double[0], 1, ae_v_len(0,nx-1)); - ae_v_move(&kdt->curboxmax.ptr.p_double[0], 1, &kdt->boxmax.ptr.p_double[0], 1, ae_v_len(0,nx-1)); - nearestneighbor_kdtreegeneratetreerec(kdt, &nodesoffs, &splitsoffs, 0, n, 8, _state); -} - - -/************************************************************************* -K-NN query: K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of actual neighbors found (either K or N, if K>N). - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryknn(kdtree* kdt, - /* Real */ ae_vector* x, - ae_int_t k, - ae_bool selfmatch, - ae_state *_state) -{ - ae_int_t result; - - - ae_assert(k>=1, "KDTreeQueryKNN: K<1!", _state); - ae_assert(x->cnt>=kdt->nx, "KDTreeQueryKNN: Length(X)nx, _state), "KDTreeQueryKNN: X contains infinite or NaN values!", _state); - result = kdtreequeryaknn(kdt, x, k, selfmatch, 0.0, _state); - return result; -} - - -/************************************************************************* -R-NN query: all points within R-sphere centered at X - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - R - radius of sphere (in corresponding norm), R>0 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of neighbors found, >=0 - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -actual results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryrnn(kdtree* kdt, - /* Real */ ae_vector* x, - double r, - ae_bool selfmatch, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t result; - - - ae_assert(ae_fp_greater(r,0), "KDTreeQueryRNN: incorrect R!", _state); - ae_assert(x->cnt>=kdt->nx, "KDTreeQueryRNN: Length(X)nx, _state), "KDTreeQueryRNN: X contains infinite or NaN values!", _state); - - /* - * Handle special case: KDT.N=0 - */ - if( kdt->n==0 ) - { - kdt->kcur = 0; - result = 0; - return result; - } - - /* - * Prepare parameters - */ - kdt->kneeded = 0; - if( kdt->normtype!=2 ) - { - kdt->rneeded = r; - } - else - { - kdt->rneeded = ae_sqr(r, _state); - } - kdt->selfmatch = selfmatch; - kdt->approxf = 1; - kdt->kcur = 0; - - /* - * calculate distance from point to current bounding box - */ - nearestneighbor_kdtreeinitbox(kdt, x, _state); - - /* - * call recursive search - * results are returned as heap - */ - nearestneighbor_kdtreequerynnrec(kdt, 0, _state); - - /* - * pop from heap to generate ordered representation - * - * last element is not pop'ed because it is already in - * its place - */ - result = kdt->kcur; - j = kdt->kcur; - for(i=kdt->kcur; i>=2; i--) - { - tagheappopi(&kdt->r, &kdt->idx, &j, _state); - } - return result; -} - - -/************************************************************************* -K-NN query: approximate K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - Eps - approximation factor, Eps>=0. eps-approximate nearest - neighbor is a neighbor whose distance from X is at - most (1+eps) times distance of true nearest neighbor. - -RESULT - number of actual neighbors found (either K or N, if K>N). - -NOTES - significant performance gain may be achieved only when Eps is is on - the order of magnitude of 1 or larger. - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryaknn(kdtree* kdt, - /* Real */ ae_vector* x, - ae_int_t k, - ae_bool selfmatch, - double eps, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t result; - - - ae_assert(k>0, "KDTreeQueryAKNN: incorrect K!", _state); - ae_assert(ae_fp_greater_eq(eps,0), "KDTreeQueryAKNN: incorrect Eps!", _state); - ae_assert(x->cnt>=kdt->nx, "KDTreeQueryAKNN: Length(X)nx, _state), "KDTreeQueryAKNN: X contains infinite or NaN values!", _state); - - /* - * Handle special case: KDT.N=0 - */ - if( kdt->n==0 ) - { - kdt->kcur = 0; - result = 0; - return result; - } - - /* - * Prepare parameters - */ - k = ae_minint(k, kdt->n, _state); - kdt->kneeded = k; - kdt->rneeded = 0; - kdt->selfmatch = selfmatch; - if( kdt->normtype==2 ) - { - kdt->approxf = 1/ae_sqr(1+eps, _state); - } - else - { - kdt->approxf = 1/(1+eps); - } - kdt->kcur = 0; - - /* - * calculate distance from point to current bounding box - */ - nearestneighbor_kdtreeinitbox(kdt, x, _state); - - /* - * call recursive search - * results are returned as heap - */ - nearestneighbor_kdtreequerynnrec(kdt, 0, _state); - - /* - * pop from heap to generate ordered representation - * - * last element is non pop'ed because it is already in - * its place - */ - result = kdt->kcur; - j = kdt->kcur; - for(i=kdt->kcur; i>=2; i--) - { - tagheappopi(&kdt->r, &kdt->idx, &j, _state); - } - return result; -} - - -/************************************************************************* -X-values from last query - -INPUT PARAMETERS - KDT - KD-tree - X - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - X - rows are filled with X-values - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsTags() tag values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsx(kdtree* kdt, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - - if( kdt->kcur==0 ) - { - return; - } - if( x->rowskcur||x->colsnx ) - { - ae_matrix_set_length(x, kdt->kcur, kdt->nx, _state); - } - k = kdt->kcur; - for(i=0; i<=k-1; i++) - { - ae_v_move(&x->ptr.pp_double[i][0], 1, &kdt->xy.ptr.pp_double[kdt->idx.ptr.p_int[i]][kdt->nx], 1, ae_v_len(0,kdt->nx-1)); - } -} - - -/************************************************************************* -X- and Y-values from last query - -INPUT PARAMETERS - KDT - KD-tree - XY - possibly pre-allocated buffer. If XY is too small to store - result, it is resized. If size(XY) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - XY - rows are filled with points: first NX columns with - X-values, next NY columns - with Y-values. - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsTags() tag values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxy(kdtree* kdt, - /* Real */ ae_matrix* xy, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - - if( kdt->kcur==0 ) - { - return; - } - if( xy->rowskcur||xy->colsnx+kdt->ny ) - { - ae_matrix_set_length(xy, kdt->kcur, kdt->nx+kdt->ny, _state); - } - k = kdt->kcur; - for(i=0; i<=k-1; i++) - { - ae_v_move(&xy->ptr.pp_double[i][0], 1, &kdt->xy.ptr.pp_double[kdt->idx.ptr.p_int[i]][kdt->nx], 1, ae_v_len(0,kdt->nx+kdt->ny-1)); - } -} - - -/************************************************************************* -Tags from last query - -INPUT PARAMETERS - KDT - KD-tree - Tags - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - Tags - filled with tags associated with points, - or, when no tags were supplied, with zeros - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultstags(kdtree* kdt, - /* Integer */ ae_vector* tags, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - - if( kdt->kcur==0 ) - { - return; - } - if( tags->cntkcur ) - { - ae_vector_set_length(tags, kdt->kcur, _state); - } - k = kdt->kcur; - for(i=0; i<=k-1; i++) - { - tags->ptr.p_int[i] = kdt->tags.ptr.p_int[kdt->idx.ptr.p_int[i]]; - } -} - - -/************************************************************************* -Distances from last query - -INPUT PARAMETERS - KDT - KD-tree - R - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - R - filled with distances (in corresponding norm) - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsTags() tag values - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsdistances(kdtree* kdt, - /* Real */ ae_vector* r, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - - if( kdt->kcur==0 ) - { - return; - } - if( r->cntkcur ) - { - ae_vector_set_length(r, kdt->kcur, _state); - } - k = kdt->kcur; - - /* - * unload norms - * - * Abs() call is used to handle cases with negative norms - * (generated during KFN requests) - */ - if( kdt->normtype==0 ) - { - for(i=0; i<=k-1; i++) - { - r->ptr.p_double[i] = ae_fabs(kdt->r.ptr.p_double[i], _state); - } - } - if( kdt->normtype==1 ) - { - for(i=0; i<=k-1; i++) - { - r->ptr.p_double[i] = ae_fabs(kdt->r.ptr.p_double[i], _state); - } - } - if( kdt->normtype==2 ) - { - for(i=0; i<=k-1; i++) - { - r->ptr.p_double[i] = ae_sqrt(ae_fabs(kdt->r.ptr.p_double[i], _state), _state); - } - } -} - - -/************************************************************************* -X-values from last query; 'interactive' variant for languages like Python -which support constructs like "X = KDTreeQueryResultsXI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxi(kdtree* kdt, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - - ae_matrix_clear(x); - - kdtreequeryresultsx(kdt, x, _state); -} - - -/************************************************************************* -XY-values from last query; 'interactive' variant for languages like Python -which support constructs like "XY = KDTreeQueryResultsXYI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxyi(kdtree* kdt, - /* Real */ ae_matrix* xy, - ae_state *_state) -{ - - ae_matrix_clear(xy); - - kdtreequeryresultsxy(kdt, xy, _state); -} - - -/************************************************************************* -Tags from last query; 'interactive' variant for languages like Python -which support constructs like "Tags = KDTreeQueryResultsTagsI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultstagsi(kdtree* kdt, - /* Integer */ ae_vector* tags, - ae_state *_state) -{ - - ae_vector_clear(tags); - - kdtreequeryresultstags(kdt, tags, _state); -} - - -/************************************************************************* -Distances from last query; 'interactive' variant for languages like Python -which support constructs like "R = KDTreeQueryResultsDistancesI(KDT)" -and interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsdistancesi(kdtree* kdt, - /* Real */ ae_vector* r, - ae_state *_state) -{ - - ae_vector_clear(r); - - kdtreequeryresultsdistances(kdt, r, _state); -} - - -/************************************************************************* -Serializer: allocation - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void kdtreealloc(ae_serializer* s, kdtree* tree, ae_state *_state) -{ - - - - /* - * Header - */ - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - - /* - * Data - */ - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - allocrealmatrix(s, &tree->xy, -1, -1, _state); - allocintegerarray(s, &tree->tags, -1, _state); - allocrealarray(s, &tree->boxmin, -1, _state); - allocrealarray(s, &tree->boxmax, -1, _state); - allocintegerarray(s, &tree->nodes, -1, _state); - allocrealarray(s, &tree->splits, -1, _state); -} - - -/************************************************************************* -Serializer: serialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void kdtreeserialize(ae_serializer* s, kdtree* tree, ae_state *_state) -{ - - - - /* - * Header - */ - ae_serializer_serialize_int(s, getkdtreeserializationcode(_state), _state); - ae_serializer_serialize_int(s, nearestneighbor_kdtreefirstversion, _state); - - /* - * Data - */ - ae_serializer_serialize_int(s, tree->n, _state); - ae_serializer_serialize_int(s, tree->nx, _state); - ae_serializer_serialize_int(s, tree->ny, _state); - ae_serializer_serialize_int(s, tree->normtype, _state); - serializerealmatrix(s, &tree->xy, -1, -1, _state); - serializeintegerarray(s, &tree->tags, -1, _state); - serializerealarray(s, &tree->boxmin, -1, _state); - serializerealarray(s, &tree->boxmax, -1, _state); - serializeintegerarray(s, &tree->nodes, -1, _state); - serializerealarray(s, &tree->splits, -1, _state); -} - - -/************************************************************************* -Serializer: unserialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void kdtreeunserialize(ae_serializer* s, kdtree* tree, ae_state *_state) -{ - ae_int_t i0; - ae_int_t i1; - - _kdtree_clear(tree); - - - /* - * check correctness of header - */ - ae_serializer_unserialize_int(s, &i0, _state); - ae_assert(i0==getkdtreeserializationcode(_state), "KDTreeUnserialize: stream header corrupted", _state); - ae_serializer_unserialize_int(s, &i1, _state); - ae_assert(i1==nearestneighbor_kdtreefirstversion, "KDTreeUnserialize: stream header corrupted", _state); - - /* - * Unserialize data - */ - ae_serializer_unserialize_int(s, &tree->n, _state); - ae_serializer_unserialize_int(s, &tree->nx, _state); - ae_serializer_unserialize_int(s, &tree->ny, _state); - ae_serializer_unserialize_int(s, &tree->normtype, _state); - unserializerealmatrix(s, &tree->xy, _state); - unserializeintegerarray(s, &tree->tags, _state); - unserializerealarray(s, &tree->boxmin, _state); - unserializerealarray(s, &tree->boxmax, _state); - unserializeintegerarray(s, &tree->nodes, _state); - unserializerealarray(s, &tree->splits, _state); - nearestneighbor_kdtreealloctemporaries(tree, tree->n, tree->nx, tree->ny, _state); -} - - -/************************************************************************* -Rearranges nodes [I1,I2) using partition in D-th dimension with S as threshold. -Returns split position I3: [I1,I3) and [I3,I2) are created as result. - -This subroutine doesn't create tree structures, just rearranges nodes. -*************************************************************************/ -static void nearestneighbor_kdtreesplit(kdtree* kdt, - ae_int_t i1, - ae_int_t i2, - ae_int_t d, - double s, - ae_int_t* i3, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t ileft; - ae_int_t iright; - double v; - - *i3 = 0; - - ae_assert(kdt->n>0, "KDTreeSplit: internal error", _state); - - /* - * split XY/Tags in two parts: - * * [ILeft,IRight] is non-processed part of XY/Tags - * - * After cycle is done, we have Ileft=IRight. We deal with - * this element separately. - * - * After this, [I1,ILeft) contains left part, and [ILeft,I2) - * contains right part. - */ - ileft = i1; - iright = i2-1; - while(ileftxy.ptr.pp_double[ileft][d],s) ) - { - - /* - * XY[ILeft] is on its place. - * Advance ILeft. - */ - ileft = ileft+1; - } - else - { - - /* - * XY[ILeft,..] must be at IRight. - * Swap and advance IRight. - */ - for(i=0; i<=2*kdt->nx+kdt->ny-1; i++) - { - v = kdt->xy.ptr.pp_double[ileft][i]; - kdt->xy.ptr.pp_double[ileft][i] = kdt->xy.ptr.pp_double[iright][i]; - kdt->xy.ptr.pp_double[iright][i] = v; - } - j = kdt->tags.ptr.p_int[ileft]; - kdt->tags.ptr.p_int[ileft] = kdt->tags.ptr.p_int[iright]; - kdt->tags.ptr.p_int[iright] = j; - iright = iright-1; - } - } - if( ae_fp_less_eq(kdt->xy.ptr.pp_double[ileft][d],s) ) - { - ileft = ileft+1; - } - else - { - iright = iright-1; - } - *i3 = ileft; -} - - -/************************************************************************* -Recursive kd-tree generation subroutine. - -PARAMETERS - KDT tree - NodesOffs unused part of Nodes[] which must be filled by tree - SplitsOffs unused part of Splits[] - I1, I2 points from [I1,I2) are processed - -NodesOffs[] and SplitsOffs[] must be large enough. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -static void nearestneighbor_kdtreegeneratetreerec(kdtree* kdt, - ae_int_t* nodesoffs, - ae_int_t* splitsoffs, - ae_int_t i1, - ae_int_t i2, - ae_int_t maxleafsize, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nx; - ae_int_t ny; - ae_int_t i; - ae_int_t j; - ae_int_t oldoffs; - ae_int_t i3; - ae_int_t cntless; - ae_int_t cntgreater; - double minv; - double maxv; - ae_int_t minidx; - ae_int_t maxidx; - ae_int_t d; - double ds; - double s; - double v; - double v0; - double v1; - - - ae_assert(kdt->n>0, "KDTreeGenerateTreeRec: internal error", _state); - ae_assert(i2>i1, "KDTreeGenerateTreeRec: internal error", _state); - - /* - * Generate leaf if needed - */ - if( i2-i1<=maxleafsize ) - { - kdt->nodes.ptr.p_int[*nodesoffs+0] = i2-i1; - kdt->nodes.ptr.p_int[*nodesoffs+1] = i1; - *nodesoffs = *nodesoffs+2; - return; - } - - /* - * Load values for easier access - */ - nx = kdt->nx; - ny = kdt->ny; - - /* - * Select dimension to split: - * * D is a dimension number - * In case bounding box has zero size, we enforce creation of the leaf node. - */ - d = 0; - ds = kdt->curboxmax.ptr.p_double[0]-kdt->curboxmin.ptr.p_double[0]; - for(i=1; i<=nx-1; i++) - { - v = kdt->curboxmax.ptr.p_double[i]-kdt->curboxmin.ptr.p_double[i]; - if( ae_fp_greater(v,ds) ) - { - ds = v; - d = i; - } - } - if( ae_fp_eq(ds,0) ) - { - kdt->nodes.ptr.p_int[*nodesoffs+0] = i2-i1; - kdt->nodes.ptr.p_int[*nodesoffs+1] = i1; - *nodesoffs = *nodesoffs+2; - return; - } - - /* - * Select split position S using sliding midpoint rule, - * rearrange points into [I1,I3) and [I3,I2). - * - * In case all points has same value of D-th component - * (MinV=MaxV) we enforce D-th dimension of bounding - * box to become exactly zero and repeat tree construction. - */ - s = kdt->curboxmin.ptr.p_double[d]+0.5*ds; - ae_v_move(&kdt->buf.ptr.p_double[0], 1, &kdt->xy.ptr.pp_double[i1][d], kdt->xy.stride, ae_v_len(0,i2-i1-1)); - n = i2-i1; - cntless = 0; - cntgreater = 0; - minv = kdt->buf.ptr.p_double[0]; - maxv = kdt->buf.ptr.p_double[0]; - minidx = i1; - maxidx = i1; - for(i=0; i<=n-1; i++) - { - v = kdt->buf.ptr.p_double[i]; - if( ae_fp_less(v,minv) ) - { - minv = v; - minidx = i1+i; - } - if( ae_fp_greater(v,maxv) ) - { - maxv = v; - maxidx = i1+i; - } - if( ae_fp_less(v,s) ) - { - cntless = cntless+1; - } - if( ae_fp_greater(v,s) ) - { - cntgreater = cntgreater+1; - } - } - if( ae_fp_eq(minv,maxv) ) - { - - /* - * In case all points has same value of D-th component - * (MinV=MaxV) we enforce D-th dimension of bounding - * box to become exactly zero and repeat tree construction. - */ - v0 = kdt->curboxmin.ptr.p_double[d]; - v1 = kdt->curboxmax.ptr.p_double[d]; - kdt->curboxmin.ptr.p_double[d] = minv; - kdt->curboxmax.ptr.p_double[d] = maxv; - nearestneighbor_kdtreegeneratetreerec(kdt, nodesoffs, splitsoffs, i1, i2, maxleafsize, _state); - kdt->curboxmin.ptr.p_double[d] = v0; - kdt->curboxmax.ptr.p_double[d] = v1; - return; - } - if( cntless>0&&cntgreater>0 ) - { - - /* - * normal midpoint split - */ - nearestneighbor_kdtreesplit(kdt, i1, i2, d, s, &i3, _state); - } - else - { - - /* - * sliding midpoint - */ - if( cntless==0 ) - { - - /* - * 1. move split to MinV, - * 2. place one point to the left bin (move to I1), - * others - to the right bin - */ - s = minv; - if( minidx!=i1 ) - { - for(i=0; i<=2*nx+ny-1; i++) - { - v = kdt->xy.ptr.pp_double[minidx][i]; - kdt->xy.ptr.pp_double[minidx][i] = kdt->xy.ptr.pp_double[i1][i]; - kdt->xy.ptr.pp_double[i1][i] = v; - } - j = kdt->tags.ptr.p_int[minidx]; - kdt->tags.ptr.p_int[minidx] = kdt->tags.ptr.p_int[i1]; - kdt->tags.ptr.p_int[i1] = j; - } - i3 = i1+1; - } - else - { - - /* - * 1. move split to MaxV, - * 2. place one point to the right bin (move to I2-1), - * others - to the left bin - */ - s = maxv; - if( maxidx!=i2-1 ) - { - for(i=0; i<=2*nx+ny-1; i++) - { - v = kdt->xy.ptr.pp_double[maxidx][i]; - kdt->xy.ptr.pp_double[maxidx][i] = kdt->xy.ptr.pp_double[i2-1][i]; - kdt->xy.ptr.pp_double[i2-1][i] = v; - } - j = kdt->tags.ptr.p_int[maxidx]; - kdt->tags.ptr.p_int[maxidx] = kdt->tags.ptr.p_int[i2-1]; - kdt->tags.ptr.p_int[i2-1] = j; - } - i3 = i2-1; - } - } - - /* - * Generate 'split' node - */ - kdt->nodes.ptr.p_int[*nodesoffs+0] = 0; - kdt->nodes.ptr.p_int[*nodesoffs+1] = d; - kdt->nodes.ptr.p_int[*nodesoffs+2] = *splitsoffs; - kdt->splits.ptr.p_double[*splitsoffs+0] = s; - oldoffs = *nodesoffs; - *nodesoffs = *nodesoffs+nearestneighbor_splitnodesize; - *splitsoffs = *splitsoffs+1; - - /* - * Recirsive generation: - * * update CurBox - * * call subroutine - * * restore CurBox - */ - kdt->nodes.ptr.p_int[oldoffs+3] = *nodesoffs; - v = kdt->curboxmax.ptr.p_double[d]; - kdt->curboxmax.ptr.p_double[d] = s; - nearestneighbor_kdtreegeneratetreerec(kdt, nodesoffs, splitsoffs, i1, i3, maxleafsize, _state); - kdt->curboxmax.ptr.p_double[d] = v; - kdt->nodes.ptr.p_int[oldoffs+4] = *nodesoffs; - v = kdt->curboxmin.ptr.p_double[d]; - kdt->curboxmin.ptr.p_double[d] = s; - nearestneighbor_kdtreegeneratetreerec(kdt, nodesoffs, splitsoffs, i3, i2, maxleafsize, _state); - kdt->curboxmin.ptr.p_double[d] = v; -} - - -/************************************************************************* -Recursive subroutine for NN queries. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -static void nearestneighbor_kdtreequerynnrec(kdtree* kdt, - ae_int_t offs, - ae_state *_state) -{ - double ptdist; - ae_int_t i; - ae_int_t j; - ae_int_t nx; - ae_int_t i1; - ae_int_t i2; - ae_int_t d; - double s; - double v; - double t1; - ae_int_t childbestoffs; - ae_int_t childworstoffs; - ae_int_t childoffs; - double prevdist; - ae_bool todive; - ae_bool bestisleft; - ae_bool updatemin; - - - ae_assert(kdt->n>0, "KDTreeQueryNNRec: internal error", _state); - - /* - * Leaf node. - * Process points. - */ - if( kdt->nodes.ptr.p_int[offs]>0 ) - { - i1 = kdt->nodes.ptr.p_int[offs+1]; - i2 = i1+kdt->nodes.ptr.p_int[offs]; - for(i=i1; i<=i2-1; i++) - { - - /* - * Calculate distance - */ - ptdist = 0; - nx = kdt->nx; - if( kdt->normtype==0 ) - { - for(j=0; j<=nx-1; j++) - { - ptdist = ae_maxreal(ptdist, ae_fabs(kdt->xy.ptr.pp_double[i][j]-kdt->x.ptr.p_double[j], _state), _state); - } - } - if( kdt->normtype==1 ) - { - for(j=0; j<=nx-1; j++) - { - ptdist = ptdist+ae_fabs(kdt->xy.ptr.pp_double[i][j]-kdt->x.ptr.p_double[j], _state); - } - } - if( kdt->normtype==2 ) - { - for(j=0; j<=nx-1; j++) - { - ptdist = ptdist+ae_sqr(kdt->xy.ptr.pp_double[i][j]-kdt->x.ptr.p_double[j], _state); - } - } - - /* - * Skip points with zero distance if self-matches are turned off - */ - if( ae_fp_eq(ptdist,0)&&!kdt->selfmatch ) - { - continue; - } - - /* - * We CAN'T process point if R-criterion isn't satisfied, - * i.e. (RNeeded<>0) AND (PtDist>R). - */ - if( ae_fp_eq(kdt->rneeded,0)||ae_fp_less_eq(ptdist,kdt->rneeded) ) - { - - /* - * R-criterion is satisfied, we must either: - * * replace worst point, if (KNeeded<>0) AND (KCur=KNeeded) - * (or skip, if worst point is better) - * * add point without replacement otherwise - */ - if( kdt->kcurkneeded||kdt->kneeded==0 ) - { - - /* - * add current point to heap without replacement - */ - tagheappushi(&kdt->r, &kdt->idx, &kdt->kcur, ptdist, i, _state); - } - else - { - - /* - * New points are added or not, depending on their distance. - * If added, they replace element at the top of the heap - */ - if( ae_fp_less(ptdist,kdt->r.ptr.p_double[0]) ) - { - if( kdt->kneeded==1 ) - { - kdt->idx.ptr.p_int[0] = i; - kdt->r.ptr.p_double[0] = ptdist; - } - else - { - tagheapreplacetopi(&kdt->r, &kdt->idx, kdt->kneeded, ptdist, i, _state); - } - } - } - } - } - return; - } - - /* - * Simple split - */ - if( kdt->nodes.ptr.p_int[offs]==0 ) - { - - /* - * Load: - * * D dimension to split - * * S split position - */ - d = kdt->nodes.ptr.p_int[offs+1]; - s = kdt->splits.ptr.p_double[kdt->nodes.ptr.p_int[offs+2]]; - - /* - * Calculate: - * * ChildBestOffs child box with best chances - * * ChildWorstOffs child box with worst chances - */ - if( ae_fp_less_eq(kdt->x.ptr.p_double[d],s) ) - { - childbestoffs = kdt->nodes.ptr.p_int[offs+3]; - childworstoffs = kdt->nodes.ptr.p_int[offs+4]; - bestisleft = ae_true; - } - else - { - childbestoffs = kdt->nodes.ptr.p_int[offs+4]; - childworstoffs = kdt->nodes.ptr.p_int[offs+3]; - bestisleft = ae_false; - } - - /* - * Navigate through childs - */ - for(i=0; i<=1; i++) - { - - /* - * Select child to process: - * * ChildOffs current child offset in Nodes[] - * * UpdateMin whether minimum or maximum value - * of bounding box is changed on update - */ - if( i==0 ) - { - childoffs = childbestoffs; - updatemin = !bestisleft; - } - else - { - updatemin = bestisleft; - childoffs = childworstoffs; - } - - /* - * Update bounding box and current distance - */ - if( updatemin ) - { - prevdist = kdt->curdist; - t1 = kdt->x.ptr.p_double[d]; - v = kdt->curboxmin.ptr.p_double[d]; - if( ae_fp_less_eq(t1,s) ) - { - if( kdt->normtype==0 ) - { - kdt->curdist = ae_maxreal(kdt->curdist, s-t1, _state); - } - if( kdt->normtype==1 ) - { - kdt->curdist = kdt->curdist-ae_maxreal(v-t1, 0, _state)+s-t1; - } - if( kdt->normtype==2 ) - { - kdt->curdist = kdt->curdist-ae_sqr(ae_maxreal(v-t1, 0, _state), _state)+ae_sqr(s-t1, _state); - } - } - kdt->curboxmin.ptr.p_double[d] = s; - } - else - { - prevdist = kdt->curdist; - t1 = kdt->x.ptr.p_double[d]; - v = kdt->curboxmax.ptr.p_double[d]; - if( ae_fp_greater_eq(t1,s) ) - { - if( kdt->normtype==0 ) - { - kdt->curdist = ae_maxreal(kdt->curdist, t1-s, _state); - } - if( kdt->normtype==1 ) - { - kdt->curdist = kdt->curdist-ae_maxreal(t1-v, 0, _state)+t1-s; - } - if( kdt->normtype==2 ) - { - kdt->curdist = kdt->curdist-ae_sqr(ae_maxreal(t1-v, 0, _state), _state)+ae_sqr(t1-s, _state); - } - } - kdt->curboxmax.ptr.p_double[d] = s; - } - - /* - * Decide: to dive into cell or not to dive - */ - if( ae_fp_neq(kdt->rneeded,0)&&ae_fp_greater(kdt->curdist,kdt->rneeded) ) - { - todive = ae_false; - } - else - { - if( kdt->kcurkneeded||kdt->kneeded==0 ) - { - - /* - * KCurcurdist,kdt->r.ptr.p_double[0]*kdt->approxf); - } - } - if( todive ) - { - nearestneighbor_kdtreequerynnrec(kdt, childoffs, _state); - } - - /* - * Restore bounding box and distance - */ - if( updatemin ) - { - kdt->curboxmin.ptr.p_double[d] = v; - } - else - { - kdt->curboxmax.ptr.p_double[d] = v; - } - kdt->curdist = prevdist; - } - return; - } -} - - -/************************************************************************* -Copies X[] to KDT.X[] -Loads distance from X[] to bounding box. -Initializes CurBox[]. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -static void nearestneighbor_kdtreeinitbox(kdtree* kdt, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t i; - double vx; - double vmin; - double vmax; - - - ae_assert(kdt->n>0, "KDTreeInitBox: internal error", _state); - - /* - * calculate distance from point to current bounding box - */ - kdt->curdist = 0; - if( kdt->normtype==0 ) - { - for(i=0; i<=kdt->nx-1; i++) - { - vx = x->ptr.p_double[i]; - vmin = kdt->boxmin.ptr.p_double[i]; - vmax = kdt->boxmax.ptr.p_double[i]; - kdt->x.ptr.p_double[i] = vx; - kdt->curboxmin.ptr.p_double[i] = vmin; - kdt->curboxmax.ptr.p_double[i] = vmax; - if( ae_fp_less(vx,vmin) ) - { - kdt->curdist = ae_maxreal(kdt->curdist, vmin-vx, _state); - } - else - { - if( ae_fp_greater(vx,vmax) ) - { - kdt->curdist = ae_maxreal(kdt->curdist, vx-vmax, _state); - } - } - } - } - if( kdt->normtype==1 ) - { - for(i=0; i<=kdt->nx-1; i++) - { - vx = x->ptr.p_double[i]; - vmin = kdt->boxmin.ptr.p_double[i]; - vmax = kdt->boxmax.ptr.p_double[i]; - kdt->x.ptr.p_double[i] = vx; - kdt->curboxmin.ptr.p_double[i] = vmin; - kdt->curboxmax.ptr.p_double[i] = vmax; - if( ae_fp_less(vx,vmin) ) - { - kdt->curdist = kdt->curdist+vmin-vx; - } - else - { - if( ae_fp_greater(vx,vmax) ) - { - kdt->curdist = kdt->curdist+vx-vmax; - } - } - } - } - if( kdt->normtype==2 ) - { - for(i=0; i<=kdt->nx-1; i++) - { - vx = x->ptr.p_double[i]; - vmin = kdt->boxmin.ptr.p_double[i]; - vmax = kdt->boxmax.ptr.p_double[i]; - kdt->x.ptr.p_double[i] = vx; - kdt->curboxmin.ptr.p_double[i] = vmin; - kdt->curboxmax.ptr.p_double[i] = vmax; - if( ae_fp_less(vx,vmin) ) - { - kdt->curdist = kdt->curdist+ae_sqr(vmin-vx, _state); - } - else - { - if( ae_fp_greater(vx,vmax) ) - { - kdt->curdist = kdt->curdist+ae_sqr(vx-vmax, _state); - } - } - } - } -} - - -/************************************************************************* -This function allocates all dataset-independent array fields of KDTree, -i.e. such array fields that their dimensions do not depend on dataset -size. - -This function do not sets KDT.NX or KDT.NY - it just allocates arrays - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -static void nearestneighbor_kdtreeallocdatasetindependent(kdtree* kdt, - ae_int_t nx, - ae_int_t ny, - ae_state *_state) -{ - - - ae_assert(kdt->n>0, "KDTreeAllocDatasetIndependent: internal error", _state); - ae_vector_set_length(&kdt->x, nx, _state); - ae_vector_set_length(&kdt->boxmin, nx, _state); - ae_vector_set_length(&kdt->boxmax, nx, _state); - ae_vector_set_length(&kdt->curboxmin, nx, _state); - ae_vector_set_length(&kdt->curboxmax, nx, _state); -} - - -/************************************************************************* -This function allocates all dataset-dependent array fields of KDTree, i.e. -such array fields that their dimensions depend on dataset size. - -This function do not sets KDT.N, KDT.NX or KDT.NY - -it just allocates arrays. - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -static void nearestneighbor_kdtreeallocdatasetdependent(kdtree* kdt, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_state *_state) -{ - - - ae_assert(n>0, "KDTreeAllocDatasetDependent: internal error", _state); - ae_matrix_set_length(&kdt->xy, n, 2*nx+ny, _state); - ae_vector_set_length(&kdt->tags, n, _state); - ae_vector_set_length(&kdt->idx, n, _state); - ae_vector_set_length(&kdt->r, n, _state); - ae_vector_set_length(&kdt->x, nx, _state); - ae_vector_set_length(&kdt->buf, ae_maxint(n, nx, _state), _state); - ae_vector_set_length(&kdt->nodes, nearestneighbor_splitnodesize*2*n, _state); - ae_vector_set_length(&kdt->splits, 2*n, _state); -} - - -/************************************************************************* -This function allocates temporaries. - -This function do not sets KDT.N, KDT.NX or KDT.NY - -it just allocates arrays. - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -static void nearestneighbor_kdtreealloctemporaries(kdtree* kdt, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_state *_state) -{ - - - ae_assert(n>0, "KDTreeAllocTemporaries: internal error", _state); - ae_vector_set_length(&kdt->x, nx, _state); - ae_vector_set_length(&kdt->idx, n, _state); - ae_vector_set_length(&kdt->r, n, _state); - ae_vector_set_length(&kdt->buf, ae_maxint(n, nx, _state), _state); - ae_vector_set_length(&kdt->curboxmin, nx, _state); - ae_vector_set_length(&kdt->curboxmax, nx, _state); -} - - -ae_bool _kdtree_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - kdtree *p = (kdtree*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->xy, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tags, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->boxmin, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->boxmax, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->nodes, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->splits, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->idx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->r, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->buf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->curboxmin, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->curboxmax, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _kdtree_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - kdtree *dst = (kdtree*)_dst; - kdtree *src = (kdtree*)_src; - dst->n = src->n; - dst->nx = src->nx; - dst->ny = src->ny; - dst->normtype = src->normtype; - if( !ae_matrix_init_copy(&dst->xy, &src->xy, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tags, &src->tags, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->boxmin, &src->boxmin, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->boxmax, &src->boxmax, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->nodes, &src->nodes, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->splits, &src->splits, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->kneeded = src->kneeded; - dst->rneeded = src->rneeded; - dst->selfmatch = src->selfmatch; - dst->approxf = src->approxf; - dst->kcur = src->kcur; - if( !ae_vector_init_copy(&dst->idx, &src->idx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->r, &src->r, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->buf, &src->buf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->curboxmin, &src->curboxmin, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->curboxmax, &src->curboxmax, _state, make_automatic) ) - return ae_false; - dst->curdist = src->curdist; - dst->debugcounter = src->debugcounter; - return ae_true; -} - - -void _kdtree_clear(void* _p) -{ - kdtree *p = (kdtree*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->xy); - ae_vector_clear(&p->tags); - ae_vector_clear(&p->boxmin); - ae_vector_clear(&p->boxmax); - ae_vector_clear(&p->nodes); - ae_vector_clear(&p->splits); - ae_vector_clear(&p->x); - ae_vector_clear(&p->idx); - ae_vector_clear(&p->r); - ae_vector_clear(&p->buf); - ae_vector_clear(&p->curboxmin); - ae_vector_clear(&p->curboxmax); -} - - -void _kdtree_destroy(void* _p) -{ - kdtree *p = (kdtree*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->xy); - ae_vector_destroy(&p->tags); - ae_vector_destroy(&p->boxmin); - ae_vector_destroy(&p->boxmax); - ae_vector_destroy(&p->nodes); - ae_vector_destroy(&p->splits); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->idx); - ae_vector_destroy(&p->r); - ae_vector_destroy(&p->buf); - ae_vector_destroy(&p->curboxmin); - ae_vector_destroy(&p->curboxmax); -} - - - -} - diff --git a/src/inc/alglib/alglibmisc.h b/src/inc/alglib/alglibmisc.h deleted file mode 100644 index 8209ac6..0000000 --- a/src/inc/alglib/alglibmisc.h +++ /dev/null @@ -1,769 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _alglibmisc_pkg_h -#define _alglibmisc_pkg_h -#include "ap.h" -#include "alglibinternal.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t magicv; -} hqrndstate; -typedef struct -{ - ae_int_t n; - ae_int_t nx; - ae_int_t ny; - ae_int_t normtype; - ae_matrix xy; - ae_vector tags; - ae_vector boxmin; - ae_vector boxmax; - ae_vector nodes; - ae_vector splits; - ae_vector x; - ae_int_t kneeded; - double rneeded; - ae_bool selfmatch; - double approxf; - ae_int_t kcur; - ae_vector idx; - ae_vector r; - ae_vector buf; - ae_vector curboxmin; - ae_vector curboxmax; - double curdist; - ae_int_t debugcounter; -} kdtree; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - -/************************************************************************* -Portable high quality random number generator state. -Initialized with HQRNDRandomize() or HQRNDSeed(). - -Fields: - S1, S2 - seed values - V - precomputed value - MagicV - 'magic' value used to determine whether State structure - was correctly initialized. -*************************************************************************/ -class _hqrndstate_owner -{ -public: - _hqrndstate_owner(); - _hqrndstate_owner(const _hqrndstate_owner &rhs); - _hqrndstate_owner& operator=(const _hqrndstate_owner &rhs); - virtual ~_hqrndstate_owner(); - alglib_impl::hqrndstate* c_ptr(); - alglib_impl::hqrndstate* c_ptr() const; -protected: - alglib_impl::hqrndstate *p_struct; -}; -class hqrndstate : public _hqrndstate_owner -{ -public: - hqrndstate(); - hqrndstate(const hqrndstate &rhs); - hqrndstate& operator=(const hqrndstate &rhs); - virtual ~hqrndstate(); - -}; - -/************************************************************************* - -*************************************************************************/ -class _kdtree_owner -{ -public: - _kdtree_owner(); - _kdtree_owner(const _kdtree_owner &rhs); - _kdtree_owner& operator=(const _kdtree_owner &rhs); - virtual ~_kdtree_owner(); - alglib_impl::kdtree* c_ptr(); - alglib_impl::kdtree* c_ptr() const; -protected: - alglib_impl::kdtree *p_struct; -}; -class kdtree : public _kdtree_owner -{ -public: - kdtree(); - kdtree(const kdtree &rhs); - kdtree& operator=(const kdtree &rhs); - virtual ~kdtree(); - -}; - -/************************************************************************* -HQRNDState initialization with random values which come from standard -RNG. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndrandomize(hqrndstate &state); - - -/************************************************************************* -HQRNDState initialization with seed values - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndseed(const ae_int_t s1, const ae_int_t s2, hqrndstate &state); - - -/************************************************************************* -This function generates random real number in (0,1), -not including interval boundaries - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double hqrnduniformr(const hqrndstate &state); - - -/************************************************************************* -This function generates random integer number in [0, N) - -1. State structure must be initialized with HQRNDRandomize() or HQRNDSeed() -2. N can be any positive number except for very large numbers: - * close to 2^31 on 32-bit systems - * close to 2^62 on 64-bit systems - An exception will be generated if N is too large. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -ae_int_t hqrnduniformi(const hqrndstate &state, const ae_int_t n); - - -/************************************************************************* -Random number generator: normal numbers - -This function generates one random number from normal distribution. -Its performance is equal to that of HQRNDNormal2() - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double hqrndnormal(const hqrndstate &state); - - -/************************************************************************* -Random number generator: random X and Y such that X^2+Y^2=1 - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndunit2(const hqrndstate &state, double &x, double &y); - - -/************************************************************************* -Random number generator: normal numbers - -This function generates two independent random numbers from normal -distribution. Its performance is equal to that of HQRNDNormal() - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void hqrndnormal2(const hqrndstate &state, double &x1, double &x2); - - -/************************************************************************* -Random number generator: exponential distribution - -State structure must be initialized with HQRNDRandomize() or HQRNDSeed(). - - -- ALGLIB -- - Copyright 11.08.2007 by Bochkanov Sergey -*************************************************************************/ -double hqrndexponential(const hqrndstate &state, const double lambdav); - - -/************************************************************************* -This function generates random number from discrete distribution given by -finite sample X. - -INPUT PARAMETERS - State - high quality random number generator, must be - initialized with HQRNDRandomize() or HQRNDSeed(). - X - finite sample - N - number of elements to use, N>=1 - -RESULT - this function returns one of the X[i] for random i=0..N-1 - - -- ALGLIB -- - Copyright 08.11.2011 by Bochkanov Sergey -*************************************************************************/ -double hqrnddiscrete(const hqrndstate &state, const real_1d_array &x, const ae_int_t n); - - -/************************************************************************* -This function generates random number from continuous distribution given -by finite sample X. - -INPUT PARAMETERS - State - high quality random number generator, must be - initialized with HQRNDRandomize() or HQRNDSeed(). - X - finite sample, array[N] (can be larger, in this case only - leading N elements are used). THIS ARRAY MUST BE SORTED BY - ASCENDING. - N - number of elements to use, N>=1 - -RESULT - this function returns random number from continuous distribution which - tries to approximate X as mush as possible. min(X)<=Result<=max(X). - - -- ALGLIB -- - Copyright 08.11.2011 by Bochkanov Sergey -*************************************************************************/ -double hqrndcontinuous(const hqrndstate &state, const real_1d_array &x, const ae_int_t n); - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void kdtreeserialize(kdtree &obj, std::string &s_out); - - -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void kdtreeunserialize(std::string &s_in, kdtree &obj); - - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values and optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - N - number of points, N>=0. - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuild(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt); -void kdtreebuild(const real_2d_array &xy, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt); - - -/************************************************************************* -KD-tree creation - -This subroutine creates KD-tree from set of X-values, integer tags and -optional Y-values - -INPUT PARAMETERS - XY - dataset, array[0..N-1,0..NX+NY-1]. - one row corresponds to one point. - first NX columns contain X-values, next NY (NY may be zero) - columns may contain associated Y-values - Tags - tags, array[0..N-1], contains integer tags associated - with points. - N - number of points, N>=0 - NX - space dimension, NX>=1. - NY - number of optional Y-values, NY>=0. - NormType- norm type: - * 0 denotes infinity-norm - * 1 denotes 1-norm - * 2 denotes 2-norm (Euclidean norm) - -OUTPUT PARAMETERS - KDT - KD-tree - -NOTES - -1. KD-tree creation have O(N*logN) complexity and O(N*(2*NX+NY)) memory - requirements. -2. Although KD-trees may be used with any combination of N and NX, they - are more efficient than brute-force search only when N >> 4^NX. So they - are most useful in low-dimensional tasks (NX=2, NX=3). NX=1 is another - inefficient case, because simple binary search (without additional - structures) is much more efficient in such tasks than KD-trees. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreebuildtagged(const real_2d_array &xy, const integer_1d_array &tags, const ae_int_t n, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt); -void kdtreebuildtagged(const real_2d_array &xy, const integer_1d_array &tags, const ae_int_t nx, const ae_int_t ny, const ae_int_t normtype, kdtree &kdt); - - -/************************************************************************* -K-NN query: K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of actual neighbors found (either K or N, if K>N). - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch); -ae_int_t kdtreequeryknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k); - - -/************************************************************************* -R-NN query: all points within R-sphere centered at X - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - R - radius of sphere (in corresponding norm), R>0 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - -RESULT - number of neighbors found, >=0 - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -actual results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r, const bool selfmatch); -ae_int_t kdtreequeryrnn(const kdtree &kdt, const real_1d_array &x, const double r); - - -/************************************************************************* -K-NN query: approximate K nearest neighbors - -INPUT PARAMETERS - KDT - KD-tree - X - point, array[0..NX-1]. - K - number of neighbors to return, K>=1 - SelfMatch - whether self-matches are allowed: - * if True, nearest neighbor may be the point itself - (if it exists in original dataset) - * if False, then only points with non-zero distance - are returned - * if not given, considered True - Eps - approximation factor, Eps>=0. eps-approximate nearest - neighbor is a neighbor whose distance from X is at - most (1+eps) times distance of true nearest neighbor. - -RESULT - number of actual neighbors found (either K or N, if K>N). - -NOTES - significant performance gain may be achieved only when Eps is is on - the order of magnitude of 1 or larger. - -This subroutine performs query and stores its result in the internal -structures of the KD-tree. You can use following subroutines to obtain -these results: -* KDTreeQueryResultsX() to get X-values -* KDTreeQueryResultsXY() to get X- and Y-values -* KDTreeQueryResultsTags() to get tag values -* KDTreeQueryResultsDistances() to get distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const bool selfmatch, const double eps); -ae_int_t kdtreequeryaknn(const kdtree &kdt, const real_1d_array &x, const ae_int_t k, const double eps); - - -/************************************************************************* -X-values from last query - -INPUT PARAMETERS - KDT - KD-tree - X - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - X - rows are filled with X-values - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsTags() tag values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsx(const kdtree &kdt, real_2d_array &x); - - -/************************************************************************* -X- and Y-values from last query - -INPUT PARAMETERS - KDT - KD-tree - XY - possibly pre-allocated buffer. If XY is too small to store - result, it is resized. If size(XY) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - XY - rows are filled with points: first NX columns with - X-values, next NY columns - with Y-values. - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsTags() tag values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxy(const kdtree &kdt, real_2d_array &xy); - - -/************************************************************************* -Tags from last query - -INPUT PARAMETERS - KDT - KD-tree - Tags - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - Tags - filled with tags associated with points, - or, when no tags were supplied, with zeros - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsDistances() distances - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultstags(const kdtree &kdt, integer_1d_array &tags); - - -/************************************************************************* -Distances from last query - -INPUT PARAMETERS - KDT - KD-tree - R - possibly pre-allocated buffer. If X is too small to store - result, it is resized. If size(X) is enough to store - result, it is left unchanged. - -OUTPUT PARAMETERS - R - filled with distances (in corresponding norm) - -NOTES -1. points are ordered by distance from the query point (first = closest) -2. if XY is larger than required to store result, only leading part will - be overwritten; trailing part will be left unchanged. So if on input - XY = [[A,B],[C,D]], and result is [1,2], then on exit we will get - XY = [[1,2],[C,D]]. This is done purposely to increase performance; if - you want function to resize array according to result size, use - function with same name and suffix 'I'. - -SEE ALSO -* KDTreeQueryResultsX() X-values -* KDTreeQueryResultsXY() X- and Y-values -* KDTreeQueryResultsTags() tag values - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsdistances(const kdtree &kdt, real_1d_array &r); - - -/************************************************************************* -X-values from last query; 'interactive' variant for languages like Python -which support constructs like "X = KDTreeQueryResultsXI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxi(const kdtree &kdt, real_2d_array &x); - - -/************************************************************************* -XY-values from last query; 'interactive' variant for languages like Python -which support constructs like "XY = KDTreeQueryResultsXYI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsxyi(const kdtree &kdt, real_2d_array &xy); - - -/************************************************************************* -Tags from last query; 'interactive' variant for languages like Python -which support constructs like "Tags = KDTreeQueryResultsTagsI(KDT)" and -interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultstagsi(const kdtree &kdt, integer_1d_array &tags); - - -/************************************************************************* -Distances from last query; 'interactive' variant for languages like Python -which support constructs like "R = KDTreeQueryResultsDistancesI(KDT)" -and interactive mode of interpreter. - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void kdtreequeryresultsdistancesi(const kdtree &kdt, real_1d_array &r); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void hqrndrandomize(hqrndstate* state, ae_state *_state); -void hqrndseed(ae_int_t s1, - ae_int_t s2, - hqrndstate* state, - ae_state *_state); -double hqrnduniformr(hqrndstate* state, ae_state *_state); -ae_int_t hqrnduniformi(hqrndstate* state, ae_int_t n, ae_state *_state); -double hqrndnormal(hqrndstate* state, ae_state *_state); -void hqrndunit2(hqrndstate* state, double* x, double* y, ae_state *_state); -void hqrndnormal2(hqrndstate* state, - double* x1, - double* x2, - ae_state *_state); -double hqrndexponential(hqrndstate* state, - double lambdav, - ae_state *_state); -double hqrnddiscrete(hqrndstate* state, - /* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -double hqrndcontinuous(hqrndstate* state, - /* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -ae_bool _hqrndstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _hqrndstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _hqrndstate_clear(void* _p); -void _hqrndstate_destroy(void* _p); -void kdtreebuild(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_int_t normtype, - kdtree* kdt, - ae_state *_state); -void kdtreebuildtagged(/* Real */ ae_matrix* xy, - /* Integer */ ae_vector* tags, - ae_int_t n, - ae_int_t nx, - ae_int_t ny, - ae_int_t normtype, - kdtree* kdt, - ae_state *_state); -ae_int_t kdtreequeryknn(kdtree* kdt, - /* Real */ ae_vector* x, - ae_int_t k, - ae_bool selfmatch, - ae_state *_state); -ae_int_t kdtreequeryrnn(kdtree* kdt, - /* Real */ ae_vector* x, - double r, - ae_bool selfmatch, - ae_state *_state); -ae_int_t kdtreequeryaknn(kdtree* kdt, - /* Real */ ae_vector* x, - ae_int_t k, - ae_bool selfmatch, - double eps, - ae_state *_state); -void kdtreequeryresultsx(kdtree* kdt, - /* Real */ ae_matrix* x, - ae_state *_state); -void kdtreequeryresultsxy(kdtree* kdt, - /* Real */ ae_matrix* xy, - ae_state *_state); -void kdtreequeryresultstags(kdtree* kdt, - /* Integer */ ae_vector* tags, - ae_state *_state); -void kdtreequeryresultsdistances(kdtree* kdt, - /* Real */ ae_vector* r, - ae_state *_state); -void kdtreequeryresultsxi(kdtree* kdt, - /* Real */ ae_matrix* x, - ae_state *_state); -void kdtreequeryresultsxyi(kdtree* kdt, - /* Real */ ae_matrix* xy, - ae_state *_state); -void kdtreequeryresultstagsi(kdtree* kdt, - /* Integer */ ae_vector* tags, - ae_state *_state); -void kdtreequeryresultsdistancesi(kdtree* kdt, - /* Real */ ae_vector* r, - ae_state *_state); -void kdtreealloc(ae_serializer* s, kdtree* tree, ae_state *_state); -void kdtreeserialize(ae_serializer* s, kdtree* tree, ae_state *_state); -void kdtreeunserialize(ae_serializer* s, kdtree* tree, ae_state *_state); -ae_bool _kdtree_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _kdtree_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _kdtree_clear(void* _p); -void _kdtree_destroy(void* _p); - -} -#endif - diff --git a/src/inc/alglib/ap.cpp b/src/inc/alglib/ap.cpp deleted file mode 100644 index cc8140e..0000000 --- a/src/inc/alglib/ap.cpp +++ /dev/null @@ -1,10661 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "ap.h" -#include -#include -using namespace std; - -#if defined(AE_CPU) -#if (AE_CPU==AE_INTEL) - -#if AE_COMPILER==AE_MSVC -#include -#endif - -#endif -#endif - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION IMPLEMENTS BASIC FUNCTIONALITY LIKE -// MEMORY MANAGEMENT FOR VECTORS/MATRICES WHICH IS -// SHARED BETWEEN C++ AND PURE C LIBRARIES -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -/* - * local definitions - */ -#define x_nb 16 -#define AE_DATA_ALIGN 16 -#define AE_PTR_ALIGN sizeof(void*) -#define DYN_BOTTOM ((void*)1) -#define DYN_FRAME ((void*)2) -#define AE_LITTLE_ENDIAN 1 -#define AE_BIG_ENDIAN 2 -#define AE_MIXED_ENDIAN 3 -#define AE_SER_ENTRY_LENGTH 11 -#define AE_SER_ENTRIES_PER_ROW 5 - -#define AE_SM_DEFAULT 0 -#define AE_SM_ALLOC 1 -#define AE_SM_READY2S 2 -#define AE_SM_TO_STRING 10 -#define AE_SM_FROM_STRING 20 -#define AE_SM_TO_CPPSTRING 11 - -#define AE_LOCK_CYCLES 512 -#define AE_LOCK_TESTS_BEFORE_YIELD 16 -#define AE_CRITICAL_ASSERT(x) if( !(x) ) abort() - - -/* - * alloc counter (if used) - */ -#ifdef AE_USE_ALLOC_COUNTER -ae_int64_t _alloc_counter = 0; -#endif -#ifdef AE_DEBUGRNG -static ae_int_t _debug_rng_s0 = 11; -static ae_int_t _debug_rng_s1 = 13; -#endif -#ifdef AE_SMP_DEBUGCOUNTERS -__declspec(align(AE_LOCK_ALIGNMENT)) volatile ae_int64_t _ae_dbg_lock_acquisitions = 0; -__declspec(align(AE_LOCK_ALIGNMENT)) volatile ae_int64_t _ae_dbg_lock_spinwaits = 0; -__declspec(align(AE_LOCK_ALIGNMENT)) volatile ae_int64_t _ae_dbg_lock_yields = 0; -#endif - -/* - * These declarations are used to ensure that - * sizeof(ae_int32_t)==4, sizeof(ae_int64_t)==8, sizeof(ae_int_t)==sizeof(void*). - * they will lead to syntax error otherwise (array size will be negative). - * - * you can remove them, if you want - they are not used anywhere. - * - */ -static char _ae_int32_t_must_be_32_bits_wide[1-2*((int)(sizeof(ae_int32_t))-4)*((int)(sizeof(ae_int32_t))-4)]; -static char _ae_int64_t_must_be_64_bits_wide[1-2*((int)(sizeof(ae_int64_t))-8)*((int)(sizeof(ae_int64_t))-8)]; -static char _ae_int_t_must_be_pointer_sized [1-2*((int)(sizeof(ae_int_t))-(int)sizeof(void*))*((int)(sizeof(ae_int_t))-(int)(sizeof(void*)))]; - -/* - * This variable is used to prevent some tricky optimizations which may degrade multithreaded performance. - * It is touched once in the ae_init_pool() function from smp.c in order to prevent optimizations. - * - */ -static volatile ae_int_t ae_never_change_it = 1; - -ae_int_t ae_misalignment(const void *ptr, size_t alignment) -{ - union _u - { - const void *ptr; - ae_int_t iptr; - } u; - u.ptr = ptr; - return (ae_int_t)(u.iptr%alignment); -} - -void* ae_align(void *ptr, size_t alignment) -{ - char *result = (char*)ptr; - if( (result-(char*)0)%alignment!=0 ) - result += alignment - (result-(char*)0)%alignment; - return result; -} - -void ae_break(ae_state *state, ae_error_type error_type, const char *msg) -{ -#ifndef AE_USE_CPP_ERROR_HANDLING - if( state!=NULL ) - { - if( state->thread_exception_handler!=NULL ) - state->thread_exception_handler(state); - ae_state_clear(state); - state->last_error = error_type; - state->error_msg = msg; - if( state->break_jump!=NULL ) - longjmp(*(state->break_jump), 1); - else - abort(); - } - else - abort(); -#else - if( state!=NULL ) - { - if( state->thread_exception_handler!=NULL ) - state->thread_exception_handler(state); - ae_state_clear(state); - state->last_error = error_type; - state->error_msg = msg; - } - throw error_type; -#endif -} - -void* aligned_malloc(size_t size, size_t alignment) -{ - if( size==0 ) - return NULL; - if( alignment<=1 ) - { - /* no alignment, just call malloc */ - void *block; - void **p; ; - block = malloc(sizeof(void*)+size); - if( block==NULL ) - return NULL; - p = (void**)block; - *p = block; -#ifdef AE_USE_ALLOC_COUNTER - _alloc_counter++; -#endif - return (void*)((char*)block+sizeof(void*)); - } - else - { - /* align */ - void *block; - char *result; - block = malloc(alignment-1+sizeof(void*)+size); - if( block==NULL ) - return NULL; - result = (char*)block+sizeof(void*); - /*if( (result-(char*)0)%alignment!=0 ) - result += alignment - (result-(char*)0)%alignment;*/ - result = (char*)ae_align(result, alignment); - *((void**)(result-sizeof(void*))) = block; -#ifdef AE_USE_ALLOC_COUNTER - _alloc_counter++; -#endif - return result; - } -} - -void aligned_free(void *block) -{ - void *p; - if( block==NULL ) - return; - p = *((void**)((char*)block-sizeof(void*))); - free(p); -#ifdef AE_USE_ALLOC_COUNTER - _alloc_counter--; -#endif -} - -/************************************************************************ -Malloc's memory with automatic alignment. - -Returns NULL when zero size is specified. - -Error handling: -* if state is NULL, returns NULL on allocation error -* if state is not NULL, calls ae_break() on allocation error -************************************************************************/ -void* ae_malloc(size_t size, ae_state *state) -{ - void *result; - if( size==0 ) - return NULL; - result = aligned_malloc(size,AE_DATA_ALIGN); - if( result==NULL && state!=NULL) - { - char buf[256]; - sprintf(buf, "ae_malloc(): out of memory (attempted to allocate %llu bytes)", (unsigned long long)size); - ae_break(state, ERR_OUT_OF_MEMORY, buf); - } - return result; -} - -void ae_free(void *p) -{ - if( p!=NULL ) - aligned_free(p); -} - -/************************************************************************ -Sets pointers to the matrix rows. - -* dst must be correctly initialized matrix -* dst->data.ptr points to the beginning of memory block allocated for - row pointers. -* dst->ptr - undefined (initialized during algorithm processing) -* storage parameter points to the beginning of actual storage -************************************************************************/ -void ae_matrix_update_row_pointers(ae_matrix *dst, void *storage) -{ - char *p_base; - void **pp_ptr; - ae_int_t i; - if( dst->rows>0 && dst->cols>0 ) - { - p_base = (char*)storage; - pp_ptr = (void**)dst->data.ptr; - dst->ptr.pp_void = pp_ptr; - for(i=0; irows; i++, p_base+=dst->stride*ae_sizeof(dst->datatype)) - pp_ptr[i] = p_base; - } - else - dst->ptr.pp_void = NULL; -} - -/************************************************************************ -Returns size of datatype. -Zero for dynamic types like strings or multiple precision types. -************************************************************************/ -ae_int_t ae_sizeof(ae_datatype datatype) -{ - switch(datatype) - { - case DT_BOOL: return (ae_int_t)sizeof(ae_bool); - case DT_INT: return (ae_int_t)sizeof(ae_int_t); - case DT_REAL: return (ae_int_t)sizeof(double); - case DT_COMPLEX: return 2*(ae_int_t)sizeof(double); - default: return 0; - } -} - - -/************************************************************************ -This dummy function is used to prevent compiler messages about unused -locals in automatically generated code. - -It makes nothing - just accepts pointer, "touches" it - and that is all. -It performs several tricky operations without side effects which confuse -compiler so it does not compain about unused locals in THIS function. -************************************************************************/ -void ae_touch_ptr(void *p) -{ - void * volatile fake_variable0 = p; - void * volatile fake_variable1 = fake_variable0; - fake_variable0 = fake_variable1; -} - -/************************************************************************ -This function initializes ALGLIB environment state. - -NOTES: -* stacks contain no frames, so ae_make_frame() must be called before - attaching dynamic blocks. Without it ae_leave_frame() will cycle - forever (which is intended behavior). -************************************************************************/ -void ae_state_init(ae_state *state) -{ - ae_int32_t *vp; - - /* - * p_next points to itself because: - * * correct program should be able to detect end of the list - * by looking at the ptr field. - * * NULL p_next may be used to distinguish automatic blocks - * (in the list) from non-automatic (not in the list) - */ - state->last_block.p_next = &(state->last_block); - state->last_block.deallocator = NULL; - state->last_block.ptr = DYN_BOTTOM; - state->p_top_block = &(state->last_block); -#ifndef AE_USE_CPP_ERROR_HANDLING - state->break_jump = NULL; -#endif - state->error_msg = ""; - - /* - * determine endianness and initialize precomputed IEEE special quantities. - */ - state->endianness = ae_get_endianness(); - if( state->endianness==AE_LITTLE_ENDIAN ) - { - vp = (ae_int32_t*)(&state->v_nan); - vp[0] = 0; - vp[1] = (ae_int32_t)0x7FF80000; - vp = (ae_int32_t*)(&state->v_posinf); - vp[0] = 0; - vp[1] = (ae_int32_t)0x7FF00000; - vp = (ae_int32_t*)(&state->v_neginf); - vp[0] = 0; - vp[1] = (ae_int32_t)0xFFF00000; - } - else if( state->endianness==AE_BIG_ENDIAN ) - { - vp = (ae_int32_t*)(&state->v_nan); - vp[1] = 0; - vp[0] = (ae_int32_t)0x7FF80000; - vp = (ae_int32_t*)(&state->v_posinf); - vp[1] = 0; - vp[0] = (ae_int32_t)0x7FF00000; - vp = (ae_int32_t*)(&state->v_neginf); - vp[1] = 0; - vp[0] = (ae_int32_t)0xFFF00000; - } - else - abort(); - - /* - * set threading information - */ - state->worker_thread = NULL; - state->parent_task = NULL; - state->thread_exception_handler = NULL; -} - - -/************************************************************************ -This function clears ALGLIB environment state. -All dynamic data controlled by state are freed. -************************************************************************/ -void ae_state_clear(ae_state *state) -{ - while( state->p_top_block->ptr!=DYN_BOTTOM ) - ae_frame_leave(state); -} - - -#ifndef AE_USE_CPP_ERROR_HANDLING -/************************************************************************ -This function sets jump buffer for error handling. - -buf may be NULL. -************************************************************************/ -void ae_state_set_break_jump(ae_state *state, jmp_buf *buf) -{ - state->break_jump = buf; -} -#endif - - -/************************************************************************ -This function makes new stack frame. - -This function takes two parameters: environment state and pointer to the -dynamic block which will be used as indicator of the frame beginning. -This dynamic block must be initialized by caller and mustn't be changed/ -deallocated/reused till ae_leave_frame called. It may be global or local -variable (local is even better). -************************************************************************/ -void ae_frame_make(ae_state *state, ae_frame *tmp) -{ - tmp->db_marker.p_next = state->p_top_block; - tmp->db_marker.deallocator = NULL; - tmp->db_marker.ptr = DYN_FRAME; - state->p_top_block = &tmp->db_marker; -} - - -/************************************************************************ -This function leaves current stack frame and deallocates all automatic -dynamic blocks which were attached to this frame. -************************************************************************/ -void ae_frame_leave(ae_state *state) -{ - while( state->p_top_block->ptr!=DYN_FRAME && state->p_top_block->ptr!=DYN_BOTTOM) - { - if( state->p_top_block->ptr!=NULL && state->p_top_block->deallocator!=NULL) - ((ae_deallocator)(state->p_top_block->deallocator))(state->p_top_block->ptr); - state->p_top_block = state->p_top_block->p_next; - } - state->p_top_block = state->p_top_block->p_next; -} - - -/************************************************************************ -This function attaches block to the dynamic block list - -block block -state ALGLIB environment state - -NOTES: -* never call it for special blocks which marks frame boundaries! -************************************************************************/ -void ae_db_attach(ae_dyn_block *block, ae_state *state) -{ - block->p_next = state->p_top_block; - state->p_top_block = block; -} - - -/************************************************************************ -This function malloc's dynamic block: - -block destination block, assumed to be uninitialized -size size (in bytes) -state ALGLIB environment state. May be NULL. -make_automatic if true, vector is added to the dynamic block list - -block is assumed to be uninitialized, its fields are ignored. - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -NOTES: -* never call it for blocks which are already in the list -************************************************************************/ -ae_bool ae_db_malloc(ae_dyn_block *block, ae_int_t size, ae_state *state, ae_bool make_automatic) -{ - /* ensure that size is >=0 - two ways to exit: 1) through ae_assert, if we have non-NULL state, 2) by returning ae_false */ - if( state!=NULL ) - ae_assert(size>=0, "ae_db_malloc(): negative size", state); - if( size<0 ) - return ae_false; - - /* alloc */ - block->ptr = ae_malloc((size_t)size, state); - if( block->ptr==NULL && size!=0 ) - return ae_false; - if( make_automatic && state!=NULL ) - ae_db_attach(block, state); - else - block->p_next = NULL; - block->deallocator = ae_free; - return ae_true; -} - - -/************************************************************************ -This function realloc's dynamic block: - -block destination block (initialized) -size new size (in bytes) -state ALGLIB environment state - -block is assumed to be initialized. - -This function: -* deletes old contents -* preserves automatic state - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -NOTES: -* never call it for special blocks which mark frame boundaries! -************************************************************************/ -ae_bool ae_db_realloc(ae_dyn_block *block, ae_int_t size, ae_state *state) -{ - /* ensure that size is >=0 - two ways to exit: 1) through ae_assert, if we have non-NULL state, 2) by returning ae_false */ - if( state!=NULL ) - ae_assert(size>=0, "ae_db_realloc(): negative size", state); - if( size<0 ) - return ae_false; - - /* realloc */ - if( block->ptr!=NULL ) - ((ae_deallocator)block->deallocator)(block->ptr); - block->ptr = ae_malloc((size_t)size, state); - if( block->ptr==NULL && size!=0 ) - return ae_false; - block->deallocator = ae_free; - return ae_true; -} - - -/************************************************************************ -This function clears dynamic block (releases all dynamically allocated -memory). Dynamic block may be in automatic management list - in this case -it will NOT be removed from list. - -block destination block (initialized) - -NOTES: -* never call it for special blocks which marks frame boundaries! -************************************************************************/ -void ae_db_free(ae_dyn_block *block) -{ - if( block->ptr!=NULL ) - ((ae_deallocator)block->deallocator)(block->ptr); - block->ptr = NULL; - block->deallocator = ae_free; -} - -/************************************************************************ -This function swaps contents of two dynamic blocks (pointers and -deallocators) leaving other parameters (automatic management settings, -etc.) unchanged. - -NOTES: -* never call it for special blocks which marks frame boundaries! -************************************************************************/ -void ae_db_swap(ae_dyn_block *block1, ae_dyn_block *block2) -{ - void (*deallocator)(void*) = NULL; - void * volatile ptr; - ptr = block1->ptr; - deallocator = block1->deallocator; - block1->ptr = block2->ptr; - block1->deallocator = block2->deallocator; - block2->ptr = ptr; - block2->deallocator = deallocator; -} - -/************************************************************************ -This function creates ae_vector. - -Vector size may be zero. Vector contents is uninitialized. - -dst destination vector -size vector size, may be zero -datatype guess what... -state ALGLIB environment state -make_automatic if true, vector is added to the dynamic block list - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -dst is assumed to be uninitialized, its fields are ignored. -************************************************************************/ -ae_bool ae_vector_init(ae_vector *dst, ae_int_t size, ae_datatype datatype, ae_state *state, ae_bool make_automatic) -{ - /* ensure that size is >=0 - two ways to exit: 1) through ae_assert, if we have non-NULL state, 2) by returning ae_false */ - if( state!=NULL ) - ae_assert(size>=0, "ae_vector_init(): negative size", state); - if( size<0 ) - return ae_false; - - /* init */ - dst->cnt = size; - dst->datatype = datatype; - if( !ae_db_malloc(&dst->data, size*ae_sizeof(datatype), state, make_automatic) ) - return ae_false; - dst->ptr.p_ptr = dst->data.ptr; - return ae_true; -} - - -/************************************************************************ -This function creates copy of ae_vector. - -dst destination vector -src well, it is source -state ALGLIB environment state -make_automatic if true, vector is added to the dynamic block list - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -dst is assumed to be uninitialized, its fields are ignored. -************************************************************************/ -ae_bool ae_vector_init_copy(ae_vector *dst, ae_vector *src, ae_state *state, ae_bool make_automatic) -{ - if( !ae_vector_init(dst, src->cnt, src->datatype, state, make_automatic) ) - return ae_false; - if( src->cnt!=0 ) - memcpy(dst->ptr.p_ptr, src->ptr.p_ptr, (size_t)(src->cnt*ae_sizeof(src->datatype))); - return ae_true; -} - -/************************************************************************ -This function creates ae_vector from x_vector: - -dst destination vector -src source, vector in x-format -state ALGLIB environment state -make_automatic if true, vector is added to the dynamic block list - -dst is assumed to be uninitialized, its fields are ignored. -************************************************************************/ -void ae_vector_init_from_x(ae_vector *dst, x_vector *src, ae_state *state, ae_bool make_automatic) -{ - ae_vector_init(dst, (ae_int_t)src->cnt, (ae_datatype)src->datatype, state, make_automatic); - if( src->cnt>0 ) - memcpy(dst->ptr.p_ptr, src->ptr, (size_t)(((ae_int_t)src->cnt)*ae_sizeof((ae_datatype)src->datatype))); -} - - -/************************************************************************ -This function changes length of ae_vector. - -dst destination vector -newsize vector size, may be zero -state ALGLIB environment state - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -NOTES: -* vector must be initialized -* all contents is destroyed during setlength() call -* new size may be zero. -************************************************************************/ -ae_bool ae_vector_set_length(ae_vector *dst, ae_int_t newsize, ae_state *state) -{ - /* ensure that size is >=0 - two ways to exit: 1) through ae_assert, if we have non-NULL state, 2) by returning ae_false */ - if( state!=NULL ) - ae_assert(newsize>=0, "ae_vector_set_length(): negative size", state); - if( newsize<0 ) - return ae_false; - - /* set length */ - if( dst->cnt==newsize ) - return ae_true; - dst->cnt = newsize; - if( !ae_db_realloc(&dst->data, newsize*ae_sizeof(dst->datatype), state) ) - return ae_false; - dst->ptr.p_ptr = dst->data.ptr; - return ae_true; -} - - -/************************************************************************ -This function provides "CLEAR" functionality for vector (contents is -cleared, but structure still left in valid state). - -The function clears vector contents (releases all dynamically allocated -memory). Vector may be in automatic management list - in this case it -will NOT be removed from list. - -IMPORTANT: this function does NOT invalidates dst; it just releases all -dynamically allocated storage, but dst still may be used after call to -ae_vector_set_length(). - -dst destination vector -************************************************************************/ -void ae_vector_clear(ae_vector *dst) -{ - dst->cnt = 0; - ae_db_free(&dst->data); - dst->ptr.p_ptr = 0; -} - - -/************************************************************************ -This function provides "DESTROY" functionality for vector (contents is -cleared, all internal structures are destroyed). For vectors it is same -as CLEAR. - -dst destination vector -************************************************************************/ -void ae_vector_destroy(ae_vector *dst) -{ - ae_vector_clear(dst); -} - - -/************************************************************************ -This function efficiently swaps contents of two vectors, leaving other -pararemeters (automatic management, etc.) unchanged. -************************************************************************/ -void ae_swap_vectors(ae_vector *vec1, ae_vector *vec2) -{ - ae_int_t cnt; - ae_datatype datatype; - void *p_ptr; - - ae_db_swap(&vec1->data, &vec2->data); - - cnt = vec1->cnt; - datatype = vec1->datatype; - p_ptr = vec1->ptr.p_ptr; - vec1->cnt = vec2->cnt; - vec1->datatype = vec2->datatype; - vec1->ptr.p_ptr = vec2->ptr.p_ptr; - vec2->cnt = cnt; - vec2->datatype = datatype; - vec2->ptr.p_ptr = p_ptr; -} - -/************************************************************************ -This function creates ae_matrix. - -Matrix size may be zero, in such cases both rows and cols are zero. -Matrix contents is uninitialized. - -dst destination natrix -rows rows count -cols cols count -datatype element type -state ALGLIB environment state -make_automatic if true, matrix is added to the dynamic block list - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -dst is assumed to be uninitialized, its fields are ignored. -************************************************************************/ -ae_bool ae_matrix_init(ae_matrix *dst, ae_int_t rows, ae_int_t cols, ae_datatype datatype, ae_state *state, ae_bool make_automatic) -{ - /* ensure that size is >=0 - two ways to exit: 1) through ae_assert, if we have non-NULL state, 2) by returning ae_false */ - if( state!=NULL ) - ae_assert(rows>=0 && cols>=0, "ae_matrix_init(): negative length", state); - if( rows<0 || cols<0 ) - return ae_false; - - /* if one of rows/cols is zero, another MUST be too */ - if( rows==0 || cols==0 ) - { - rows = 0; - cols = 0; - } - - /* init */ - dst->rows = rows; - dst->cols = cols; - dst->stride = cols; - while( dst->stride*ae_sizeof(datatype)%AE_DATA_ALIGN!=0 ) - dst->stride++; - dst->datatype = datatype; - if( !ae_db_malloc(&dst->data, dst->rows*((ae_int_t)sizeof(void*)+dst->stride*ae_sizeof(datatype))+AE_DATA_ALIGN-1, state, make_automatic) ) - return ae_false; - ae_matrix_update_row_pointers(dst, ae_align((char*)dst->data.ptr+dst->rows*sizeof(void*),AE_DATA_ALIGN)); - return ae_true; -} - - -/************************************************************************ -This function creates copy of ae_matrix. - -dst destination matrix -src well, it is source -state ALGLIB environment state -make_automatic if true, matrix is added to the dynamic block list - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -dst is assumed to be uninitialized, its fields are ignored. -************************************************************************/ -ae_bool ae_matrix_init_copy(ae_matrix *dst, ae_matrix *src, ae_state *state, ae_bool make_automatic) -{ - ae_int_t i; - if( !ae_matrix_init(dst, src->rows, src->cols, src->datatype, state, make_automatic) ) - return ae_false; - if( src->rows!=0 && src->cols!=0 ) - { - if( dst->stride==src->stride ) - memcpy(dst->ptr.pp_void[0], src->ptr.pp_void[0], (size_t)(src->rows*src->stride*ae_sizeof(src->datatype))); - else - for(i=0; irows; i++) - memcpy(dst->ptr.pp_void[i], src->ptr.pp_void[i], (size_t)(dst->cols*ae_sizeof(dst->datatype))); - } - return ae_true; -} - - -void ae_matrix_init_from_x(ae_matrix *dst, x_matrix *src, ae_state *state, ae_bool make_automatic) -{ - char *p_src_row; - char *p_dst_row; - ae_int_t row_size; - ae_int_t i; - ae_matrix_init(dst, (ae_int_t)src->rows, (ae_int_t)src->cols, (ae_datatype)src->datatype, state, make_automatic); - if( src->rows!=0 && src->cols!=0 ) - { - p_src_row = (char*)src->ptr; - p_dst_row = (char*)(dst->ptr.pp_void[0]); - row_size = ae_sizeof((ae_datatype)src->datatype)*(ae_int_t)src->cols; - for(i=0; irows; i++, p_src_row+=src->stride*ae_sizeof((ae_datatype)src->datatype), p_dst_row+=dst->stride*ae_sizeof((ae_datatype)src->datatype)) - memcpy(p_dst_row, p_src_row, (size_t)(row_size)); - } -} - - -/************************************************************************ -This function changes length of ae_matrix. - -dst destination matrix -rows size, may be zero -cols size, may be zero -state ALGLIB environment state - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -NOTES: -* matrix must be initialized -* all contents is destroyed during setlength() call -* new size may be zero. -************************************************************************/ -ae_bool ae_matrix_set_length(ae_matrix *dst, ae_int_t rows, ae_int_t cols, ae_state *state) -{ - /* ensure that size is >=0 - two ways to exit: 1) through ae_assert, if we have non-NULL state, 2) by returning ae_false */ - if( state!=NULL ) - ae_assert(rows>=0 && cols>=0, "ae_matrix_set_length(): negative length", state); - if( rows<0 || cols<0 ) - return ae_false; - - if( dst->rows==rows && dst->cols==cols ) - return ae_true; - dst->rows = rows; - dst->cols = cols; - dst->stride = cols; - while( dst->stride*ae_sizeof(dst->datatype)%AE_DATA_ALIGN!=0 ) - dst->stride++; - if( !ae_db_realloc(&dst->data, dst->rows*((ae_int_t)sizeof(void*)+dst->stride*ae_sizeof(dst->datatype))+AE_DATA_ALIGN-1, state) ) - return ae_false; - ae_matrix_update_row_pointers(dst, ae_align((char*)dst->data.ptr+dst->rows*sizeof(void*),AE_DATA_ALIGN)); - return ae_true; -} - - -/************************************************************************ -This function provides "CLEAR" functionality for vector (contents is -cleared, but structure still left in valid state). - -The function clears matrix contents (releases all dynamically allocated -memory). Matrix may be in automatic management list - in this case it -will NOT be removed from list. - -IMPORTANT: this function does NOT invalidates dst; it just releases all -dynamically allocated storage, but dst still may be used after call to -ae_matrix_set_length(). - -dst destination matrix -************************************************************************/ -void ae_matrix_clear(ae_matrix *dst) -{ - dst->rows = 0; - dst->cols = 0; - dst->stride = 0; - ae_db_free(&dst->data); - dst->ptr.p_ptr = 0; -} - - -/************************************************************************ -This function provides "DESTROY" functionality for matrix (contents is -cleared, but structure still left in valid state). - -For matrices it is same as CLEAR. - -dst destination matrix -************************************************************************/ -void ae_matrix_destroy(ae_matrix *dst) -{ - ae_matrix_clear(dst); -} - - -/************************************************************************ -This function efficiently swaps contents of two vectors, leaving other -pararemeters (automatic management, etc.) unchanged. -************************************************************************/ -void ae_swap_matrices(ae_matrix *mat1, ae_matrix *mat2) -{ - ae_int_t rows; - ae_int_t cols; - ae_int_t stride; - ae_datatype datatype; - void *p_ptr; - - ae_db_swap(&mat1->data, &mat2->data); - - rows = mat1->rows; - cols = mat1->cols; - stride = mat1->stride; - datatype = mat1->datatype; - p_ptr = mat1->ptr.p_ptr; - - mat1->rows = mat2->rows; - mat1->cols = mat2->cols; - mat1->stride = mat2->stride; - mat1->datatype = mat2->datatype; - mat1->ptr.p_ptr = mat2->ptr.p_ptr; - - mat2->rows = rows; - mat2->cols = cols; - mat2->stride = stride; - mat2->datatype = datatype; - mat2->ptr.p_ptr = p_ptr; -} - - -/************************************************************************ -This function creates smart pointer structure. - -dst destination smart pointer. - already allocated, but not initialized. -subscriber pointer to pointer which receives updates in the - internal object stored in ae_smart_ptr. Any update to - dst->ptr is translated to subscriber. Can be NULL. -state ALGLIB environment state -make_automatic if true, smart pointer is added to the dynamic block list - -After initialization, smart pointer stores NULL pointer. - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success -************************************************************************/ -ae_bool ae_smart_ptr_init(ae_smart_ptr *dst, void **subscriber, ae_state *state, ae_bool make_automatic) -{ - dst->subscriber = subscriber; - dst->ptr = NULL; - if( dst->subscriber!=NULL ) - *(dst->subscriber) = dst->ptr; - dst->is_owner = ae_false; - dst->is_dynamic = ae_false; - dst->frame_entry.deallocator = ae_smart_ptr_destroy; - dst->frame_entry.ptr = dst; - if( make_automatic && state!=NULL ) - ae_db_attach(&dst->frame_entry, state); - return ae_true; -} - - -/************************************************************************ -This function clears smart pointer structure. - -dst destination smart pointer. - -After call to this function smart pointer contains NULL reference, which -is propagated to its subscriber (in cases non-NULL subscruber was -specified during pointer creation). -************************************************************************/ -void ae_smart_ptr_clear(void *_dst) -{ - ae_smart_ptr *dst = (ae_smart_ptr*)_dst; - if( dst->is_owner && dst->ptr!=NULL ) - { - dst->destroy(dst->ptr); - if( dst->is_dynamic ) - ae_free(dst->ptr); - } - dst->is_owner = ae_false; - dst->is_dynamic = ae_false; - dst->ptr = NULL; - dst->destroy = NULL; - if( dst->subscriber!=NULL ) - *(dst->subscriber) = NULL; -} - - -/************************************************************************ -This function dstroys smart pointer structure (same as clearing it). - -dst destination smart pointer. -************************************************************************/ -void ae_smart_ptr_destroy(void *_dst) -{ - ae_smart_ptr_clear(_dst); -} - - -/************************************************************************ -This function assigns pointer to ae_smart_ptr structure. - -dst destination smart pointer. -new_ptr new pointer to assign -is_owner whether smart pointer owns new_ptr -is_dynamic whether object is dynamic - clearing such object - requires BOTH calling destructor function AND calling - ae_free() for memory occupied by object. -destroy destructor function - -In case smart pointer already contains non-NULL value and owns this value, -it is freed before assigning new pointer. - -Changes in pointer are propagated to its subscriber (in case non-NULL -subscriber was specified during pointer creation). - -You can specify NULL new_ptr, in which case is_owner/destroy are ignored. -************************************************************************/ -void ae_smart_ptr_assign(ae_smart_ptr *dst, void *new_ptr, ae_bool is_owner, ae_bool is_dynamic, void (*destroy)(void*)) -{ - if( dst->is_owner && dst->ptr!=NULL ) - dst->destroy(dst->ptr); - if( new_ptr!=NULL ) - { - dst->ptr = new_ptr; - dst->is_owner = is_owner; - dst->is_dynamic = is_dynamic; - dst->destroy = destroy; - } - else - { - dst->ptr = NULL; - dst->is_owner = ae_false; - dst->is_dynamic = ae_false; - dst->destroy = NULL; - } - if( dst->subscriber!=NULL ) - *(dst->subscriber) = dst->ptr; -} - - -/************************************************************************ -This function releases pointer owned by ae_smart_ptr structure: -* all internal fields are set to NULL -* destructor function for internal pointer is NOT called even when we own - this pointer. After this call ae_smart_ptr releases ownership of its - pointer and passes it to caller. -* changes in pointer are propagated to its subscriber (in case non-NULL - subscriber was specified during pointer creation). - -dst destination smart pointer. -************************************************************************/ -void ae_smart_ptr_release(ae_smart_ptr *dst) -{ - dst->is_owner = ae_false; - dst->is_dynamic = ae_false; - dst->ptr = NULL; - dst->destroy = NULL; - if( dst->subscriber!=NULL ) - *(dst->subscriber) = NULL; -} - -/************************************************************************ -This function fills x_vector by ae_vector's contents: - -dst destination vector -src source, vector in x-format -state ALGLIB environment state - -NOTES: -* dst is assumed to be initialized. Its contents is freed before copying - data from src (if size / type are different) or overwritten (if - possible given destination size). -************************************************************************/ -void ae_x_set_vector(x_vector *dst, ae_vector *src, ae_state *state) -{ - if( dst->cnt!=src->cnt || dst->datatype!=src->datatype ) - { - if( dst->owner==OWN_AE ) - ae_free(dst->ptr); - dst->ptr = ae_malloc((size_t)(src->cnt*ae_sizeof(src->datatype)), state); - dst->last_action = ACT_NEW_LOCATION; - dst->cnt = src->cnt; - dst->datatype = src->datatype; - dst->owner = OWN_AE; - } - else - dst->last_action = ACT_SAME_LOCATION; - if( src->cnt ) - memcpy(dst->ptr, src->ptr.p_ptr, (size_t)(src->cnt*ae_sizeof(src->datatype))); -} - -/************************************************************************ -This function fills x_matrix by ae_matrix's contents: - -dst destination vector -src source, matrix in x-format -state ALGLIB environment state - -NOTES: -* dst is assumed to be initialized. Its contents is freed before copying - data from src (if size / type are different) or overwritten (if - possible given destination size). -************************************************************************/ -void ae_x_set_matrix(x_matrix *dst, ae_matrix *src, ae_state *state) -{ - char *p_src_row; - char *p_dst_row; - ae_int_t i; - ae_int_t row_size; - if( dst->rows!=src->rows || dst->cols!=src->cols || dst->datatype!=src->datatype ) - { - if( dst->owner==OWN_AE ) - ae_free(dst->ptr); - dst->rows = src->rows; - dst->cols = src->cols; - dst->stride = src->cols; - dst->datatype = src->datatype; - dst->ptr = ae_malloc((size_t)(dst->rows*((ae_int_t)dst->stride)*ae_sizeof(src->datatype)), state); - dst->last_action = ACT_NEW_LOCATION; - dst->owner = OWN_AE; - } - else - dst->last_action = ACT_SAME_LOCATION; - if( src->rows!=0 && src->cols!=0 ) - { - p_src_row = (char*)(src->ptr.pp_void[0]); - p_dst_row = (char*)dst->ptr; - row_size = ae_sizeof(src->datatype)*src->cols; - for(i=0; irows; i++, p_src_row+=src->stride*ae_sizeof(src->datatype), p_dst_row+=dst->stride*ae_sizeof(src->datatype)) - memcpy(p_dst_row, p_src_row, (size_t)(row_size)); - } -} - -/************************************************************************ -This function attaches x_vector to ae_vector's contents. -Ownership of memory allocated is not changed (it is still managed by -ae_matrix). - -dst destination vector -src source, vector in x-format -state ALGLIB environment state - -NOTES: -* dst is assumed to be initialized. Its contents is freed before - attaching to src. -* this function doesn't need ae_state parameter because it can't fail - (assuming correctly initialized src) -************************************************************************/ -void ae_x_attach_to_vector(x_vector *dst, ae_vector *src) -{ - if( dst->owner==OWN_AE ) - ae_free(dst->ptr); - dst->ptr = src->ptr.p_ptr; - dst->last_action = ACT_NEW_LOCATION; - dst->cnt = src->cnt; - dst->datatype = src->datatype; - dst->owner = OWN_CALLER; -} - -/************************************************************************ -This function attaches x_matrix to ae_matrix's contents. -Ownership of memory allocated is not changed (it is still managed by -ae_matrix). - -dst destination vector -src source, matrix in x-format -state ALGLIB environment state - -NOTES: -* dst is assumed to be initialized. Its contents is freed before - attaching to src. -* this function doesn't need ae_state parameter because it can't fail - (assuming correctly initialized src) -************************************************************************/ -void ae_x_attach_to_matrix(x_matrix *dst, ae_matrix *src) -{ - if( dst->owner==OWN_AE ) - ae_free(dst->ptr); - dst->rows = src->rows; - dst->cols = src->cols; - dst->stride = src->stride; - dst->datatype = src->datatype; - dst->ptr = &(src->ptr.pp_double[0][0]); - dst->last_action = ACT_NEW_LOCATION; - dst->owner = OWN_CALLER; -} - -/************************************************************************ -This function clears x_vector. It does nothing if vector is not owned by -ALGLIB environment. - -dst vector -************************************************************************/ -void x_vector_clear(x_vector *dst) -{ - if( dst->owner==OWN_AE ) - aligned_free(dst->ptr); - dst->ptr = NULL; - dst->cnt = 0; -} - -/************************************************************************ -Assertion -************************************************************************/ -void ae_assert(ae_bool cond, const char *msg, ae_state *state) -{ - if( !cond ) - ae_break(state, ERR_ASSERTION_FAILED, msg); -} - -/************************************************************************ -CPUID - -Returns information about features CPU and compiler support. - -You must tell ALGLIB what CPU family is used by defining AE_CPU symbol -(without this hint zero will be returned). - -Note: results of this function depend on both CPU and compiler; -if compiler doesn't support SSE intrinsics, function won't set -corresponding flag. -************************************************************************/ -static volatile ae_bool _ae_cpuid_initialized = ae_false; -static volatile ae_bool _ae_cpuid_has_sse2 = ae_false; -ae_int_t ae_cpuid() -{ - /* - * to speed up CPU detection we cache results from previous attempts - * there is no synchronization, but it is still thread safe. - * - * thread safety is guaranteed on all modern architectures which - * have following property: simultaneous writes by different cores - * to the same location will be executed in serial manner. - * - */ - ae_int_t result; - - /* - * if not initialized, determine system properties - */ - if( !_ae_cpuid_initialized ) - { - /* - * SSE2 - */ -#if defined(AE_CPU) -#if (AE_CPU==AE_INTEL) && defined(AE_HAS_SSE2_INTRINSICS) -#if AE_COMPILER==AE_MSVC - { - int CPUInfo[4]; - __cpuid(CPUInfo, 1); - if( (CPUInfo[3]&0x04000000)!=0 ) - _ae_cpuid_has_sse2 = ae_true; - } -#elif AE_COMPILER==AE_GNUC - { - ae_int_t a,b,c,d; - __asm__ __volatile__ ("cpuid": "=a" (a), "=b" (b), "=c" (c), "=d" (d) : "a" (1)); - if( (d&0x04000000)!=0 ) - _ae_cpuid_has_sse2 = ae_true; - } -#elif AE_COMPILER==AE_SUNC - { - ae_int_t a,b,c,d; - __asm__ __volatile__ ("cpuid": "=a" (a), "=b" (b), "=c" (c), "=d" (d) : "a" (1)); - if( (d&0x04000000)!=0 ) - _ae_cpuid_has_sse2 = ae_true; - } -#else -#endif -#endif -#endif - /* - * set initialization flag - */ - _ae_cpuid_initialized = ae_true; - } - - /* - * return - */ - result = 0; - if( _ae_cpuid_has_sse2 ) - result = result|CPU_SSE2; - return result; -} - -/************************************************************************ -Real math functions -************************************************************************/ -ae_bool ae_fp_eq(double v1, double v2) -{ - /* IEEE-strict floating point comparison */ - volatile double x = v1; - volatile double y = v2; - return x==y; -} - -ae_bool ae_fp_neq(double v1, double v2) -{ - /* IEEE-strict floating point comparison */ - return !ae_fp_eq(v1,v2); -} - -ae_bool ae_fp_less(double v1, double v2) -{ - /* IEEE-strict floating point comparison */ - volatile double x = v1; - volatile double y = v2; - return xy; -} - -ae_bool ae_fp_greater_eq(double v1, double v2) -{ - /* IEEE-strict floating point comparison */ - volatile double x = v1; - volatile double y = v2; - return x>=y; -} - -ae_bool ae_isfinite_stateless(double x, ae_int_t endianness) -{ - union _u - { - double a; - ae_int32_t p[2]; - } u; - ae_int32_t high; - u.a = x; - if( endianness==AE_LITTLE_ENDIAN ) - high = u.p[1]; - else - high = u.p[0]; - return (high & (ae_int32_t)0x7FF00000)!=(ae_int32_t)0x7FF00000; -} - -ae_bool ae_isnan_stateless(double x, ae_int_t endianness) -{ - union _u - { - double a; - ae_int32_t p[2]; - } u; - ae_int32_t high, low; - u.a = x; - if( endianness==AE_LITTLE_ENDIAN ) - { - high = u.p[1]; - low = u.p[0]; - } - else - { - high = u.p[0]; - low = u.p[1]; - } - return ((high &0x7FF00000)==0x7FF00000) && (((high &0x000FFFFF)!=0) || (low!=0)); -} - -ae_bool ae_isinf_stateless(double x, ae_int_t endianness) -{ - union _u - { - double a; - ae_int32_t p[2]; - } u; - ae_int32_t high, low; - u.a = x; - if( endianness==AE_LITTLE_ENDIAN ) - { - high = u.p[1]; - low = u.p[0]; - } - else - { - high = u.p[0]; - low = u.p[1]; - } - - /* 31 least significant bits of high are compared */ - return ((high&0x7FFFFFFF)==0x7FF00000) && (low==0); -} - -ae_bool ae_isposinf_stateless(double x, ae_int_t endianness) -{ - union _u - { - double a; - ae_int32_t p[2]; - } u; - ae_int32_t high, low; - u.a = x; - if( endianness==AE_LITTLE_ENDIAN ) - { - high = u.p[1]; - low = u.p[0]; - } - else - { - high = u.p[0]; - low = u.p[1]; - } - - /* all 32 bits of high are compared */ - return (high==(ae_int32_t)0x7FF00000) && (low==0); -} - -ae_bool ae_isneginf_stateless(double x, ae_int_t endianness) -{ - union _u - { - double a; - ae_int32_t p[2]; - } u; - ae_int32_t high, low; - u.a = x; - if( endianness==AE_LITTLE_ENDIAN ) - { - high = u.p[1]; - low = u.p[0]; - } - else - { - high = u.p[0]; - low = u.p[1]; - } - - /* this code is a bit tricky to avoid comparison of high with 0xFFF00000, which may be unsafe with some buggy compilers */ - return ((high&0x7FFFFFFF)==0x7FF00000) && (high!=(ae_int32_t)0x7FF00000) && (low==0); -} - -ae_int_t ae_get_endianness() -{ - union - { - double a; - ae_int32_t p[2]; - } u; - - /* - * determine endianness - * two types are supported: big-endian and little-endian. - * mixed-endian hardware is NOT supported. - * - * 1983 is used as magic number because its non-periodic double - * representation allow us to easily distinguish between upper - * and lower halfs and to detect mixed endian hardware. - * - */ - u.a = 1.0/1983.0; - if( u.p[1]==(ae_int32_t)0x3f408642 ) - return AE_LITTLE_ENDIAN; - if( u.p[0]==(ae_int32_t)0x3f408642 ) - return AE_BIG_ENDIAN; - return AE_MIXED_ENDIAN; -} - -ae_bool ae_isfinite(double x,ae_state *state) -{ - return ae_isfinite_stateless(x, state->endianness); -} - -ae_bool ae_isnan(double x, ae_state *state) -{ - return ae_isnan_stateless(x, state->endianness); -} - -ae_bool ae_isinf(double x, ae_state *state) -{ - return ae_isinf_stateless(x, state->endianness); -} - -ae_bool ae_isposinf(double x,ae_state *state) -{ - return ae_isposinf_stateless(x, state->endianness); -} - -ae_bool ae_isneginf(double x,ae_state *state) -{ - return ae_isneginf_stateless(x, state->endianness); -} - -double ae_fabs(double x, ae_state *state) -{ - return fabs(x); -} - -ae_int_t ae_iabs(ae_int_t x, ae_state *state) -{ - return x>=0 ? x : -x; -} - -double ae_sqr(double x, ae_state *state) -{ - return x*x; -} - -double ae_sqrt(double x, ae_state *state) -{ - return sqrt(x); -} - -ae_int_t ae_sign(double x, ae_state *state) -{ - if( x>0 ) return 1; - if( x<0 ) return -1; - return 0; -} - -ae_int_t ae_round(double x, ae_state *state) -{ - return (ae_int_t)(ae_ifloor(x+0.5,state)); -} - -ae_int_t ae_trunc(double x, ae_state *state) -{ - return (ae_int_t)(x>0 ? ae_ifloor(x,state) : ae_iceil(x,state)); -} - -ae_int_t ae_ifloor(double x, ae_state *state) -{ - return (ae_int_t)(floor(x)); -} - -ae_int_t ae_iceil(double x, ae_state *state) -{ - return (ae_int_t)(ceil(x)); -} - -ae_int_t ae_maxint(ae_int_t m1, ae_int_t m2, ae_state *state) -{ - return m1>m2 ? m1 : m2; -} - -ae_int_t ae_minint(ae_int_t m1, ae_int_t m2, ae_state *state) -{ - return m1>m2 ? m2 : m1; -} - -double ae_maxreal(double m1, double m2, ae_state *state) -{ - return m1>m2 ? m1 : m2; -} - -double ae_minreal(double m1, double m2, ae_state *state) -{ - return m1>m2 ? m2 : m1; -} - -#ifdef AE_DEBUGRNG -ae_int_t ae_debugrng() -{ - ae_int_t k; - ae_int_t result; - k = _debug_rng_s0/53668; - _debug_rng_s0 = 40014*(_debug_rng_s0-k*53668)-k*12211; - if( _debug_rng_s0<0 ) - _debug_rng_s0 = _debug_rng_s0+2147483563; - k = _debug_rng_s1/52774; - _debug_rng_s1 = 40692*(_debug_rng_s1-k*52774)-k*3791; - if( _debug_rng_s1<0 ) - _debug_rng_s1 = _debug_rng_s1+2147483399; - result = _debug_rng_s0-_debug_rng_s1; - if( result<1 ) - result = result+2147483562; - return result; -} -#endif - -double ae_randomreal(ae_state *state) -{ -#ifdef AE_DEBUGRNG - return ae_debugrng()/2147483563.0; -#else - int i1 = rand(); - int i2 = rand(); - double mx = (double)(RAND_MAX)+1.0; - volatile double tmp0 = i2/mx; - volatile double tmp1 = i1+tmp0; - return tmp1/mx; -#endif -} - -ae_int_t ae_randominteger(ae_int_t maxv, ae_state *state) -{ -#ifdef AE_DEBUGRNG - return (ae_debugrng()-1)%maxv; -#else - return rand()%maxv; -#endif -} - -double ae_sin(double x, ae_state *state) -{ - return sin(x); -} - -double ae_cos(double x, ae_state *state) -{ - return cos(x); -} - -double ae_tan(double x, ae_state *state) -{ - return tan(x); -} - -double ae_sinh(double x, ae_state *state) -{ - return sinh(x); -} - -double ae_cosh(double x, ae_state *state) -{ - return cosh(x); -} -double ae_tanh(double x, ae_state *state) -{ - return tanh(x); -} - -double ae_asin(double x, ae_state *state) -{ - return asin(x); -} - -double ae_acos(double x, ae_state *state) -{ - return acos(x); -} - -double ae_atan(double x, ae_state *state) -{ - return atan(x); -} - -double ae_atan2(double y, double x, ae_state *state) -{ - return atan2(y,x); -} - -double ae_log(double x, ae_state *state) -{ - return log(x); -} - -double ae_pow(double x, double y, ae_state *state) -{ - return pow(x,y); -} - -double ae_exp(double x, ae_state *state) -{ - return exp(x); -} - -/************************************************************************ -Symmetric/Hermitian properties: check and force -************************************************************************/ -static void x_split_length(ae_int_t n, ae_int_t nb, ae_int_t* n1, ae_int_t* n2) -{ - ae_int_t r; - if( n<=nb ) - { - *n1 = n; - *n2 = 0; - } - else - { - if( n%nb!=0 ) - { - *n2 = n%nb; - *n1 = n-(*n2); - } - else - { - *n2 = n/2; - *n1 = n-(*n2); - if( *n1%nb==0 ) - { - return; - } - r = nb-*n1%nb; - *n1 = *n1+r; - *n2 = *n2-r; - } - } -} -static double x_safepythag2(double x, double y) -{ - double w; - double xabs; - double yabs; - double z; - xabs = fabs(x); - yabs = fabs(y); - w = xabs>yabs ? xabs : yabs; - z = xabsx_nb || len1>x_nb ) - { - ae_int_t n1, n2; - if( len0>len1 ) - { - x_split_length(len0, x_nb, &n1, &n2); - is_symmetric_rec_off_stat(a, offset0, offset1, n1, len1, nonfinite, mx, err, _state); - is_symmetric_rec_off_stat(a, offset0+n1, offset1, n2, len1, nonfinite, mx, err, _state); - } - else - { - x_split_length(len1, x_nb, &n1, &n2); - is_symmetric_rec_off_stat(a, offset0, offset1, len0, n1, nonfinite, mx, err, _state); - is_symmetric_rec_off_stat(a, offset0, offset1+n1, len0, n2, nonfinite, mx, err, _state); - } - return; - } - else - { - /* base case */ - double *p1, *p2, *prow, *pcol; - double v; - ae_int_t i, j; - - p1 = (double*)(a->ptr)+offset0*a->stride+offset1; - p2 = (double*)(a->ptr)+offset1*a->stride+offset0; - for(i=0; istride; - for(j=0; jv ? *mx : v; - v = fabs(*prow); - *mx = *mx>v ? *mx : v; - v = fabs(*pcol-*prow); - *err = *err>v ? *err : v; - } - pcol += a->stride; - prow++; - } - } - } -} -/* - * this function checks that diagonal block A0 is symmetric. - * Block A0 is specified by its offset and size. - * - * [ . ] - * [ A0 ] - * A = [ . ] - * [ . ] - * - * this subroutine updates current values of: - * a) mx maximum value of A[i,j] found so far - * b) err componentwise difference between A0 and A0^T - * - */ -static void is_symmetric_rec_diag_stat(x_matrix *a, ae_int_t offset, ae_int_t len, ae_bool *nonfinite, double *mx, double *err, ae_state *_state) -{ - double *p, *prow, *pcol; - double v; - ae_int_t i, j; - - /* try to split problem into two smaller ones */ - if( len>x_nb ) - { - ae_int_t n1, n2; - x_split_length(len, x_nb, &n1, &n2); - is_symmetric_rec_diag_stat(a, offset, n1, nonfinite, mx, err, _state); - is_symmetric_rec_diag_stat(a, offset+n1, n2, nonfinite, mx, err, _state); - is_symmetric_rec_off_stat(a, offset+n1, offset, n2, n1, nonfinite, mx, err, _state); - return; - } - - /* base case */ - p = (double*)(a->ptr)+offset*a->stride+offset; - for(i=0; istride; - for(j=0; jstride,prow++) - { - if( !ae_isfinite(*pcol,_state) || !ae_isfinite(*prow,_state) ) - { - *nonfinite = ae_true; - } - else - { - v = fabs(*pcol); - *mx = *mx>v ? *mx : v; - v = fabs(*prow); - *mx = *mx>v ? *mx : v; - v = fabs(*pcol-*prow); - *err = *err>v ? *err : v; - } - } - v = fabs(p[i+i*a->stride]); - *mx = *mx>v ? *mx : v; - } -} -/* - * this function checks difference between offdiagonal blocks BL and BU - * (see below). Block BL is specified by offsets (offset0,offset1) and - * sizes (len0,len1). - * - * [ . ] - * [ A0 BU ] - * A = [ BL A1 ] - * [ . ] - * - * this subroutine updates current values of: - * a) mx maximum value of A[i,j] found so far - * b) err componentwise difference between elements of BL and BU^H - * - */ -static void is_hermitian_rec_off_stat(x_matrix *a, ae_int_t offset0, ae_int_t offset1, ae_int_t len0, ae_int_t len1, ae_bool *nonfinite, double *mx, double *err, ae_state *_state) -{ - /* try to split problem into two smaller ones */ - if( len0>x_nb || len1>x_nb ) - { - ae_int_t n1, n2; - if( len0>len1 ) - { - x_split_length(len0, x_nb, &n1, &n2); - is_hermitian_rec_off_stat(a, offset0, offset1, n1, len1, nonfinite, mx, err, _state); - is_hermitian_rec_off_stat(a, offset0+n1, offset1, n2, len1, nonfinite, mx, err, _state); - } - else - { - x_split_length(len1, x_nb, &n1, &n2); - is_hermitian_rec_off_stat(a, offset0, offset1, len0, n1, nonfinite, mx, err, _state); - is_hermitian_rec_off_stat(a, offset0, offset1+n1, len0, n2, nonfinite, mx, err, _state); - } - return; - } - else - { - /* base case */ - ae_complex *p1, *p2, *prow, *pcol; - double v; - ae_int_t i, j; - - p1 = (ae_complex*)(a->ptr)+offset0*a->stride+offset1; - p2 = (ae_complex*)(a->ptr)+offset1*a->stride+offset0; - for(i=0; istride; - for(j=0; jx, _state) || !ae_isfinite(pcol->y, _state) || !ae_isfinite(prow->x, _state) || !ae_isfinite(prow->y, _state) ) - { - *nonfinite = ae_true; - } - else - { - v = x_safepythag2(pcol->x, pcol->y); - *mx = *mx>v ? *mx : v; - v = x_safepythag2(prow->x, prow->y); - *mx = *mx>v ? *mx : v; - v = x_safepythag2(pcol->x-prow->x, pcol->y+prow->y); - *err = *err>v ? *err : v; - } - pcol += a->stride; - prow++; - } - } - } -} -/* - * this function checks that diagonal block A0 is Hermitian. - * Block A0 is specified by its offset and size. - * - * [ . ] - * [ A0 ] - * A = [ . ] - * [ . ] - * - * this subroutine updates current values of: - * a) mx maximum value of A[i,j] found so far - * b) err componentwise difference between A0 and A0^H - * - */ -static void is_hermitian_rec_diag_stat(x_matrix *a, ae_int_t offset, ae_int_t len, ae_bool *nonfinite, double *mx, double *err, ae_state *_state) -{ - ae_complex *p, *prow, *pcol; - double v; - ae_int_t i, j; - - /* try to split problem into two smaller ones */ - if( len>x_nb ) - { - ae_int_t n1, n2; - x_split_length(len, x_nb, &n1, &n2); - is_hermitian_rec_diag_stat(a, offset, n1, nonfinite, mx, err, _state); - is_hermitian_rec_diag_stat(a, offset+n1, n2, nonfinite, mx, err, _state); - is_hermitian_rec_off_stat(a, offset+n1, offset, n2, n1, nonfinite, mx, err, _state); - return; - } - - /* base case */ - p = (ae_complex*)(a->ptr)+offset*a->stride+offset; - for(i=0; istride; - for(j=0; jstride,prow++) - { - if( !ae_isfinite(pcol->x, _state) || !ae_isfinite(pcol->y, _state) || !ae_isfinite(prow->x, _state) || !ae_isfinite(prow->y, _state) ) - { - *nonfinite = ae_true; - } - else - { - v = x_safepythag2(pcol->x, pcol->y); - *mx = *mx>v ? *mx : v; - v = x_safepythag2(prow->x, prow->y); - *mx = *mx>v ? *mx : v; - v = x_safepythag2(pcol->x-prow->x, pcol->y+prow->y); - *err = *err>v ? *err : v; - } - } - if( !ae_isfinite(p[i+i*a->stride].x, _state) || !ae_isfinite(p[i+i*a->stride].y, _state) ) - { - *nonfinite = ae_true; - } - else - { - v = fabs(p[i+i*a->stride].x); - *mx = *mx>v ? *mx : v; - v = fabs(p[i+i*a->stride].y); - *err = *err>v ? *err : v; - } - } -} -/* - * this function copies offdiagonal block BL to its symmetric counterpart - * BU (see below). Block BL is specified by offsets (offset0,offset1) - * and sizes (len0,len1). - * - * [ . ] - * [ A0 BU ] - * A = [ BL A1 ] - * [ . ] - * - */ -static void force_symmetric_rec_off_stat(x_matrix *a, ae_int_t offset0, ae_int_t offset1, ae_int_t len0, ae_int_t len1) -{ - /* try to split problem into two smaller ones */ - if( len0>x_nb || len1>x_nb ) - { - ae_int_t n1, n2; - if( len0>len1 ) - { - x_split_length(len0, x_nb, &n1, &n2); - force_symmetric_rec_off_stat(a, offset0, offset1, n1, len1); - force_symmetric_rec_off_stat(a, offset0+n1, offset1, n2, len1); - } - else - { - x_split_length(len1, x_nb, &n1, &n2); - force_symmetric_rec_off_stat(a, offset0, offset1, len0, n1); - force_symmetric_rec_off_stat(a, offset0, offset1+n1, len0, n2); - } - return; - } - else - { - /* base case */ - double *p1, *p2, *prow, *pcol; - ae_int_t i, j; - - p1 = (double*)(a->ptr)+offset0*a->stride+offset1; - p2 = (double*)(a->ptr)+offset1*a->stride+offset0; - for(i=0; istride; - for(j=0; jstride; - prow++; - } - } - } -} -/* - * this function copies lower part of diagonal block A0 to its upper part - * Block is specified by offset and size. - * - * [ . ] - * [ A0 ] - * A = [ . ] - * [ . ] - * - */ -static void force_symmetric_rec_diag_stat(x_matrix *a, ae_int_t offset, ae_int_t len) -{ - double *p, *prow, *pcol; - ae_int_t i, j; - - /* try to split problem into two smaller ones */ - if( len>x_nb ) - { - ae_int_t n1, n2; - x_split_length(len, x_nb, &n1, &n2); - force_symmetric_rec_diag_stat(a, offset, n1); - force_symmetric_rec_diag_stat(a, offset+n1, n2); - force_symmetric_rec_off_stat(a, offset+n1, offset, n2, n1); - return; - } - - /* base case */ - p = (double*)(a->ptr)+offset*a->stride+offset; - for(i=0; istride; - for(j=0; jstride,prow++) - *pcol = *prow; - } -} -/* - * this function copies Hermitian transpose of offdiagonal block BL to - * its symmetric counterpart BU (see below). Block BL is specified by - * offsets (offset0,offset1) and sizes (len0,len1). - * - * [ . ] - * [ A0 BU ] - * A = [ BL A1 ] - * [ . ] - */ -static void force_hermitian_rec_off_stat(x_matrix *a, ae_int_t offset0, ae_int_t offset1, ae_int_t len0, ae_int_t len1) -{ - /* try to split problem into two smaller ones */ - if( len0>x_nb || len1>x_nb ) - { - ae_int_t n1, n2; - if( len0>len1 ) - { - x_split_length(len0, x_nb, &n1, &n2); - force_hermitian_rec_off_stat(a, offset0, offset1, n1, len1); - force_hermitian_rec_off_stat(a, offset0+n1, offset1, n2, len1); - } - else - { - x_split_length(len1, x_nb, &n1, &n2); - force_hermitian_rec_off_stat(a, offset0, offset1, len0, n1); - force_hermitian_rec_off_stat(a, offset0, offset1+n1, len0, n2); - } - return; - } - else - { - /* base case */ - ae_complex *p1, *p2, *prow, *pcol; - ae_int_t i, j; - - p1 = (ae_complex*)(a->ptr)+offset0*a->stride+offset1; - p2 = (ae_complex*)(a->ptr)+offset1*a->stride+offset0; - for(i=0; istride; - for(j=0; jstride; - prow++; - } - } - } -} -/* - * this function copies Hermitian transpose of lower part of - * diagonal block A0 to its upper part Block is specified by offset and size. - * - * [ . ] - * [ A0 ] - * A = [ . ] - * [ . ] - * - */ -static void force_hermitian_rec_diag_stat(x_matrix *a, ae_int_t offset, ae_int_t len) -{ - ae_complex *p, *prow, *pcol; - ae_int_t i, j; - - /* try to split problem into two smaller ones */ - if( len>x_nb ) - { - ae_int_t n1, n2; - x_split_length(len, x_nb, &n1, &n2); - force_hermitian_rec_diag_stat(a, offset, n1); - force_hermitian_rec_diag_stat(a, offset+n1, n2); - force_hermitian_rec_off_stat(a, offset+n1, offset, n2, n1); - return; - } - - /* base case */ - p = (ae_complex*)(a->ptr)+offset*a->stride+offset; - for(i=0; istride; - for(j=0; jstride,prow++) - *pcol = *prow; - } -} -ae_bool x_is_symmetric(x_matrix *a) -{ - double mx, err; - ae_bool nonfinite; - ae_state _alglib_env_state; - if( a->datatype!=DT_REAL ) - return ae_false; - if( a->cols!=a->rows ) - return ae_false; - if( a->cols==0 || a->rows==0 ) - return ae_true; - ae_state_init(&_alglib_env_state); - mx = 0; - err = 0; - nonfinite = ae_false; - is_symmetric_rec_diag_stat(a, 0, (ae_int_t)a->rows, &nonfinite, &mx, &err, &_alglib_env_state); - if( nonfinite ) - return ae_false; - if( mx==0 ) - return ae_true; - return err/mx<=1.0E-14; -} -ae_bool x_is_hermitian(x_matrix *a) -{ - double mx, err; - ae_bool nonfinite; - ae_state _alglib_env_state; - if( a->datatype!=DT_COMPLEX ) - return ae_false; - if( a->cols!=a->rows ) - return ae_false; - if( a->cols==0 || a->rows==0 ) - return ae_true; - ae_state_init(&_alglib_env_state); - mx = 0; - err = 0; - nonfinite = ae_false; - is_hermitian_rec_diag_stat(a, 0, (ae_int_t)a->rows, &nonfinite, &mx, &err, &_alglib_env_state); - if( nonfinite ) - return ae_false; - if( mx==0 ) - return ae_true; - return err/mx<=1.0E-14; -} -ae_bool x_force_symmetric(x_matrix *a) -{ - if( a->datatype!=DT_REAL ) - return ae_false; - if( a->cols!=a->rows ) - return ae_false; - if( a->cols==0 || a->rows==0 ) - return ae_true; - force_symmetric_rec_diag_stat(a, 0, (ae_int_t)a->rows); - return ae_true; -} -ae_bool x_force_hermitian(x_matrix *a) -{ - if( a->datatype!=DT_COMPLEX ) - return ae_false; - if( a->cols!=a->rows ) - return ae_false; - if( a->cols==0 || a->rows==0 ) - return ae_true; - force_hermitian_rec_diag_stat(a, 0, (ae_int_t)a->rows); - return ae_true; -} - -ae_bool ae_is_symmetric(ae_matrix *a) -{ - x_matrix x; - x.owner = OWN_CALLER; - ae_x_attach_to_matrix(&x, a); - return x_is_symmetric(&x); -} - -ae_bool ae_is_hermitian(ae_matrix *a) -{ - x_matrix x; - x.owner = OWN_CALLER; - ae_x_attach_to_matrix(&x, a); - return x_is_hermitian(&x); -} - -ae_bool ae_force_symmetric(ae_matrix *a) -{ - x_matrix x; - x.owner = OWN_CALLER; - ae_x_attach_to_matrix(&x, a); - return x_force_symmetric(&x); -} - -ae_bool ae_force_hermitian(ae_matrix *a) -{ - x_matrix x; - x.owner = OWN_CALLER; - ae_x_attach_to_matrix(&x, a); - return x_force_hermitian(&x); -} - -/************************************************************************ -This function converts six-bit value (from 0 to 63) to character (only -digits, lowercase and uppercase letters, minus and underscore are used). - -If v is negative or greater than 63, this function returns '?'. -************************************************************************/ -static char _sixbits2char_tbl[64] = { - '0', '1', '2', '3', '4', '5', '6', '7', - '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', - 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', - 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', - 'W', 'X', 'Y', 'Z', 'a', 'b', 'c', 'd', - 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', - 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', - 'u', 'v', 'w', 'x', 'y', 'z', '-', '_' }; - -char ae_sixbits2char(ae_int_t v) -{ - - if( v<0 || v>63 ) - return '?'; - return _sixbits2char_tbl[v]; - - /* v is correct, process it */ - /*if( v<10 ) - return '0'+v; - v -= 10; - if( v<26 ) - return 'A'+v; - v -= 26; - if( v<26 ) - return 'a'+v; - v -= 26; - return v==0 ? '-' : '_';*/ -} - -/************************************************************************ -This function converts character to six-bit value (from 0 to 63). - -This function is inverse of ae_sixbits2char() -If c is not correct character, this function returns -1. -************************************************************************/ -static ae_int_t _ae_char2sixbits_tbl[] = { - -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, -1, -1, -1, - -1, -1, -1, -1, -1, 62, -1, -1, - 0, 1, 2, 3, 4, 5, 6, 7, - 8, 9, -1, -1, -1, -1, -1, -1, - -1, 10, 11, 12, 13, 14, 15, 16, - 17, 18, 19, 20, 21, 22, 23, 24, - 25, 26, 27, 28, 29, 30, 31, 32, - 33, 34, 35, -1, -1, -1, -1, 63, - -1, 36, 37, 38, 39, 40, 41, 42, - 43, 44, 45, 46, 47, 48, 49, 50, - 51, 52, 53, 54, 55, 56, 57, 58, - 59, 60, 61, -1, -1, -1, -1, -1 }; -ae_int_t ae_char2sixbits(char c) -{ - return (c>=0 && c<127) ? _ae_char2sixbits_tbl[(int)c] : -1; -} - -/************************************************************************ -This function converts three bytes (24 bits) to four six-bit values -(24 bits again). - -src pointer to three bytes -dst pointer to four ints -************************************************************************/ -void ae_threebytes2foursixbits(const unsigned char *src, ae_int_t *dst) -{ - dst[0] = src[0] & 0x3F; - dst[1] = (src[0]>>6) | ((src[1]&0x0F)<<2); - dst[2] = (src[1]>>4) | ((src[2]&0x03)<<4); - dst[3] = src[2]>>2; -} - -/************************************************************************ -This function converts four six-bit values (24 bits) to three bytes -(24 bits again). - -src pointer to four ints -dst pointer to three bytes -************************************************************************/ -void ae_foursixbits2threebytes(const ae_int_t *src, unsigned char *dst) -{ - dst[0] = (unsigned char)( src[0] | ((src[1]&0x03)<<6)); - dst[1] = (unsigned char)((src[1]>>2) | ((src[2]&0x0F)<<4)); - dst[2] = (unsigned char)((src[2]>>4) | (src[3]<<2)); -} - -/************************************************************************ -This function serializes boolean value into buffer - -v boolean value to be serialized -buf buffer, at least 12 characters wide - (11 chars for value, one for trailing zero) -state ALGLIB environment state -************************************************************************/ -void ae_bool2str(ae_bool v, char *buf, ae_state *state) -{ - char c = v ? '1' : '0'; - ae_int_t i; - for(i=0; iendianness==AE_BIG_ENDIAN ) - { - for(i=0; i<(ae_int_t)(sizeof(ae_int_t)/2); i++) - { - unsigned char tc; - tc = u.bytes[i]; - u.bytes[i] = u.bytes[sizeof(ae_int_t)-1-i]; - u.bytes[sizeof(ae_int_t)-1-i] = tc; - } - } - - /* - * convert to six-bit representation, output - * - * NOTE: last 12th element of sixbits is always zero, we do not output it - */ - ae_threebytes2foursixbits(u.bytes+0, sixbits+0); - ae_threebytes2foursixbits(u.bytes+3, sixbits+4); - ae_threebytes2foursixbits(u.bytes+6, sixbits+8); - for(i=0; i=AE_SER_ENTRY_LENGTH ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - sixbits[sixbitsread] = d; - sixbitsread++; - buf++; - } - *pasttheend = buf; - if( sixbitsread==0 ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - for(i=sixbitsread; i<12; i++) - sixbits[i] = 0; - ae_foursixbits2threebytes(sixbits+0, u.bytes+0); - ae_foursixbits2threebytes(sixbits+4, u.bytes+3); - ae_foursixbits2threebytes(sixbits+8, u.bytes+6); - if( state->endianness==AE_BIG_ENDIAN ) - { - for(i=0; i<(ae_int_t)(sizeof(ae_int_t)/2); i++) - { - unsigned char tc; - tc = u.bytes[i]; - u.bytes[i] = u.bytes[sizeof(ae_int_t)-1-i]; - u.bytes[sizeof(ae_int_t)-1-i] = tc; - } - } - return u.ival; -} - - -/************************************************************************ -This function serializes double value into buffer - -v double value to be serialized -buf buffer, at least 12 characters wide - (11 chars for value, one for trailing zero) -state ALGLIB environment state -************************************************************************/ -void ae_double2str(double v, char *buf, ae_state *state) -{ - union _u - { - double dval; - unsigned char bytes[9]; - } u; - ae_int_t i; - ae_int_t sixbits[12]; - - /* - * handle special quantities - */ - if( ae_isnan(v, state) ) - { - const char *s = ".nan_______"; - memcpy(buf, s, strlen(s)+1); - return; - } - if( ae_isposinf(v, state) ) - { - const char *s = ".posinf____"; - memcpy(buf, s, strlen(s)+1); - return; - } - if( ae_isneginf(v, state) ) - { - const char *s = ".neginf____"; - memcpy(buf, s, strlen(s)+1); - return; - } - - /* - * process general case: - * 1. copy v to array of chars - * 2. set 9th byte of u.bytes to zero in order to - * simplify conversion to six-bit representation - * 3. convert to little endian (if needed) - * 4. convert to six-bit representation - * (last 12th element of sixbits is always zero, we do not output it) - */ - u.dval = v; - u.bytes[8] = 0; - if( state->endianness==AE_BIG_ENDIAN ) - { - for(i=0; i<(ae_int_t)(sizeof(double)/2); i++) - { - unsigned char tc; - tc = u.bytes[i]; - u.bytes[i] = u.bytes[sizeof(double)-1-i]; - u.bytes[sizeof(double)-1-i] = tc; - } - } - ae_threebytes2foursixbits(u.bytes+0, sixbits+0); - ae_threebytes2foursixbits(u.bytes+3, sixbits+4); - ae_threebytes2foursixbits(u.bytes+6, sixbits+8); - for(i=0; iv_nan; - } - if( strncmp(buf, s_posinf, strlen(s_posinf))==0 ) - { - *pasttheend = buf+strlen(s_posinf); - return state->v_posinf; - } - if( strncmp(buf, s_neginf, strlen(s_neginf))==0 ) - { - *pasttheend = buf+strlen(s_neginf); - return state->v_neginf; - } - ae_break(state, ERR_ASSERTION_FAILED, emsg); - } - - /* - * General case: - * 1. read and decode six-bit digits - * 2. check that all 11 digits were read - * 3. set last 12th digit to zero (needed for simplicity of conversion) - * 4. convert to 8 bytes - * 5. convert to big endian representation, if needed - */ - sixbitsread = 0; - while( *buf!=' ' && *buf!='\t' && *buf!='\n' && *buf!='\r' && *buf!=0 ) - { - ae_int_t d; - d = ae_char2sixbits(*buf); - if( d<0 || sixbitsread>=AE_SER_ENTRY_LENGTH ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - sixbits[sixbitsread] = d; - sixbitsread++; - buf++; - } - *pasttheend = buf; - if( sixbitsread!=AE_SER_ENTRY_LENGTH ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - sixbits[AE_SER_ENTRY_LENGTH] = 0; - ae_foursixbits2threebytes(sixbits+0, u.bytes+0); - ae_foursixbits2threebytes(sixbits+4, u.bytes+3); - ae_foursixbits2threebytes(sixbits+8, u.bytes+6); - if( state->endianness==AE_BIG_ENDIAN ) - { - for(i=0; i<(ae_int_t)(sizeof(double)/2); i++) - { - unsigned char tc; - tc = u.bytes[i]; - u.bytes[i] = u.bytes[sizeof(double)-1-i]; - u.bytes[sizeof(double)-1-i] = tc; - } - } - return u.dval; -} - - -/************************************************************************ -This function performs given number of spin-wait iterations -************************************************************************/ -void ae_spin_wait(ae_int_t cnt) -{ - /* - * these strange operations with ae_never_change_it are necessary to - * prevent compiler optimization of the loop. - */ - volatile ae_int_t i; - - /* very unlikely because no one will wait for such amount of cycles */ - if( cnt>0x12345678 ) - ae_never_change_it = cnt%10; - - /* spin wait, test condition which will never be true */ - for(i=0; i0 ) - ae_never_change_it--; -} - - -/************************************************************************ -This function causes the calling thread to relinquish the CPU. The thread -is moved to the end of the queue and some other thread gets to run. - -NOTE: this function should NOT be called when AE_OS is AE_UNKNOWN - the - whole program will be abnormally terminated. -************************************************************************/ -void ae_yield() -{ -#if AE_OS==AE_WINDOWS - if( !SwitchToThread() ) - Sleep(0); -#elif AE_OS==AE_POSIX - sched_yield(); -#else - abort(); -#endif -} - -/************************************************************************ -This function initializes ae_lock structure and sets lock in a free mode. -************************************************************************/ -void ae_init_lock(ae_lock *lock) -{ -#if AE_OS==AE_WINDOWS - lock->p_lock = (ae_int_t*)ae_align((void*)(&lock->buf),AE_LOCK_ALIGNMENT); - lock->p_lock[0] = 0; -#elif AE_OS==AE_POSIX - pthread_mutex_init(&lock->mutex, NULL); -#else - lock->is_locked = ae_false; -#endif -} - - -/************************************************************************ -This function acquires lock. In case lock is busy, we perform several -iterations inside tight loop before trying again. -************************************************************************/ -void ae_acquire_lock(ae_lock *lock) -{ -#if AE_OS==AE_WINDOWS - ae_int_t cnt = 0; -#ifdef AE_SMP_DEBUGCOUNTERS - InterlockedIncrement((LONG volatile *)&_ae_dbg_lock_acquisitions); -#endif - for(;;) - { - if( InterlockedCompareExchange((LONG volatile *)lock->p_lock, 1, 0)==0 ) - return; - ae_spin_wait(AE_LOCK_CYCLES); -#ifdef AE_SMP_DEBUGCOUNTERS - InterlockedIncrement((LONG volatile *)&_ae_dbg_lock_spinwaits); -#endif - cnt++; - if( cnt%AE_LOCK_TESTS_BEFORE_YIELD==0 ) - { -#ifdef AE_SMP_DEBUGCOUNTERS - InterlockedIncrement((LONG volatile *)&_ae_dbg_lock_yields); -#endif - ae_yield(); - } - } -#elif AE_OS==AE_POSIX - ae_int_t cnt = 0; - for(;;) - { - if( pthread_mutex_trylock(&lock->mutex)==0 ) - return; - ae_spin_wait(AE_LOCK_CYCLES); - cnt++; - if( cnt%AE_LOCK_TESTS_BEFORE_YIELD==0 ) - ae_yield(); - } - ; -#else - AE_CRITICAL_ASSERT(!lock->is_locked); - lock->is_locked = ae_true; -#endif -} - - -/************************************************************************ -This function releases lock. -************************************************************************/ -void ae_release_lock(ae_lock *lock) -{ -#if AE_OS==AE_WINDOWS - InterlockedExchange((LONG volatile *)lock->p_lock, 0); -#elif AE_OS==AE_POSIX - pthread_mutex_unlock(&lock->mutex); -#else - lock->is_locked = ae_false; -#endif -} - - -/************************************************************************ -This function frees ae_lock structure. -************************************************************************/ -void ae_free_lock(ae_lock *lock) -{ -#if AE_OS==AE_POSIX - pthread_mutex_destroy(&lock->mutex); -#endif -} - - -/************************************************************************ -This function creates ae_shared_pool structure. - -dst destination shared pool; - already allocated, but not initialized. -state ALGLIB environment state -make_automatic if true, pool is added to the dynamic block list - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -dst is assumed to be uninitialized, its fields are ignored. -************************************************************************/ -ae_bool ae_shared_pool_init(void *_dst, ae_state *state, ae_bool make_automatic) -{ - ae_shared_pool *dst; - - dst = (ae_shared_pool*)_dst; - - /* init */ - dst->seed_object = NULL; - dst->recycled_objects = NULL; - dst->recycled_entries = NULL; - dst->enumeration_counter = NULL; - dst->size_of_object = 0; - dst->init = NULL; - dst->init_copy = NULL; - dst->destroy = NULL; - dst->frame_entry.deallocator = ae_shared_pool_destroy; - dst->frame_entry.ptr = dst; - if( make_automatic && state!=NULL ) - ae_db_attach(&dst->frame_entry, state); - ae_init_lock(&dst->pool_lock); - return ae_true; -} - - -/************************************************************************ -This function clears all dynamically allocated fields of the pool except -for the lock. It does NOT try to acquire pool_lock. - -NOTE: this function is NOT thread-safe, it is not protected by lock. -************************************************************************/ -static void ae_shared_pool_internalclear(ae_shared_pool *dst) -{ - ae_shared_pool_entry *ptr, *tmp; - - /* destroy seed */ - if( dst->seed_object!=NULL ) - { - dst->destroy((void*)dst->seed_object); - ae_free((void*)dst->seed_object); - dst->seed_object = NULL; - } - - /* destroy recycled objects */ - for(ptr=dst->recycled_objects; ptr!=NULL;) - { - tmp = (ae_shared_pool_entry*)ptr->next_entry; - dst->destroy(ptr->obj); - ae_free(ptr->obj); - ae_free(ptr); - ptr = tmp; - } - dst->recycled_objects = NULL; - - /* destroy recycled entries */ - for(ptr=dst->recycled_entries; ptr!=NULL;) - { - tmp = (ae_shared_pool_entry*)ptr->next_entry; - ae_free(ptr); - ptr = tmp; - } - dst->recycled_entries = NULL; -} - - -/************************************************************************ -This function creates copy of ae_shared_pool. - -dst destination pool, allocated but not initialized -src source pool -state ALGLIB environment state -make_automatic if true, pool is added to the dynamic block list - -Error handling: -* if state is NULL, returns ae_false on allocation error -* if state is not NULL, calls ae_break() on allocation error -* returns ae_true on success - -dst is assumed to be uninitialized, its fields are ignored. - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -ae_bool ae_shared_pool_init_copy(void *_dst, void *_src, ae_state *state, ae_bool make_automatic) -{ - ae_shared_pool *dst, *src; - ae_shared_pool_entry *ptr; - - dst = (ae_shared_pool*)_dst; - src = (ae_shared_pool*)_src; - if( !ae_shared_pool_init(dst, state, make_automatic) ) - return ae_false; - - /* copy non-pointer fields */ - dst->size_of_object = src->size_of_object; - dst->init = src->init; - dst->init_copy = src->init_copy; - dst->destroy = src->destroy; - ae_init_lock(&dst->pool_lock); - - /* copy seed object */ - if( src->seed_object!=NULL ) - { - dst->seed_object = ae_malloc(dst->size_of_object, state); - if( dst->seed_object==NULL ) - return ae_false; - if( !dst->init_copy(dst->seed_object, src->seed_object, state, ae_false) ) - return ae_false; - } - - /* copy recycled objects */ - dst->recycled_objects = NULL; - for(ptr=src->recycled_objects; ptr!=NULL; ptr=(ae_shared_pool_entry*)ptr->next_entry) - { - ae_shared_pool_entry *tmp; - tmp = (ae_shared_pool_entry*)ae_malloc(sizeof(ae_shared_pool_entry), state); - if( tmp==NULL ) - return ae_false; - tmp->obj = ae_malloc(dst->size_of_object, state); - if( tmp->obj==NULL ) - return ae_false; - if( !dst->init_copy(tmp->obj, ptr->obj, state, ae_false) ) - return ae_false; - tmp->next_entry = dst->recycled_objects; - dst->recycled_objects = tmp; - } - - /* recycled entries are not copied because they do not store any information */ - dst->recycled_entries = NULL; - - /* enumeration counter is reset on copying */ - dst->enumeration_counter = NULL; - - /* initialize frame record */ - dst->frame_entry.deallocator = ae_shared_pool_destroy; - dst->frame_entry.ptr = dst; - - /* return */ - return ae_true; -} - - -/************************************************************************ -This function clears contents of the pool, but pool remain usable. - -IMPORTANT: this function invalidates dst, it can not be used after it is - cleared. - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -void ae_shared_pool_clear(void *_dst) -{ - ae_shared_pool *dst = (ae_shared_pool*)_dst; - - /* clear seed and lists */ - ae_shared_pool_internalclear(dst); - - /* clear fields */ - dst->seed_object = NULL; - dst->recycled_objects = NULL; - dst->recycled_entries = NULL; - dst->enumeration_counter = NULL; - dst->size_of_object = 0; - dst->init = NULL; - dst->init_copy = NULL; - dst->destroy = NULL; -} - - -/************************************************************************ -This function destroys pool (object is left in invalid state, all -dynamically allocated memory is freed). - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -void ae_shared_pool_destroy(void *_dst) -{ - ae_shared_pool *dst = (ae_shared_pool*)_dst; - ae_shared_pool_clear(_dst); - ae_free_lock(&dst->pool_lock); -} - - -/************************************************************************ -This function returns True, if internal seed object was set. It returns -False for un-seeded pool. - -dst destination pool (initialized by constructor function) - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -ae_bool ae_shared_pool_is_initialized(void *_dst) -{ - ae_shared_pool *dst = (ae_shared_pool*)_dst; - return dst->seed_object!=NULL; -} - - -/************************************************************************ -This function sets internal seed object. All objects owned by the pool -(current seed object, recycled objects) are automatically freed. - -dst destination pool (initialized by constructor function) -seed_object new seed object -size_of_object sizeof(), used to allocate memory -init constructor function -init_copy copy constructor -clear destructor function -state ALGLIB environment state - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -void ae_shared_pool_set_seed( - ae_shared_pool *dst, - void *seed_object, - ae_int_t size_of_object, - ae_bool (*init)(void* dst, ae_state* state, ae_bool make_automatic), - ae_bool (*init_copy)(void* dst, void* src, ae_state* state, ae_bool make_automatic), - void (*destroy)(void* ptr), - ae_state *state) -{ - /* destroy internal objects */ - ae_shared_pool_internalclear(dst); - - /* set non-pointer fields */ - dst->size_of_object = size_of_object; - dst->init = init; - dst->init_copy = init_copy; - dst->destroy = destroy; - - /* set seed object */ - dst->seed_object = ae_malloc(size_of_object, state); - ae_assert(dst->seed_object!=NULL, "ALGLIB: unable to allocate memory for ae_shared_pool_set_seed()", state); - ae_assert( - init_copy(dst->seed_object, seed_object, state, ae_false), - "ALGLIB: unable to initialize seed in ae_shared_pool_set_seed()", - state); -} - - -/************************************************************************ -This function retrieves a copy of the seed object from the pool and -stores it to target smart pointer ptr. - -In case target pointer owns non-NULL value, it is deallocated before -storing value retrieved from pool. Target pointer becomes owner of the -value which was retrieved from pool. - -pool pool -pptr pointer to ae_smart_ptr structure -state ALGLIB environment state - -NOTE: this function IS thread-safe. It acquires pool lock during its - operation and can be used simultaneously from several threads. -************************************************************************/ -void ae_shared_pool_retrieve( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state) -{ - void *new_obj; - - /* assert that pool was seeded */ - ae_assert( - pool->seed_object!=NULL, - "ALGLIB: shared pool is not seeded, PoolRetrieve() failed", - state); - - /* acquire lock */ - ae_acquire_lock(&pool->pool_lock); - - /* try to reuse recycled objects */ - if( pool->recycled_objects!=NULL ) - { - void *new_obj; - ae_shared_pool_entry *result; - - /* retrieve entry/object from list of recycled objects */ - result = pool->recycled_objects; - pool->recycled_objects = (ae_shared_pool_entry*)pool->recycled_objects->next_entry; - new_obj = result->obj; - result->obj = NULL; - - /* move entry to list of recycled entries */ - result->next_entry = pool->recycled_entries; - pool->recycled_entries = result; - - /* release lock */ - ae_release_lock(&pool->pool_lock); - - /* assign object to smart pointer */ - ae_smart_ptr_assign(pptr, new_obj, ae_true, ae_true, pool->destroy); - return; - } - - /* release lock; we do not need it anymore because copy constructor does not modify source variable */ - ae_release_lock(&pool->pool_lock); - - /* create new object from seed */ - new_obj = ae_malloc(pool->size_of_object, state); - ae_assert(new_obj!=NULL, "ALGLIB: unable to allocate memory for ae_shared_pool_retrieve()", state); - ae_assert( - pool->init_copy(new_obj, pool->seed_object, state, ae_false), - "ALGLIB: unable to initialize object in ae_shared_pool_retrieve()", - state); - - /* assign object to smart pointer and return */ - ae_smart_ptr_assign(pptr, new_obj, ae_true, ae_true, pool->destroy); -} - - -/************************************************************************ -This function recycles object owned by smart pointer by moving it to -internal storage of the shared pool. - -Source pointer must own the object. After function is over, it owns NULL -pointer. - -pool pool -pptr pointer to ae_smart_ptr structure -state ALGLIB environment state - -NOTE: this function IS thread-safe. It acquires pool lock during its - operation and can be used simultaneously from several threads. -************************************************************************/ -void ae_shared_pool_recycle( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state) -{ - ae_shared_pool_entry *new_entry; - - /* assert that pool was seeded */ - ae_assert( - pool->seed_object!=NULL, - "ALGLIB: shared pool is not seeded, PoolRecycle() failed", - state); - - /* assert that pointer non-null and owns the object */ - ae_assert(pptr->is_owner, "ALGLIB: pptr in ae_shared_pool_recycle() does not own its pointer", state); - ae_assert(pptr->ptr!=NULL, "ALGLIB: pptr in ae_shared_pool_recycle() is NULL", state); - - /* acquire lock */ - ae_acquire_lock(&pool->pool_lock); - - /* acquire shared pool entry (reuse one from recycled_entries or malloc new one) */ - if( pool->recycled_entries!=NULL ) - { - /* reuse previously allocated entry */ - new_entry = pool->recycled_entries; - pool->recycled_entries = (ae_shared_pool_entry*)new_entry->next_entry; - } - else - { - /* - * Allocate memory for new entry. - * - * NOTE: we release pool lock during allocation because ae_malloc() may raise - * exception and we do not want our pool to be left in the locked state. - */ - ae_release_lock(&pool->pool_lock); - new_entry = (ae_shared_pool_entry*)ae_malloc(sizeof(ae_shared_pool_entry), state); - ae_assert(new_entry!=NULL, "ALGLIB: unable to allocate memory in ae_shared_pool_recycle()", state); - ae_acquire_lock(&pool->pool_lock); - } - - /* add object to the list of recycled objects */ - new_entry->obj = pptr->ptr; - new_entry->next_entry = pool->recycled_objects; - pool->recycled_objects = new_entry; - - /* release lock object */ - ae_release_lock(&pool->pool_lock); - - /* release source pointer */ - ae_smart_ptr_release(pptr); -} - - -/************************************************************************ -This function clears internal list of recycled objects, but does not -change seed object managed by the pool. - -pool pool -state ALGLIB environment state - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -void ae_shared_pool_clear_recycled( - ae_shared_pool *pool, - ae_state *state) -{ - ae_shared_pool_entry *ptr, *tmp; - - /* clear recycled objects */ - for(ptr=pool->recycled_objects; ptr!=NULL;) - { - tmp = (ae_shared_pool_entry*)ptr->next_entry; - pool->destroy(ptr->obj); - ae_free(ptr->obj); - ae_free(ptr); - ptr = tmp; - } - pool->recycled_objects = NULL; -} - - -/************************************************************************ -This function allows to enumerate recycled elements of the shared pool. -It stores pointer to the first recycled object in the smart pointer. - -IMPORTANT: -* in case target pointer owns non-NULL value, it is deallocated before - storing value retrieved from pool. -* recycled object IS NOT removed from pool -* target pointer DOES NOT become owner of the new value -* this function IS NOT thread-safe -* you SHOULD NOT modify shared pool during enumeration (although you can - modify state of the objects retrieved from pool) -* in case there is no recycled objects in the pool, NULL is stored to pptr -* in case pool is not seeded, NULL is stored to pptr - -pool pool -pptr pointer to ae_smart_ptr structure -state ALGLIB environment state -************************************************************************/ -void ae_shared_pool_first_recycled( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state) -{ - /* modify internal enumeration counter */ - pool->enumeration_counter = pool->recycled_objects; - - /* exit on empty list */ - if( pool->enumeration_counter==NULL ) - { - ae_smart_ptr_assign(pptr, NULL, ae_false, ae_false, NULL); - return; - } - - /* assign object to smart pointer */ - ae_smart_ptr_assign(pptr, pool->enumeration_counter->obj, ae_false, ae_false, pool->destroy); -} - - -/************************************************************************ -This function allows to enumerate recycled elements of the shared pool. -It stores pointer to the next recycled object in the smart pointer. - -IMPORTANT: -* in case target pointer owns non-NULL value, it is deallocated before - storing value retrieved from pool. -* recycled object IS NOT removed from pool -* target pointer DOES NOT become owner of the new value -* this function IS NOT thread-safe -* you SHOULD NOT modify shared pool during enumeration (although you can - modify state of the objects retrieved from pool) -* in case there is no recycled objects left in the pool, NULL is stored. -* in case pool is not seeded, NULL is stored. - -pool pool -pptr pointer to ae_smart_ptr structure -state ALGLIB environment state -************************************************************************/ -void ae_shared_pool_next_recycled( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state) -{ - /* exit on end of list */ - if( pool->enumeration_counter==NULL ) - { - ae_smart_ptr_assign(pptr, NULL, ae_false, ae_false, NULL); - return; - } - - /* modify internal enumeration counter */ - pool->enumeration_counter = (ae_shared_pool_entry*)pool->enumeration_counter->next_entry; - - /* exit on empty list */ - if( pool->enumeration_counter==NULL ) - { - ae_smart_ptr_assign(pptr, NULL, ae_false, ae_false, NULL); - return; - } - - /* assign object to smart pointer */ - ae_smart_ptr_assign(pptr, pool->enumeration_counter->obj, ae_false, ae_false, pool->destroy); -} - - - -/************************************************************************ -This function clears internal list of recycled objects and seed object. -However, pool still can be used (after initialization with another seed). - -pool pool -state ALGLIB environment state - -NOTE: this function is NOT thread-safe. It does not acquire pool lock, so - you should NOT call it when lock can be used by another thread. -************************************************************************/ -void ae_shared_pool_reset( - ae_shared_pool *pool, - ae_state *state) -{ - /* clear seed and lists */ - ae_shared_pool_internalclear(pool); - - /* clear fields */ - pool->seed_object = NULL; - pool->recycled_objects = NULL; - pool->recycled_entries = NULL; - pool->enumeration_counter = NULL; - pool->size_of_object = 0; - pool->init = NULL; - pool->init_copy = NULL; - pool->destroy = NULL; -} - - -/************************************************************************ -This function initializes serializer -************************************************************************/ -void ae_serializer_init(ae_serializer *serializer) -{ - serializer->mode = AE_SM_DEFAULT; - serializer->entries_needed = 0; - serializer->bytes_asked = 0; -} - -void ae_serializer_clear(ae_serializer *serializer) -{ -} - -void ae_serializer_alloc_start(ae_serializer *serializer) -{ - serializer->entries_needed = 0; - serializer->bytes_asked = 0; - serializer->mode = AE_SM_ALLOC; -} - -void ae_serializer_alloc_entry(ae_serializer *serializer) -{ - serializer->entries_needed++; -} - -ae_int_t ae_serializer_get_alloc_size(ae_serializer *serializer) -{ - ae_int_t rows, lastrowsize, result; - - serializer->mode = AE_SM_READY2S; - - /* if no entries needes (degenerate case) */ - if( serializer->entries_needed==0 ) - { - serializer->bytes_asked = 1; - return serializer->bytes_asked; - } - - /* non-degenerate case */ - rows = serializer->entries_needed/AE_SER_ENTRIES_PER_ROW; - lastrowsize = AE_SER_ENTRIES_PER_ROW; - if( serializer->entries_needed%AE_SER_ENTRIES_PER_ROW ) - { - lastrowsize = serializer->entries_needed%AE_SER_ENTRIES_PER_ROW; - rows++; - } - - /* calculate result size */ - result = ((rows-1)*AE_SER_ENTRIES_PER_ROW+lastrowsize)*AE_SER_ENTRY_LENGTH; - result += (rows-1)*(AE_SER_ENTRIES_PER_ROW-1)+(lastrowsize-1); - result += rows*2; - serializer->bytes_asked = result; - return result; -} - -#ifdef AE_USE_CPP_SERIALIZATION -void ae_serializer_sstart_str(ae_serializer *serializer, std::string *buf) -{ - serializer->mode = AE_SM_TO_CPPSTRING; - serializer->out_cppstr = buf; - serializer->entries_saved = 0; - serializer->bytes_written = 0; -} -#endif - -#ifdef AE_USE_CPP_SERIALIZATION -void ae_serializer_ustart_str(ae_serializer *serializer, const std::string *buf) -{ - serializer->mode = AE_SM_FROM_STRING; - serializer->in_str = buf->c_str(); -} -#endif - -void ae_serializer_sstart_str(ae_serializer *serializer, char *buf) -{ - serializer->mode = AE_SM_TO_STRING; - serializer->out_str = buf; - serializer->out_str[0] = 0; - serializer->entries_saved = 0; - serializer->bytes_written = 0; -} - -void ae_serializer_ustart_str(ae_serializer *serializer, const char *buf) -{ - serializer->mode = AE_SM_FROM_STRING; - serializer->in_str = buf; -} - -void ae_serializer_serialize_bool(ae_serializer *serializer, ae_bool v, ae_state *state) -{ - char buf[AE_SER_ENTRY_LENGTH+2+1]; - const char *emsg = "ALGLIB: serialization integrity error"; - ae_int_t bytes_appended; - - /* prepare serialization, check consistency */ - ae_bool2str(v, buf, state); - serializer->entries_saved++; - if( serializer->entries_saved%AE_SER_ENTRIES_PER_ROW ) - strcat(buf, " "); - else - strcat(buf, "\r\n"); - bytes_appended = (ae_int_t)strlen(buf); - if( serializer->bytes_written+bytes_appended > serializer->bytes_asked ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - serializer->bytes_written += bytes_appended; - - /* append to buffer */ -#ifdef AE_USE_CPP_SERIALIZATION - if( serializer->mode==AE_SM_TO_CPPSTRING ) - { - *(serializer->out_cppstr) += buf; - return; - } -#endif - if( serializer->mode==AE_SM_TO_STRING ) - { - strcat(serializer->out_str, buf); - serializer->out_str += bytes_appended; - return; - } - ae_break(state, ERR_ASSERTION_FAILED, emsg); -} - -void ae_serializer_serialize_int(ae_serializer *serializer, ae_int_t v, ae_state *state) -{ - char buf[AE_SER_ENTRY_LENGTH+2+1]; - const char *emsg = "ALGLIB: serialization integrity error"; - ae_int_t bytes_appended; - - /* prepare serialization, check consistency */ - ae_int2str(v, buf, state); - serializer->entries_saved++; - if( serializer->entries_saved%AE_SER_ENTRIES_PER_ROW ) - strcat(buf, " "); - else - strcat(buf, "\r\n"); - bytes_appended = (ae_int_t)strlen(buf); - if( serializer->bytes_written+bytes_appended > serializer->bytes_asked ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - serializer->bytes_written += bytes_appended; - - /* append to buffer */ -#ifdef AE_USE_CPP_SERIALIZATION - if( serializer->mode==AE_SM_TO_CPPSTRING ) - { - *(serializer->out_cppstr) += buf; - return; - } -#endif - if( serializer->mode==AE_SM_TO_STRING ) - { - strcat(serializer->out_str, buf); - serializer->out_str += bytes_appended; - return; - } - ae_break(state, ERR_ASSERTION_FAILED, emsg); -} - -void ae_serializer_serialize_double(ae_serializer *serializer, double v, ae_state *state) -{ - char buf[AE_SER_ENTRY_LENGTH+2+1]; - const char *emsg = "ALGLIB: serialization integrity error"; - ae_int_t bytes_appended; - - /* prepare serialization, check consistency */ - ae_double2str(v, buf, state); - serializer->entries_saved++; - if( serializer->entries_saved%AE_SER_ENTRIES_PER_ROW ) - strcat(buf, " "); - else - strcat(buf, "\r\n"); - bytes_appended = (ae_int_t)strlen(buf); - if( serializer->bytes_written+bytes_appended > serializer->bytes_asked ) - ae_break(state, ERR_ASSERTION_FAILED, emsg); - serializer->bytes_written += bytes_appended; - - /* append to buffer */ -#ifdef AE_USE_CPP_SERIALIZATION - if( serializer->mode==AE_SM_TO_CPPSTRING ) - { - *(serializer->out_cppstr) += buf; - return; - } -#endif - if( serializer->mode==AE_SM_TO_STRING ) - { - strcat(serializer->out_str, buf); - serializer->out_str += bytes_appended; - return; - } - ae_break(state, ERR_ASSERTION_FAILED, emsg); -} - -void ae_serializer_unserialize_bool(ae_serializer *serializer, ae_bool *v, ae_state *state) -{ - *v = ae_str2bool(serializer->in_str, state, &serializer->in_str); -} - -void ae_serializer_unserialize_int(ae_serializer *serializer, ae_int_t *v, ae_state *state) -{ - *v = ae_str2int(serializer->in_str, state, &serializer->in_str); -} - -void ae_serializer_unserialize_double(ae_serializer *serializer, double *v, ae_state *state) -{ - *v = ae_str2double(serializer->in_str, state, &serializer->in_str); -} - -void ae_serializer_stop(ae_serializer *serializer) -{ -} - - -/************************************************************************ -Complex math functions -************************************************************************/ -ae_complex ae_complex_from_d(double v) -{ - ae_complex r; - r.x = v; - r.y = 0.0; - return r; -} - -ae_complex ae_c_neg(ae_complex lhs) -{ - ae_complex result; - result.x = -lhs.x; - result.y = -lhs.y; - return result; -} - -ae_complex ae_c_conj(ae_complex lhs, ae_state *state) -{ - ae_complex result; - result.x = +lhs.x; - result.y = -lhs.y; - return result; -} - -ae_complex ae_c_sqr(ae_complex lhs, ae_state *state) -{ - ae_complex result; - result.x = lhs.x*lhs.x-lhs.y*lhs.y; - result.y = 2*lhs.x*lhs.y; - return result; -} - -double ae_c_abs(ae_complex z, ae_state *state) -{ - double w; - double xabs; - double yabs; - double v; - - xabs = fabs(z.x); - yabs = fabs(z.y); - w = xabs>yabs ? xabs : yabs; - v = xabsx; - v0y = -v0->y; - v1x = v1->x; - v1y = -v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - if( !bconj0 && bconj1 ) - { - double v0x, v0y, v1x, v1y; - for(i=0; ix; - v0y = v0->y; - v1x = v1->x; - v1y = -v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - if( bconj0 && !bconj1 ) - { - double v0x, v0y, v1x, v1y; - for(i=0; ix; - v0y = -v0->y; - v1x = v1->x; - v1y = v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - if( !bconj0 && !bconj1 ) - { - double v0x, v0y, v1x, v1y; - for(i=0; ix; - v0y = v0->y; - v1x = v1->x; - v1y = v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - result.x = rx; - result.y = ry; - return result; -} - -void ae_v_cmove(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - if( bconj ) - { - for(i=0; ix = vsrc->x; - vdst->y = -vsrc->y; - } - } - else - { - for(i=0; ix = vsrc->x; - vdst->y = -vsrc->y; - } - } - else - { - for(i=0; ix = -vsrc->x; - vdst->y = vsrc->y; - } - } - else - { - for(i=0; ix = -vsrc->x; - vdst->y = -vsrc->y; - } - } - } - else - { - /* - * optimized case - */ - if( bconj ) - { - for(i=0; ix = -vsrc->x; - vdst->y = vsrc->y; - } - } - else - { - for(i=0; ix = -vsrc->x; - vdst->y = -vsrc->y; - } - } - } -} - -void ae_v_cmoved(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - if( bconj ) - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = -alpha*vsrc->y; - } - } - else - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = alpha*vsrc->y; - } - } - } - else - { - /* - * optimized case - */ - if( bconj ) - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = -alpha*vsrc->y; - } - } - else - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = alpha*vsrc->y; - } - } - } -} - -void ae_v_cmovec(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, ae_complex alpha) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - if( bconj ) - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x+ay*vsrc->y; - vdst->y = -ax*vsrc->y+ay*vsrc->x; - } - } - else - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x-ay*vsrc->y; - vdst->y = ax*vsrc->y+ay*vsrc->x; - } - } - } - else - { - /* - * highly optimized case - */ - if( bconj ) - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x+ay*vsrc->y; - vdst->y = -ax*vsrc->y+ay*vsrc->x; - } - } - else - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x-ay*vsrc->y; - vdst->y = ax*vsrc->y+ay*vsrc->x; - } - } - } -} - -void ae_v_cadd(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - if( bconj ) - { - for(i=0; ix += vsrc->x; - vdst->y -= vsrc->y; - } - } - else - { - for(i=0; ix += vsrc->x; - vdst->y += vsrc->y; - } - } - } - else - { - /* - * optimized case - */ - if( bconj ) - { - for(i=0; ix += vsrc->x; - vdst->y -= vsrc->y; - } - } - else - { - for(i=0; ix += vsrc->x; - vdst->y += vsrc->y; - } - } - } -} - -void ae_v_caddd(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - if( bconj ) - { - for(i=0; ix += alpha*vsrc->x; - vdst->y -= alpha*vsrc->y; - } - } - else - { - for(i=0; ix += alpha*vsrc->x; - vdst->y += alpha*vsrc->y; - } - } - } - else - { - /* - * optimized case - */ - if( bconj ) - { - for(i=0; ix += alpha*vsrc->x; - vdst->y -= alpha*vsrc->y; - } - } - else - { - for(i=0; ix += alpha*vsrc->x; - vdst->y += alpha*vsrc->y; - } - } - } -} - -void ae_v_caddc(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, ae_complex alpha) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - double ax = alpha.x, ay = alpha.y; - if( bconj ) - { - for(i=0; ix += ax*vsrc->x+ay*vsrc->y; - vdst->y -= ax*vsrc->y-ay*vsrc->x; - } - } - else - { - for(i=0; ix += ax*vsrc->x-ay*vsrc->y; - vdst->y += ax*vsrc->y+ay*vsrc->x; - } - } - } - else - { - /* - * highly optimized case - */ - double ax = alpha.x, ay = alpha.y; - if( bconj ) - { - for(i=0; ix += ax*vsrc->x+ay*vsrc->y; - vdst->y -= ax*vsrc->y-ay*vsrc->x; - } - } - else - { - for(i=0; ix += ax*vsrc->x-ay*vsrc->y; - vdst->y += ax*vsrc->y+ay*vsrc->x; - } - } - } -} - -void ae_v_csub(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n) -{ - ae_bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - /* - * general unoptimized case - */ - if( bconj ) - { - for(i=0; ix -= vsrc->x; - vdst->y += vsrc->y; - } - } - else - { - for(i=0; ix -= vsrc->x; - vdst->y -= vsrc->y; - } - } - } - else - { - /* - * highly optimized case - */ - if( bconj ) - { - for(i=0; ix -= vsrc->x; - vdst->y += vsrc->y; - } - } - else - { - for(i=0; ix -= vsrc->x; - vdst->y -= vsrc->y; - } - } - } -} - -void ae_v_csubd(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha) -{ - ae_v_caddd(vdst, stride_dst, vsrc, stride_src, conj_src, n, -alpha); -} - -void ae_v_csubc(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, ae_complex alpha) -{ - alpha.x = -alpha.x; - alpha.y = -alpha.y; - ae_v_caddc(vdst, stride_dst, vsrc, stride_src, conj_src, n, alpha); -} - -void ae_v_cmuld(ae_complex *vdst, ae_int_t stride_dst, ae_int_t n, double alpha) -{ - ae_int_t i; - if( stride_dst!=1 ) - { - /* - * general unoptimized case - */ - for(i=0; ix *= alpha; - vdst->y *= alpha; - } - } - else - { - /* - * optimized case - */ - for(i=0; ix *= alpha; - vdst->y *= alpha; - } - } -} - -void ae_v_cmulc(ae_complex *vdst, ae_int_t stride_dst, ae_int_t n, ae_complex alpha) -{ - ae_int_t i; - if( stride_dst!=1 ) - { - /* - * general unoptimized case - */ - double ax = alpha.x, ay = alpha.y; - for(i=0; ix, dsty = vdst->y; - vdst->x = ax*dstx-ay*dsty; - vdst->y = ax*dsty+ay*dstx; - } - } - else - { - /* - * highly optimized case - */ - double ax = alpha.x, ay = alpha.y; - for(i=0; ix, dsty = vdst->y; - vdst->x = ax*dstx-ay*dsty; - vdst->y = ax*dsty+ay*dstx; - } - } -} - -/************************************************************************ -Real BLAS operations -************************************************************************/ -double ae_v_dotproduct(const double *v0, ae_int_t stride0, const double *v1, ae_int_t stride1, ae_int_t n) -{ - double result = 0; - ae_int_t i; - if( stride0!=1 || stride1!=1 ) - { - /* - * slow general code - */ - for(i=0; iba, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ia, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ra, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ca, 0, DT_COMPLEX, _state, make_automatic) ) - return ae_false; - return ae_true; -} - -ae_bool _rcommstate_init_copy(rcommstate* dst, rcommstate* src, ae_state *_state, ae_bool make_automatic) -{ - if( !ae_vector_init_copy(&dst->ba, &src->ba, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ia, &src->ia, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ra, &src->ra, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ca, &src->ca, _state, make_automatic) ) - return ae_false; - dst->stage = src->stage; - return ae_true; -} - -void _rcommstate_clear(rcommstate* p) -{ - ae_vector_clear(&p->ba); - ae_vector_clear(&p->ia); - ae_vector_clear(&p->ra); - ae_vector_clear(&p->ca); -} - -void _rcommstate_destroy(rcommstate* p) -{ - _rcommstate_clear(p); -} - -#ifdef AE_DEBUG4WINDOWS -int _tickcount() -{ - return GetTickCount(); -} -#endif - -#ifdef AE_DEBUG4POSIX -#include -int _tickcount() -{ - struct timespec now; - if (clock_gettime(CLOCK_MONOTONIC, &now) ) - return 0; - return now.tv_sec * 1000.0 + now.tv_nsec / 1000000.0; -} -#endif - -#ifdef AE_DEBUGRNG -void ae_set_seed(ae_int_t s0, ae_int_t s1) -{ - ae_int_t hqrnd_hqrndm1 = 2147483563; - ae_int_t hqrnd_hqrndm2 = 2147483399; - - while(s0<1) - s0 += hqrnd_hqrndm1-1; - while(s0>hqrnd_hqrndm1-1) - s0 -= hqrnd_hqrndm1-1; - - while(s1<1) - s1 += hqrnd_hqrndm2-1; - while(s1>hqrnd_hqrndm2-1) - s1 -= hqrnd_hqrndm2-1; - - _debug_rng_s0 = s0; - _debug_rng_s1 = s1; -} - -void ae_get_seed(ae_int_t *s0, ae_int_t *s1) -{ - *s0 = _debug_rng_s0; - *s1 = _debug_rng_s1; -} -#endif - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ RELATED FUNCTIONALITY -// -///////////////////////////////////////////////////////////////////////// -/******************************************************************** -Internal forwards -********************************************************************/ -namespace alglib -{ - double get_aenv_nan(); - double get_aenv_posinf(); - double get_aenv_neginf(); - ae_int_t my_stricmp(const char *s1, const char *s2); - char* filter_spaces(const char *s); - void str_vector_create(const char *src, bool match_head_only, std::vector *p_vec); - void str_matrix_create(const char *src, std::vector< std::vector > *p_mat); - - ae_bool parse_bool_delim(const char *s, const char *delim); - ae_int_t parse_int_delim(const char *s, const char *delim); - bool _parse_real_delim(const char *s, const char *delim, double *result, const char **new_s); - double parse_real_delim(const char *s, const char *delim); - alglib::complex parse_complex_delim(const char *s, const char *delim); - - std::string arraytostring(const bool *ptr, ae_int_t n); - std::string arraytostring(const ae_int_t *ptr, ae_int_t n); - std::string arraytostring(const double *ptr, ae_int_t n, int dps); - std::string arraytostring(const alglib::complex *ptr, ae_int_t n, int dps); -} - -/******************************************************************** -Global and local constants -********************************************************************/ -const double alglib::machineepsilon = 5E-16; -const double alglib::maxrealnumber = 1E300; -const double alglib::minrealnumber = 1E-300; -const alglib::ae_int_t alglib::endianness = alglib_impl::ae_get_endianness(); -const double alglib::fp_nan = alglib::get_aenv_nan(); -const double alglib::fp_posinf = alglib::get_aenv_posinf(); -const double alglib::fp_neginf = alglib::get_aenv_neginf(); - - -/******************************************************************** -ap_error -********************************************************************/ -alglib::ap_error::ap_error() -{ -} - -alglib::ap_error::ap_error(const char *s) -{ - msg = s; -} - -void alglib::ap_error::make_assertion(bool bClause) -{ - if(!bClause) - throw ap_error(); -} - -void alglib::ap_error::make_assertion(bool bClause, const char *msg) -{ - if(!bClause) - throw ap_error(msg); -} - - -/******************************************************************** -Complex number with double precision. -********************************************************************/ -alglib::complex::complex():x(0.0),y(0.0) -{ -} - -alglib::complex::complex(const double &_x):x(_x),y(0.0) -{ -} - -alglib::complex::complex(const double &_x, const double &_y):x(_x),y(_y) -{ -} - -alglib::complex::complex(const alglib::complex &z):x(z.x),y(z.y) -{ -} - -alglib::complex& alglib::complex::operator= (const double& v) -{ - x = v; - y = 0.0; - return *this; -} - -alglib::complex& alglib::complex::operator+=(const double& v) -{ - x += v; - return *this; -} - -alglib::complex& alglib::complex::operator-=(const double& v) -{ - x -= v; - return *this; -} - -alglib::complex& alglib::complex::operator*=(const double& v) -{ - x *= v; - y *= v; - return *this; -} - -alglib::complex& alglib::complex::operator/=(const double& v) -{ - x /= v; - y /= v; - return *this; -} - -alglib::complex& alglib::complex::operator= (const alglib::complex& z) -{ - x = z.x; - y = z.y; - return *this; -} - -alglib::complex& alglib::complex::operator+=(const alglib::complex& z) -{ - x += z.x; - y += z.y; - return *this; -} - -alglib::complex& alglib::complex::operator-=(const alglib::complex& z) -{ - x -= z.x; - y -= z.y; - return *this; -} - -alglib::complex& alglib::complex::operator*=(const alglib::complex& z) -{ - double t = x*z.x-y*z.y; - y = x*z.y+y*z.x; - x = t; - return *this; -} - -alglib::complex& alglib::complex::operator/=(const alglib::complex& z) -{ - alglib::complex result; - double e; - double f; - if( fabs(z.y)=0 ? _dps : -_dps; - if( dps<=0 || dps>=20 ) - throw ap_error("complex::tostring(): incorrect dps"); - - // handle IEEE special quantities - if( fp_isnan(x) || fp_isnan(y) ) - return "NAN"; - if( fp_isinf(x) || fp_isinf(y) ) - return "INF"; - - // generate mask - if( sprintf(mask, "%%.%d%s", dps, _dps>=0 ? "f" : "e")>=(int)sizeof(mask) ) - throw ap_error("complex::tostring(): buffer overflow"); - - // print |x|, |y| and zero with same mask and compare - if( sprintf(buf_x, mask, (double)(fabs(x)))>=(int)sizeof(buf_x) ) - throw ap_error("complex::tostring(): buffer overflow"); - if( sprintf(buf_y, mask, (double)(fabs(y)))>=(int)sizeof(buf_y) ) - throw ap_error("complex::tostring(): buffer overflow"); - if( sprintf(buf_zero, mask, (double)0)>=(int)sizeof(buf_zero) ) - throw ap_error("complex::tostring(): buffer overflow"); - - // different zero/nonzero patterns - if( strcmp(buf_x,buf_zero)!=0 && strcmp(buf_y,buf_zero)!=0 ) - return std::string(x>0 ? "" : "-")+buf_x+(y>0 ? "+" : "-")+buf_y+"i"; - if( strcmp(buf_x,buf_zero)!=0 && strcmp(buf_y,buf_zero)==0 ) - return std::string(x>0 ? "" : "-")+buf_x; - if( strcmp(buf_x,buf_zero)==0 && strcmp(buf_y,buf_zero)!=0 ) - return std::string(y>0 ? "" : "-")+buf_y+"i"; - return std::string("0"); -} - -const bool alglib::operator==(const alglib::complex& lhs, const alglib::complex& rhs) -{ - volatile double x1 = lhs.x; - volatile double x2 = rhs.x; - volatile double y1 = lhs.y; - volatile double y2 = rhs.y; - return x1==x2 && y1==y2; -} - -const bool alglib::operator!=(const alglib::complex& lhs, const alglib::complex& rhs) -{ return !(lhs==rhs); } - -const alglib::complex alglib::operator+(const alglib::complex& lhs) -{ return lhs; } - -const alglib::complex alglib::operator-(const alglib::complex& lhs) -{ return alglib::complex(-lhs.x, -lhs.y); } - -const alglib::complex alglib::operator+(const alglib::complex& lhs, const alglib::complex& rhs) -{ alglib::complex r = lhs; r += rhs; return r; } - -const alglib::complex alglib::operator+(const alglib::complex& lhs, const double& rhs) -{ alglib::complex r = lhs; r += rhs; return r; } - -const alglib::complex alglib::operator+(const double& lhs, const alglib::complex& rhs) -{ alglib::complex r = rhs; r += lhs; return r; } - -const alglib::complex alglib::operator-(const alglib::complex& lhs, const alglib::complex& rhs) -{ alglib::complex r = lhs; r -= rhs; return r; } - -const alglib::complex alglib::operator-(const alglib::complex& lhs, const double& rhs) -{ alglib::complex r = lhs; r -= rhs; return r; } - -const alglib::complex alglib::operator-(const double& lhs, const alglib::complex& rhs) -{ alglib::complex r = lhs; r -= rhs; return r; } - -const alglib::complex alglib::operator*(const alglib::complex& lhs, const alglib::complex& rhs) -{ return alglib::complex(lhs.x*rhs.x - lhs.y*rhs.y, lhs.x*rhs.y + lhs.y*rhs.x); } - -const alglib::complex alglib::operator*(const alglib::complex& lhs, const double& rhs) -{ return alglib::complex(lhs.x*rhs, lhs.y*rhs); } - -const alglib::complex alglib::operator*(const double& lhs, const alglib::complex& rhs) -{ return alglib::complex(lhs*rhs.x, lhs*rhs.y); } - -const alglib::complex alglib::operator/(const alglib::complex& lhs, const alglib::complex& rhs) -{ - alglib::complex result; - double e; - double f; - if( fabs(rhs.y)yabs ? xabs : yabs; - v = xabsx; - v0y = -v0->y; - v1x = v1->x; - v1y = -v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - if( !bconj0 && bconj1 ) - { - double v0x, v0y, v1x, v1y; - for(i=0; ix; - v0y = v0->y; - v1x = v1->x; - v1y = -v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - if( bconj0 && !bconj1 ) - { - double v0x, v0y, v1x, v1y; - for(i=0; ix; - v0y = -v0->y; - v1x = v1->x; - v1y = v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - if( !bconj0 && !bconj1 ) - { - double v0x, v0y, v1x, v1y; - for(i=0; ix; - v0y = v0->y; - v1x = v1->x; - v1y = v1->y; - rx += v0x*v1x-v0y*v1y; - ry += v0x*v1y+v0y*v1x; - } - } - return alglib::complex(rx,ry); -} - -alglib::complex alglib::vdotproduct(const alglib::complex *v1, const alglib::complex *v2, ae_int_t N) -{ - return vdotproduct(v1, 1, "N", v2, 1, "N", N); -} - -void alglib::vmove(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n) -{ - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - for(i=0; ix = vsrc->x; - vdst->y = -vsrc->y; - } - } - else - { - for(i=0; ix = vsrc->x; - vdst->y = -vsrc->y; - } - } - else - { - for(i=0; ix = -vsrc->x; - vdst->y = vsrc->y; - } - } - else - { - for(i=0; ix = -vsrc->x; - vdst->y = -vsrc->y; - } - } - } - else - { - // - // optimized case - // - if( bconj ) - { - for(i=0; ix = -vsrc->x; - vdst->y = vsrc->y; - } - } - else - { - for(i=0; ix = -vsrc->x; - vdst->y = -vsrc->y; - } - } - } -} - -void alglib::vmoveneg(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N) -{ - vmoveneg(vdst, 1, vsrc, 1, "N", N); -} - -void alglib::vmove(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n, double alpha) -{ - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - for(i=0; ix = alpha*vsrc->x; - vdst->y = -alpha*vsrc->y; - } - } - else - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = alpha*vsrc->y; - } - } - } - else - { - // - // optimized case - // - if( bconj ) - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = -alpha*vsrc->y; - } - } - else - { - for(i=0; ix = alpha*vsrc->x; - vdst->y = alpha*vsrc->y; - } - } - } -} - -void alglib::vmove(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, double alpha) -{ - vmove(vdst, 1, vsrc, 1, "N", N, alpha); -} - -void alglib::vmove(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, alglib::complex alpha) -{ - bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - if( bconj ) - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x+ay*vsrc->y; - vdst->y = -ax*vsrc->y+ay*vsrc->x; - } - } - else - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x-ay*vsrc->y; - vdst->y = ax*vsrc->y+ay*vsrc->x; - } - } - } - else - { - // - // optimized case - // - if( bconj ) - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x+ay*vsrc->y; - vdst->y = -ax*vsrc->y+ay*vsrc->x; - } - } - else - { - double ax = alpha.x, ay = alpha.y; - for(i=0; ix = ax*vsrc->x-ay*vsrc->y; - vdst->y = ax*vsrc->y+ay*vsrc->x; - } - } - } -} - -void alglib::vmove(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, alglib::complex alpha) -{ - vmove(vdst, 1, vsrc, 1, "N", N, alpha); -} - -void alglib::vadd(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n) -{ - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - for(i=0; ix += vsrc->x; - vdst->y -= vsrc->y; - } - } - else - { - for(i=0; ix += vsrc->x; - vdst->y += vsrc->y; - } - } - } - else - { - // - // optimized case - // - if( bconj ) - { - for(i=0; ix += vsrc->x; - vdst->y -= vsrc->y; - } - } - else - { - for(i=0; ix += vsrc->x; - vdst->y += vsrc->y; - } - } - } -} - -void alglib::vadd(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N) -{ - vadd(vdst, 1, vsrc, 1, "N", N); -} - -void alglib::vadd(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n, double alpha) -{ - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - for(i=0; ix += alpha*vsrc->x; - vdst->y -= alpha*vsrc->y; - } - } - else - { - for(i=0; ix += alpha*vsrc->x; - vdst->y += alpha*vsrc->y; - } - } - } - else - { - // - // optimized case - // - if( bconj ) - { - for(i=0; ix += alpha*vsrc->x; - vdst->y -= alpha*vsrc->y; - } - } - else - { - for(i=0; ix += alpha*vsrc->x; - vdst->y += alpha*vsrc->y; - } - } - } -} - -void alglib::vadd(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, double alpha) -{ - vadd(vdst, 1, vsrc, 1, "N", N, alpha); -} - -void alglib::vadd(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, alglib::complex alpha) -{ - bool bconj = !((conj_src[0]=='N') || (conj_src[0]=='n')); - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - double ax = alpha.x, ay = alpha.y; - if( bconj ) - { - for(i=0; ix += ax*vsrc->x+ay*vsrc->y; - vdst->y -= ax*vsrc->y-ay*vsrc->x; - } - } - else - { - for(i=0; ix += ax*vsrc->x-ay*vsrc->y; - vdst->y += ax*vsrc->y+ay*vsrc->x; - } - } - } - else - { - // - // optimized case - // - double ax = alpha.x, ay = alpha.y; - if( bconj ) - { - for(i=0; ix += ax*vsrc->x+ay*vsrc->y; - vdst->y -= ax*vsrc->y-ay*vsrc->x; - } - } - else - { - for(i=0; ix += ax*vsrc->x-ay*vsrc->y; - vdst->y += ax*vsrc->y+ay*vsrc->x; - } - } - } -} - -void alglib::vadd(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, alglib::complex alpha) -{ - vadd(vdst, 1, vsrc, 1, "N", N, alpha); -} - -void alglib::vsub(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n) -{ - ae_int_t i; - if( stride_dst!=1 || stride_src!=1 ) - { - // - // general unoptimized case - // - for(i=0; ix -= vsrc->x; - vdst->y += vsrc->y; - } - } - else - { - for(i=0; ix -= vsrc->x; - vdst->y -= vsrc->y; - } - } - } - else - { - // - // optimized case - // - if( bconj ) - { - for(i=0; ix -= vsrc->x; - vdst->y += vsrc->y; - } - } - else - { - for(i=0; ix -= vsrc->x; - vdst->y -= vsrc->y; - } - } - } -} - -void alglib::vsub(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N) -{ - vsub(vdst, 1, vsrc, 1, "N", N); -} - -void alglib::vsub(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n, double alpha) -{ - vadd(vdst, stride_dst, vsrc, stride_src, n, -alpha); -} - -void alglib::vsub(double *vdst, const double *vsrc, ae_int_t N, double alpha) -{ - vadd(vdst, 1, vsrc, 1, N, -alpha); -} - -void alglib::vsub(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha) -{ - vadd(vdst, stride_dst, vsrc, stride_src, conj_src, n, -alpha); -} - -void alglib::vsub(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t n, double alpha) -{ - vadd(vdst, 1, vsrc, 1, "N", n, -alpha); -} - -void alglib::vsub(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, alglib::complex alpha) -{ - vadd(vdst, stride_dst, vsrc, stride_src, conj_src, n, -alpha); -} - -void alglib::vsub(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t n, alglib::complex alpha) -{ - vadd(vdst, 1, vsrc, 1, "N", n, -alpha); -} -void alglib::vmul(double *vdst, ae_int_t stride_dst, ae_int_t n, double alpha) -{ - ae_int_t i; - if( stride_dst!=1 ) - { - // - // general unoptimized case - // - for(i=0; ix *= alpha; - vdst->y *= alpha; - } - } - else - { - // - // optimized case - // - for(i=0; ix *= alpha; - vdst->y *= alpha; - } - } -} - -void alglib::vmul(alglib::complex *vdst, ae_int_t N, double alpha) -{ - vmul(vdst, 1, N, alpha); -} - -void alglib::vmul(alglib::complex *vdst, ae_int_t stride_dst, ae_int_t n, alglib::complex alpha) -{ - ae_int_t i; - if( stride_dst!=1 ) - { - // - // general unoptimized case - // - double ax = alpha.x, ay = alpha.y; - for(i=0; ix, dsty = vdst->y; - vdst->x = ax*dstx-ay*dsty; - vdst->y = ax*dsty+ay*dstx; - } - } - else - { - // - // optimized case - // - double ax = alpha.x, ay = alpha.y; - for(i=0; ix, dsty = vdst->y; - vdst->x = ax*dstx-ay*dsty; - vdst->y = ax*dsty+ay*dstx; - } - } -} - -void alglib::vmul(alglib::complex *vdst, ae_int_t N, alglib::complex alpha) -{ - vmul(vdst, 1, N, alpha); -} - - -/******************************************************************** -Matrices and vectors -********************************************************************/ -alglib::ae_vector_wrapper::ae_vector_wrapper() -{ - p_vec = NULL; -} - -alglib::ae_vector_wrapper::~ae_vector_wrapper() -{ - if( p_vec==&vec ) - ae_vector_clear(p_vec); -} - -void alglib::ae_vector_wrapper::setlength(ae_int_t iLen) -{ - if( p_vec==NULL ) - throw alglib::ap_error("ALGLIB: setlength() error, p_vec==NULL (array was not correctly initialized)"); - if( p_vec!=&vec ) - throw alglib::ap_error("ALGLIB: setlength() error, p_vec!=&vec (attempt to resize frozen array)"); - if( !ae_vector_set_length(p_vec, iLen, NULL) ) - throw alglib::ap_error("ALGLIB: malloc error"); -} - -alglib::ae_int_t alglib::ae_vector_wrapper::length() const -{ - if( p_vec==NULL ) - return 0; - return p_vec->cnt; -} - -void alglib::ae_vector_wrapper::attach_to(alglib_impl::ae_vector *ptr) -{ - if( ptr==&vec ) - throw alglib::ap_error("ALGLIB: attempt to attach vector to itself"); - if( p_vec==&vec ) - ae_vector_clear(p_vec); - p_vec = ptr; -} - -void alglib::ae_vector_wrapper::allocate_own(ae_int_t size, alglib_impl::ae_datatype datatype) -{ - if( p_vec==&vec ) - ae_vector_clear(p_vec); - p_vec = &vec; - if( !ae_vector_init(p_vec, size, datatype, NULL, false) ) - throw alglib::ap_error("ALGLIB: malloc error"); -} - -const alglib_impl::ae_vector* alglib::ae_vector_wrapper::c_ptr() const -{ - return p_vec; -} - -alglib_impl::ae_vector* alglib::ae_vector_wrapper::c_ptr() -{ - return p_vec; -} - -void alglib::ae_vector_wrapper::create(const alglib::ae_vector_wrapper &rhs) -{ - if( rhs.p_vec!=NULL ) - { - p_vec = &vec; - if( !ae_vector_init_copy(p_vec, rhs.p_vec, NULL, ae_false) ) - throw alglib::ap_error("ALGLIB: malloc error!"); - } - else - p_vec = NULL; -} - -void alglib::ae_vector_wrapper::create(const char *s, alglib_impl::ae_datatype datatype) -{ - std::vector svec; - size_t i; - char *p = filter_spaces(s); - try - { - str_vector_create(p, true, &svec); - allocate_own((ae_int_t)(svec.size()), datatype); - for(i=0; iptr.p_bool[i] = parse_bool_delim(svec[i],",]"); - if( datatype==alglib_impl::DT_INT ) - p_vec->ptr.p_int[i] = parse_int_delim(svec[i],",]"); - if( datatype==alglib_impl::DT_REAL ) - p_vec->ptr.p_double[i] = parse_real_delim(svec[i],",]"); - if( datatype==alglib_impl::DT_COMPLEX ) - { - alglib::complex t = parse_complex_delim(svec[i],",]"); - p_vec->ptr.p_complex[i].x = t.x; - p_vec->ptr.p_complex[i].y = t.y; - } - } - alglib_impl::ae_free(p); - } - catch(...) - { - alglib_impl::ae_free(p); - throw; - } -} - -void alglib::ae_vector_wrapper::assign(const alglib::ae_vector_wrapper &rhs) -{ - if( this==&rhs ) - return; - if( p_vec==&vec || p_vec==NULL ) - { - // - // Assignment to non-proxy object - // - ae_vector_clear(p_vec); - if( rhs.p_vec!=NULL ) - { - p_vec = &vec; - if( !ae_vector_init_copy(p_vec, rhs.p_vec, NULL, ae_false) ) - throw alglib::ap_error("ALGLIB: malloc error!"); - } - else - p_vec = NULL; - } - else - { - // - // Assignment to proxy object - // - if( rhs.p_vec==NULL ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (sizes dont match)"); - if( rhs.p_vec->datatype!=p_vec->datatype ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (types dont match)"); - if( rhs.p_vec->cnt!=p_vec->cnt ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (sizes dont match)"); - memcpy(p_vec->ptr.p_ptr, rhs.p_vec->ptr.p_ptr, p_vec->cnt*alglib_impl::ae_sizeof(p_vec->datatype)); - } -} - -alglib::boolean_1d_array::boolean_1d_array() -{ - allocate_own(0, alglib_impl::DT_BOOL); -} - -alglib::boolean_1d_array::boolean_1d_array(const char *s) -{ - create(s, alglib_impl::DT_BOOL); -} - -alglib::boolean_1d_array::boolean_1d_array(const alglib::boolean_1d_array &rhs) -{ - create(rhs); -} - -alglib::boolean_1d_array::boolean_1d_array(alglib_impl::ae_vector *p) -{ - p_vec = NULL; - attach_to(p); -} - -const alglib::boolean_1d_array& alglib::boolean_1d_array::operator=(const alglib::boolean_1d_array &rhs) -{ - assign(rhs); - return *this; -} - -alglib::boolean_1d_array::~boolean_1d_array() -{ -} - -const ae_bool& alglib::boolean_1d_array::operator()(ae_int_t i) const -{ - return p_vec->ptr.p_bool[i]; -} - -ae_bool& alglib::boolean_1d_array::operator()(ae_int_t i) -{ - return p_vec->ptr.p_bool[i]; -} - -const ae_bool& alglib::boolean_1d_array::operator[](ae_int_t i) const -{ - return p_vec->ptr.p_bool[i]; -} - -ae_bool& alglib::boolean_1d_array::operator[](ae_int_t i) -{ - return p_vec->ptr.p_bool[i]; -} - -void alglib::boolean_1d_array::setcontent(ae_int_t iLen, const bool *pContent ) -{ - ae_int_t i; - setlength(iLen); - for(i=0; iptr.p_bool[i] = pContent[i]; -} - -ae_bool* alglib::boolean_1d_array::getcontent() -{ - return p_vec->ptr.p_bool; -} - -const ae_bool* alglib::boolean_1d_array::getcontent() const -{ - return p_vec->ptr.p_bool; -} - -std::string alglib::boolean_1d_array::tostring() const -{ - if( length()==0 ) - return "[]"; - return arraytostring(&(operator()(0)), length()); -} - -alglib::integer_1d_array::integer_1d_array() -{ - allocate_own(0, alglib_impl::DT_INT); -} - -alglib::integer_1d_array::integer_1d_array(alglib_impl::ae_vector *p) -{ - p_vec = NULL; - attach_to(p); -} - -alglib::integer_1d_array::integer_1d_array(const char *s) -{ - create(s, alglib_impl::DT_INT); -} - -alglib::integer_1d_array::integer_1d_array(const alglib::integer_1d_array &rhs) -{ - create(rhs); -} - -const alglib::integer_1d_array& alglib::integer_1d_array::operator=(const alglib::integer_1d_array &rhs) -{ - assign(rhs); - return *this; -} - -alglib::integer_1d_array::~integer_1d_array() -{ -} - -const alglib::ae_int_t& alglib::integer_1d_array::operator()(ae_int_t i) const -{ - return p_vec->ptr.p_int[i]; -} - -alglib::ae_int_t& alglib::integer_1d_array::operator()(ae_int_t i) -{ - return p_vec->ptr.p_int[i]; -} - -const alglib::ae_int_t& alglib::integer_1d_array::operator[](ae_int_t i) const -{ - return p_vec->ptr.p_int[i]; -} - -alglib::ae_int_t& alglib::integer_1d_array::operator[](ae_int_t i) -{ - return p_vec->ptr.p_int[i]; -} - -void alglib::integer_1d_array::setcontent(ae_int_t iLen, const ae_int_t *pContent ) -{ - ae_int_t i; - setlength(iLen); - for(i=0; iptr.p_int[i] = pContent[i]; -} - -alglib::ae_int_t* alglib::integer_1d_array::getcontent() -{ - return p_vec->ptr.p_int; -} - -const alglib::ae_int_t* alglib::integer_1d_array::getcontent() const -{ - return p_vec->ptr.p_int; -} - -std::string alglib::integer_1d_array::tostring() const -{ - if( length()==0 ) - return "[]"; - return arraytostring(&operator()(0), length()); -} - -alglib::real_1d_array::real_1d_array() -{ - allocate_own(0, alglib_impl::DT_REAL); -} - -alglib::real_1d_array::real_1d_array(alglib_impl::ae_vector *p) -{ - p_vec = NULL; - attach_to(p); -} - -alglib::real_1d_array::real_1d_array(const char *s) -{ - create(s, alglib_impl::DT_REAL); -} - -alglib::real_1d_array::real_1d_array(const alglib::real_1d_array &rhs) -{ - create(rhs); -} - -const alglib::real_1d_array& alglib::real_1d_array::operator=(const alglib::real_1d_array &rhs) -{ - assign(rhs); - return *this; -} - -alglib::real_1d_array::~real_1d_array() -{ -} - -const double& alglib::real_1d_array::operator()(ae_int_t i) const -{ - return p_vec->ptr.p_double[i]; -} - -double& alglib::real_1d_array::operator()(ae_int_t i) -{ - return p_vec->ptr.p_double[i]; -} - -const double& alglib::real_1d_array::operator[](ae_int_t i) const -{ - return p_vec->ptr.p_double[i]; -} - -double& alglib::real_1d_array::operator[](ae_int_t i) -{ - return p_vec->ptr.p_double[i]; -} - -void alglib::real_1d_array::setcontent(ae_int_t iLen, const double *pContent ) -{ - ae_int_t i; - setlength(iLen); - for(i=0; iptr.p_double[i] = pContent[i]; -} - -double* alglib::real_1d_array::getcontent() -{ - return p_vec->ptr.p_double; -} - -const double* alglib::real_1d_array::getcontent() const -{ - return p_vec->ptr.p_double; -} - -std::string alglib::real_1d_array::tostring(int dps) const -{ - if( length()==0 ) - return "[]"; - return arraytostring(&operator()(0), length(), dps); -} - -alglib::complex_1d_array::complex_1d_array() -{ - allocate_own(0, alglib_impl::DT_COMPLEX); -} - -alglib::complex_1d_array::complex_1d_array(alglib_impl::ae_vector *p) -{ - p_vec = NULL; - attach_to(p); -} - -alglib::complex_1d_array::complex_1d_array(const char *s) -{ - create(s, alglib_impl::DT_COMPLEX); -} - -alglib::complex_1d_array::complex_1d_array(const alglib::complex_1d_array &rhs) -{ - create(rhs); -} - -const alglib::complex_1d_array& alglib::complex_1d_array::operator=(const alglib::complex_1d_array &rhs) -{ - assign(rhs); - return *this; -} - -alglib::complex_1d_array::~complex_1d_array() -{ -} - -const alglib::complex& alglib::complex_1d_array::operator()(ae_int_t i) const -{ - return *((const alglib::complex*)(p_vec->ptr.p_complex+i)); -} - -alglib::complex& alglib::complex_1d_array::operator()(ae_int_t i) -{ - return *((alglib::complex*)(p_vec->ptr.p_complex+i)); -} - -const alglib::complex& alglib::complex_1d_array::operator[](ae_int_t i) const -{ - return *((const alglib::complex*)(p_vec->ptr.p_complex+i)); -} - -alglib::complex& alglib::complex_1d_array::operator[](ae_int_t i) -{ - return *((alglib::complex*)(p_vec->ptr.p_complex+i)); -} - -void alglib::complex_1d_array::setcontent(ae_int_t iLen, const alglib::complex *pContent ) -{ - ae_int_t i; - setlength(iLen); - for(i=0; iptr.p_complex[i].x = pContent[i].x; - p_vec->ptr.p_complex[i].y = pContent[i].y; - } -} - - alglib::complex* alglib::complex_1d_array::getcontent() -{ - return (alglib::complex*)p_vec->ptr.p_complex; -} - -const alglib::complex* alglib::complex_1d_array::getcontent() const -{ - return (const alglib::complex*)p_vec->ptr.p_complex; -} - -std::string alglib::complex_1d_array::tostring(int dps) const -{ - if( length()==0 ) - return "[]"; - return arraytostring(&operator()(0), length(), dps); -} - -alglib::ae_matrix_wrapper::ae_matrix_wrapper() -{ - p_mat = NULL; -} - -alglib::ae_matrix_wrapper::~ae_matrix_wrapper() -{ - if( p_mat==&mat ) - ae_matrix_clear(p_mat); -} - -const alglib::ae_matrix_wrapper& alglib::ae_matrix_wrapper::operator=(const alglib::ae_matrix_wrapper &rhs) -{ - assign(rhs); - return *this; -} - -void alglib::ae_matrix_wrapper::create(const ae_matrix_wrapper &rhs) -{ - if( rhs.p_mat!=NULL ) - { - p_mat = &mat; - if( !ae_matrix_init_copy(p_mat, rhs.p_mat, NULL, ae_false) ) - throw alglib::ap_error("ALGLIB: malloc error!"); - } - else - p_mat = NULL; -} - -void alglib::ae_matrix_wrapper::create(const char *s, alglib_impl::ae_datatype datatype) -{ - std::vector< std::vector > smat; - size_t i, j; - char *p = filter_spaces(s); - try - { - str_matrix_create(p, &smat); - if( smat.size()!=0 ) - { - allocate_own((ae_int_t)(smat.size()), (ae_int_t)(smat[0].size()), datatype); - for(i=0; iptr.pp_bool[i][j] = parse_bool_delim(smat[i][j],",]"); - if( datatype==alglib_impl::DT_INT ) - p_mat->ptr.pp_int[i][j] = parse_int_delim(smat[i][j],",]"); - if( datatype==alglib_impl::DT_REAL ) - p_mat->ptr.pp_double[i][j] = parse_real_delim(smat[i][j],",]"); - if( datatype==alglib_impl::DT_COMPLEX ) - { - alglib::complex t = parse_complex_delim(smat[i][j],",]"); - p_mat->ptr.pp_complex[i][j].x = t.x; - p_mat->ptr.pp_complex[i][j].y = t.y; - } - } - } - else - allocate_own(0, 0, datatype); - alglib_impl::ae_free(p); - } - catch(...) - { - alglib_impl::ae_free(p); - throw; - } -} - -void alglib::ae_matrix_wrapper::assign(const alglib::ae_matrix_wrapper &rhs) -{ - if( this==&rhs ) - return; - if( p_mat==&mat || p_mat==NULL ) - { - // - // Assignment to non-proxy object - // - ae_matrix_clear(p_mat); - if( rhs.p_mat!=NULL ) - { - p_mat = &mat; - if( !ae_matrix_init_copy(p_mat, rhs.p_mat, NULL, ae_false) ) - throw alglib::ap_error("ALGLIB: malloc error!"); - } - else - p_mat = NULL; - } - else - { - // - // Assignment to proxy object - // - ae_int_t i; - if( rhs.p_mat==NULL ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (sizes dont match)"); - if( rhs.p_mat->datatype!=p_mat->datatype ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (types dont match)"); - if( rhs.p_mat->rows!=p_mat->rows ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (sizes dont match)"); - if( rhs.p_mat->cols!=p_mat->cols ) - throw alglib::ap_error("ALGLIB: incorrect assignment to array (sizes dont match)"); - for(i=0; irows; i++) - memcpy(p_mat->ptr.pp_void[i], rhs.p_mat->ptr.pp_void[i], p_mat->cols*alglib_impl::ae_sizeof(p_mat->datatype)); - } -} - -void alglib::ae_matrix_wrapper::setlength(ae_int_t rows, ae_int_t cols) -{ - if( p_mat==NULL ) - throw alglib::ap_error("ALGLIB: setlength() error, p_mat==NULL (array was not correctly initialized)"); - if( p_mat!=&mat ) - throw alglib::ap_error("ALGLIB: setlength() error, p_mat!=&mat (attempt to resize frozen array)"); - if( !ae_matrix_set_length(p_mat, rows, cols, NULL) ) - throw alglib::ap_error("ALGLIB: malloc error"); -} - -alglib::ae_int_t alglib::ae_matrix_wrapper::rows() const -{ - if( p_mat==NULL ) - return 0; - return p_mat->rows; -} - -alglib::ae_int_t alglib::ae_matrix_wrapper::cols() const -{ - if( p_mat==NULL ) - return 0; - return p_mat->cols; -} - -bool alglib::ae_matrix_wrapper::isempty() const -{ - return rows()==0 || cols()==0; -} - -alglib::ae_int_t alglib::ae_matrix_wrapper::getstride() const -{ - if( p_mat==NULL ) - return 0; - return p_mat->stride; -} - -void alglib::ae_matrix_wrapper::attach_to(alglib_impl::ae_matrix *ptr) -{ - if( ptr==&mat ) - throw alglib::ap_error("ALGLIB: attempt to attach matrix to itself"); - if( p_mat==&mat ) - ae_matrix_clear(p_mat); - p_mat = ptr; -} - -void alglib::ae_matrix_wrapper::allocate_own(ae_int_t rows, ae_int_t cols, alglib_impl::ae_datatype datatype) -{ - if( p_mat==&mat ) - ae_matrix_clear(p_mat); - p_mat = &mat; - if( !ae_matrix_init(p_mat, rows, cols, datatype, NULL, false) ) - throw alglib::ap_error("ALGLIB: malloc error"); -} - -const alglib_impl::ae_matrix* alglib::ae_matrix_wrapper::c_ptr() const -{ - return p_mat; -} - -alglib_impl::ae_matrix* alglib::ae_matrix_wrapper::c_ptr() -{ - return p_mat; -} - -alglib::boolean_2d_array::boolean_2d_array() -{ - allocate_own(0, 0, alglib_impl::DT_BOOL); -} - -alglib::boolean_2d_array::boolean_2d_array(const alglib::boolean_2d_array &rhs) -{ - create(rhs); -} - -alglib::boolean_2d_array::boolean_2d_array(alglib_impl::ae_matrix *p) -{ - p_mat = NULL; - attach_to(p); -} - -alglib::boolean_2d_array::boolean_2d_array(const char *s) -{ - create(s, alglib_impl::DT_BOOL); -} - -alglib::boolean_2d_array::~boolean_2d_array() -{ -} - -const ae_bool& alglib::boolean_2d_array::operator()(ae_int_t i, ae_int_t j) const -{ - return p_mat->ptr.pp_bool[i][j]; -} - -ae_bool& alglib::boolean_2d_array::operator()(ae_int_t i, ae_int_t j) -{ - return p_mat->ptr.pp_bool[i][j]; -} - -const ae_bool* alglib::boolean_2d_array::operator[](ae_int_t i) const -{ - return p_mat->ptr.pp_bool[i]; -} - -ae_bool* alglib::boolean_2d_array::operator[](ae_int_t i) -{ - return p_mat->ptr.pp_bool[i]; -} - -void alglib::boolean_2d_array::setcontent(ae_int_t irows, ae_int_t icols, const bool *pContent ) -{ - ae_int_t i, j; - setlength(irows, icols); - for(i=0; iptr.pp_bool[i][j] = pContent[i*icols+j]; -} - -std::string alglib::boolean_2d_array::tostring() const -{ - std::string result; - ae_int_t i; - if( isempty() ) - return "[[]]"; - result = "["; - for(i=0; iptr.pp_int[i][j]; -} - -alglib::ae_int_t& alglib::integer_2d_array::operator()(ae_int_t i, ae_int_t j) -{ - return p_mat->ptr.pp_int[i][j]; -} - -const alglib::ae_int_t* alglib::integer_2d_array::operator[](ae_int_t i) const -{ - return p_mat->ptr.pp_int[i]; -} - -alglib::ae_int_t* alglib::integer_2d_array::operator[](ae_int_t i) -{ - return p_mat->ptr.pp_int[i]; -} - -void alglib::integer_2d_array::setcontent(ae_int_t irows, ae_int_t icols, const ae_int_t *pContent ) -{ - ae_int_t i, j; - setlength(irows, icols); - for(i=0; iptr.pp_int[i][j] = pContent[i*icols+j]; -} - -std::string alglib::integer_2d_array::tostring() const -{ - std::string result; - ae_int_t i; - if( isempty() ) - return "[[]]"; - result = "["; - for(i=0; iptr.pp_double[i][j]; -} - -double& alglib::real_2d_array::operator()(ae_int_t i, ae_int_t j) -{ - return p_mat->ptr.pp_double[i][j]; -} - -const double* alglib::real_2d_array::operator[](ae_int_t i) const -{ - return p_mat->ptr.pp_double[i]; -} - -double* alglib::real_2d_array::operator[](ae_int_t i) -{ - return p_mat->ptr.pp_double[i]; -} - -void alglib::real_2d_array::setcontent(ae_int_t irows, ae_int_t icols, const double *pContent ) -{ - ae_int_t i, j; - setlength(irows, icols); - for(i=0; iptr.pp_double[i][j] = pContent[i*icols+j]; -} - -std::string alglib::real_2d_array::tostring(int dps) const -{ - std::string result; - ae_int_t i; - if( isempty() ) - return "[[]]"; - result = "["; - for(i=0; iptr.pp_complex[i]+j)); -} - -alglib::complex& alglib::complex_2d_array::operator()(ae_int_t i, ae_int_t j) -{ - return *((alglib::complex*)(p_mat->ptr.pp_complex[i]+j)); -} - -const alglib::complex* alglib::complex_2d_array::operator[](ae_int_t i) const -{ - return (const alglib::complex*)(p_mat->ptr.pp_complex[i]); -} - -alglib::complex* alglib::complex_2d_array::operator[](ae_int_t i) -{ - return (alglib::complex*)(p_mat->ptr.pp_complex[i]); -} - -void alglib::complex_2d_array::setcontent(ae_int_t irows, ae_int_t icols, const alglib::complex *pContent ) -{ - ae_int_t i, j; - setlength(irows, icols); - for(i=0; iptr.pp_complex[i][j].x = pContent[i*icols+j].x; - p_mat->ptr.pp_complex[i][j].y = pContent[i*icols+j].y; - } -} - -std::string alglib::complex_2d_array::tostring(int dps) const -{ - std::string result; - ae_int_t i; - if( isempty() ) - return "[[]]"; - result = "["; - for(i=0; ic2 ) - return +1; - } -} - -char* alglib::filter_spaces(const char *s) -{ - size_t i, n; - char *r; - char *r0; - n = strlen(s); - r = (char*)alglib_impl::ae_malloc(n+1, NULL); - if( r==NULL ) - throw ap_error("malloc error"); - for(i=0,r0=r; i<=n; i++,s++) - if( !isspace(*s) ) - { - *r0 = *s; - r0++; - } - return r; -} - -void alglib::str_vector_create(const char *src, bool match_head_only, std::vector *p_vec) -{ - // - // parse beginning of the string. - // try to handle "[]" string - // - p_vec->clear(); - if( *src!='[' ) - throw alglib::ap_error("Incorrect initializer for vector"); - src++; - if( *src==']' ) - return; - p_vec->push_back(src); - for(;;) - { - if( *src==0 ) - throw alglib::ap_error("Incorrect initializer for vector"); - if( *src==']' ) - { - if( src[1]==0 || !match_head_only) - return; - throw alglib::ap_error("Incorrect initializer for vector"); - } - if( *src==',' ) - { - p_vec->push_back(src+1); - src++; - continue; - } - src++; - } -} - -void alglib::str_matrix_create(const char *src, std::vector< std::vector > *p_mat) -{ - p_mat->clear(); - - // - // Try to handle "[[]]" string - // - if( strcmp(src, "[[]]")==0 ) - return; - - // - // Parse non-empty string - // - if( *src!='[' ) - throw alglib::ap_error("Incorrect initializer for matrix"); - src++; - for(;;) - { - p_mat->push_back(std::vector()); - str_vector_create(src, false, &p_mat->back()); - if( p_mat->back().size()==0 || p_mat->back().size()!=(*p_mat)[0].size() ) - throw alglib::ap_error("Incorrect initializer for matrix"); - src = strchr(src, ']'); - if( src==NULL ) - throw alglib::ap_error("Incorrect initializer for matrix"); - src++; - if( *src==',' ) - { - src++; - continue; - } - if( *src==']' ) - break; - throw alglib::ap_error("Incorrect initializer for matrix"); - } - src++; - if( *src!=0 ) - throw alglib::ap_error("Incorrect initializer for matrix"); -} - -ae_bool alglib::parse_bool_delim(const char *s, const char *delim) -{ - const char *p; - char buf[8]; - - // try to parse false - p = "false"; - memset(buf, 0, sizeof(buf)); - strncpy(buf, s, strlen(p)); - if( my_stricmp(buf, p)==0 ) - { - if( s[strlen(p)]==0 || strchr(delim,s[strlen(p)])==NULL ) - throw alglib::ap_error("Cannot parse value"); - return ae_false; - } - - // try to parse true - p = "true"; - memset(buf, 0, sizeof(buf)); - strncpy(buf, s, strlen(p)); - if( my_stricmp(buf, p)==0 ) - { - if( s[strlen(p)]==0 || strchr(delim,s[strlen(p)])==NULL ) - throw alglib::ap_error("Cannot parse value"); - return ae_true; - } - - // error - throw alglib::ap_error("Cannot parse value"); -} - -alglib::ae_int_t alglib::parse_int_delim(const char *s, const char *delim) -{ - const char *p; - long long_val; - volatile ae_int_t ae_val; - - p = s; - - // - // check string structure: - // * leading sign - // * at least one digit - // * delimiter - // - if( *s=='-' || *s=='+' ) - s++; - if( *s==0 || strchr("1234567890",*s)==NULL) - throw alglib::ap_error("Cannot parse value"); - while( *s!=0 && strchr("1234567890",*s)!=NULL ) - s++; - if( *s==0 || strchr(delim,*s)==NULL ) - throw alglib::ap_error("Cannot parse value"); - - // convert and ensure that value fits into ae_int_t - s = p; - long_val = atol(s); - ae_val = long_val; - if( ae_val!=long_val ) - throw alglib::ap_error("Cannot parse value"); - return ae_val; -} - -bool alglib::_parse_real_delim(const char *s, const char *delim, double *result, const char **new_s) -{ - const char *p; - char *t; - bool has_digits; - char buf[64]; - int isign; - lconv *loc; - - p = s; - - // - // check string structure and decide what to do - // - isign = 1; - if( *s=='-' || *s=='+' ) - { - isign = *s=='-' ? -1 : +1; - s++; - } - memset(buf, 0, sizeof(buf)); - strncpy(buf, s, 3); - if( my_stricmp(buf,"nan")!=0 && my_stricmp(buf,"inf")!=0 ) - { - // - // [sign] [ddd] [.] [ddd] [e|E[sign]ddd] - // - has_digits = false; - if( *s!=0 && strchr("1234567890",*s)!=NULL ) - { - has_digits = true; - while( *s!=0 && strchr("1234567890",*s)!=NULL ) - s++; - } - if( *s=='.' ) - s++; - if( *s!=0 && strchr("1234567890",*s)!=NULL ) - { - has_digits = true; - while( *s!=0 && strchr("1234567890",*s)!=NULL ) - s++; - } - if (!has_digits ) - return false; - if( *s=='e' || *s=='E' ) - { - s++; - if( *s=='-' || *s=='+' ) - s++; - if( *s==0 || strchr("1234567890",*s)==NULL ) - return false; - while( *s!=0 && strchr("1234567890",*s)!=NULL ) - s++; - } - if( *s==0 || strchr(delim,*s)==NULL ) - return false; - *new_s = s; - - // - // finite value conversion - // - if( *new_s-p>=(int)sizeof(buf) ) - return false; - strncpy(buf, p, (size_t)(*new_s-p)); - buf[*new_s-p] = 0; - loc = localeconv(); - t = strchr(buf,'.'); - if( t!=NULL ) - *t = *loc->decimal_point; - *result = atof(buf); - return true; - } - else - { - // - // check delimiter and update *new_s - // - s += 3; - if( *s==0 || strchr(delim,*s)==NULL ) - return false; - *new_s = s; - - // - // NAN, INF conversion - // - if( my_stricmp(buf,"nan")==0 ) - *result = fp_nan; - if( my_stricmp(buf,"inf")==0 ) - *result = isign>0 ? fp_posinf : fp_neginf; - return true; - } -} - -double alglib::parse_real_delim(const char *s, const char *delim) -{ - double result; - const char *new_s; - if( !_parse_real_delim(s, delim, &result, &new_s) ) - throw alglib::ap_error("Cannot parse value"); - return result; -} - -alglib::complex alglib::parse_complex_delim(const char *s, const char *delim) -{ - double d_result; - const char *new_s; - alglib::complex c_result; - - // parse as real value - if( _parse_real_delim(s, delim, &d_result, &new_s) ) - return d_result; - - // parse as "a+bi" or "a-bi" - if( _parse_real_delim(s, "+-", &c_result.x, &new_s) ) - { - s = new_s; - if( !_parse_real_delim(s, "i", &c_result.y, &new_s) ) - throw alglib::ap_error("Cannot parse value"); - s = new_s+1; - if( *s==0 || strchr(delim,*s)==NULL ) - throw alglib::ap_error("Cannot parse value"); - return c_result; - } - - // parse as complex value "bi+a" or "bi-a" - if( _parse_real_delim(s, "i", &c_result.y, &new_s) ) - { - s = new_s+1; - if( *s==0 ) - throw alglib::ap_error("Cannot parse value"); - if( strchr(delim,*s)!=NULL ) - { - c_result.x = 0; - return c_result; - } - if( strchr("+-",*s)!=NULL ) - { - if( !_parse_real_delim(s, delim, &c_result.x, &new_s) ) - throw alglib::ap_error("Cannot parse value"); - return c_result; - } - throw alglib::ap_error("Cannot parse value"); - } - - // error - throw alglib::ap_error("Cannot parse value"); -} - -std::string alglib::arraytostring(const bool *ptr, ae_int_t n) -{ - std::string result; - ae_int_t i; - result = "["; - for(i=0; i=(int)sizeof(buf) ) - throw ap_error("arraytostring(): buffer overflow"); - result += buf; - } - result += "]"; - return result; -} - -std::string alglib::arraytostring(const double *ptr, ae_int_t n, int _dps) -{ - std::string result; - ae_int_t i; - char buf[64]; - char mask1[64]; - char mask2[64]; - int dps = _dps>=0 ? _dps : -_dps; - result = "["; - if( sprintf(mask1, "%%.%d%s", dps, _dps>=0 ? "f" : "e")>=(int)sizeof(mask1) ) - throw ap_error("arraytostring(): buffer overflow"); - if( sprintf(mask2, ",%s", mask1)>=(int)sizeof(mask2) ) - throw ap_error("arraytostring(): buffer overflow"); - for(i=0; i=(int)sizeof(buf) ) - throw ap_error("arraytostring(): buffer overflow"); - } - else if( fp_isnan(ptr[i]) ) - strcpy(buf, i==0 ? "NAN" : ",NAN"); - else if( fp_isposinf(ptr[i]) ) - strcpy(buf, i==0 ? "+INF" : ",+INF"); - else if( fp_isneginf(ptr[i]) ) - strcpy(buf, i==0 ? "-INF" : ",-INF"); - result += buf; - } - result += "]"; - return result; -} - -std::string alglib::arraytostring(const alglib::complex *ptr, ae_int_t n, int dps) -{ - std::string result; - ae_int_t i; - result = "["; - for(i=0; i0 ) return 1; - if( x<0 ) return -1; - return 0; -} - -double alglib::randomreal() -{ -#ifdef AE_DEBUGRNG - return alglib_impl::ae_debugrng()/2147483563.0; -#else - int i1 = rand(); - int i2 = rand(); - double mx = (double)(RAND_MAX)+1.0; - volatile double tmp0 = i2/mx; - volatile double tmp1 = i1+tmp0; - return tmp1/mx; -#endif -} - -alglib::ae_int_t alglib::randominteger(alglib::ae_int_t maxv) -{ -#ifdef AE_DEBUGRNG - return ((alglib::ae_int_t)(alglib_impl::ae_debugrng()-1))%maxv; -#else - return ((alglib::ae_int_t)rand())%maxv; -#endif -} - -int alglib::round(double x) -{ return int(floor(x+0.5)); } - -int alglib::trunc(double x) -{ return int(x>0 ? floor(x) : ceil(x)); } - -int alglib::ifloor(double x) -{ return int(floor(x)); } - -int alglib::iceil(double x) -{ return int(ceil(x)); } - -double alglib::pi() -{ return 3.14159265358979323846; } - -double alglib::sqr(double x) -{ return x*x; } - -int alglib::maxint(int m1, int m2) -{ - return m1>m2 ? m1 : m2; -} - -int alglib::minint(int m1, int m2) -{ - return m1>m2 ? m2 : m1; -} - -double alglib::maxreal(double m1, double m2) -{ - return m1>m2 ? m1 : m2; -} - -double alglib::minreal(double m1, double m2) -{ - return m1>m2 ? m2 : m1; -} - -bool alglib::fp_eq(double v1, double v2) -{ - // IEEE-strict floating point comparison - volatile double x = v1; - volatile double y = v2; - return x==y; -} - -bool alglib::fp_neq(double v1, double v2) -{ - // IEEE-strict floating point comparison - return !fp_eq(v1,v2); -} - -bool alglib::fp_less(double v1, double v2) -{ - // IEEE-strict floating point comparison - volatile double x = v1; - volatile double y = v2; - return xy; -} - -bool alglib::fp_greater_eq(double v1, double v2) -{ - // IEEE-strict floating point comparison - volatile double x = v1; - volatile double y = v2; - return x>=y; -} - -bool alglib::fp_isnan(double x) -{ - return alglib_impl::ae_isnan_stateless(x,endianness); -} - -bool alglib::fp_isposinf(double x) -{ - return alglib_impl::ae_isposinf_stateless(x,endianness); -} - -bool alglib::fp_isneginf(double x) -{ - return alglib_impl::ae_isneginf_stateless(x,endianness); -} - -bool alglib::fp_isinf(double x) -{ - return alglib_impl::ae_isinf_stateless(x,endianness); -} - -bool alglib::fp_isfinite(double x) -{ - return alglib_impl::ae_isfinite_stateless(x,endianness); -} - -/******************************************************************** -Dataset functions -********************************************************************/ -/*bool alglib::readstrings(std::string file, std::list *pOutput) -{ - return readstrings(file, pOutput, ""); -} - -bool alglib::readstrings(std::string file, std::list *pOutput, std::string comment) -{ - std::string cmd, s; - FILE *f; - char buf[32768]; - char *str; - - f = fopen(file.c_str(), "rb"); - if( !f ) - return false; - s = ""; - pOutput->clear(); - while(str=fgets(buf, sizeof(buf), f)) - { - // TODO: read file by small chunks, combine in one large string - if( strlen(str)==0 ) - continue; - - // - // trim trailing newline chars - // - char *eos = str+strlen(str)-1; - if( *eos=='\n' ) - { - *eos = 0; - eos--; - } - if( *eos=='\r' ) - { - *eos = 0; - eos--; - } - s = str; - - // - // skip comments - // - if( comment.length()>0 ) - if( strncmp(s.c_str(), comment.c_str(), comment.length())==0 ) - { - s = ""; - continue; - } - - // - // read data - // - if( s.length()<1 ) - { - fclose(f); - throw alglib::ap_error("internal error in read_strings"); - } - pOutput->push_back(s); - } - fclose(f); - return true; -} - -void alglib::explodestring(std::string s, char sep, std::vector *pOutput) -{ - std::string tmp; - int i; - tmp = ""; - pOutput->clear(); - for(i=0; ipush_back(tmp); - tmp = ""; - } - if( tmp.length()!=0 ) - pOutput->push_back(tmp); -} - -std::string alglib::strtolower(const std::string &s) -{ - std::string r = s; - for(int i=0; i Lines; - std::vector Values, RowsArr, ColsArr, VarsArr, HeadArr; - std::list::iterator i; - std::string s; - int TrnFirst, TrnLast, ValFirst, ValLast, TstFirst, TstLast, LinesRead, j; - - // - // Read data - // - if( pdataset==NULL ) - return false; - if( !readstrings(file, &Lines, "//") ) - return false; - i = Lines.begin(); - *pdataset = dataset(); - - // - // Read header - // - if( i==Lines.end() ) - return false; - s = alglib::xtrim(*i); - alglib::explodestring(s, '#', &HeadArr); - if( HeadArr.size()!=2 ) - return false; - - // - // Rows info - // - alglib::explodestring(alglib::xtrim(HeadArr[0]), ' ', &RowsArr); - if( RowsArr.size()==0 || RowsArr.size()>3 ) - return false; - if( RowsArr.size()==1 ) - { - pdataset->totalsize = atol(RowsArr[0].c_str()); - pdataset->trnsize = pdataset->totalsize; - } - if( RowsArr.size()==2 ) - { - pdataset->trnsize = atol(RowsArr[0].c_str()); - pdataset->tstsize = atol(RowsArr[1].c_str()); - pdataset->totalsize = pdataset->trnsize + pdataset->tstsize; - } - if( RowsArr.size()==3 ) - { - pdataset->trnsize = atol(RowsArr[0].c_str()); - pdataset->valsize = atol(RowsArr[1].c_str()); - pdataset->tstsize = atol(RowsArr[2].c_str()); - pdataset->totalsize = pdataset->trnsize + pdataset->valsize + pdataset->tstsize; - } - if( pdataset->totalsize<=0 || pdataset->trnsize<0 || pdataset->valsize<0 || pdataset->tstsize<0 ) - return false; - TrnFirst = 0; - TrnLast = TrnFirst + pdataset->trnsize; - ValFirst = TrnLast; - ValLast = ValFirst + pdataset->valsize; - TstFirst = ValLast; - TstLast = TstFirst + pdataset->tstsize; - - // - // columns - // - alglib::explodestring(alglib::xtrim(HeadArr[1]), ' ', &ColsArr); - if( ColsArr.size()!=1 && ColsArr.size()!=4 ) - return false; - if( ColsArr.size()==1 ) - { - pdataset->nin = atoi(ColsArr[0].c_str()); - if( pdataset->nin<=0 ) - return false; - } - if( ColsArr.size()==4 ) - { - if( alglib::strtolower(ColsArr[0])!="reg" && alglib::strtolower(ColsArr[0])!="cls" ) - return false; - if( ColsArr[2]!="=>" ) - return false; - pdataset->nin = atol(ColsArr[1].c_str()); - if( pdataset->nin<1 ) - return false; - if( alglib::strtolower(ColsArr[0])=="reg" ) - { - pdataset->nclasses = 0; - pdataset->nout = atol(ColsArr[3].c_str()); - if( pdataset->nout<1 ) - return false; - } - else - { - pdataset->nclasses = atol(ColsArr[3].c_str()); - pdataset->nout = 1; - if( pdataset->nclasses<2 ) - return false; - } - } - - // - // initialize arrays - // - pdataset->all.setlength(pdataset->totalsize, pdataset->nin+pdataset->nout); - if( pdataset->trnsize>0 ) pdataset->trn.setlength(pdataset->trnsize, pdataset->nin+pdataset->nout); - if( pdataset->valsize>0 ) pdataset->val.setlength(pdataset->valsize, pdataset->nin+pdataset->nout); - if( pdataset->tstsize>0 ) pdataset->tst.setlength(pdataset->tstsize, pdataset->nin+pdataset->nout); - - // - // read data - // - for(LinesRead=0, i++; i!=Lines.end() && LinesReadtotalsize; i++, LinesRead++) - { - std::string sss = *i; - alglib::explodestring(alglib::xtrim(*i), ' ', &VarsArr); - if( VarsArr.size()!=pdataset->nin+pdataset->nout ) - return false; - int tmpc = alglib::round(atof(VarsArr[pdataset->nin+pdataset->nout-1].c_str())); - if( pdataset->nclasses>0 && (tmpc<0 || tmpc>=pdataset->nclasses) ) - return false; - for(j=0; jnin+pdataset->nout; j++) - { - pdataset->all(LinesRead,j) = atof(VarsArr[j].c_str()); - if( LinesRead>=TrnFirst && LinesReadtrn(LinesRead-TrnFirst,j) = atof(VarsArr[j].c_str()); - if( LinesRead>=ValFirst && LinesReadval(LinesRead-ValFirst,j) = atof(VarsArr[j].c_str()); - if( LinesRead>=TstFirst && LinesReadtst(LinesRead-TstFirst,j) = atof(VarsArr[j].c_str()); - } - } - if( LinesRead!=pdataset->totalsize ) - return false; - return true; -}*/ - -/* -previous variant -bool alglib::opendataset(std::string file, dataset *pdataset) -{ - std::list Lines; - std::vector Values; - std::list::iterator i; - int nCol, nRow, nSplitted; - int nColumns, nRows; - - // - // Read data - // - if( pdataset==NULL ) - return false; - if( !readstrings(file, &Lines, "//") ) - return false; - i = Lines.begin(); - *pdataset = dataset(); - - // - // Read columns info - // - if( i==Lines.end() ) - return false; - if( sscanf(i->c_str(), " columns = %d %d ", &pdataset->nin, &pdataset->nout)!=2 ) - return false; - if( pdataset->nin<=0 || pdataset->nout==0 || pdataset->nout==-1) - return false; - if( pdataset->nout<0 ) - { - pdataset->nclasses = -pdataset->nout; - pdataset->nout = 1; - pdataset->iscls = true; - } - else - { - pdataset->isreg = true; - } - nColumns = pdataset->nin+pdataset->nout; - i++; - - // - // Read rows info - // - if( i==Lines.end() ) - return false; - if( sscanf(i->c_str(), " rows = %d %d %d ", &pdataset->trnsize, &pdataset->valsize, &pdataset->tstsize)!=3 ) - return false; - if( (pdataset->trnsize<0) || (pdataset->valsize<0) || (pdataset->tstsize<0) ) - return false; - if( (pdataset->trnsize==0) && (pdataset->valsize==0) && (pdataset->tstsize==0) ) - return false; - nRows = pdataset->trnsize+pdataset->valsize+pdataset->tstsize; - pdataset->size = nRows; - if( Lines.size()!=nRows+2 ) - return false; - i++; - - // - // Read all cases - // - alglib::real_2d_array &arr = pdataset->all; - arr.setbounds(0, nRows-1, 0, nColumns-1); - for(nRow=0; nRowiscls && ((round(v)<0) || (round(v)>=pdataset->nclasses)) ) - return false; - if( (nCol==nColumns-1) && pdataset->iscls ) - arr(nRow, nCol) = round(v); - else - arr(nRow, nCol) = v; - } - i++; - } - - // - // Split to training, validation and test sets - // - if( pdataset->trnsize>0 ) - pdataset->trn.setbounds(0, pdataset->trnsize-1, 0, nColumns-1); - if( pdataset->valsize>0 ) - pdataset->val.setbounds(0, pdataset->valsize-1, 0, nColumns-1); - if( pdataset->tstsize>0 ) - pdataset->tst.setbounds(0, pdataset->tstsize-1, 0, nColumns-1); - nSplitted=0; - for(nRow=0; nRow<=pdataset->trnsize-1; nRow++, nSplitted++) - for(nCol=0; nCol<=nColumns-1; nCol++) - pdataset->trn(nRow,nCol) = arr(nSplitted,nCol); - for(nRow=0; nRow<=pdataset->valsize-1; nRow++, nSplitted++) - for(nCol=0; nCol<=nColumns-1; nCol++) - pdataset->val(nRow,nCol) = arr(nSplitted,nCol); - for(nRow=0; nRow<=pdataset->tstsize-1; nRow++, nSplitted++) - for(nCol=0; nCol<=nColumns-1; nCol++) - pdataset->tst(nRow,nCol) = arr(nSplitted,nCol); - return true; -}*/ - -alglib::ae_int_t alglib::vlen(ae_int_t n1, ae_int_t n2) -{ - return n2-n1+1; -} - - - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTIONS CONTAINS OPTIMIZED LINEAR ALGEBRA CODE -// IT IS SHARED BETWEEN C++ AND PURE C LIBRARIES -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -#define alglib_simd_alignment 16 - -#define alglib_r_block 32 -#define alglib_half_r_block 16 -#define alglib_twice_r_block 64 - -#define alglib_c_block 24 -#define alglib_half_c_block 12 -#define alglib_twice_c_block 48 - - -/******************************************************************** -This subroutine calculates fast 32x32 real matrix-vector product: - - y := beta*y + alpha*A*x - -using either generic C code or native optimizations (if available) - -IMPORTANT: -* A must be stored in row-major order, - stride is alglib_r_block, - aligned on alglib_simd_alignment boundary -* X must be aligned on alglib_simd_alignment boundary -* Y may be non-aligned -********************************************************************/ -void _ialglib_mv_32(const double *a, const double *x, double *y, ae_int_t stride, double alpha, double beta) -{ - ae_int_t i, k; - const double *pa0, *pa1, *pb; - - pa0 = a; - pa1 = a+alglib_r_block; - pb = x; - for(i=0; i<16; i++) - { - double v0 = 0, v1 = 0; - for(k=0; k<4; k++) - { - v0 += pa0[0]*pb[0]; - v1 += pa1[0]*pb[0]; - v0 += pa0[1]*pb[1]; - v1 += pa1[1]*pb[1]; - v0 += pa0[2]*pb[2]; - v1 += pa1[2]*pb[2]; - v0 += pa0[3]*pb[3]; - v1 += pa1[3]*pb[3]; - v0 += pa0[4]*pb[4]; - v1 += pa1[4]*pb[4]; - v0 += pa0[5]*pb[5]; - v1 += pa1[5]*pb[5]; - v0 += pa0[6]*pb[6]; - v1 += pa1[6]*pb[6]; - v0 += pa0[7]*pb[7]; - v1 += pa1[7]*pb[7]; - pa0 += 8; - pa1 += 8; - pb += 8; - } - y[0] = beta*y[0]+alpha*v0; - y[stride] = beta*y[stride]+alpha*v1; - - /* - * now we've processed rows I and I+1, - * pa0 and pa1 are pointing to rows I+1 and I+2. - * move to I+2 and I+3. - */ - pa0 += alglib_r_block; - pa1 += alglib_r_block; - pb = x; - y+=2*stride; - } -} - - -/************************************************************************* -This function calculates MxN real matrix-vector product: - - y := beta*y + alpha*A*x - -using generic C code. It calls _ialglib_mv_32 if both M=32 and N=32. - -If beta is zero, we do not use previous values of y (they are overwritten -by alpha*A*x without ever being read). If alpha is zero, no matrix-vector -product is calculated (only beta is updated); however, this update is not -efficient and this function should NOT be used for multiplication of -vector and scalar. - -IMPORTANT: -* 0<=M<=alglib_r_block, 0<=N<=alglib_r_block -* A must be stored in row-major order with stride equal to alglib_r_block -*************************************************************************/ -void _ialglib_rmv(ae_int_t m, ae_int_t n, const double *a, const double *x, double *y, ae_int_t stride, double alpha, double beta) -{ - /* - * Handle special cases: - * - alpha is zero or n is zero - * - m is zero - */ - if( m==0 ) - return; - if( alpha==0.0 || n==0 ) - { - ae_int_t i; - if( beta==0.0 ) - { - for(i=0; ix-beta.y*cy->y)+(alpha.x*v0-alpha.y*v1); - double ty = (beta.x*cy->y+beta.y*cy->x)+(alpha.x*v1+alpha.y*v0); - cy->x = tx; - cy->y = ty; - cy+=stride; - } - else - { - double tx = (beta.x*dy[0]-beta.y*dy[1])+(alpha.x*v0-alpha.y*v1); - double ty = (beta.x*dy[1]+beta.y*dy[0])+(alpha.x*v1+alpha.y*v0); - dy[0] = tx; - dy[1] = ty; - dy += 2*stride; - } - parow += 2*alglib_c_block; - } -} - - -/************************************************************************* -This subroutine calculates fast MxN complex matrix-vector product: - - y := beta*y + alpha*A*x - -using generic C code, where A, x, y, alpha and beta are complex. - -If beta is zero, we do not use previous values of y (they are overwritten -by alpha*A*x without ever being read). However, when alpha is zero, we -still calculate A*x and multiply it by alpha (this distinction can be -important when A or x contain infinities/NANs). - -IMPORTANT: -* 0<=M<=alglib_c_block, 0<=N<=alglib_c_block -* A must be stored in row-major order, as sequence of double precision - pairs. Stride is alglib_c_block (it is measured in pairs of doubles, not - in doubles). -* Y may be referenced by cy (pointer to ae_complex) or - dy (pointer to array of double precision pair) depending on what type of - output you wish. Pass pointer to Y as one of these parameters, - AND SET OTHER PARAMETER TO NULL. -* both A and x must be aligned; y may be non-aligned. - -This function supports SSE2; it can be used when: -1. AE_HAS_SSE2_INTRINSICS was defined (checked at compile-time) -2. ae_cpuid() result contains CPU_SSE2 (checked at run-time) - -If (1) is failed, this function will be undefined. If (2) is failed, call -to this function will probably crash your system. - -If you want to know whether it is safe to call it, you should check -results of ae_cpuid(). If CPU_SSE2 bit is set, this function is callable -and will do its work. -*************************************************************************/ -#if defined(AE_HAS_SSE2_INTRINSICS) -void _ialglib_cmv_sse2(ae_int_t m, ae_int_t n, const double *a, const double *x, ae_complex *cy, double *dy, ae_int_t stride, ae_complex alpha, ae_complex beta) -{ - ae_int_t i, j, m2; - const double *pa0, *pa1, *parow, *pb; - __m128d vbeta, vbetax, vbetay; - __m128d valpha, valphax, valphay; - - m2 = m/2; - parow = a; - if( cy!=NULL ) - { - dy = (double*)cy; - cy = NULL; - } - vbeta = _mm_loadh_pd(_mm_load_sd(&beta.x),&beta.y); - vbetax = _mm_unpacklo_pd(vbeta,vbeta); - vbetay = _mm_unpackhi_pd(vbeta,vbeta); - valpha = _mm_loadh_pd(_mm_load_sd(&alpha.x),&alpha.y); - valphax = _mm_unpacklo_pd(valpha,valpha); - valphay = _mm_unpackhi_pd(valpha,valpha); - for(i=0; ix = 0.0; - p->y = 0.0; - } - } - else - { - for(i=0; ix = 0.0; - p->y = 0.0; - } - } -} - - -/******************************************************************** -This subroutine copies unaligned real vector -********************************************************************/ -void _ialglib_vcopy(ae_int_t n, const double *a, ae_int_t stridea, double *b, ae_int_t strideb) -{ - ae_int_t i, n2; - if( stridea==1 && strideb==1 ) - { - n2 = n/2; - for(i=n2; i!=0; i--, a+=2, b+=2) - { - b[0] = a[0]; - b[1] = a[1]; - } - if( n%2!=0 ) - b[0] = a[0]; - } - else - { - for(i=0; ix; - b[1] = a->y; - } - } - else - { - for(i=0; ix; - b[1] = -a->y; - } - } -} - - -/******************************************************************** -This subroutine copies unaligned complex vector (passed as double*) - -1. strideb is stride measured in complex numbers, not doubles -2. conj may be "N" (no conj.) or "C" (conj.) -********************************************************************/ -void _ialglib_vcopy_dcomplex(ae_int_t n, const double *a, ae_int_t stridea, double *b, ae_int_t strideb, const char *conj) -{ - ae_int_t i; - - /* - * more general case - */ - if( conj[0]=='N' || conj[0]=='n' ) - { - for(i=0; ix; - pdst[1] = psrc->y; - } - } - if( op==1 ) - { - for(i=0,psrc=a; ix; - pdst[1] = psrc->y; - } - } - if( op==2 ) - { - for(i=0,psrc=a; ix; - pdst[1] = -psrc->y; - } - } - if( op==3 ) - { - for(i=0,psrc=a; ix; - pdst[1] = -psrc->y; - } - } -} - - -/******************************************************************** -This subroutine copies matrix from aligned contigous storage to -non-aligned non-contigous storage - -A: -* 2*alglib_c_block*alglib_c_block doubles (only MxN submatrix is used) -* aligned -* stride is alglib_c_block -* pointer to double is passed -* may be transformed during copying (as prescribed by op) - -B: -* MxN -* non-aligned -* non-contigous -* pointer to ae_complex is passed - -Transformation types: -* 0 - no transform -* 1 - transposition -* 2 - conjugate transposition -* 3 - conjugate, but no transposition -********************************************************************/ -void _ialglib_mcopyunblock_complex(ae_int_t m, ae_int_t n, const double *a, ae_int_t op, ae_complex* b, ae_int_t stride) -{ - ae_int_t i, j; - const double *psrc; - ae_complex *pdst; - if( op==0 ) - { - for(i=0,psrc=a; ix = psrc[0]; - pdst->y = psrc[1]; - } - } - if( op==1 ) - { - for(i=0,psrc=a; ix = psrc[0]; - pdst->y = psrc[1]; - } - } - if( op==2 ) - { - for(i=0,psrc=a; ix = psrc[0]; - pdst->y = -psrc[1]; - } - } - if( op==3 ) - { - for(i=0,psrc=a; ix = psrc[0]; - pdst->y = -psrc[1]; - } - } -} - - -/******************************************************************** -Real GEMM kernel -********************************************************************/ -ae_bool _ialglib_rmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - double *_a, - ae_int_t _a_stride, - ae_int_t optypea, - double *_b, - ae_int_t _b_stride, - ae_int_t optypeb, - double beta, - double *_c, - ae_int_t _c_stride) -{ - int i; - double *crow; - double _abuf[alglib_r_block+alglib_simd_alignment]; - double _bbuf[alglib_r_block*alglib_r_block+alglib_simd_alignment]; - double * const abuf = (double * const) ae_align(_abuf,alglib_simd_alignment); - double * const b = (double * const) ae_align(_bbuf,alglib_simd_alignment); - void (*rmv)(ae_int_t, ae_int_t, const double *, const double *, double *, ae_int_t, double, double) = &_ialglib_rmv; - void (*mcopyblock)(ae_int_t, ae_int_t, const double *, ae_int_t, ae_int_t, double *) = &_ialglib_mcopyblock; - - if( m>alglib_r_block || n>alglib_r_block || k>alglib_r_block || m<=0 || n<=0 || k<=0 || alpha==0.0 ) - return ae_false; - - /* - * Check for SSE2 support - */ -#ifdef AE_HAS_SSE2_INTRINSICS - if( ae_cpuid() & CPU_SSE2 ) - { - rmv = &_ialglib_rmv_sse2; - mcopyblock = &_ialglib_mcopyblock_sse2; - } -#endif - - /* - * copy b - */ - if( optypeb==0 ) - mcopyblock(k, n, _b, 1, _b_stride, b); - else - mcopyblock(n, k, _b, 0, _b_stride, b); - - /* - * multiply B by A (from the right, by rows) - * and store result in C - */ - crow = _c; - if( optypea==0 ) - { - const double *arow = _a; - for(i=0; ialglib_c_block || n>alglib_c_block || k>alglib_c_block ) - return ae_false; - - /* - * Check for SSE2 support - */ -#ifdef AE_HAS_SSE2_INTRINSICS - if( ae_cpuid() & CPU_SSE2 ) - { - cmv = &_ialglib_cmv_sse2; - } -#endif - - /* - * copy b - */ - brows = optypeb==0 ? k : n; - bcols = optypeb==0 ? n : k; - if( optypeb==0 ) - _ialglib_mcopyblock_complex(brows, bcols, _b, 1, _b_stride, b); - if( optypeb==1 ) - _ialglib_mcopyblock_complex(brows, bcols, _b, 0, _b_stride, b); - if( optypeb==2 ) - _ialglib_mcopyblock_complex(brows, bcols, _b, 3, _b_stride, b); - - /* - * multiply B by A (from the right, by rows) - * and store result in C - */ - arow = _a; - crow = _c; - for(i=0; ialglib_c_block || n>alglib_c_block ) - return ae_false; - - /* - * Check for SSE2 support - */ -#ifdef AE_HAS_SSE2_INTRINSICS - if( ae_cpuid() & CPU_SSE2 ) - { - cmv = &_ialglib_cmv_sse2; - } -#endif - - /* - * Prepare - */ - _ialglib_mcopyblock_complex(n, n, _a, optype, _a_stride, abuf); - _ialglib_mcopyblock_complex(m, n, _x, 0, _x_stride, xbuf); - if( isunit ) - for(i=0,pdiag=abuf; i=0; i--,pdiag-=2*(alglib_c_block+1)) - { - ae_complex tmp_c; - ae_complex beta; - ae_complex alpha; - tmp_c.x = pdiag[0]; - tmp_c.y = pdiag[1]; - beta = ae_c_d_div(1.0, tmp_c); - alpha.x = -beta.x; - alpha.y = -beta.y; - _ialglib_vcopy_dcomplex(n-1-i, pdiag+2*alglib_c_block, alglib_c_block, tmpbuf, 1, "No conj"); - cmv(m, n-1-i, xbuf+2*(i+1), tmpbuf, NULL, xbuf+2*i, alglib_c_block, alpha, beta); - } - _ialglib_mcopyunblock_complex(m, n, xbuf, 0, _x, _x_stride); - } - return ae_true; -} - - -/******************************************************************** -real TRSM kernel -********************************************************************/ -ae_bool _ialglib_rmatrixrighttrsm(ae_int_t m, - ae_int_t n, - double *_a, - ae_int_t _a_stride, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - double *_x, - ae_int_t _x_stride) -{ - /* - * local buffers - */ - double *pdiag; - ae_int_t i; - double _loc_abuf[alglib_r_block*alglib_r_block+alglib_simd_alignment]; - double _loc_xbuf[alglib_r_block*alglib_r_block+alglib_simd_alignment]; - double _loc_tmpbuf[alglib_r_block+alglib_simd_alignment]; - double * const abuf = (double * const) ae_align(_loc_abuf, alglib_simd_alignment); - double * const xbuf = (double * const) ae_align(_loc_xbuf, alglib_simd_alignment); - double * const tmpbuf = (double * const) ae_align(_loc_tmpbuf,alglib_simd_alignment); - ae_bool uppera; - void (*rmv)(ae_int_t, ae_int_t, const double *, const double *, double *, ae_int_t, double, double) = &_ialglib_rmv; - void (*mcopyblock)(ae_int_t, ae_int_t, const double *, ae_int_t, ae_int_t, double *) = &_ialglib_mcopyblock; - - if( m>alglib_r_block || n>alglib_r_block ) - return ae_false; - - /* - * Check for SSE2 support - */ -#ifdef AE_HAS_SSE2_INTRINSICS - if( ae_cpuid() & CPU_SSE2 ) - { - rmv = &_ialglib_rmv_sse2; - mcopyblock = &_ialglib_mcopyblock_sse2; - } -#endif - - /* - * Prepare - */ - mcopyblock(n, n, _a, optype, _a_stride, abuf); - mcopyblock(m, n, _x, 0, _x_stride, xbuf); - if( isunit ) - for(i=0,pdiag=abuf; i=0; i--,pdiag-=alglib_r_block+1) - { - double beta = 1.0/(*pdiag); - double alpha = -beta; - _ialglib_vcopy(n-1-i, pdiag+alglib_r_block, alglib_r_block, tmpbuf+i+1, 1); - rmv(m, n-1-i, xbuf+i+1, tmpbuf+i+1, xbuf+i, alglib_r_block, alpha, beta); - } - _ialglib_mcopyunblock(m, n, xbuf, 0, _x, _x_stride); - } - return ae_true; -} - - -/******************************************************************** -complex TRSM kernel -********************************************************************/ -ae_bool _ialglib_cmatrixlefttrsm(ae_int_t m, - ae_int_t n, - ae_complex *_a, - ae_int_t _a_stride, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_complex *_x, - ae_int_t _x_stride) -{ - /* - * local buffers - */ - double *pdiag, *arow; - ae_int_t i; - double _loc_abuf[2*alglib_c_block*alglib_c_block+alglib_simd_alignment]; - double _loc_xbuf[2*alglib_c_block*alglib_c_block+alglib_simd_alignment]; - double _loc_tmpbuf[2*alglib_c_block+alglib_simd_alignment]; - double * const abuf = (double * const) ae_align(_loc_abuf, alglib_simd_alignment); - double * const xbuf = (double * const) ae_align(_loc_xbuf, alglib_simd_alignment); - double * const tmpbuf = (double * const) ae_align(_loc_tmpbuf,alglib_simd_alignment); - ae_bool uppera; - void (*cmv)(ae_int_t, ae_int_t, const double *, const double *, ae_complex *, double *, ae_int_t, ae_complex, ae_complex) = &_ialglib_cmv; - - if( m>alglib_c_block || n>alglib_c_block ) - return ae_false; - - /* - * Check for SSE2 support - */ -#ifdef AE_HAS_SSE2_INTRINSICS - if( ae_cpuid() & CPU_SSE2 ) - { - cmv = &_ialglib_cmv_sse2; - } -#endif - - /* - * Prepare - * Transpose X (so we may use mv, which calculates A*x, but not x*A) - */ - _ialglib_mcopyblock_complex(m, m, _a, optype, _a_stride, abuf); - _ialglib_mcopyblock_complex(m, n, _x, 1, _x_stride, xbuf); - if( isunit ) - for(i=0,pdiag=abuf; i=0; i--,pdiag-=2*(alglib_c_block+1)) - { - ae_complex tmp_c; - ae_complex beta; - ae_complex alpha; - tmp_c.x = pdiag[0]; - tmp_c.y = pdiag[1]; - beta = ae_c_d_div(1.0, tmp_c); - alpha.x = -beta.x; - alpha.y = -beta.y; - _ialglib_vcopy_dcomplex(m-1-i, pdiag+2, 1, tmpbuf, 1, "No conj"); - cmv(n, m-1-i, xbuf+2*(i+1), tmpbuf, NULL, xbuf+2*i, alglib_c_block, alpha, beta); - } - _ialglib_mcopyunblock_complex(m, n, xbuf, 1, _x, _x_stride); - } - else - { for(i=0,pdiag=abuf,arow=abuf; ialglib_r_block || n>alglib_r_block ) - return ae_false; - - /* - * Check for SSE2 support - */ -#ifdef AE_HAS_SSE2_INTRINSICS - if( ae_cpuid() & CPU_SSE2 ) - { - rmv = &_ialglib_rmv_sse2; - mcopyblock = &_ialglib_mcopyblock_sse2; - } -#endif - - /* - * Prepare - * Transpose X (so we may use mv, which calculates A*x, but not x*A) - */ - mcopyblock(m, m, _a, optype, _a_stride, abuf); - mcopyblock(m, n, _x, 1, _x_stride, xbuf); - if( isunit ) - for(i=0,pdiag=abuf; i=0; i--,pdiag-=alglib_r_block+1) - { - double beta = 1.0/(*pdiag); - double alpha = -beta; - _ialglib_vcopy(m-1-i, pdiag+1, 1, tmpbuf+i+1, 1); - rmv(n, m-1-i, xbuf+i+1, tmpbuf+i+1, xbuf+i, alglib_r_block, alpha, beta); - } - _ialglib_mcopyunblock(m, n, xbuf, 1, _x, _x_stride); - } - else - { for(i=0,pdiag=abuf,arow=abuf; ialglib_c_block || k>alglib_c_block ) - return ae_false; - if( n==0 ) - return ae_true; - - /* - * copy A and C, task is transformed to "A*A^H"-form. - * if beta==0, then C is filled by zeros (and not referenced) - * - * alpha==0 or k==0 are correctly processed (A is not referenced) - */ - c_alpha.x = alpha; - c_alpha.y = 0; - c_beta.x = beta; - c_beta.y = 0; - if( alpha==0 ) - k = 0; - if( k>0 ) - { - if( optypea==0 ) - _ialglib_mcopyblock_complex(n, k, _a, 3, _a_stride, abuf); - else - _ialglib_mcopyblock_complex(k, n, _a, 1, _a_stride, abuf); - } - _ialglib_mcopyblock_complex(n, n, _c, 0, _c_stride, cbuf); - if( beta==0 ) - { - for(i=0,crow=cbuf; ialglib_r_block || k>alglib_r_block ) - return ae_false; - if( n==0 ) - return ae_true; - - /* - * copy A and C, task is transformed to "A*A^T"-form. - * if beta==0, then C is filled by zeros (and not referenced) - * - * alpha==0 or k==0 are correctly processed (A is not referenced) - */ - if( alpha==0 ) - k = 0; - if( k>0 ) - { - if( optypea==0 ) - _ialglib_mcopyblock(n, k, _a, 0, _a_stride, abuf); - else - _ialglib_mcopyblock(k, n, _a, 1, _a_stride, abuf); - } - _ialglib_mcopyblock(n, n, _c, 0, _c_stride, cbuf); - if( beta==0 ) - { - for(i=0,crow=cbuf; iptr.pp_double[ia]+ja, _a->stride, optypea, _b->ptr.pp_double[ib]+jb, _b->stride, optypeb, beta, _c->ptr.pp_double[ic]+jc, _c->stride); -} - -ae_bool _ialglib_i_cmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - ae_matrix *_a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - ae_matrix *_b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - ae_matrix *_c, - ae_int_t ic, - ae_int_t jc) -{ - return _ialglib_cmatrixgemm(m, n, k, alpha, _a->ptr.pp_complex[ia]+ja, _a->stride, optypea, _b->ptr.pp_complex[ib]+jb, _b->stride, optypeb, beta, _c->ptr.pp_complex[ic]+jc, _c->stride); -} - -ae_bool _ialglib_i_cmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2) -{ - return _ialglib_cmatrixrighttrsm(m, n, &a->ptr.pp_complex[i1][j1], a->stride, isupper, isunit, optype, &x->ptr.pp_complex[i2][j2], x->stride); -} - -ae_bool _ialglib_i_rmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2) -{ - return _ialglib_rmatrixrighttrsm(m, n, &a->ptr.pp_double[i1][j1], a->stride, isupper, isunit, optype, &x->ptr.pp_double[i2][j2], x->stride); -} - -ae_bool _ialglib_i_cmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2) -{ - return _ialglib_cmatrixlefttrsm(m, n, &a->ptr.pp_complex[i1][j1], a->stride, isupper, isunit, optype, &x->ptr.pp_complex[i2][j2], x->stride); -} - -ae_bool _ialglib_i_rmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2) -{ - return _ialglib_rmatrixlefttrsm(m, n, &a->ptr.pp_double[i1][j1], a->stride, isupper, isunit, optype, &x->ptr.pp_double[i2][j2], x->stride); -} - -ae_bool _ialglib_i_cmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - ae_matrix *c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper) -{ - return _ialglib_cmatrixsyrk(n, k, alpha, &a->ptr.pp_complex[ia][ja], a->stride, optypea, beta, &c->ptr.pp_complex[ic][jc], c->stride, isupper); -} - -ae_bool _ialglib_i_rmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - ae_matrix *c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper) -{ - return _ialglib_rmatrixsyrk(n, k, alpha, &a->ptr.pp_double[ia][ja], a->stride, optypea, beta, &c->ptr.pp_double[ic][jc], c->stride, isupper); -} - -ae_bool _ialglib_i_cmatrixrank1f(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_vector *u, - ae_int_t uoffs, - ae_vector *v, - ae_int_t voffs) -{ - return _ialglib_cmatrixrank1(m, n, &a->ptr.pp_complex[ia][ja], a->stride, &u->ptr.p_complex[uoffs], &v->ptr.p_complex[voffs]); -} - -ae_bool _ialglib_i_rmatrixrank1f(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_vector *u, - ae_int_t uoffs, - ae_vector *v, - ae_int_t voffs) -{ - return _ialglib_rmatrixrank1(m, n, &a->ptr.pp_double[ia][ja], a->stride, &u->ptr.p_double[uoffs], &v->ptr.p_double[voffs]); -} - - - - -/******************************************************************** -This function reads rectangular matrix A given by two column pointers -col0 and col1 and stride src_stride and moves it into contiguous row- -by-row storage given by dst. - -It can handle following special cases: -* col1==NULL in this case second column of A is filled by zeros -********************************************************************/ -void _ialglib_pack_n2( - double *col0, - double *col1, - ae_int_t n, - ae_int_t src_stride, - double *dst) -{ - ae_int_t n2, j, stride2; - - /* - * handle special case - */ - if( col1==NULL ) - { - for(j=0; j>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _ap_h -#define _ap_h - -#include -#include -#include -#include -#include -#include - -#ifdef __BORLANDC__ -#include -#include -#else -#include -#include -#endif - -#define AE_USE_CPP -/* Definitions */ -#define AE_UNKNOWN 0 -#define AE_MSVC 1 -#define AE_GNUC 2 -#define AE_SUNC 3 -#define AE_INTEL 1 -#define AE_SPARC 2 -#define AE_WINDOWS 1 -#define AE_POSIX 2 -#define AE_LOCK_ALIGNMENT 16 - -/* in case no OS is defined, use AE_UNKNOWN */ -#ifndef AE_OS -#define AE_OS AE_UNKNOWN -#endif - -/* automatically determine compiler */ -#define AE_COMPILER AE_UNKNOWN -#ifdef __GNUC__ -#undef AE_COMPILER -#define AE_COMPILER AE_GNUC -#endif -#if defined(__SUNPRO_C)||defined(__SUNPRO_CC) -#undef AE_COMPILER -#define AE_COMPILER AE_SUNC -#endif -#ifdef _MSC_VER -#undef AE_COMPILER -#define AE_COMPILER AE_MSVC -#endif - -/* now we are ready to include headers */ -#include -#include -#include -#include -#include -#include - -#if AE_OS==AE_WINDOWS -#include -#include -#elif AE_OS==AE_POSIX -#include -#include -#include -#include -#endif - -#if defined(AE_HAVE_STDINT) -#include -#endif - -/* - * SSE2 intrinsics - * - * Preprocessor directives below: - * - include headers for SSE2 intrinsics - * - define AE_HAS_SSE2_INTRINSICS definition - * - * These actions are performed when we have: - * - x86 architecture definition (AE_CPU==AE_INTEL) - * - compiler which supports intrinsics - * - * Presence of AE_HAS_SSE2_INTRINSICS does NOT mean that our CPU - * actually supports SSE2 - such things should be determined at runtime - * with ae_cpuid() call. It means that we are working under Intel and - * out compiler can issue SSE2-capable code. - * - */ -#if defined(AE_CPU) -#if AE_CPU==AE_INTEL -#if AE_COMPILER==AE_MSVC -#include -#define AE_HAS_SSE2_INTRINSICS -#endif -#if AE_COMPILER==AE_GNUC -#include -#define AE_HAS_SSE2_INTRINSICS -#endif -#if AE_COMPILER==AE_SUNC -#include -#include -#define AE_HAS_SSE2_INTRINSICS -#endif -#endif -#endif - -/* Debugging helpers for Windows */ -#ifdef AE_DEBUG4WINDOWS -#include -#include -#endif - - - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS DECLARATIONS FOR BASIC FUNCTIONALITY -// LIKE MEMORY MANAGEMENT FOR VECTORS/MATRICES WHICH IS SHARED -// BETWEEN C++ AND PURE C LIBRARIES -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - -/* if we work under C++ environment, define several conditions */ -#ifdef AE_USE_CPP -#define AE_USE_CPP_BOOL -#define AE_USE_CPP_ERROR_HANDLING -#define AE_USE_CPP_SERIALIZATION -#endif - -/* - * define ae_int32_t, ae_int64_t, ae_int_t, ae_bool, ae_complex, ae_error_type and ae_datatype - */ - -#if defined(AE_INT32_T) -typedef AE_INT32_T ae_int32_t; -#endif -#if defined(AE_HAVE_STDINT) && !defined(AE_INT32_T) -typedef int32_t ae_int32_t; -#endif -#if !defined(AE_HAVE_STDINT) && !defined(AE_INT32_T) -#if AE_COMPILER==AE_MSVC -typedef _int32 ae_int32_t; -#endif -#if (AE_COMPILER==AE_GNUC) || (AE_COMPILER==AE_SUNC) || (AE_COMPILER==AE_UNKNOWN) -typedef int ae_int32_t; -#endif -#endif - -#if defined(AE_INT64_T) -typedef AE_INT64_T ae_int64_t; -#endif -#if defined(AE_HAVE_STDINT) && !defined(AE_INT64_T) -typedef int64_t ae_int64_t; -#endif -#if !defined(AE_HAVE_STDINT) && !defined(AE_INT64_T) -#if AE_COMPILER==AE_MSVC -typedef _int64 ae_int64_t; -#endif -#if (AE_COMPILER==AE_GNUC) || (AE_COMPILER==AE_SUNC) || (AE_COMPILER==AE_UNKNOWN) -typedef signed long long ae_int64_t; -#endif -#endif - -#if !defined(AE_INT_T) -typedef ptrdiff_t ae_int_t; -#endif - -#if !defined(AE_USE_CPP_BOOL) -#define ae_bool char -#define ae_true 1 -#define ae_false 0 -#else -#define ae_bool bool -#define ae_true true -#define ae_false false -#endif - -typedef struct { double x, y; } ae_complex; - -typedef enum -{ - ERR_OK = 0, - ERR_OUT_OF_MEMORY = 1, - ERR_XARRAY_TOO_LARGE = 2, - ERR_ASSERTION_FAILED = 3 -} ae_error_type; - -typedef ae_int_t ae_datatype; - -/* - * other definitions - */ -enum { OWN_CALLER=1, OWN_AE=2 }; -enum { ACT_UNCHANGED=1, ACT_SAME_LOCATION=2, ACT_NEW_LOCATION=3 }; -enum { DT_BOOL=1, DT_INT=2, DT_REAL=3, DT_COMPLEX=4 }; -enum { CPU_SSE2=1 }; - -/************************************************************************ -x-string (zero-terminated): - owner OWN_CALLER or OWN_AE. Determines what to do on realloc(). - If vector is owned by caller, X-interface will just set - ptr to NULL before realloc(). If it is owned by X, it - will call ae_free/x_free/aligned_free family functions. - - last_action ACT_UNCHANGED, ACT_SAME_LOCATION, ACT_NEW_LOCATION - contents is either: unchanged, stored at the same location, - stored at the new location. - this field is set on return from X. - - ptr pointer to the actual data - -Members of this structure are ae_int64_t to avoid alignment problems. -************************************************************************/ -typedef struct -{ - ae_int64_t owner; - ae_int64_t last_action; - char *ptr; -} x_string; - -/************************************************************************ -x-vector: - cnt number of elements - - datatype one of the DT_XXXX values - - owner OWN_CALLER or OWN_AE. Determines what to do on realloc(). - If vector is owned by caller, X-interface will just set - ptr to NULL before realloc(). If it is owned by X, it - will call ae_free/x_free/aligned_free family functions. - - last_action ACT_UNCHANGED, ACT_SAME_LOCATION, ACT_NEW_LOCATION - contents is either: unchanged, stored at the same location, - stored at the new location. - this field is set on return from X interface and may be - used by caller as hint when deciding what to do with data - (if it was ACT_UNCHANGED or ACT_SAME_LOCATION, no array - reallocation or copying is required). - - ptr pointer to the actual data - -Members of this structure are ae_int64_t to avoid alignment problems. -************************************************************************/ -typedef struct -{ - ae_int64_t cnt; - ae_int64_t datatype; - ae_int64_t owner; - ae_int64_t last_action; - void *ptr; -} x_vector; - - -/************************************************************************ -x-matrix: - rows number of rows. may be zero only when cols is zero too. - - cols number of columns. may be zero only when rows is zero too. - - stride stride, i.e. distance between first elements of rows (in bytes) - - datatype one of the DT_XXXX values - - owner OWN_CALLER or OWN_AE. Determines what to do on realloc(). - If vector is owned by caller, X-interface will just set - ptr to NULL before realloc(). If it is owned by X, it - will call ae_free/x_free/aligned_free family functions. - - last_action ACT_UNCHANGED, ACT_SAME_LOCATION, ACT_NEW_LOCATION - contents is either: unchanged, stored at the same location, - stored at the new location. - this field is set on return from X interface and may be - used by caller as hint when deciding what to do with data - (if it was ACT_UNCHANGED or ACT_SAME_LOCATION, no array - reallocation or copying is required). - - ptr pointer to the actual data, stored rowwise - -Members of this structure are ae_int64_t to avoid alignment problems. -************************************************************************/ -typedef struct -{ - ae_int64_t rows; - ae_int64_t cols; - ae_int64_t stride; - ae_int64_t datatype; - ae_int64_t owner; - ae_int64_t last_action; - void *ptr; -} x_matrix; - - -/************************************************************************ -dynamic block which may be automatically deallocated during stack unwinding - -p_next next block in the stack unwinding list. - NULL means that this block is not in the list -deallocator deallocator function which should be used to deallocate block. - NULL for "special" blocks (frame/stack boundaries) -ptr pointer which should be passed to the deallocator. - may be null (for zero-size block), DYN_BOTTOM or DYN_FRAME - for "special" blocks (frame/stack boundaries). - -************************************************************************/ -typedef struct ae_dyn_block -{ - struct ae_dyn_block * volatile p_next; - /* void *deallocator; */ - void (*deallocator)(void*); - void * volatile ptr; -} ae_dyn_block; - -/************************************************************************ -frame marker -************************************************************************/ -typedef struct ae_frame -{ - ae_dyn_block db_marker; -} ae_frame; - -/************************************************************************ -ALGLIB environment state -************************************************************************/ -typedef struct ae_state -{ - /* - * endianness type: AE_LITTLE_ENDIAN or AE_BIG_ENDIAN - */ - ae_int_t endianness; - - /* - * double value for NAN - */ - double v_nan; - - /* - * double value for +INF - */ - double v_posinf; - - /* - * double value for -INF - */ - double v_neginf; - - /* - * pointer to the top block in a stack of frames - * which hold dynamically allocated objects - */ - ae_dyn_block * volatile p_top_block; - ae_dyn_block last_block; - - /* - * jmp_buf for cases when C-style exception handling is used - */ -#ifndef AE_USE_CPP_ERROR_HANDLING - jmp_buf * volatile break_jump; -#endif - - /* - * ae_error_type of the last error (filled when exception is thrown) - */ - ae_error_type volatile last_error; - - /* - * human-readable message (filled when exception is thrown) - */ - const char* volatile error_msg; - - /* - * threading information: - * a) current thread pool - * b) current worker thread - * c) parent task (one we are solving right now) - * d) thread exception handler (function which must be called - * by ae_assert before raising exception). - * - * NOTE: we use void* to store pointers in order to avoid explicit dependency on smp.h - */ - void *worker_thread; - void *parent_task; - void (*thread_exception_handler)(void*); - -} ae_state; - -/************************************************************************ -Serializer -************************************************************************/ -typedef struct -{ - ae_int_t mode; - ae_int_t entries_needed; - ae_int_t entries_saved; - ae_int_t bytes_asked; - ae_int_t bytes_written; - -#ifdef AE_USE_CPP_SERIALIZATION - std::string *out_cppstr; -#endif - char *out_str; - const char *in_str; -} ae_serializer; - -typedef void(*ae_deallocator)(void*); - -typedef struct ae_vector -{ - ae_int_t cnt; - ae_datatype datatype; - ae_dyn_block data; - union - { - void *p_ptr; - ae_bool *p_bool; - ae_int_t *p_int; - double *p_double; - ae_complex *p_complex; - } ptr; -} ae_vector; - -typedef struct ae_matrix -{ - ae_int_t rows; - ae_int_t cols; - ae_int_t stride; - ae_datatype datatype; - ae_dyn_block data; - union - { - void *p_ptr; - void **pp_void; - ae_bool **pp_bool; - ae_int_t **pp_int; - double **pp_double; - ae_complex **pp_complex; - } ptr; -} ae_matrix; - -typedef struct ae_smart_ptr -{ - /* pointer to subscriber; all changes in ptr are translated to subscriber */ - void **subscriber; - - /* pointer to object */ - void *ptr; - - /* whether smart pointer owns ptr */ - ae_bool is_owner; - - /* whether object pointed by ptr is dynamic - clearing such object requires BOTH - calling destructor function AND calling ae_free for memory occupied by object. */ - ae_bool is_dynamic; - - /* destructor function for pointer; clears all dynamically allocated memory */ - void (*destroy)(void*); - - /* frame entry; used to ensure automatic deallocation of smart pointer in case of exception/exit */ - ae_dyn_block frame_entry; -} ae_smart_ptr; - - -/************************************************************************* -Lock. - -This structure provides OS-independent non-reentrant lock: -* under Windows/Posix systems it uses system-provided locks -* under Boost it uses OS-independent lock provided by Boost package -* when no OS is defined, it uses "fake lock" (just stub which is not thread-safe): - a) "fake lock" can be in locked or free mode - b) "fake lock" can be used only from one thread - one which created lock - c) when thread acquires free lock, it immediately returns - d) when thread acquires busy lock, program is terminated - (because lock is already acquired and no one else can free it) -*************************************************************************/ -typedef struct -{ -#if AE_OS==AE_WINDOWS - volatile ae_int_t * volatile p_lock; - char buf[sizeof(ae_int_t)+AE_LOCK_ALIGNMENT]; -#elif AE_OS==AE_POSIX - pthread_mutex_t mutex; -#else - ae_bool is_locked; -#endif -} ae_lock; - - -/************************************************************************* -Shared pool: data structure used to provide thread-safe access to pool of -temporary variables. -*************************************************************************/ -typedef struct ae_shared_pool_entry -{ - void * volatile obj; - void * volatile next_entry; -} ae_shared_pool_entry; - -typedef struct ae_shared_pool -{ - /* lock object which protects pool */ - ae_lock pool_lock; - - /* seed object (used to create new instances of temporaries) */ - void * volatile seed_object; - - /* - * list of recycled OBJECTS: - * 1. entries in this list store pointers to recycled objects - * 2. every time we retrieve object, we retrieve first entry from this list, - * move it to recycled_entries and return its obj field to caller/ - */ - ae_shared_pool_entry * volatile recycled_objects; - - /* - * list of recycled ENTRIES: - * 1. this list holds entries which are not used to store recycled objects; - * every time recycled object is retrieved, its entry is moved to this list. - * 2. every time object is recycled, we try to fetch entry for him from this list - * before allocating it with malloc() - */ - ae_shared_pool_entry * volatile recycled_entries; - - /* enumeration pointer, points to current recycled object*/ - ae_shared_pool_entry * volatile enumeration_counter; - - /* size of object; this field is used when we call malloc() for new objects */ - ae_int_t size_of_object; - - /* initializer function; accepts pointer to malloc'ed object, initializes its fields */ - ae_bool (*init)(void* dst, ae_state* state, ae_bool make_automatic); - - /* copy constructor; accepts pointer to malloc'ed, but not initialized object */ - ae_bool (*init_copy)(void* dst, void* src, ae_state* state, ae_bool make_automatic); - - /* destructor function; */ - void (*destroy)(void* ptr); - - /* frame entry; contains pointer to the pool object itself */ - ae_dyn_block frame_entry; -} ae_shared_pool; - -ae_int_t ae_misalignment(const void *ptr, size_t alignment); -void* ae_align(void *ptr, size_t alignment); -void* aligned_malloc(size_t size, size_t alignment); -void aligned_free(void *block); - -void* ae_malloc(size_t size, ae_state *state); -void ae_free(void *p); -ae_int_t ae_sizeof(ae_datatype datatype); -void ae_touch_ptr(void *p); - -void ae_state_init(ae_state *state); -void ae_state_clear(ae_state *state); -#ifndef AE_USE_CPP_ERROR_HANDLING -void ae_state_set_break_jump(ae_state *state, jmp_buf *buf); -#endif -void ae_break(ae_state *state, ae_error_type error_type, const char *msg); - -void ae_frame_make(ae_state *state, ae_frame *tmp); -void ae_frame_leave(ae_state *state); - -void ae_db_attach(ae_dyn_block *block, ae_state *state); -ae_bool ae_db_malloc(ae_dyn_block *block, ae_int_t size, ae_state *state, ae_bool make_automatic); -ae_bool ae_db_realloc(ae_dyn_block *block, ae_int_t size, ae_state *state); -void ae_db_free(ae_dyn_block *block); -void ae_db_swap(ae_dyn_block *block1, ae_dyn_block *block2); - -ae_bool ae_vector_init(ae_vector *dst, ae_int_t size, ae_datatype datatype, ae_state *state, ae_bool make_automatic); -ae_bool ae_vector_init_copy(ae_vector *dst, ae_vector *src, ae_state *state, ae_bool make_automatic); -void ae_vector_init_from_x(ae_vector *dst, x_vector *src, ae_state *state, ae_bool make_automatic); -ae_bool ae_vector_set_length(ae_vector *dst, ae_int_t newsize, ae_state *state); -void ae_vector_clear(ae_vector *dst); -void ae_vector_destroy(ae_vector *dst); -void ae_swap_vectors(ae_vector *vec1, ae_vector *vec2); - -ae_bool ae_matrix_init(ae_matrix *dst, ae_int_t rows, ae_int_t cols, ae_datatype datatype, ae_state *state, ae_bool make_automatic); -ae_bool ae_matrix_init_copy(ae_matrix *dst, ae_matrix *src, ae_state *state, ae_bool make_automatic); -void ae_matrix_init_from_x(ae_matrix *dst, x_matrix *src, ae_state *state, ae_bool make_automatic); -ae_bool ae_matrix_set_length(ae_matrix *dst, ae_int_t rows, ae_int_t cols, ae_state *state); -void ae_matrix_clear(ae_matrix *dst); -void ae_matrix_destroy(ae_matrix *dst); -void ae_swap_matrices(ae_matrix *mat1, ae_matrix *mat2); - -ae_bool ae_smart_ptr_init(ae_smart_ptr *dst, void **subscriber, ae_state *state, ae_bool make_automatic); -void ae_smart_ptr_clear(void *_dst); /* accepts ae_smart_ptr* */ -void ae_smart_ptr_destroy(void *_dst); -void ae_smart_ptr_assign(ae_smart_ptr *dst, void *new_ptr, ae_bool is_owner, ae_bool is_dynamic, void (*destroy)(void*)); -void ae_smart_ptr_release(ae_smart_ptr *dst); - -void ae_yield(); -void ae_init_lock(ae_lock *lock); -void ae_acquire_lock(ae_lock *lock); -void ae_release_lock(ae_lock *lock); -void ae_free_lock(ae_lock *lock); - -ae_bool ae_shared_pool_init(void *_dst, ae_state *state, ae_bool make_automatic); -ae_bool ae_shared_pool_init_copy(void *_dst, void *_src, ae_state *state, ae_bool make_automatic); -void ae_shared_pool_clear(void *dst); -void ae_shared_pool_destroy(void *dst); -ae_bool ae_shared_pool_is_initialized(void *_dst); -void ae_shared_pool_set_seed( - ae_shared_pool *dst, - void *seed_object, - ae_int_t size_of_object, - ae_bool (*init)(void* dst, ae_state* state, ae_bool make_automatic), - ae_bool (*init_copy)(void* dst, void* src, ae_state* state, ae_bool make_automatic), - void (*destroy)(void* ptr), - ae_state *state); -void ae_shared_pool_retrieve( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state); -void ae_shared_pool_recycle( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state); -void ae_shared_pool_clear_recycled( - ae_shared_pool *pool, - ae_state *state); -void ae_shared_pool_first_recycled( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state); -void ae_shared_pool_next_recycled( - ae_shared_pool *pool, - ae_smart_ptr *pptr, - ae_state *state); -void ae_shared_pool_reset( - ae_shared_pool *pool, - ae_state *state); - -void ae_x_set_vector(x_vector *dst, ae_vector *src, ae_state *state); -void ae_x_set_matrix(x_matrix *dst, ae_matrix *src, ae_state *state); -void ae_x_attach_to_vector(x_vector *dst, ae_vector *src); -void ae_x_attach_to_matrix(x_matrix *dst, ae_matrix *src); - -void x_vector_clear(x_vector *dst); - -ae_bool x_is_symmetric(x_matrix *a); -ae_bool x_is_hermitian(x_matrix *a); -ae_bool x_force_symmetric(x_matrix *a); -ae_bool x_force_hermitian(x_matrix *a); -ae_bool ae_is_symmetric(ae_matrix *a); -ae_bool ae_is_hermitian(ae_matrix *a); -ae_bool ae_force_symmetric(ae_matrix *a); -ae_bool ae_force_hermitian(ae_matrix *a); - -void ae_serializer_init(ae_serializer *serializer); -void ae_serializer_clear(ae_serializer *serializer); - -void ae_serializer_alloc_start(ae_serializer *serializer); -void ae_serializer_alloc_entry(ae_serializer *serializer); -ae_int_t ae_serializer_get_alloc_size(ae_serializer *serializer); - -#ifdef AE_USE_CPP_SERIALIZATION -void ae_serializer_sstart_str(ae_serializer *serializer, std::string *buf); -void ae_serializer_ustart_str(ae_serializer *serializer, const std::string *buf); -#endif -void ae_serializer_sstart_str(ae_serializer *serializer, char *buf); -void ae_serializer_ustart_str(ae_serializer *serializer, const char *buf); - -void ae_serializer_serialize_bool(ae_serializer *serializer, ae_bool v, ae_state *state); -void ae_serializer_serialize_int(ae_serializer *serializer, ae_int_t v, ae_state *state); -void ae_serializer_serialize_double(ae_serializer *serializer, double v, ae_state *state); -void ae_serializer_unserialize_bool(ae_serializer *serializer, ae_bool *v, ae_state *state); -void ae_serializer_unserialize_int(ae_serializer *serializer, ae_int_t *v, ae_state *state); -void ae_serializer_unserialize_double(ae_serializer *serializer, double *v, ae_state *state); - -void ae_serializer_stop(ae_serializer *serializer); - -/************************************************************************ -Service functions -************************************************************************/ -void ae_assert(ae_bool cond, const char *msg, ae_state *state); -ae_int_t ae_cpuid(); - -/************************************************************************ -Real math functions: -* IEEE-compliant floating point comparisons -* standard functions -************************************************************************/ -ae_bool ae_fp_eq(double v1, double v2); -ae_bool ae_fp_neq(double v1, double v2); -ae_bool ae_fp_less(double v1, double v2); -ae_bool ae_fp_less_eq(double v1, double v2); -ae_bool ae_fp_greater(double v1, double v2); -ae_bool ae_fp_greater_eq(double v1, double v2); - -ae_bool ae_isfinite_stateless(double x, ae_int_t endianness); -ae_bool ae_isnan_stateless(double x, ae_int_t endianness); -ae_bool ae_isinf_stateless(double x, ae_int_t endianness); -ae_bool ae_isposinf_stateless(double x, ae_int_t endianness); -ae_bool ae_isneginf_stateless(double x, ae_int_t endianness); - -ae_int_t ae_get_endianness(); - -ae_bool ae_isfinite(double x,ae_state *state); -ae_bool ae_isnan(double x, ae_state *state); -ae_bool ae_isinf(double x, ae_state *state); -ae_bool ae_isposinf(double x,ae_state *state); -ae_bool ae_isneginf(double x,ae_state *state); - -double ae_fabs(double x, ae_state *state); -ae_int_t ae_iabs(ae_int_t x, ae_state *state); -double ae_sqr(double x, ae_state *state); -double ae_sqrt(double x, ae_state *state); - -ae_int_t ae_sign(double x, ae_state *state); -ae_int_t ae_round(double x, ae_state *state); -ae_int_t ae_trunc(double x, ae_state *state); -ae_int_t ae_ifloor(double x, ae_state *state); -ae_int_t ae_iceil(double x, ae_state *state); - -ae_int_t ae_maxint(ae_int_t m1, ae_int_t m2, ae_state *state); -ae_int_t ae_minint(ae_int_t m1, ae_int_t m2, ae_state *state); -double ae_maxreal(double m1, double m2, ae_state *state); -double ae_minreal(double m1, double m2, ae_state *state); -double ae_randomreal(ae_state *state); -ae_int_t ae_randominteger(ae_int_t maxv, ae_state *state); - -double ae_sin(double x, ae_state *state); -double ae_cos(double x, ae_state *state); -double ae_tan(double x, ae_state *state); -double ae_sinh(double x, ae_state *state); -double ae_cosh(double x, ae_state *state); -double ae_tanh(double x, ae_state *state); -double ae_asin(double x, ae_state *state); -double ae_acos(double x, ae_state *state); -double ae_atan(double x, ae_state *state); -double ae_atan2(double y, double x, ae_state *state); - -double ae_log(double x, ae_state *state); -double ae_pow(double x, double y, ae_state *state); -double ae_exp(double x, ae_state *state); - -/************************************************************************ -Complex math functions: -* basic arithmetic operations -* standard functions -************************************************************************/ -ae_complex ae_complex_from_d(double v); - -ae_complex ae_c_neg(ae_complex lhs); -ae_bool ae_c_eq(ae_complex lhs, ae_complex rhs); -ae_bool ae_c_neq(ae_complex lhs, ae_complex rhs); -ae_complex ae_c_add(ae_complex lhs, ae_complex rhs); -ae_complex ae_c_mul(ae_complex lhs, ae_complex rhs); -ae_complex ae_c_sub(ae_complex lhs, ae_complex rhs); -ae_complex ae_c_div(ae_complex lhs, ae_complex rhs); -ae_bool ae_c_eq_d(ae_complex lhs, double rhs); -ae_bool ae_c_neq_d(ae_complex lhs, double rhs); -ae_complex ae_c_add_d(ae_complex lhs, double rhs); -ae_complex ae_c_mul_d(ae_complex lhs, double rhs); -ae_complex ae_c_sub_d(ae_complex lhs, double rhs); -ae_complex ae_c_d_sub(double lhs, ae_complex rhs); -ae_complex ae_c_div_d(ae_complex lhs, double rhs); -ae_complex ae_c_d_div(double lhs, ae_complex rhs); - -ae_complex ae_c_conj(ae_complex lhs, ae_state *state); -ae_complex ae_c_sqr(ae_complex lhs, ae_state *state); -double ae_c_abs(ae_complex z, ae_state *state); - -/************************************************************************ -Complex BLAS operations -************************************************************************/ -ae_complex ae_v_cdotproduct(const ae_complex *v0, ae_int_t stride0, const char *conj0, const ae_complex *v1, ae_int_t stride1, const char *conj1, ae_int_t n); -void ae_v_cmove(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void ae_v_cmoveneg(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void ae_v_cmoved(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha); -void ae_v_cmovec(ae_complex *vdst, ae_int_t stride_dst, const ae_complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, ae_complex alpha); -void ae_v_cadd(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void ae_v_caddd(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha); -void ae_v_caddc(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, ae_complex alpha); -void ae_v_csub(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void ae_v_csubd(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha); -void ae_v_csubc(ae_complex *vdst, ae_int_t stride_dst, const ae_complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, ae_complex alpha); -void ae_v_cmuld(ae_complex *vdst, ae_int_t stride_dst, ae_int_t n, double alpha); -void ae_v_cmulc(ae_complex *vdst, ae_int_t stride_dst, ae_int_t n, ae_complex alpha); - -/************************************************************************ -Real BLAS operations -************************************************************************/ -double ae_v_dotproduct(const double *v0, ae_int_t stride0, const double *v1, ae_int_t stride1, ae_int_t n); -void ae_v_move(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n); -void ae_v_moveneg(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n); -void ae_v_moved(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n, double alpha); -void ae_v_add(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n); -void ae_v_addd(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n, double alpha); -void ae_v_sub(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n); -void ae_v_subd(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n, double alpha); -void ae_v_muld(double *vdst, ae_int_t stride_dst, ae_int_t n, double alpha); - -/************************************************************************ -Other functions -************************************************************************/ -ae_int_t ae_v_len(ae_int_t a, ae_int_t b); - -/* -extern const double ae_machineepsilon; -extern const double ae_maxrealnumber; -extern const double ae_minrealnumber; -extern const double ae_pi; -*/ -#define ae_machineepsilon 5E-16 -#define ae_maxrealnumber 1E300 -#define ae_minrealnumber 1E-300 -#define ae_pi 3.1415926535897932384626433832795 - - -/************************************************************************ -RComm functions -************************************************************************/ -typedef struct rcommstate -{ - int stage; - ae_vector ia; - ae_vector ba; - ae_vector ra; - ae_vector ca; -} rcommstate; -ae_bool _rcommstate_init(rcommstate* p, ae_state *_state, ae_bool make_automatic); -ae_bool _rcommstate_init_copy(rcommstate* dst, rcommstate* src, ae_state *_state, ae_bool make_automatic); -void _rcommstate_clear(rcommstate* p); -void _rcommstate_destroy(rcommstate* p); - -#ifdef AE_USE_ALLOC_COUNTER -extern ae_int64_t _alloc_counter; -#endif - - -/************************************************************************ -debug functions (must be turned on by preprocessor definitions): -* tickcount(), which is wrapper around GetTickCount() -* flushconsole(), fluches console -* ae_debugrng(), returns random number generated with high-quality random numbers generator -* ae_set_seed(), sets seed of the debug RNG (NON-THREAD-SAFE!!!) -* ae_get_seed(), returns two seed values of the debug RNG (NON-THREAD-SAFE!!!) -************************************************************************/ -#ifdef AE_DEBUG4WINDOWS -#define flushconsole(s) fflush(stdout) -#define tickcount(s) _tickcount() -int _tickcount(); -#endif -#ifdef AE_DEBUG4POSIX -#define flushconsole(s) fflush(stdout) -#define tickcount(s) _tickcount() -int _tickcount(); -#endif -#ifdef AE_DEBUGRNG -ae_int_t ae_debugrng(); -void ae_set_seed(ae_int_t s0, ae_int_t s1); -void ae_get_seed(ae_int_t *s0, ae_int_t *s1); -#endif - - -} - - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS DECLARATIONS FOR C++ RELATED FUNCTIONALITY -// -///////////////////////////////////////////////////////////////////////// - -namespace alglib -{ - -typedef alglib_impl::ae_int_t ae_int_t; - -/******************************************************************** -Class forwards -********************************************************************/ -class complex; - -ae_int_t vlen(ae_int_t n1, ae_int_t n2); - -/******************************************************************** -Exception class. -********************************************************************/ -class ap_error -{ -public: - std::string msg; - - ap_error(); - ap_error(const char *s); - static void make_assertion(bool bClause); - static void make_assertion(bool bClause, const char *msg); -private: -}; - -/******************************************************************** -Complex number with double precision. -********************************************************************/ -class complex -{ -public: - complex(); - complex(const double &_x); - complex(const double &_x, const double &_y); - complex(const complex &z); - - complex& operator= (const double& v); - complex& operator+=(const double& v); - complex& operator-=(const double& v); - complex& operator*=(const double& v); - complex& operator/=(const double& v); - - complex& operator= (const complex& z); - complex& operator+=(const complex& z); - complex& operator-=(const complex& z); - complex& operator*=(const complex& z); - complex& operator/=(const complex& z); - - alglib_impl::ae_complex* c_ptr(); - const alglib_impl::ae_complex* c_ptr() const; - - std::string tostring(int dps) const; - - double x, y; -}; - -const alglib::complex operator/(const alglib::complex& lhs, const alglib::complex& rhs); -const bool operator==(const alglib::complex& lhs, const alglib::complex& rhs); -const bool operator!=(const alglib::complex& lhs, const alglib::complex& rhs); -const alglib::complex operator+(const alglib::complex& lhs); -const alglib::complex operator-(const alglib::complex& lhs); -const alglib::complex operator+(const alglib::complex& lhs, const alglib::complex& rhs); -const alglib::complex operator+(const alglib::complex& lhs, const double& rhs); -const alglib::complex operator+(const double& lhs, const alglib::complex& rhs); -const alglib::complex operator-(const alglib::complex& lhs, const alglib::complex& rhs); -const alglib::complex operator-(const alglib::complex& lhs, const double& rhs); -const alglib::complex operator-(const double& lhs, const alglib::complex& rhs); -const alglib::complex operator*(const alglib::complex& lhs, const alglib::complex& rhs); -const alglib::complex operator*(const alglib::complex& lhs, const double& rhs); -const alglib::complex operator*(const double& lhs, const alglib::complex& rhs); -const alglib::complex operator/(const alglib::complex& lhs, const alglib::complex& rhs); -const alglib::complex operator/(const double& lhs, const alglib::complex& rhs); -const alglib::complex operator/(const alglib::complex& lhs, const double& rhs); -double abscomplex(const alglib::complex &z); -alglib::complex conj(const alglib::complex &z); -alglib::complex csqr(const alglib::complex &z); -void setnworkers(alglib::ae_int_t nworkers); - -/******************************************************************** -Level 1 BLAS functions - -NOTES: -* destination and source should NOT overlap -* stride is assumed to be positive, but it is not - assert'ed within function -* conj_src parameter specifies whether complex source is conjugated - before processing or not. Pass string which starts with 'N' or 'n' - ("No conj", for example) to use unmodified parameter. All other - values will result in conjugation of input, but it is recommended - to use "Conj" in such cases. -********************************************************************/ -double vdotproduct(const double *v0, ae_int_t stride0, const double *v1, ae_int_t stride1, ae_int_t n); -double vdotproduct(const double *v1, const double *v2, ae_int_t N); - -alglib::complex vdotproduct(const alglib::complex *v0, ae_int_t stride0, const char *conj0, const alglib::complex *v1, ae_int_t stride1, const char *conj1, ae_int_t n); -alglib::complex vdotproduct(const alglib::complex *v1, const alglib::complex *v2, ae_int_t N); - -void vmove(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n); -void vmove(double *vdst, const double* vsrc, ae_int_t N); - -void vmove(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void vmove(alglib::complex *vdst, const alglib::complex* vsrc, ae_int_t N); - -void vmoveneg(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n); -void vmoveneg(double *vdst, const double *vsrc, ae_int_t N); - -void vmoveneg(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void vmoveneg(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N); - -void vmove(double *vdst, ae_int_t stride_dst, const double* vsrc, ae_int_t stride_src, ae_int_t n, double alpha); -void vmove(double *vdst, const double *vsrc, ae_int_t N, double alpha); - -void vmove(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha); -void vmove(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, double alpha); - -void vmove(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex* vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, alglib::complex alpha); -void vmove(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, alglib::complex alpha); - -void vadd(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n); -void vadd(double *vdst, const double *vsrc, ae_int_t N); - -void vadd(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void vadd(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N); - -void vadd(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n, double alpha); -void vadd(double *vdst, const double *vsrc, ae_int_t N, double alpha); - -void vadd(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha); -void vadd(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, double alpha); - -void vadd(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, alglib::complex alpha); -void vadd(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, alglib::complex alpha); - -void vsub(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n); -void vsub(double *vdst, const double *vsrc, ae_int_t N); - -void vsub(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n); -void vsub(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N); - -void vsub(double *vdst, ae_int_t stride_dst, const double *vsrc, ae_int_t stride_src, ae_int_t n, double alpha); -void vsub(double *vdst, const double *vsrc, ae_int_t N, double alpha); - -void vsub(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, double alpha); -void vsub(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, double alpha); - -void vsub(alglib::complex *vdst, ae_int_t stride_dst, const alglib::complex *vsrc, ae_int_t stride_src, const char *conj_src, ae_int_t n, alglib::complex alpha); -void vsub(alglib::complex *vdst, const alglib::complex *vsrc, ae_int_t N, alglib::complex alpha); - -void vmul(double *vdst, ae_int_t stride_dst, ae_int_t n, double alpha); -void vmul(double *vdst, ae_int_t N, double alpha); - -void vmul(alglib::complex *vdst, ae_int_t stride_dst, ae_int_t n, double alpha); -void vmul(alglib::complex *vdst, ae_int_t N, double alpha); - -void vmul(alglib::complex *vdst, ae_int_t stride_dst, ae_int_t n, alglib::complex alpha); -void vmul(alglib::complex *vdst, ae_int_t N, alglib::complex alpha); - - - -/******************************************************************** -string conversion functions !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -********************************************************************/ - -/******************************************************************** -1- and 2-dimensional arrays -********************************************************************/ -class ae_vector_wrapper -{ -public: - ae_vector_wrapper(); - virtual ~ae_vector_wrapper(); - - void setlength(ae_int_t iLen); - ae_int_t length() const; - - void attach_to(alglib_impl::ae_vector *ptr); - void allocate_own(ae_int_t size, alglib_impl::ae_datatype datatype); - const alglib_impl::ae_vector* c_ptr() const; - alglib_impl::ae_vector* c_ptr(); -private: - ae_vector_wrapper(const ae_vector_wrapper &rhs); - const ae_vector_wrapper& operator=(const ae_vector_wrapper &rhs); -protected: - // - // Copies source vector RHS into current object. - // - // Current object is considered empty (this function should be - // called from copy constructor). - // - void create(const ae_vector_wrapper &rhs); - - // - // Copies array given by string into current object. Additional - // parameter DATATYPE contains information about type of the data - // in S and type of the array to create. - // - // Current object is considered empty (this function should be - // called from copy constructor). - // - void create(const char *s, alglib_impl::ae_datatype datatype); - - // - // Assigns RHS to current object. - // - // It has several branches depending on target object status: - // * in case it is proxy object, data are copied into memory pointed by - // proxy. Function checks that source has exactly same size as target - // (exception is thrown on failure). - // * in case it is non-proxy object, data allocated by object are cleared - // and a copy of RHS is created in target. - // - // NOTE: this function correctly handles assignments of the object to itself. - // - void assign(const ae_vector_wrapper &rhs); - - alglib_impl::ae_vector *p_vec; - alglib_impl::ae_vector vec; -}; - -class boolean_1d_array : public ae_vector_wrapper -{ -public: - boolean_1d_array(); - boolean_1d_array(const char *s); - boolean_1d_array(const boolean_1d_array &rhs); - boolean_1d_array(alglib_impl::ae_vector *p); - const boolean_1d_array& operator=(const boolean_1d_array &rhs); - virtual ~boolean_1d_array() ; - - const ae_bool& operator()(ae_int_t i) const; - ae_bool& operator()(ae_int_t i); - - const ae_bool& operator[](ae_int_t i) const; - ae_bool& operator[](ae_int_t i); - - void setcontent(ae_int_t iLen, const bool *pContent ); - ae_bool* getcontent(); - const ae_bool* getcontent() const; - - std::string tostring() const; -}; - -class integer_1d_array : public ae_vector_wrapper -{ -public: - integer_1d_array(); - integer_1d_array(const char *s); - integer_1d_array(const integer_1d_array &rhs); - integer_1d_array(alglib_impl::ae_vector *p); - const integer_1d_array& operator=(const integer_1d_array &rhs); - virtual ~integer_1d_array(); - - const ae_int_t& operator()(ae_int_t i) const; - ae_int_t& operator()(ae_int_t i); - - const ae_int_t& operator[](ae_int_t i) const; - ae_int_t& operator[](ae_int_t i); - - void setcontent(ae_int_t iLen, const ae_int_t *pContent ); - - ae_int_t* getcontent(); - const ae_int_t* getcontent() const; - - std::string tostring() const; -}; - -class real_1d_array : public ae_vector_wrapper -{ -public: - real_1d_array(); - real_1d_array(const char *s); - real_1d_array(const real_1d_array &rhs); - real_1d_array(alglib_impl::ae_vector *p); - const real_1d_array& operator=(const real_1d_array &rhs); - virtual ~real_1d_array(); - - const double& operator()(ae_int_t i) const; - double& operator()(ae_int_t i); - - const double& operator[](ae_int_t i) const; - double& operator[](ae_int_t i); - - void setcontent(ae_int_t iLen, const double *pContent ); - double* getcontent(); - const double* getcontent() const; - - std::string tostring(int dps) const; -}; - -class complex_1d_array : public ae_vector_wrapper -{ -public: - complex_1d_array(); - complex_1d_array(const char *s); - complex_1d_array(const complex_1d_array &rhs); - complex_1d_array(alglib_impl::ae_vector *p); - const complex_1d_array& operator=(const complex_1d_array &rhs); - virtual ~complex_1d_array(); - - const alglib::complex& operator()(ae_int_t i) const; - alglib::complex& operator()(ae_int_t i); - - const alglib::complex& operator[](ae_int_t i) const; - alglib::complex& operator[](ae_int_t i); - - void setcontent(ae_int_t iLen, const alglib::complex *pContent ); - alglib::complex* getcontent(); - const alglib::complex* getcontent() const; - - std::string tostring(int dps) const; -}; - -class ae_matrix_wrapper -{ -public: - ae_matrix_wrapper(); - virtual ~ae_matrix_wrapper(); - const ae_matrix_wrapper& operator=(const ae_matrix_wrapper &rhs); - - void setlength(ae_int_t rows, ae_int_t cols); - ae_int_t rows() const; - ae_int_t cols() const; - bool isempty() const; - ae_int_t getstride() const; - - void attach_to(alglib_impl::ae_matrix *ptr); - void allocate_own(ae_int_t rows, ae_int_t cols, alglib_impl::ae_datatype datatype); - const alglib_impl::ae_matrix* c_ptr() const; - alglib_impl::ae_matrix* c_ptr(); -private: - ae_matrix_wrapper(const ae_matrix_wrapper &rhs); -protected: - // - // Copies source matrix RHS into current object. - // - // Current object is considered empty (this function should be - // called from copy constructor). - // - void create(const ae_matrix_wrapper &rhs); - - // - // Copies array given by string into current object. Additional - // parameter DATATYPE contains information about type of the data - // in S and type of the array to create. - // - // Current object is considered empty (this function should be - // called from copy constructor). - // - void create(const char *s, alglib_impl::ae_datatype datatype); - - // - // Assigns RHS to current object. - // - // It has several branches depending on target object status: - // * in case it is proxy object, data are copied into memory pointed by - // proxy. Function checks that source has exactly same size as target - // (exception is thrown on failure). - // * in case it is non-proxy object, data allocated by object are cleared - // and a copy of RHS is created in target. - // - // NOTE: this function correctly handles assignments of the object to itself. - // - void assign(const ae_matrix_wrapper &rhs); - - alglib_impl::ae_matrix *p_mat; - alglib_impl::ae_matrix mat; -}; - -class boolean_2d_array : public ae_matrix_wrapper -{ -public: - boolean_2d_array(); - boolean_2d_array(const boolean_2d_array &rhs); - boolean_2d_array(alglib_impl::ae_matrix *p); - boolean_2d_array(const char *s); - virtual ~boolean_2d_array(); - - const ae_bool& operator()(ae_int_t i, ae_int_t j) const; - ae_bool& operator()(ae_int_t i, ae_int_t j); - - const ae_bool* operator[](ae_int_t i) const; - ae_bool* operator[](ae_int_t i); - - void setcontent(ae_int_t irows, ae_int_t icols, const bool *pContent ); - - std::string tostring() const ; -}; - -class integer_2d_array : public ae_matrix_wrapper -{ -public: - integer_2d_array(); - integer_2d_array(const integer_2d_array &rhs); - integer_2d_array(alglib_impl::ae_matrix *p); - integer_2d_array(const char *s); - virtual ~integer_2d_array(); - - const ae_int_t& operator()(ae_int_t i, ae_int_t j) const; - ae_int_t& operator()(ae_int_t i, ae_int_t j); - - const ae_int_t* operator[](ae_int_t i) const; - ae_int_t* operator[](ae_int_t i); - - void setcontent(ae_int_t irows, ae_int_t icols, const ae_int_t *pContent ); - - std::string tostring() const; -}; - -class real_2d_array : public ae_matrix_wrapper -{ -public: - real_2d_array(); - real_2d_array(const real_2d_array &rhs); - real_2d_array(alglib_impl::ae_matrix *p); - real_2d_array(const char *s); - virtual ~real_2d_array(); - - const double& operator()(ae_int_t i, ae_int_t j) const; - double& operator()(ae_int_t i, ae_int_t j); - - const double* operator[](ae_int_t i) const; - double* operator[](ae_int_t i); - - void setcontent(ae_int_t irows, ae_int_t icols, const double *pContent ); - - std::string tostring(int dps) const; -}; - -class complex_2d_array : public ae_matrix_wrapper -{ -public: - complex_2d_array(); - complex_2d_array(const complex_2d_array &rhs); - complex_2d_array(alglib_impl::ae_matrix *p); - complex_2d_array(const char *s); - virtual ~complex_2d_array(); - - const alglib::complex& operator()(ae_int_t i, ae_int_t j) const; - alglib::complex& operator()(ae_int_t i, ae_int_t j); - - const alglib::complex* operator[](ae_int_t i) const; - alglib::complex* operator[](ae_int_t i); - - void setcontent(ae_int_t irows, ae_int_t icols, const alglib::complex *pContent ); - - std::string tostring(int dps) const; -}; - - - -/******************************************************************** -dataset information. - -can store regression dataset, classification dataset, or non-labeled -task: -* nout==0 means non-labeled task (clustering, for example) -* nout>0 && nclasses==0 means regression task -* nout>0 && nclasses>0 means classification task -********************************************************************/ -/*class dataset -{ -public: - dataset():nin(0), nout(0), nclasses(0), trnsize(0), valsize(0), tstsize(0), totalsize(0){}; - - int nin, nout, nclasses; - - int trnsize; - int valsize; - int tstsize; - int totalsize; - - alglib::real_2d_array trn; - alglib::real_2d_array val; - alglib::real_2d_array tst; - alglib::real_2d_array all; -}; - -bool opendataset(std::string file, dataset *pdataset); - -// -// internal functions -// -std::string strtolower(const std::string &s); -bool readstrings(std::string file, std::list *pOutput); -bool readstrings(std::string file, std::list *pOutput, std::string comment); -void explodestring(std::string s, char sep, std::vector *pOutput); -std::string xtrim(std::string s);*/ - -/******************************************************************** -Constants and functions introduced for compatibility with AlgoPascal -********************************************************************/ -extern const double machineepsilon; -extern const double maxrealnumber; -extern const double minrealnumber; -extern const double fp_nan; -extern const double fp_posinf; -extern const double fp_neginf; -extern const ae_int_t endianness; - -int sign(double x); -double randomreal(); -ae_int_t randominteger(ae_int_t maxv); -int round(double x); -int trunc(double x); -int ifloor(double x); -int iceil(double x); -double pi(); -double sqr(double x); -int maxint(int m1, int m2); -int minint(int m1, int m2); -double maxreal(double m1, double m2); -double minreal(double m1, double m2); - -bool fp_eq(double v1, double v2); -bool fp_neq(double v1, double v2); -bool fp_less(double v1, double v2); -bool fp_less_eq(double v1, double v2); -bool fp_greater(double v1, double v2); -bool fp_greater_eq(double v1, double v2); - -bool fp_isnan(double x); -bool fp_isposinf(double x); -bool fp_isneginf(double x); -bool fp_isinf(double x); -bool fp_isfinite(double x); - - -}//namespace alglib - - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTIONS CONTAINS DECLARATIONS FOR OPTIMIZED LINEAR ALGEBRA CODES -// IT IS SHARED BETWEEN C++ AND PURE C LIBRARIES -// -///////////////////////////////////////////////////////////////////////// - -namespace alglib_impl -{ -#define ALGLIB_INTERCEPTS_ABLAS -void _ialglib_vzero(ae_int_t n, double *p, ae_int_t stride); -void _ialglib_vzero_complex(ae_int_t n, ae_complex *p, ae_int_t stride); -void _ialglib_vcopy(ae_int_t n, const double *a, ae_int_t stridea, double *b, ae_int_t strideb); -void _ialglib_vcopy_complex(ae_int_t n, const ae_complex *a, ae_int_t stridea, double *b, ae_int_t strideb, const char *conj); -void _ialglib_vcopy_dcomplex(ae_int_t n, const double *a, ae_int_t stridea, double *b, ae_int_t strideb, const char *conj); -void _ialglib_mcopyblock(ae_int_t m, ae_int_t n, const double *a, ae_int_t op, ae_int_t stride, double *b); -void _ialglib_mcopyunblock(ae_int_t m, ae_int_t n, const double *a, ae_int_t op, double *b, ae_int_t stride); -void _ialglib_mcopyblock_complex(ae_int_t m, ae_int_t n, const ae_complex *a, ae_int_t op, ae_int_t stride, double *b); -void _ialglib_mcopyunblock_complex(ae_int_t m, ae_int_t n, const double *a, ae_int_t op, ae_complex* b, ae_int_t stride); - -ae_bool _ialglib_i_rmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - ae_matrix *b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - ae_matrix *c, - ae_int_t ic, - ae_int_t jc); -ae_bool _ialglib_i_cmatrixgemmf(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - ae_matrix *b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - ae_matrix *c, - ae_int_t ic, - ae_int_t jc); -ae_bool _ialglib_i_cmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2); -ae_bool _ialglib_i_rmatrixrighttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2); -ae_bool _ialglib_i_cmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2); -ae_bool _ialglib_i_rmatrixlefttrsmf(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - ae_matrix *x, - ae_int_t i2, - ae_int_t j2); -ae_bool _ialglib_i_cmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - ae_matrix *c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper); -ae_bool _ialglib_i_rmatrixsyrkf(ae_int_t n, - ae_int_t k, - double alpha, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - ae_matrix *c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper); -ae_bool _ialglib_i_cmatrixrank1f(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_vector *u, - ae_int_t uoffs, - ae_vector *v, - ae_int_t voffs); -ae_bool _ialglib_i_rmatrixrank1f(ae_int_t m, - ae_int_t n, - ae_matrix *a, - ae_int_t ia, - ae_int_t ja, - ae_vector *u, - ae_int_t uoffs, - ae_vector *v, - ae_int_t voffs); - - - -} - - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS PARALLEL SUBROUTINES -// -///////////////////////////////////////////////////////////////////////// - -namespace alglib_impl -{ - -} - - -#endif - diff --git a/src/inc/alglib/dataanalysis.cpp b/src/inc/alglib/dataanalysis.cpp deleted file mode 100644 index 1ef4452..0000000 --- a/src/inc/alglib/dataanalysis.cpp +++ /dev/null @@ -1,35078 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "dataanalysis.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Optimal binary classification - -Algorithms finds optimal (=with minimal cross-entropy) binary partition. -Internal subroutine. - -INPUT PARAMETERS: - A - array[0..N-1], variable - C - array[0..N-1], class numbers (0 or 1). - N - array size - -OUTPUT PARAMETERS: - Info - completetion code: - * -3, all values of A[] are same (partition is impossible) - * -2, one of C[] is incorrect (<0, >1) - * -1, incorrect pararemets were passed (N<=0). - * 1, OK - Threshold- partiton boundary. Left part contains values which are - strictly less than Threshold. Right part contains values - which are greater than or equal to Threshold. - PAL, PBL- probabilities P(0|v=Threshold) and P(1|v>=Threshold) - CVE - cross-validation estimate of cross-entropy - - -- ALGLIB -- - Copyright 22.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplit2(const real_1d_array &a, const integer_1d_array &c, const ae_int_t n, ae_int_t &info, double &threshold, double &pal, double &pbl, double &par, double &pbr, double &cve) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::dsoptimalsplit2(const_cast(a.c_ptr()), const_cast(c.c_ptr()), n, &info, &threshold, &pal, &pbl, &par, &pbr, &cve, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Optimal partition, internal subroutine. Fast version. - -Accepts: - A array[0..N-1] array of attributes array[0..N-1] - C array[0..N-1] array of class labels - TiesBuf array[0..N] temporaries (ties) - CntBuf array[0..2*NC-1] temporaries (counts) - Alpha centering factor (0<=alpha<=1, recommended value - 0.05) - BufR array[0..N-1] temporaries - BufI array[0..N-1] temporaries - -Output: - Info error code (">0"=OK, "<0"=bad) - RMS training set RMS error - CVRMS leave-one-out RMS error - -Note: - content of all arrays is changed by subroutine; - it doesn't allocate temporaries. - - -- ALGLIB -- - Copyright 11.12.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplit2fast(real_1d_array &a, integer_1d_array &c, integer_1d_array &tiesbuf, integer_1d_array &cntbuf, real_1d_array &bufr, integer_1d_array &bufi, const ae_int_t n, const ae_int_t nc, const double alpha, ae_int_t &info, double &threshold, double &rms, double &cvrms) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::dsoptimalsplit2fast(const_cast(a.c_ptr()), const_cast(c.c_ptr()), const_cast(tiesbuf.c_ptr()), const_cast(cntbuf.c_ptr()), const_cast(bufr.c_ptr()), const_cast(bufi.c_ptr()), n, nc, alpha, &info, &threshold, &rms, &cvrms, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This structure is a clusterization engine. - -You should not try to access its fields directly. -Use ALGLIB functions in order to work with this object. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -_clusterizerstate_owner::_clusterizerstate_owner() -{ - p_struct = (alglib_impl::clusterizerstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::clusterizerstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_clusterizerstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_clusterizerstate_owner::_clusterizerstate_owner(const _clusterizerstate_owner &rhs) -{ - p_struct = (alglib_impl::clusterizerstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::clusterizerstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_clusterizerstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_clusterizerstate_owner& _clusterizerstate_owner::operator=(const _clusterizerstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_clusterizerstate_clear(p_struct); - if( !alglib_impl::_clusterizerstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_clusterizerstate_owner::~_clusterizerstate_owner() -{ - alglib_impl::_clusterizerstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::clusterizerstate* _clusterizerstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::clusterizerstate* _clusterizerstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -clusterizerstate::clusterizerstate() : _clusterizerstate_owner() -{ -} - -clusterizerstate::clusterizerstate(const clusterizerstate &rhs):_clusterizerstate_owner(rhs) -{ -} - -clusterizerstate& clusterizerstate::operator=(const clusterizerstate &rhs) -{ - if( this==&rhs ) - return *this; - _clusterizerstate_owner::operator=(rhs); - return *this; -} - -clusterizerstate::~clusterizerstate() -{ -} - - -/************************************************************************* -This structure is used to store results of the agglomerative hierarchical -clustering (AHC). - -Following information is returned: - -* NPoints contains number of points in the original dataset - -* Z contains information about merges performed (see below). Z contains - indexes from the original (unsorted) dataset and it can be used when you - need to know what points were merged. However, it is not convenient when - you want to build a dendrograd (see below). - -* if you want to build dendrogram, you can use Z, but it is not good - option, because Z contains indexes from unsorted dataset. Dendrogram - built from such dataset is likely to have intersections. So, you have to - reorder you points before building dendrogram. - Permutation which reorders point is returned in P. Another representation - of merges, which is more convenient for dendorgram construction, is - returned in PM. - -* more information on format of Z, P and PM can be found below and in the - examples from ALGLIB Reference Manual. - -FORMAL DESCRIPTION OF FIELDS: - NPoints number of points - Z array[NPoints-1,2], contains indexes of clusters - linked in pairs to form clustering tree. I-th row - corresponds to I-th merge: - * Z[I,0] - index of the first cluster to merge - * Z[I,1] - index of the second cluster to merge - * Z[I,0](rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_ahcreport_owner& _ahcreport_owner::operator=(const _ahcreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_ahcreport_clear(p_struct); - if( !alglib_impl::_ahcreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_ahcreport_owner::~_ahcreport_owner() -{ - alglib_impl::_ahcreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::ahcreport* _ahcreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::ahcreport* _ahcreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -ahcreport::ahcreport() : _ahcreport_owner() ,npoints(p_struct->npoints),p(&p_struct->p),z(&p_struct->z),pz(&p_struct->pz),pm(&p_struct->pm),mergedist(&p_struct->mergedist) -{ -} - -ahcreport::ahcreport(const ahcreport &rhs):_ahcreport_owner(rhs) ,npoints(p_struct->npoints),p(&p_struct->p),z(&p_struct->z),pz(&p_struct->pz),pm(&p_struct->pm),mergedist(&p_struct->mergedist) -{ -} - -ahcreport& ahcreport::operator=(const ahcreport &rhs) -{ - if( this==&rhs ) - return *this; - _ahcreport_owner::operator=(rhs); - return *this; -} - -ahcreport::~ahcreport() -{ -} - - -/************************************************************************* -This structure is used to store results of the k-means++ clustering -algorithm. - -Following information is always returned: -* NPoints contains number of points in the original dataset -* TerminationType contains completion code, negative on failure, positive - on success -* K contains number of clusters - -For positive TerminationType we return: -* NFeatures contains number of variables in the original dataset -* C, which contains centers found by algorithm -* CIdx, which maps points of the original dataset to clusters - -FORMAL DESCRIPTION OF FIELDS: - NPoints number of points, >=0 - NFeatures number of variables, >=1 - TerminationType completion code: - * -5 if distance type is anything different from - Euclidean metric - * -3 for degenerate dataset: a) less than K distinct - points, b) K=0 for non-empty dataset. - * +1 for successful completion - K number of clusters - C array[K,NFeatures], rows of the array store centers - CIdx array[NPoints], which contains cluster indexes - - -- ALGLIB -- - Copyright 27.11.2012 by Bochkanov Sergey -*************************************************************************/ -_kmeansreport_owner::_kmeansreport_owner() -{ - p_struct = (alglib_impl::kmeansreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::kmeansreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_kmeansreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_kmeansreport_owner::_kmeansreport_owner(const _kmeansreport_owner &rhs) -{ - p_struct = (alglib_impl::kmeansreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::kmeansreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_kmeansreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_kmeansreport_owner& _kmeansreport_owner::operator=(const _kmeansreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_kmeansreport_clear(p_struct); - if( !alglib_impl::_kmeansreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_kmeansreport_owner::~_kmeansreport_owner() -{ - alglib_impl::_kmeansreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::kmeansreport* _kmeansreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::kmeansreport* _kmeansreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -kmeansreport::kmeansreport() : _kmeansreport_owner() ,npoints(p_struct->npoints),nfeatures(p_struct->nfeatures),terminationtype(p_struct->terminationtype),k(p_struct->k),c(&p_struct->c),cidx(&p_struct->cidx) -{ -} - -kmeansreport::kmeansreport(const kmeansreport &rhs):_kmeansreport_owner(rhs) ,npoints(p_struct->npoints),nfeatures(p_struct->nfeatures),terminationtype(p_struct->terminationtype),k(p_struct->k),c(&p_struct->c),cidx(&p_struct->cidx) -{ -} - -kmeansreport& kmeansreport::operator=(const kmeansreport &rhs) -{ - if( this==&rhs ) - return *this; - _kmeansreport_owner::operator=(rhs); - return *this; -} - -kmeansreport::~kmeansreport() -{ -} - -/************************************************************************* -This function initializes clusterizer object. Newly initialized object is -empty, i.e. it does not contain dataset. You should use it as follows: -1. creation -2. dataset is added with ClusterizerSetPoints() -3. additional parameters are set -3. clusterization is performed with one of the clustering functions - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizercreate(clusterizerstate &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizercreate(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds dataset to the clusterizer structure. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -NOTE 1: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - -NOTE 2: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric - * k-means++ clustering algorithm may be used only with Euclidean - distance function - Thus, list of specific clustering algorithms you may use depends - on distance function you specify when you set your dataset. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetpoints(const clusterizerstate &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizersetpoints(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), npoints, nfeatures, disttype, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds dataset to the clusterizer structure. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -NOTE 1: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - -NOTE 2: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric - * k-means++ clustering algorithm may be used only with Euclidean - distance function - Thus, list of specific clustering algorithms you may use depends - on distance function you specify when you set your dataset. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetpoints(const clusterizerstate &s, const real_2d_array &xy, const ae_int_t disttype) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t npoints; - ae_int_t nfeatures; - - npoints = xy.rows(); - nfeatures = xy.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizersetpoints(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), npoints, nfeatures, disttype, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds dataset given by distance matrix to the clusterizer -structure. It is important that dataset is not given explicitly - only -distance matrix is given. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - D - array[NPoints,NPoints], distance matrix given by its upper - or lower triangle (main diagonal is ignored because its - entries are expected to be zero). - NPoints - number of points - IsUpper - whether upper or lower triangle of D is given. - -NOTE 1: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric, including one which is given by - distance matrix - * k-means++ clustering algorithm may be used only with Euclidean - distance function and explicitly given points - it can not be - used with dataset given by distance matrix - Thus, if you call this function, you will be unable to use k-means - clustering algorithm to process your problem. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetdistances(const clusterizerstate &s, const real_2d_array &d, const ae_int_t npoints, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizersetdistances(const_cast(s.c_ptr()), const_cast(d.c_ptr()), npoints, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds dataset given by distance matrix to the clusterizer -structure. It is important that dataset is not given explicitly - only -distance matrix is given. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - D - array[NPoints,NPoints], distance matrix given by its upper - or lower triangle (main diagonal is ignored because its - entries are expected to be zero). - NPoints - number of points - IsUpper - whether upper or lower triangle of D is given. - -NOTE 1: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric, including one which is given by - distance matrix - * k-means++ clustering algorithm may be used only with Euclidean - distance function and explicitly given points - it can not be - used with dataset given by distance matrix - Thus, if you call this function, you will be unable to use k-means - clustering algorithm to process your problem. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetdistances(const clusterizerstate &s, const real_2d_array &d, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t npoints; - if( (d.rows()!=d.cols())) - throw ap_error("Error while calling 'clusterizersetdistances': looks like one of arguments has wrong size"); - npoints = d.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizersetdistances(const_cast(s.c_ptr()), const_cast(d.c_ptr()), npoints, isupper, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets agglomerative hierarchical clustering algorithm - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - Algo - algorithm type: - * 0 complete linkage (default algorithm) - * 1 single linkage - * 2 unweighted average linkage - * 3 weighted average linkage - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetahcalgo(const clusterizerstate &s, const ae_int_t algo) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizersetahcalgo(const_cast(s.c_ptr()), algo, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets k-means++ properties : number of restarts and maximum -number of iterations per one run. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - Restarts- restarts count, >=1. - k-means++ algorithm performs several restarts and chooses - best set of centers (one with minimum squared distance). - MaxIts - maximum number of k-means iterations performed during one - run. >=0, zero value means that algorithm performs unlimited - number of iterations. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetkmeanslimits(const clusterizerstate &s, const ae_int_t restarts, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizersetkmeanslimits(const_cast(s.c_ptr()), restarts, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function performs agglomerative hierarchical clustering - -FOR USERS OF SMP EDITION: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Multicore version is pretty efficient on large - ! problems which need more than 1.000.000 operations to be solved, - ! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU - ! cycles), but gives no speed-up for small problems (less than 100.000 - ! operations). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - -OUTPUT PARAMETERS: - Rep - clustering results; see description of AHCReport - structure for more information. - -NOTE 1: hierarchical clustering algorithms require large amounts of memory. - In particular, this implementation needs sizeof(double)*NPoints^2 - bytes, which are used to store distance matrix. In case we work - with user-supplied matrix, this amount is multiplied by 2 (we have - to store original matrix and to work with its copy). - - For example, problem with 10000 points would require 800M of RAM, - even when working in a 1-dimensional space. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizerrunahc(const clusterizerstate &s, ahcreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizerrunahc(const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_clusterizerrunahc(const clusterizerstate &s, ahcreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_clusterizerrunahc(const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function performs clustering by k-means++ algorithm. - -You may change algorithm properties like number of restarts or iterations -limit by calling ClusterizerSetKMeansLimits() functions. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - K - number of clusters, K>=0. - K can be zero only when algorithm is called for empty - dataset, in this case completion code is set to - success (+1). - If K=0 and dataset size is non-zero, we can not - meaningfully assign points to some center (there are no - centers because K=0) and return -3 as completion code - (failure). - -OUTPUT PARAMETERS: - Rep - clustering results; see description of KMeansReport - structure for more information. - -NOTE 1: k-means clustering can be performed only for datasets with - Euclidean distance function. Algorithm will return negative - completion code in Rep.TerminationType in case dataset was added - to clusterizer with DistType other than Euclidean (or dataset was - specified by distance matrix instead of explicitly given points). - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizerrunkmeans(const clusterizerstate &s, const ae_int_t k, kmeansreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizerrunkmeans(const_cast(s.c_ptr()), k, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns distance matrix for dataset - -FOR USERS OF SMP EDITION: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Multicore version is pretty efficient on large - ! problems which need more than 1.000.000 operations to be solved, - ! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU - ! cycles), but gives no speed-up for small problems (less than 100.000 - ! operations). - -INPUT PARAMETERS: - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -OUTPUT PARAMETERS: - D - array[NPoints,NPoints], distance matrix - (full matrix is returned, with lower and upper triangles) - -NOTES: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizergetdistances(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype, real_2d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::clusterizergetdistances(const_cast(xy.c_ptr()), npoints, nfeatures, disttype, const_cast(d.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_clusterizergetdistances(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype, real_2d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_clusterizergetdistances(const_cast(xy.c_ptr()), npoints, nfeatures, disttype, const_cast(d.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function takes as input clusterization report Rep, desired clusters -count K, and builds top K clusters from hierarchical clusterization tree. -It returns assignment of points to clusters (array of cluster indexes). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - K - desired number of clusters, 1<=K<=NPoints. - K can be zero only when NPoints=0. - -OUTPUT PARAMETERS: - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I](rep.c_ptr()), k, const_cast(cidx.c_ptr()), const_cast(cz.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function accepts AHC report Rep, desired minimum intercluster -distance and returns top clusters from hierarchical clusterization tree -which are separated by distance R or HIGHER. - -It returns assignment of points to clusters (array of cluster indexes). - -There is one more function with similar name - ClusterizerSeparatedByCorr, -which returns clusters with intercluster correlation equal to R or LOWER -(note: higher for distance, lower for correlation). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - R - desired minimum intercluster distance, R>=0 - -OUTPUT PARAMETERS: - K - number of clusters, 1<=K<=NPoints - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I](rep.c_ptr()), r, &k, const_cast(cidx.c_ptr()), const_cast(cz.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function accepts AHC report Rep, desired maximum intercluster -correlation and returns top clusters from hierarchical clusterization tree -which are separated by correlation R or LOWER. - -It returns assignment of points to clusters (array of cluster indexes). - -There is one more function with similar name - ClusterizerSeparatedByDist, -which returns clusters with intercluster distance equal to R or HIGHER -(note: higher for distance, lower for correlation). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - R - desired maximum intercluster correlation, -1<=R<=+1 - -OUTPUT PARAMETERS: - K - number of clusters, 1<=K<=NPoints - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I](rep.c_ptr()), r, &k, const_cast(cidx.c_ptr()), const_cast(cz.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -k-means++ clusterization. -Backward compatibility function, we recommend to use CLUSTERING subpackage -as better replacement. - - -- ALGLIB -- - Copyright 21.03.2009 by Bochkanov Sergey -*************************************************************************/ -void kmeansgenerate(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t k, const ae_int_t restarts, ae_int_t &info, real_2d_array &c, integer_1d_array &xyc) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::kmeansgenerate(const_cast(xy.c_ptr()), npoints, nvars, k, restarts, &info, const_cast(c.c_ptr()), const_cast(xyc.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_decisionforest_owner::_decisionforest_owner() -{ - p_struct = (alglib_impl::decisionforest*)alglib_impl::ae_malloc(sizeof(alglib_impl::decisionforest), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_decisionforest_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_decisionforest_owner::_decisionforest_owner(const _decisionforest_owner &rhs) -{ - p_struct = (alglib_impl::decisionforest*)alglib_impl::ae_malloc(sizeof(alglib_impl::decisionforest), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_decisionforest_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_decisionforest_owner& _decisionforest_owner::operator=(const _decisionforest_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_decisionforest_clear(p_struct); - if( !alglib_impl::_decisionforest_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_decisionforest_owner::~_decisionforest_owner() -{ - alglib_impl::_decisionforest_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::decisionforest* _decisionforest_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::decisionforest* _decisionforest_owner::c_ptr() const -{ - return const_cast(p_struct); -} -decisionforest::decisionforest() : _decisionforest_owner() -{ -} - -decisionforest::decisionforest(const decisionforest &rhs):_decisionforest_owner(rhs) -{ -} - -decisionforest& decisionforest::operator=(const decisionforest &rhs) -{ - if( this==&rhs ) - return *this; - _decisionforest_owner::operator=(rhs); - return *this; -} - -decisionforest::~decisionforest() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_dfreport_owner::_dfreport_owner() -{ - p_struct = (alglib_impl::dfreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::dfreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_dfreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_dfreport_owner::_dfreport_owner(const _dfreport_owner &rhs) -{ - p_struct = (alglib_impl::dfreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::dfreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_dfreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_dfreport_owner& _dfreport_owner::operator=(const _dfreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_dfreport_clear(p_struct); - if( !alglib_impl::_dfreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_dfreport_owner::~_dfreport_owner() -{ - alglib_impl::_dfreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::dfreport* _dfreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::dfreport* _dfreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -dfreport::dfreport() : _dfreport_owner() ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),oobrelclserror(p_struct->oobrelclserror),oobavgce(p_struct->oobavgce),oobrmserror(p_struct->oobrmserror),oobavgerror(p_struct->oobavgerror),oobavgrelerror(p_struct->oobavgrelerror) -{ -} - -dfreport::dfreport(const dfreport &rhs):_dfreport_owner(rhs) ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),oobrelclserror(p_struct->oobrelclserror),oobavgce(p_struct->oobavgce),oobrmserror(p_struct->oobrmserror),oobavgerror(p_struct->oobavgerror),oobavgrelerror(p_struct->oobavgrelerror) -{ -} - -dfreport& dfreport::operator=(const dfreport &rhs) -{ - if( this==&rhs ) - return *this; - _dfreport_owner::operator=(rhs); - return *this; -} - -dfreport::~dfreport() -{ -} - - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void dfserialize(decisionforest &obj, std::string &s_out) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - alglib_impl::ae_int_t ssize; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_alloc_start(&serializer); - alglib_impl::dfalloc(&serializer, obj.c_ptr(), &state); - ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer); - s_out.clear(); - s_out.reserve((size_t)(ssize+1)); - alglib_impl::ae_serializer_sstart_str(&serializer, &s_out); - alglib_impl::dfserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - if( s_out.length()>(size_t)ssize ) - throw ap_error("ALGLIB: serialization integrity error"); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void dfunserialize(std::string &s_in, decisionforest &obj) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_ustart_str(&serializer, &s_in); - alglib_impl::dfunserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} - -/************************************************************************* -This subroutine builds random decision forest. - -INPUT PARAMETERS: - XY - training set - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - task type: - * NClasses=1 - regression task with one - dependent variable - * NClasses>1 - classification task with - NClasses classes. - NTrees - number of trees in a forest, NTrees>=1. - recommended values: 50-100. - R - percent of a training set used to build - individual trees. 01). - * 1, if task has been solved - DF - model built - Rep - training report, contains error on a training set - and out-of-bag estimates of generalization error. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfbuildrandomdecisionforest(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const ae_int_t ntrees, const double r, ae_int_t &info, decisionforest &df, dfreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::dfbuildrandomdecisionforest(const_cast(xy.c_ptr()), npoints, nvars, nclasses, ntrees, r, &info, const_cast(df.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds random decision forest. -This function gives ability to tune number of variables used when choosing -best split. - -INPUT PARAMETERS: - XY - training set - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - task type: - * NClasses=1 - regression task with one - dependent variable - * NClasses>1 - classification task with - NClasses classes. - NTrees - number of trees in a forest, NTrees>=1. - recommended values: 50-100. - NRndVars - number of variables used when choosing best split - R - percent of a training set used to build - individual trees. 01). - * 1, if task has been solved - DF - model built - Rep - training report, contains error on a training set - and out-of-bag estimates of generalization error. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfbuildrandomdecisionforestx1(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const ae_int_t ntrees, const ae_int_t nrndvars, const double r, ae_int_t &info, decisionforest &df, dfreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::dfbuildrandomdecisionforestx1(const_cast(xy.c_ptr()), npoints, nvars, nclasses, ntrees, nrndvars, r, &info, const_cast(df.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - DF - decision forest model - X - input vector, array[0..NVars-1]. - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - -See also DFProcessI. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfprocess(const decisionforest &df, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::dfprocess(const_cast(df.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -'interactive' variant of DFProcess for languages like Python which support -constructs like "Y = DFProcessI(DF,X)" and interactive mode of interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void dfprocessi(const decisionforest &df, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::dfprocessi(const_cast(df.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - Zero if model solves regression task. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfrelclserror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::dfrelclserror(const_cast(df.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*LN(2)). - Zero if model solves regression task. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgce(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::dfavgce(const_cast(df.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - Its meaning for regression task is obvious. As for - classification task, RMS error means error when estimating posterior - probabilities. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfrmserror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::dfrmserror(const_cast(df.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for - classification task, it means average error when estimating posterior - probabilities. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgerror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::dfavgerror(const_cast(df.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for - classification task, it means average relative error when estimating - posterior probability of belonging to the correct class. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgrelerror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::dfavgrelerror(const_cast(df.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_linearmodel_owner::_linearmodel_owner() -{ - p_struct = (alglib_impl::linearmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::linearmodel), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_linearmodel_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_linearmodel_owner::_linearmodel_owner(const _linearmodel_owner &rhs) -{ - p_struct = (alglib_impl::linearmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::linearmodel), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_linearmodel_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_linearmodel_owner& _linearmodel_owner::operator=(const _linearmodel_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_linearmodel_clear(p_struct); - if( !alglib_impl::_linearmodel_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_linearmodel_owner::~_linearmodel_owner() -{ - alglib_impl::_linearmodel_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::linearmodel* _linearmodel_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::linearmodel* _linearmodel_owner::c_ptr() const -{ - return const_cast(p_struct); -} -linearmodel::linearmodel() : _linearmodel_owner() -{ -} - -linearmodel::linearmodel(const linearmodel &rhs):_linearmodel_owner(rhs) -{ -} - -linearmodel& linearmodel::operator=(const linearmodel &rhs) -{ - if( this==&rhs ) - return *this; - _linearmodel_owner::operator=(rhs); - return *this; -} - -linearmodel::~linearmodel() -{ -} - - -/************************************************************************* -LRReport structure contains additional information about linear model: -* C - covariation matrix, array[0..NVars,0..NVars]. - C[i,j] = Cov(A[i],A[j]) -* RMSError - root mean square error on a training set -* AvgError - average error on a training set -* AvgRelError - average relative error on a training set (excluding - observations with zero function value). -* CVRMSError - leave-one-out cross-validation estimate of - generalization error. Calculated using fast algorithm - with O(NVars*NPoints) complexity. -* CVAvgError - cross-validation estimate of average error -* CVAvgRelError - cross-validation estimate of average relative error - -All other fields of the structure are intended for internal use and should -not be used outside ALGLIB. -*************************************************************************/ -_lrreport_owner::_lrreport_owner() -{ - p_struct = (alglib_impl::lrreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lrreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lrreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lrreport_owner::_lrreport_owner(const _lrreport_owner &rhs) -{ - p_struct = (alglib_impl::lrreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lrreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lrreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lrreport_owner& _lrreport_owner::operator=(const _lrreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_lrreport_clear(p_struct); - if( !alglib_impl::_lrreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_lrreport_owner::~_lrreport_owner() -{ - alglib_impl::_lrreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::lrreport* _lrreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::lrreport* _lrreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -lrreport::lrreport() : _lrreport_owner() ,c(&p_struct->c),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),cvrmserror(p_struct->cvrmserror),cvavgerror(p_struct->cvavgerror),cvavgrelerror(p_struct->cvavgrelerror),ncvdefects(p_struct->ncvdefects),cvdefects(&p_struct->cvdefects) -{ -} - -lrreport::lrreport(const lrreport &rhs):_lrreport_owner(rhs) ,c(&p_struct->c),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),cvrmserror(p_struct->cvrmserror),cvavgerror(p_struct->cvavgerror),cvavgrelerror(p_struct->cvavgrelerror),ncvdefects(p_struct->ncvdefects),cvdefects(&p_struct->cvdefects) -{ -} - -lrreport& lrreport::operator=(const lrreport &rhs) -{ - if( this==&rhs ) - return *this; - _lrreport_owner::operator=(rhs); - return *this; -} - -lrreport::~lrreport() -{ -} - -/************************************************************************* -Linear regression - -Subroutine builds model: - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] + A(N) - -and model found in ALGLIB format, covariation matrix, training set errors -(rms, average, average relative) and leave-one-out cross-validation -estimate of the generalization error. CV estimate calculated using fast -algorithm with O(NPoints*NVars) complexity. - -When covariation matrix is calculated standard deviations of function -values are assumed to be equal to RMS error on the training set. - -INPUT PARAMETERS: - XY - training set, array [0..NPoints-1,0..NVars]: - * NVars columns - independent variables - * last column - dependent variable - NPoints - training set size, NPoints>NVars+1 - NVars - number of independent variables - -OUTPUT PARAMETERS: - Info - return code: - * -255, in case of unknown internal error - * -4, if internal SVD subroutine haven't converged - * -1, if incorrect parameters was passed (NPoints(xy.c_ptr()), npoints, nvars, &info, const_cast(lm.c_ptr()), const_cast(ar.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Linear regression - -Variant of LRBuild which uses vector of standatd deviations (errors in -function values). - -INPUT PARAMETERS: - XY - training set, array [0..NPoints-1,0..NVars]: - * NVars columns - independent variables - * last column - dependent variable - S - standard deviations (errors in function values) - array[0..NPoints-1], S[i]>0. - NPoints - training set size, NPoints>NVars+1 - NVars - number of independent variables - -OUTPUT PARAMETERS: - Info - return code: - * -255, in case of unknown internal error - * -4, if internal SVD subroutine haven't converged - * -1, if incorrect parameters was passed (NPoints(xy.c_ptr()), const_cast(s.c_ptr()), npoints, nvars, &info, const_cast(lm.c_ptr()), const_cast(ar.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like LRBuildS, but builds model - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] - -i.e. with zero constant term. - - -- ALGLIB -- - Copyright 30.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lrbuildzs(const real_2d_array &xy, const real_1d_array &s, const ae_int_t npoints, const ae_int_t nvars, ae_int_t &info, linearmodel &lm, lrreport &ar) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lrbuildzs(const_cast(xy.c_ptr()), const_cast(s.c_ptr()), npoints, nvars, &info, const_cast(lm.c_ptr()), const_cast(ar.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like LRBuild but builds model - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] - -i.e. with zero constant term. - - -- ALGLIB -- - Copyright 30.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lrbuildz(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, ae_int_t &info, linearmodel &lm, lrreport &ar) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lrbuildz(const_cast(xy.c_ptr()), npoints, nvars, &info, const_cast(lm.c_ptr()), const_cast(ar.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacks coefficients of linear model. - -INPUT PARAMETERS: - LM - linear model in ALGLIB format - -OUTPUT PARAMETERS: - V - coefficients, array[0..NVars] - constant term (intercept) is stored in the V[NVars]. - NVars - number of independent variables (one less than number - of coefficients) - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -void lrunpack(const linearmodel &lm, real_1d_array &v, ae_int_t &nvars) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lrunpack(const_cast(lm.c_ptr()), const_cast(v.c_ptr()), &nvars, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -"Packs" coefficients and creates linear model in ALGLIB format (LRUnpack -reversed). - -INPUT PARAMETERS: - V - coefficients, array[0..NVars] - NVars - number of independent variables - -OUTPUT PAREMETERS: - LM - linear model. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -void lrpack(const real_1d_array &v, const ae_int_t nvars, linearmodel &lm) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lrpack(const_cast(v.c_ptr()), nvars, const_cast(lm.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - LM - linear model - X - input vector, array[0..NVars-1]. - -Result: - value of linear model regression estimate - - -- ALGLIB -- - Copyright 03.09.2008 by Bochkanov Sergey -*************************************************************************/ -double lrprocess(const linearmodel &lm, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::lrprocess(const_cast(lm.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - LM - linear model - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double lrrmserror(const linearmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::lrrmserror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - LM - linear model - XY - test set - NPoints - test set size - -RESULT: - average error. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double lravgerror(const linearmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::lravgerror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - LM - linear model - XY - test set - NPoints - test set size - -RESULT: - average relative error. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double lravgrelerror(const linearmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::lravgrelerror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Filters: simple moving averages (unsymmetric). - -This filter replaces array by results of SMA(K) filter. SMA(K) is defined -as filter which averages at most K previous points (previous - not points -AROUND central point) - or less, in case of the first K-1 points. - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filtersma(real_1d_array &x, const ae_int_t n, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::filtersma(const_cast(x.c_ptr()), n, k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Filters: simple moving averages (unsymmetric). - -This filter replaces array by results of SMA(K) filter. SMA(K) is defined -as filter which averages at most K previous points (previous - not points -AROUND central point) - or less, in case of the first K-1 points. - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filtersma(real_1d_array &x, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::filtersma(const_cast(x.c_ptr()), n, k, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Filters: exponential moving averages. - -This filter replaces array by results of EMA(alpha) filter. EMA(alpha) is -defined as filter which replaces X[] by S[]: - S[0] = X[0] - S[t] = alpha*X[t] + (1-alpha)*S[t-1] - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - alpha - 0(x.c_ptr()), n, alpha, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Filters: exponential moving averages. - -This filter replaces array by results of EMA(alpha) filter. EMA(alpha) is -defined as filter which replaces X[] by S[]: - S[0] = X[0] - S[t] = alpha*X[t] + (1-alpha)*S[t-1] - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - alpha - 0(x.c_ptr()), n, alpha, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Filters: linear regression moving averages. - -This filter replaces array by results of LRMA(K) filter. - -LRMA(K) is defined as filter which, for each data point, builds linear -regression model using K prevous points (point itself is included in -these K points) and calculates value of this linear model at the point in -question. - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filterlrma(real_1d_array &x, const ae_int_t n, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::filterlrma(const_cast(x.c_ptr()), n, k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Filters: linear regression moving averages. - -This filter replaces array by results of LRMA(K) filter. - -LRMA(K) is defined as filter which, for each data point, builds linear -regression model using K prevous points (point itself is included in -these K points) and calculates value of this linear model at the point in -question. - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filterlrma(real_1d_array &x, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::filterlrma(const_cast(x.c_ptr()), n, k, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiclass Fisher LDA - -Subroutine finds coefficients of linear combination which optimally separates -training set on classes. - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars]. - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - - -OUTPUT PARAMETERS: - Info - return code: - * -4, if internal EVD subroutine hasn't converged - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed (NPoints<0, - NVars<1, NClasses<2) - * 1, if task has been solved - * 2, if there was a multicollinearity in training set, - but task has been solved. - W - linear combination coefficients, array[0..NVars-1] - - -- ALGLIB -- - Copyright 31.05.2008 by Bochkanov Sergey -*************************************************************************/ -void fisherlda(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, ae_int_t &info, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fisherlda(const_cast(xy.c_ptr()), npoints, nvars, nclasses, &info, const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -N-dimensional multiclass Fisher LDA - -Subroutine finds coefficients of linear combinations which optimally separates -training set on classes. It returns N-dimensional basis whose vector are sorted -by quality of training set separation (in descending order). - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars]. - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - - -OUTPUT PARAMETERS: - Info - return code: - * -4, if internal EVD subroutine hasn't converged - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed (NPoints<0, - NVars<1, NClasses<2) - * 1, if task has been solved - * 2, if there was a multicollinearity in training set, - but task has been solved. - W - basis, array[0..NVars-1,0..NVars-1] - columns of matrix stores basis vectors, sorted by - quality of training set separation (in descending order) - - -- ALGLIB -- - Copyright 31.05.2008 by Bochkanov Sergey -*************************************************************************/ -void fisherldan(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, ae_int_t &info, real_2d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fisherldan(const_cast(xy.c_ptr()), npoints, nvars, nclasses, &info, const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Model's errors: - * RelCLSError - fraction of misclassified cases. - * AvgCE - acerage cross-entropy - * RMSError - root-mean-square error - * AvgError - average error - * AvgRelError - average relative error - -NOTE 1: RelCLSError/AvgCE are zero on regression problems. - -NOTE 2: on classification problems RMSError/AvgError/AvgRelError contain - errors in prediction of posterior probabilities -*************************************************************************/ -_modelerrors_owner::_modelerrors_owner() -{ - p_struct = (alglib_impl::modelerrors*)alglib_impl::ae_malloc(sizeof(alglib_impl::modelerrors), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_modelerrors_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_modelerrors_owner::_modelerrors_owner(const _modelerrors_owner &rhs) -{ - p_struct = (alglib_impl::modelerrors*)alglib_impl::ae_malloc(sizeof(alglib_impl::modelerrors), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_modelerrors_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_modelerrors_owner& _modelerrors_owner::operator=(const _modelerrors_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_modelerrors_clear(p_struct); - if( !alglib_impl::_modelerrors_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_modelerrors_owner::~_modelerrors_owner() -{ - alglib_impl::_modelerrors_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::modelerrors* _modelerrors_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::modelerrors* _modelerrors_owner::c_ptr() const -{ - return const_cast(p_struct); -} -modelerrors::modelerrors() : _modelerrors_owner() ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror) -{ -} - -modelerrors::modelerrors(const modelerrors &rhs):_modelerrors_owner(rhs) ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror) -{ -} - -modelerrors& modelerrors::operator=(const modelerrors &rhs) -{ - if( this==&rhs ) - return *this; - _modelerrors_owner::operator=(rhs); - return *this; -} - -modelerrors::~modelerrors() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_multilayerperceptron_owner::_multilayerperceptron_owner() -{ - p_struct = (alglib_impl::multilayerperceptron*)alglib_impl::ae_malloc(sizeof(alglib_impl::multilayerperceptron), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_multilayerperceptron_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_multilayerperceptron_owner::_multilayerperceptron_owner(const _multilayerperceptron_owner &rhs) -{ - p_struct = (alglib_impl::multilayerperceptron*)alglib_impl::ae_malloc(sizeof(alglib_impl::multilayerperceptron), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_multilayerperceptron_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_multilayerperceptron_owner& _multilayerperceptron_owner::operator=(const _multilayerperceptron_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_multilayerperceptron_clear(p_struct); - if( !alglib_impl::_multilayerperceptron_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_multilayerperceptron_owner::~_multilayerperceptron_owner() -{ - alglib_impl::_multilayerperceptron_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::multilayerperceptron* _multilayerperceptron_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::multilayerperceptron* _multilayerperceptron_owner::c_ptr() const -{ - return const_cast(p_struct); -} -multilayerperceptron::multilayerperceptron() : _multilayerperceptron_owner() -{ -} - -multilayerperceptron::multilayerperceptron(const multilayerperceptron &rhs):_multilayerperceptron_owner(rhs) -{ -} - -multilayerperceptron& multilayerperceptron::operator=(const multilayerperceptron &rhs) -{ - if( this==&rhs ) - return *this; - _multilayerperceptron_owner::operator=(rhs); - return *this; -} - -multilayerperceptron::~multilayerperceptron() -{ -} - - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void mlpserialize(multilayerperceptron &obj, std::string &s_out) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - alglib_impl::ae_int_t ssize; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_alloc_start(&serializer); - alglib_impl::mlpalloc(&serializer, obj.c_ptr(), &state); - ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer); - s_out.clear(); - s_out.reserve((size_t)(ssize+1)); - alglib_impl::ae_serializer_sstart_str(&serializer, &s_out); - alglib_impl::mlpserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - if( s_out.length()>(size_t)ssize ) - throw ap_error("ALGLIB: serialization integrity error"); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void mlpunserialize(std::string &s_in, multilayerperceptron &obj) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_ustart_str(&serializer, &s_in); - alglib_impl::mlpunserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers, with linear output layer. Network weights are filled with small -random values. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate0(const ae_int_t nin, const ae_int_t nout, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreate0(nin, nout, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreate0, but with one hidden layer (NHid neurons) with -non-linear activation function. Output layer is linear. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreate1(nin, nhid, nout, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreate0, but with two hidden layers (NHid1 and NHid2 neurons) -with non-linear activation function. Output layer is linear. - $ALL - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreate2(nin, nhid1, nhid2, nout, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers with non-linear output layer. Network weights are filled with small -random values. - -Activation function of the output layer takes values: - - (B, +INF), if D>=0 - -or - - (-INF, B), if D<0. - - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb0(const ae_int_t nin, const ae_int_t nout, const double b, const double d, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreateb0(nin, nout, b, d, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreateB0 but with non-linear hidden layer. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double b, const double d, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreateb1(nin, nhid, nout, b, d, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreateB0 but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double b, const double d, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreateb2(nin, nhid1, nhid2, nout, b, d, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers with non-linear output layer. Network weights are filled with small -random values. Activation function of the output layer takes values [A,B]. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater0(const ae_int_t nin, const ae_int_t nout, const double a, const double b, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreater0(nin, nout, a, b, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreateR0, but with non-linear hidden layer. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double a, const double b, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreater1(nin, nhid, nout, a, b, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreateR0, but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double a, const double b, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreater2(nin, nhid1, nhid2, nout, a, b, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Creates classifier network with NIn inputs and NOut possible classes. -Network contains no hidden layers and linear output layer with SOFTMAX- -normalization (so outputs sums up to 1.0 and converge to posterior -probabilities). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec0(const ae_int_t nin, const ae_int_t nout, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreatec0(nin, nout, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreateC0, but with one non-linear hidden layer. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreatec1(nin, nhid, nout, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Same as MLPCreateC0, but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreatec2(nin, nhid1, nhid2, nout, const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Randomization of neural network weights - - -- ALGLIB -- - Copyright 06.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlprandomize(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlprandomize(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Randomization of neural network weights and standartisator - - -- ALGLIB -- - Copyright 10.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlprandomizefull(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlprandomizefull(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns information about initialized network: number of inputs, outputs, -weights. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpproperties(const multilayerperceptron &network, ae_int_t &nin, ae_int_t &nout, ae_int_t &wcount) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpproperties(const_cast(network.c_ptr()), &nin, &nout, &wcount, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns number of inputs. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetinputscount(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mlpgetinputscount(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns number of outputs. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetoutputscount(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mlpgetoutputscount(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns number of weights. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetweightscount(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mlpgetweightscount(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Tells whether network is SOFTMAX-normalized (i.e. classifier) or not. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -bool mlpissoftmax(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::mlpissoftmax(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns total number of layers (including input, hidden and -output layers). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetlayerscount(const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mlpgetlayerscount(const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns size of K-th layer. - -K=0 corresponds to input layer, K=CNT-1 corresponds to output layer. - -Size of the output layer is always equal to the number of outputs, although -when we have softmax-normalized network, last neuron doesn't have any -connections - it is just zero. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetlayersize(const multilayerperceptron &network, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mlpgetlayersize(const_cast(network.c_ptr()), k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns offset/scaling coefficients for I-th input of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - -OUTPUT PARAMETERS: - Mean - mean term - Sigma - sigma term, guaranteed to be nonzero. - -I-th input is passed through linear transformation - IN[i] = (IN[i]-Mean)/Sigma -before feeding to the network - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetinputscaling(const multilayerperceptron &network, const ae_int_t i, double &mean, double &sigma) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgetinputscaling(const_cast(network.c_ptr()), i, &mean, &sigma, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns offset/scaling coefficients for I-th output of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - -OUTPUT PARAMETERS: - Mean - mean term - Sigma - sigma term, guaranteed to be nonzero. - -I-th output is passed through linear transformation - OUT[i] = OUT[i]*Sigma+Mean -before returning it to user. In case we have SOFTMAX-normalized network, -we return (Mean,Sigma)=(0.0,1.0). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetoutputscaling(const multilayerperceptron &network, const ae_int_t i, double &mean, double &sigma) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgetoutputscaling(const_cast(network.c_ptr()), i, &mean, &sigma, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns information about Ith neuron of Kth layer - -INPUT PARAMETERS: - Network - network - K - layer index - I - neuron index (within layer) - -OUTPUT PARAMETERS: - FKind - activation function type (used by MLPActivationFunction()) - this value is zero for input or linear neurons - Threshold - also called offset, bias - zero for input neurons - -NOTE: this function throws exception if layer or neuron with given index -do not exists. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetneuroninfo(const multilayerperceptron &network, const ae_int_t k, const ae_int_t i, ae_int_t &fkind, double &threshold) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgetneuroninfo(const_cast(network.c_ptr()), k, i, &fkind, &threshold, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns information about connection from I0-th neuron of -K0-th layer to I1-th neuron of K1-th layer. - -INPUT PARAMETERS: - Network - network - K0 - layer index - I0 - neuron index (within layer) - K1 - layer index - I1 - neuron index (within layer) - -RESULT: - connection weight (zero for non-existent connections) - -This function: -1. throws exception if layer or neuron with given index do not exists. -2. returns zero if neurons exist, but there is no connection between them - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -double mlpgetweight(const multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpgetweight(const_cast(network.c_ptr()), k0, i0, k1, i1, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets offset/scaling coefficients for I-th input of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - Mean - mean term - Sigma - sigma term (if zero, will be replaced by 1.0) - -NTE: I-th input is passed through linear transformation - IN[i] = (IN[i]-Mean)/Sigma -before feeding to the network. This function sets Mean and Sigma. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetinputscaling(const multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetinputscaling(const_cast(network.c_ptr()), i, mean, sigma, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets offset/scaling coefficients for I-th output of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - Mean - mean term - Sigma - sigma term (if zero, will be replaced by 1.0) - -OUTPUT PARAMETERS: - -NOTE: I-th output is passed through linear transformation - OUT[i] = OUT[i]*Sigma+Mean -before returning it to user. This function sets Sigma/Mean. In case we -have SOFTMAX-normalized network, you can not set (Sigma,Mean) to anything -other than(0.0,1.0) - this function will throw exception. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetoutputscaling(const multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetoutputscaling(const_cast(network.c_ptr()), i, mean, sigma, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function modifies information about Ith neuron of Kth layer - -INPUT PARAMETERS: - Network - network - K - layer index - I - neuron index (within layer) - FKind - activation function type (used by MLPActivationFunction()) - this value must be zero for input neurons - (you can not set activation function for input neurons) - Threshold - also called offset, bias - this value must be zero for input neurons - (you can not set threshold for input neurons) - -NOTES: -1. this function throws exception if layer or neuron with given index do - not exists. -2. this function also throws exception when you try to set non-linear - activation function for input neurons (any kind of network) or for output - neurons of classifier network. -3. this function throws exception when you try to set non-zero threshold for - input neurons (any kind of network). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetneuroninfo(const multilayerperceptron &network, const ae_int_t k, const ae_int_t i, const ae_int_t fkind, const double threshold) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetneuroninfo(const_cast(network.c_ptr()), k, i, fkind, threshold, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function modifies information about connection from I0-th neuron of -K0-th layer to I1-th neuron of K1-th layer. - -INPUT PARAMETERS: - Network - network - K0 - layer index - I0 - neuron index (within layer) - K1 - layer index - I1 - neuron index (within layer) - W - connection weight (must be zero for non-existent - connections) - -This function: -1. throws exception if layer or neuron with given index do not exists. -2. throws exception if you try to set non-zero weight for non-existent - connection - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetweight(const multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1, const double w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetweight(const_cast(network.c_ptr()), k0, i0, k1, i1, w, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Neural network activation function - -INPUT PARAMETERS: - NET - neuron input - K - function index (zero for linear function) - -OUTPUT PARAMETERS: - F - function - DF - its derivative - D2F - its second derivative - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpactivationfunction(const double net, const ae_int_t k, double &f, double &df, double &d2f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpactivationfunction(net, k, &f, &df, &d2f, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - Network - neural network - X - input vector, array[0..NIn-1]. - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - -See also MLPProcessI - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpprocess(const multilayerperceptron &network, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpprocess(const_cast(network.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -'interactive' variant of MLPProcess for languages like Python which -support constructs like "Y = MLPProcess(NN,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 21.09.2010 by Bochkanov Sergey -*************************************************************************/ -void mlpprocessi(const multilayerperceptron &network, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpprocessi(const_cast(network.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x, depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlperror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlperror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlperror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlperror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Error of the neural network on dataset given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x, depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0 - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlperrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlperrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlperrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Natural error function for neural network, internal subroutine. - -NOTE: this function is single-threaded. Unlike other error function, it -receives no speed-up from being executed in SMP mode. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlperrorn(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlperrorn(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Classification error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: - classification error (number of misclassified cases) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mlpclserror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -ae_int_t smp_mlpclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::_pexec_mlpclserror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Relative classification error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Percent of incorrectly classified cases. Works both for classifier -networks and general purpose networks used as classifiers. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 25.12.2008 by Bochkanov Sergey -*************************************************************************/ -double mlprelclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlprelclserror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlprelclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlprelclserror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Relative classification error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. Sparse matrix must use CRS format - for storage. - NPoints - points count, >=0. - -RESULT: -Percent of incorrectly classified cases. Works both for classifier -networks and general purpose networks used as classifiers. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlprelclserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlprelclserrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlprelclserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlprelclserrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -CrossEntropy/(NPoints*LN(2)). -Zero if network solves regression task. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 08.01.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpavgce(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpavgce(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlpavgce(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlpavgce(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set given by -sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -CrossEntropy/(NPoints*LN(2)). -Zero if network solves regression task. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 9.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgcesparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpavgcesparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlpavgcesparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlpavgcesparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set given. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Root mean square error. Its meaning for regression task is obvious. As for -classification task, RMS error means error when estimating posterior -probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlprmserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlprmserror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlprmserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlprmserror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Root mean square error. Its meaning for regression task is obvious. As for -classification task, RMS error means error when estimating posterior -probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlprmserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlprmserrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlprmserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlprmserrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average absolute error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average error when estimating posterior probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 11.03.2008 by Bochkanov Sergey -*************************************************************************/ -double mlpavgerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpavgerror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlpavgerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlpavgerror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average absolute error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average error when estimating posterior probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpavgerrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlpavgerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlpavgerrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average relative error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average relative error when estimating posterior probability of -belonging to the correct class. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 11.03.2008 by Bochkanov Sergey -*************************************************************************/ -double mlpavgrelerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpavgrelerror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlpavgrelerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlpavgrelerror(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average relative error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average relative error when estimating posterior probability of -belonging to the correct class. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgrelerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpavgrelerrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlpavgrelerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlpavgrelerrorsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Gradient calculation - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - X - input vector, length of array must be at least NIn - DesiredY- desired outputs, length of array must be at least NOut - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgrad(const multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgrad(const_cast(network.c_ptr()), const_cast(x.c_ptr()), const_cast(desiredy.c_ptr()), &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Gradient calculation (natural error function is used) - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - X - input vector, length of array must be at least NIn - DesiredY- desired outputs, length of array must be at least NOut - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, sum-of-squares for regression networks, - cross-entropy for classification networks. - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradn(const multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgradn(const_cast(network.c_ptr()), const_cast(x.c_ptr()), const_cast(desiredy.c_ptr()), &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in dense format; one sample = one row: - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgradbatch(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpgradbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpgradbatch(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs given by sparse -matrices - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in sparse format; one sample = one row: - * MATRIX MUST BE STORED IN CRS FORMAT - * first NIn columns contain inputs. - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgradbatchsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpgradbatchsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpgradbatchsparse(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch gradient calculation for a subset of dataset - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in dense format; one sample = one row: - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array: - * positive value means that subset given by Idx[] is processed - * zero value results in zero gradient - * negative value means that full dataset is processed - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, - array[WCount] - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgradbatchsubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(idx.c_ptr()), subsetsize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpgradbatchsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpgradbatchsubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(idx.c_ptr()), subsetsize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs for a subset of -dataset given by set of indexes. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in sparse format; one sample = one row: - * MATRIX MUST BE STORED IN CRS FORMAT - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array: - * positive value means that subset given by Idx[] is processed - * zero value results in zero gradient - * negative value means that full dataset is processed - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, - array[WCount] - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatchSparse - function. - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgradbatchsparsesubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(idx.c_ptr()), subsetsize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpgradbatchsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpgradbatchsparsesubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(idx.c_ptr()), subsetsize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs -(natural error function is used) - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - set of inputs/outputs; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, sum-of-squares for regression networks, - cross-entropy for classification networks. - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradnbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpgradnbatch(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch Hessian calculation (natural error function) using R-algorithm. -Internal subroutine. - - -- ALGLIB -- - Copyright 26.01.2008 by Bochkanov Sergey. - - Hessian calculation based on R-algorithm described in - "Fast Exact Multiplication by the Hessian", - B. A. Pearlmutter, - Neural Computation, 1994. -*************************************************************************/ -void mlphessiannbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlphessiannbatch(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), const_cast(h.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Batch Hessian calculation using R-algorithm. -Internal subroutine. - - -- ALGLIB -- - Copyright 26.01.2008 by Bochkanov Sergey. - - Hessian calculation based on R-algorithm described in - "Fast Exact Multiplication by the Hessian", - B. A. Pearlmutter, - Neural Computation, 1994. -*************************************************************************/ -void mlphessianbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlphessianbatch(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), ssize, &e, const_cast(grad.c_ptr()), const_cast(h.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of all types of errors. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -OUTPUT PARAMETERS: - Rep - it contains all type of errors. - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function. - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpallerrorssubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpallerrorssubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpallerrorssubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpallerrorssubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of all types of errors on sparse dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset given by sparse matrix; - one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -OUTPUT PARAMETERS: - Rep - it contains all type of errors. - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function. - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpallerrorssparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpallerrorssparsesubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpallerrorssparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpallerrorssparsesubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlperrorsubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlperrorsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlperrorsubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Error of the neural network on sparse dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - SetSize - real size of XY, SetSize>=0; - it is used when SubsetSize<0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlperrorsparsesubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -double smp_mlperrorsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::_pexec_mlperrorsparsesubset(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), setsize, const_cast(subset.c_ptr()), subsetsize, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_logitmodel_owner::_logitmodel_owner() -{ - p_struct = (alglib_impl::logitmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::logitmodel), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_logitmodel_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_logitmodel_owner::_logitmodel_owner(const _logitmodel_owner &rhs) -{ - p_struct = (alglib_impl::logitmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::logitmodel), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_logitmodel_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_logitmodel_owner& _logitmodel_owner::operator=(const _logitmodel_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_logitmodel_clear(p_struct); - if( !alglib_impl::_logitmodel_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_logitmodel_owner::~_logitmodel_owner() -{ - alglib_impl::_logitmodel_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::logitmodel* _logitmodel_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::logitmodel* _logitmodel_owner::c_ptr() const -{ - return const_cast(p_struct); -} -logitmodel::logitmodel() : _logitmodel_owner() -{ -} - -logitmodel::logitmodel(const logitmodel &rhs):_logitmodel_owner(rhs) -{ -} - -logitmodel& logitmodel::operator=(const logitmodel &rhs) -{ - if( this==&rhs ) - return *this; - _logitmodel_owner::operator=(rhs); - return *this; -} - -logitmodel::~logitmodel() -{ -} - - -/************************************************************************* -MNLReport structure contains information about training process: -* NGrad - number of gradient calculations -* NHess - number of Hessian calculations -*************************************************************************/ -_mnlreport_owner::_mnlreport_owner() -{ - p_struct = (alglib_impl::mnlreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mnlreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mnlreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mnlreport_owner::_mnlreport_owner(const _mnlreport_owner &rhs) -{ - p_struct = (alglib_impl::mnlreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mnlreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mnlreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mnlreport_owner& _mnlreport_owner::operator=(const _mnlreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mnlreport_clear(p_struct); - if( !alglib_impl::_mnlreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mnlreport_owner::~_mnlreport_owner() -{ - alglib_impl::_mnlreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mnlreport* _mnlreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mnlreport* _mnlreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mnlreport::mnlreport() : _mnlreport_owner() ,ngrad(p_struct->ngrad),nhess(p_struct->nhess) -{ -} - -mnlreport::mnlreport(const mnlreport &rhs):_mnlreport_owner(rhs) ,ngrad(p_struct->ngrad),nhess(p_struct->nhess) -{ -} - -mnlreport& mnlreport::operator=(const mnlreport &rhs) -{ - if( this==&rhs ) - return *this; - _mnlreport_owner::operator=(rhs); - return *this; -} - -mnlreport::~mnlreport() -{ -} - -/************************************************************************* -This subroutine trains logit model. - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars] - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - -OUTPUT PARAMETERS: - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints(xy.c_ptr()), npoints, nvars, nclasses, &info, const_cast(lm.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - LM - logit model, passed by non-constant reference - (some fields of structure are used as temporaries - when calculating model output). - X - input vector, array[0..NVars-1]. - Y - (possibly) preallocated buffer; if size of Y is less than - NClasses, it will be reallocated.If it is large enough, it - is NOT reallocated, so we can save some time on reallocation. - -OUTPUT PARAMETERS: - Y - result, array[0..NClasses-1] - Vector of posterior probabilities for classification task. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -void mnlprocess(const logitmodel &lm, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mnlprocess(const_cast(lm.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -'interactive' variant of MNLProcess for languages like Python which -support constructs like "Y = MNLProcess(LM,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -void mnlprocessi(const logitmodel &lm, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mnlprocessi(const_cast(lm.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacks coefficients of logit model. Logit model have form: - - P(class=i) = S(i) / (S(0) + S(1) + ... +S(M-1)) - S(i) = Exp(A[i,0]*X[0] + ... + A[i,N-1]*X[N-1] + A[i,N]), when i(lm.c_ptr()), const_cast(a.c_ptr()), &nvars, &nclasses, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -"Packs" coefficients and creates logit model in ALGLIB format (MNLUnpack -reversed). - -INPUT PARAMETERS: - A - model (see MNLUnpack) - NVars - number of independent variables - NClasses - number of classes - -OUTPUT PARAMETERS: - LM - logit model. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -void mnlpack(const real_2d_array &a, const ae_int_t nvars, const ae_int_t nclasses, logitmodel &lm) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mnlpack(const_cast(a.c_ptr()), nvars, nclasses, const_cast(lm.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*ln(2)). - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlavgce(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mnlavgce(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlrelclserror(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mnlrelclserror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - root mean square error (error when estimating posterior probabilities). - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlrmserror(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mnlrmserror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - average error (error when estimating posterior probabilities). - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlavgerror(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mnlavgerror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - average relative error (error when estimating posterior probabilities). - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlavgrelerror(const logitmodel &lm, const real_2d_array &xy, const ae_int_t ssize) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mnlavgrelerror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), ssize, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Classification error on test set = MNLRelClsError*NPoints - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mnlclserror(const logitmodel &lm, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::mnlclserror(const_cast(lm.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This structure is a MCPD (Markov Chains for Population Data) solver. - -You should use ALGLIB functions in order to work with this object. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -_mcpdstate_owner::_mcpdstate_owner() -{ - p_struct = (alglib_impl::mcpdstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::mcpdstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mcpdstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mcpdstate_owner::_mcpdstate_owner(const _mcpdstate_owner &rhs) -{ - p_struct = (alglib_impl::mcpdstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::mcpdstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mcpdstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mcpdstate_owner& _mcpdstate_owner::operator=(const _mcpdstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mcpdstate_clear(p_struct); - if( !alglib_impl::_mcpdstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mcpdstate_owner::~_mcpdstate_owner() -{ - alglib_impl::_mcpdstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mcpdstate* _mcpdstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mcpdstate* _mcpdstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mcpdstate::mcpdstate() : _mcpdstate_owner() -{ -} - -mcpdstate::mcpdstate(const mcpdstate &rhs):_mcpdstate_owner(rhs) -{ -} - -mcpdstate& mcpdstate::operator=(const mcpdstate &rhs) -{ - if( this==&rhs ) - return *this; - _mcpdstate_owner::operator=(rhs); - return *this; -} - -mcpdstate::~mcpdstate() -{ -} - - -/************************************************************************* -This structure is a MCPD training report: - InnerIterationsCount - number of inner iterations of the - underlying optimization algorithm - OuterIterationsCount - number of outer iterations of the - underlying optimization algorithm - NFEV - number of merit function evaluations - TerminationType - termination type - (same as for MinBLEIC optimizer, positive - values denote success, negative ones - - failure) - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -_mcpdreport_owner::_mcpdreport_owner() -{ - p_struct = (alglib_impl::mcpdreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mcpdreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mcpdreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mcpdreport_owner::_mcpdreport_owner(const _mcpdreport_owner &rhs) -{ - p_struct = (alglib_impl::mcpdreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mcpdreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mcpdreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mcpdreport_owner& _mcpdreport_owner::operator=(const _mcpdreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mcpdreport_clear(p_struct); - if( !alglib_impl::_mcpdreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mcpdreport_owner::~_mcpdreport_owner() -{ - alglib_impl::_mcpdreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mcpdreport* _mcpdreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mcpdreport* _mcpdreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mcpdreport::mcpdreport() : _mcpdreport_owner() ,inneriterationscount(p_struct->inneriterationscount),outeriterationscount(p_struct->outeriterationscount),nfev(p_struct->nfev),terminationtype(p_struct->terminationtype) -{ -} - -mcpdreport::mcpdreport(const mcpdreport &rhs):_mcpdreport_owner(rhs) ,inneriterationscount(p_struct->inneriterationscount),outeriterationscount(p_struct->outeriterationscount),nfev(p_struct->nfev),terminationtype(p_struct->terminationtype) -{ -} - -mcpdreport& mcpdreport::operator=(const mcpdreport &rhs) -{ - if( this==&rhs ) - return *this; - _mcpdreport_owner::operator=(rhs); - return *this; -} - -mcpdreport::~mcpdreport() -{ -} - -/************************************************************************* -DESCRIPTION: - -This function creates MCPD (Markov Chains for Population Data) solver. - -This solver can be used to find transition matrix P for N-dimensional -prediction problem where transition from X[i] to X[i+1] is modelled as - X[i+1] = P*X[i] -where X[i] and X[i+1] are N-dimensional population vectors (components of -each X are non-negative), and P is a N*N transition matrix (elements of P -are non-negative, each column sums to 1.0). - -Such models arise when when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is constant, i.e. there is no new individuals and no one - leaves population -* you want to model transitions of individuals from one state into another - -USAGE: - -Here we give very brief outline of the MCPD. We strongly recommend you to -read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide -on data analysis which is available at http://www.alglib.net/dataanalysis/ - -1. User initializes algorithm state with MCPDCreate() call - -2. User adds one or more tracks - sequences of states which describe - evolution of a system being modelled from different starting conditions - -3. User may add optional boundary, equality and/or linear constraints on - the coefficients of P by calling one of the following functions: - * MCPDSetEC() to set equality constraints - * MCPDSetBC() to set bound constraints - * MCPDSetLC() to set linear constraints - -4. Optionally, user may set custom weights for prediction errors (by - default, algorithm assigns non-equal, automatically chosen weights for - errors in the prediction of different components of X). It can be done - with a call of MCPDSetPredictionWeights() function. - -5. User calls MCPDSolve() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. - -6. User calls MCPDResults() to get solution - -INPUT PARAMETERS: - N - problem dimension, N>=1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreate(const ae_int_t n, mcpdstate &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdcreate(n, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Entry-state" model, i.e. model where transition from X[i] to X[i+1] -is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -and one selected component of X[] is called "entry" state and is treated -in a special way: - system state always transits from "entry" state to some another state - system state can not transit from any state into "entry" state -Such conditions basically mean that row of P which corresponds to "entry" -state is zero. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant - at every moment of time there is some - (unpredictable) amount of "new" individuals, which can transit into one - of the states at the next turn, but still no one leaves population -* you want to model transitions of individuals from one state into another -* but you do NOT want to predict amount of "new" individuals because it - does not depends on individuals already present (hence system can not - transit INTO entry state - it can only transit FROM it). - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - EntryState- index of entry state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateentry(const ae_int_t n, const ae_int_t entrystate, mcpdstate &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdcreateentry(n, entrystate, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Exit-state" model, i.e. model where transition from X[i] to X[i+1] -is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -and one selected component of X[] is called "exit" state and is treated -in a special way: - system state can transit from any state into "exit" state - system state can not transit from "exit" state into any other state - transition operator discards "exit" state (makes it zero at each turn) -Such conditions basically mean that column of P which corresponds to -"exit" state is zero. Multiplication by such P may decrease sum of vector -components. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant - individuals can move into "exit" state - and leave population at the next turn, but there are no new individuals -* amount of individuals which leave population can be predicted -* you want to model transitions of individuals from one state into another - (including transitions into the "exit" state) - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - ExitState- index of exit state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateexit(const ae_int_t n, const ae_int_t exitstate, mcpdstate &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdcreateexit(n, exitstate, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Entry-Exit-states" model, i.e. model where transition from X[i] to -X[i+1] is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -one selected component of X[] is called "entry" state and is treated in a -special way: - system state always transits from "entry" state to some another state - system state can not transit from any state into "entry" state -and another one component of X[] is called "exit" state and is treated in -a special way too: - system state can transit from any state into "exit" state - system state can not transit from "exit" state into any other state - transition operator discards "exit" state (makes it zero at each turn) -Such conditions basically mean that: - row of P which corresponds to "entry" state is zero - column of P which corresponds to "exit" state is zero -Multiplication by such P may decrease sum of vector components. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant -* at every moment of time there is some (unpredictable) amount of "new" - individuals, which can transit into one of the states at the next turn -* some individuals can move (predictably) into "exit" state and leave - population at the next turn -* you want to model transitions of individuals from one state into another, - including transitions from the "entry" state and into the "exit" state. -* but you do NOT want to predict amount of "new" individuals because it - does not depends on individuals already present (hence system can not - transit INTO entry state - it can only transit FROM it). - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - EntryState- index of entry state, in 0..N-1 - ExitState- index of exit state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateentryexit(const ae_int_t n, const ae_int_t entrystate, const ae_int_t exitstate, mcpdstate &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdcreateentryexit(n, entrystate, exitstate, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to add a track - sequence of system states at the -different moments of its evolution. - -You may add one or several tracks to the MCPD solver. In case you have -several tracks, they won't overwrite each other. For example, if you pass -two tracks, A1-A2-A3 (system at t=A+1, t=A+2 and t=A+3) and B1-B2-B3, then -solver will try to model transitions from t=A+1 to t=A+2, t=A+2 to t=A+3, -t=B+1 to t=B+2, t=B+2 to t=B+3. But it WONT mix these two tracks - i.e. it -wont try to model transition from t=A+3 to t=B+1. - -INPUT PARAMETERS: - S - solver - XY - track, array[K,N]: - * I-th row is a state at t=I - * elements of XY must be non-negative (exception will be - thrown on negative elements) - K - number of points in a track - * if given, only leading K rows of XY are used - * if not given, automatically determined from size of XY - -NOTES: - -1. Track may contain either proportional or population data: - * with proportional data all rows of XY must sum to 1.0, i.e. we have - proportions instead of absolute population values - * with population data rows of XY contain population counts and generally - do not sum to 1.0 (although they still must be non-negative) - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddtrack(const mcpdstate &s, const real_2d_array &xy, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdaddtrack(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to add a track - sequence of system states at the -different moments of its evolution. - -You may add one or several tracks to the MCPD solver. In case you have -several tracks, they won't overwrite each other. For example, if you pass -two tracks, A1-A2-A3 (system at t=A+1, t=A+2 and t=A+3) and B1-B2-B3, then -solver will try to model transitions from t=A+1 to t=A+2, t=A+2 to t=A+3, -t=B+1 to t=B+2, t=B+2 to t=B+3. But it WONT mix these two tracks - i.e. it -wont try to model transition from t=A+3 to t=B+1. - -INPUT PARAMETERS: - S - solver - XY - track, array[K,N]: - * I-th row is a state at t=I - * elements of XY must be non-negative (exception will be - thrown on negative elements) - K - number of points in a track - * if given, only leading K rows of XY are used - * if not given, automatically determined from size of XY - -NOTES: - -1. Track may contain either proportional or population data: - * with proportional data all rows of XY must sum to 1.0, i.e. we have - proportions instead of absolute population values - * with population data rows of XY contain population counts and generally - do not sum to 1.0 (although they still must be non-negative) - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddtrack(const mcpdstate &s, const real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t k; - - k = xy.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdaddtrack(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), k, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to add equality constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to place equality constraints on arbitrary -subset of elements of P. Set of constraints is specified by EC, which may -contain either NAN's or finite numbers from [0,1]. NAN denotes absence of -constraint, finite number denotes equality constraint on specific element -of P. - -You can also use MCPDAddEC() function which allows to ADD equality -constraint for one element of P without changing constraints for other -elements. - -These functions (MCPDSetEC and MCPDAddEC) interact as follows: -* there is internal matrix of equality constraints which is stored in the - MCPD solver -* MCPDSetEC() replaces this matrix by another one (SET) -* MCPDAddEC() modifies one element of this matrix and leaves other ones - unchanged (ADD) -* thus MCPDAddEC() call preserves all modifications done by previous - calls, while MCPDSetEC() completely discards all changes done to the - equality constraints. - -INPUT PARAMETERS: - S - solver - EC - equality constraints, array[N,N]. Elements of EC can be - either NAN's or finite numbers from [0,1]. NAN denotes - absence of constraints, while finite value denotes - equality constraint on the corresponding element of P. - -NOTES: - -1. infinite values of EC will lead to exception being thrown. Values less -than 0.0 or greater than 1.0 will lead to error code being returned after -call to MCPDSolve(). - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetec(const mcpdstate &s, const real_2d_array &ec) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsetec(const_cast(s.c_ptr()), const_cast(ec.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to add equality constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to ADD equality constraint for one element of P -without changing constraints for other elements. - -You can also use MCPDSetEC() function which allows you to specify -arbitrary set of equality constraints in one call. - -These functions (MCPDSetEC and MCPDAddEC) interact as follows: -* there is internal matrix of equality constraints which is stored in the - MCPD solver -* MCPDSetEC() replaces this matrix by another one (SET) -* MCPDAddEC() modifies one element of this matrix and leaves other ones - unchanged (ADD) -* thus MCPDAddEC() call preserves all modifications done by previous - calls, while MCPDSetEC() completely discards all changes done to the - equality constraints. - -INPUT PARAMETERS: - S - solver - I - row index of element being constrained - J - column index of element being constrained - C - value (constraint for P[I,J]). Can be either NAN (no - constraint) or finite value from [0,1]. - -NOTES: - -1. infinite values of C will lead to exception being thrown. Values less -than 0.0 or greater than 1.0 will lead to error code being returned after -call to MCPDSolve(). - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddec(const mcpdstate &s, const ae_int_t i, const ae_int_t j, const double c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdaddec(const_cast(s.c_ptr()), i, j, c, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to add bound constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to place bound constraints on arbitrary -subset of elements of P. Set of constraints is specified by BndL/BndU -matrices, which may contain arbitrary combination of finite numbers or -infinities (like -INF(s.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to add bound constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to ADD bound constraint for one element of P -without changing constraints for other elements. - -You can also use MCPDSetBC() function which allows to place bound -constraints on arbitrary subset of elements of P. Set of constraints is -specified by BndL/BndU matrices, which may contain arbitrary combination -of finite numbers or infinities (like -INF(s.c_ptr()), i, j, bndl, bndu, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to set linear equality/inequality constraints on the -elements of the transition matrix P. - -This function can be used to set one or several general linear constraints -on the elements of P. Two types of constraints are supported: -* equality constraints -* inequality constraints (both less-or-equal and greater-or-equal) - -Coefficients of constraints are specified by matrix C (one of the -parameters). One row of C corresponds to one constraint. Because -transition matrix P has N*N elements, we need N*N columns to store all -coefficients (they are stored row by row), and one more column to store -right part - hence C has N*N+1 columns. Constraint kind is stored in the -CT array. - -Thus, I-th linear constraint is - P[0,0]*C[I,0] + P[0,1]*C[I,1] + .. + P[0,N-1]*C[I,N-1] + - + P[1,0]*C[I,N] + P[1,1]*C[I,N+1] + ... + - + P[N-1,N-1]*C[I,N*N-1] ?=? C[I,N*N] -where ?=? can be either "=" (CT[i]=0), "<=" (CT[i]<0) or ">=" (CT[i]>0). - -Your constraint may involve only some subset of P (less than N*N elements). -For example it can be something like - P[0,0] + P[0,1] = 0.5 -In this case you still should pass matrix with N*N+1 columns, but all its -elements (except for C[0,0], C[0,1] and C[0,N*N-1]) will be zero. - -INPUT PARAMETERS: - S - solver - C - array[K,N*N+1] - coefficients of constraints - (see above for complete description) - CT - array[K] - constraint types - (see above for complete description) - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetlc(const mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsetlc(const_cast(s.c_ptr()), const_cast(c.c_ptr()), const_cast(ct.c_ptr()), k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to set linear equality/inequality constraints on the -elements of the transition matrix P. - -This function can be used to set one or several general linear constraints -on the elements of P. Two types of constraints are supported: -* equality constraints -* inequality constraints (both less-or-equal and greater-or-equal) - -Coefficients of constraints are specified by matrix C (one of the -parameters). One row of C corresponds to one constraint. Because -transition matrix P has N*N elements, we need N*N columns to store all -coefficients (they are stored row by row), and one more column to store -right part - hence C has N*N+1 columns. Constraint kind is stored in the -CT array. - -Thus, I-th linear constraint is - P[0,0]*C[I,0] + P[0,1]*C[I,1] + .. + P[0,N-1]*C[I,N-1] + - + P[1,0]*C[I,N] + P[1,1]*C[I,N+1] + ... + - + P[N-1,N-1]*C[I,N*N-1] ?=? C[I,N*N] -where ?=? can be either "=" (CT[i]=0), "<=" (CT[i]<0) or ">=" (CT[i]>0). - -Your constraint may involve only some subset of P (less than N*N elements). -For example it can be something like - P[0,0] + P[0,1] = 0.5 -In this case you still should pass matrix with N*N+1 columns, but all its -elements (except for C[0,0], C[0,1] and C[0,N*N-1]) will be zero. - -INPUT PARAMETERS: - S - solver - C - array[K,N*N+1] - coefficients of constraints - (see above for complete description) - CT - array[K] - constraint types - (see above for complete description) - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetlc(const mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t k; - if( (c.rows()!=ct.length())) - throw ap_error("Error while calling 'mcpdsetlc': looks like one of arguments has wrong size"); - k = c.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsetlc(const_cast(s.c_ptr()), const_cast(c.c_ptr()), const_cast(ct.c_ptr()), k, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function allows to tune amount of Tikhonov regularization being -applied to your problem. - -By default, regularizing term is equal to r*||P-prior_P||^2, where r is a -small non-zero value, P is transition matrix, prior_P is identity matrix, -||X||^2 is a sum of squared elements of X. - -This function allows you to change coefficient r. You can also change -prior values with MCPDSetPrior() function. - -INPUT PARAMETERS: - S - solver - V - regularization coefficient, finite non-negative value. It - is not recommended to specify zero value unless you are - pretty sure that you want it. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsettikhonovregularizer(const mcpdstate &s, const double v) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsettikhonovregularizer(const_cast(s.c_ptr()), v, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function allows to set prior values used for regularization of your -problem. - -By default, regularizing term is equal to r*||P-prior_P||^2, where r is a -small non-zero value, P is transition matrix, prior_P is identity matrix, -||X||^2 is a sum of squared elements of X. - -This function allows you to change prior values prior_P. You can also -change r with MCPDSetTikhonovRegularizer() function. - -INPUT PARAMETERS: - S - solver - PP - array[N,N], matrix of prior values: - 1. elements must be real numbers from [0,1] - 2. columns must sum to 1.0. - First property is checked (exception is thrown otherwise), - while second one is not checked/enforced. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetprior(const mcpdstate &s, const real_2d_array &pp) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsetprior(const_cast(s.c_ptr()), const_cast(pp.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to change prediction weights - -MCPD solver scales prediction errors as follows - Error(P) = ||W*(y-P*x)||^2 -where - x is a system state at time t - y is a system state at time t+1 - P is a transition matrix - W is a diagonal scaling matrix - -By default, weights are chosen in order to minimize relative prediction -error instead of absolute one. For example, if one component of state is -about 0.5 in magnitude and another one is about 0.05, then algorithm will -make corresponding weights equal to 2.0 and 20.0. - -INPUT PARAMETERS: - S - solver - PW - array[N], weights: - * must be non-negative values (exception will be thrown otherwise) - * zero values will be replaced by automatically chosen values - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetpredictionweights(const mcpdstate &s, const real_1d_array &pw) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsetpredictionweights(const_cast(s.c_ptr()), const_cast(pw.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to start solution of the MCPD problem. - -After return from this function, you can use MCPDResults() to get solution -and completion code. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsolve(const mcpdstate &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdsolve(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -MCPD results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - P - array[N,N], transition matrix - Rep - optimization report. You should check Rep.TerminationType - in order to distinguish successful termination from - unsuccessful one. Speaking short, positive values denote - success, negative ones are failures. - More information about fields of this structure can be - found in the comments on MCPDReport datatype. - - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdresults(const mcpdstate &s, real_2d_array &p, mcpdreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mcpdresults(const_cast(s.c_ptr()), const_cast(p.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Neural networks ensemble -*************************************************************************/ -_mlpensemble_owner::_mlpensemble_owner() -{ - p_struct = (alglib_impl::mlpensemble*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlpensemble), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlpensemble_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlpensemble_owner::_mlpensemble_owner(const _mlpensemble_owner &rhs) -{ - p_struct = (alglib_impl::mlpensemble*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlpensemble), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlpensemble_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlpensemble_owner& _mlpensemble_owner::operator=(const _mlpensemble_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mlpensemble_clear(p_struct); - if( !alglib_impl::_mlpensemble_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mlpensemble_owner::~_mlpensemble_owner() -{ - alglib_impl::_mlpensemble_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mlpensemble* _mlpensemble_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mlpensemble* _mlpensemble_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mlpensemble::mlpensemble() : _mlpensemble_owner() -{ -} - -mlpensemble::mlpensemble(const mlpensemble &rhs):_mlpensemble_owner(rhs) -{ -} - -mlpensemble& mlpensemble::operator=(const mlpensemble &rhs) -{ - if( this==&rhs ) - return *this; - _mlpensemble_owner::operator=(rhs); - return *this; -} - -mlpensemble::~mlpensemble() -{ -} - - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void mlpeserialize(mlpensemble &obj, std::string &s_out) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - alglib_impl::ae_int_t ssize; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_alloc_start(&serializer); - alglib_impl::mlpealloc(&serializer, obj.c_ptr(), &state); - ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer); - s_out.clear(); - s_out.reserve((size_t)(ssize+1)); - alglib_impl::ae_serializer_sstart_str(&serializer, &s_out); - alglib_impl::mlpeserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - if( s_out.length()>(size_t)ssize ) - throw ap_error("ALGLIB: serialization integrity error"); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void mlpeunserialize(std::string &s_in, mlpensemble &obj) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_ustart_str(&serializer, &s_in); - alglib_impl::mlpeunserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} - -/************************************************************************* -Like MLPCreate0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate0(const ae_int_t nin, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreate0(nin, nout, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreate1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreate1(nin, nhid, nout, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreate2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreate2(nin, nhid1, nhid2, nout, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateB0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb0(const ae_int_t nin, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreateb0(nin, nout, b, d, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateB1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreateb1(nin, nhid, nout, b, d, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateB2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreateb2(nin, nhid1, nhid2, nout, b, d, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateR0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater0(const ae_int_t nin, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreater0(nin, nout, a, b, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateR1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreater1(nin, nhid, nout, a, b, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateR2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreater2(nin, nhid1, nhid2, nout, a, b, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateC0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec0(const ae_int_t nin, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreatec0(nin, nout, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateC1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreatec1(nin, nhid, nout, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Like MLPCreateC2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreatec2(nin, nhid1, nhid2, nout, ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Creates ensemble from network. Only network geometry is copied. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatefromnetwork(const multilayerperceptron &network, const ae_int_t ensemblesize, mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpecreatefromnetwork(const_cast(network.c_ptr()), ensemblesize, const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Randomization of MLP ensemble - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlperandomize(const mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlperandomize(const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Return ensemble properties (number of inputs and outputs). - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeproperties(const mlpensemble &ensemble, ae_int_t &nin, ae_int_t &nout) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpeproperties(const_cast(ensemble.c_ptr()), &nin, &nout, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Return normalization type (whether ensemble is SOFTMAX-normalized or not). - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -bool mlpeissoftmax(const mlpensemble &ensemble) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::mlpeissoftmax(const_cast(ensemble.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - Ensemble- neural networks ensemble - X - input vector, array[0..NIn-1]. - Y - (possibly) preallocated buffer; if size of Y is less than - NOut, it will be reallocated. If it is large enough, it - is NOT reallocated, so we can save some time on reallocation. - - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeprocess(const mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpeprocess(const_cast(ensemble.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -'interactive' variant of MLPEProcess for languages like Python which -support constructs like "Y = MLPEProcess(LM,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeprocessi(const mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpeprocessi(const_cast(ensemble.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - Works both for classifier betwork and for regression networks which -are used as classifiers. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlperelclserror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlperelclserror(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*LN(2)). - Zero if ensemble solves regression task. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgce(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpeavgce(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - Its meaning for regression task is obvious. As for classification task -RMS error means error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpermserror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpermserror(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for classification task -it means average error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgerror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpeavgerror(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for classification task -it means average relative error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgrelerror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::mlpeavgrelerror(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Training report: - * RelCLSError - fraction of misclassified cases. - * AvgCE - acerage cross-entropy - * RMSError - root-mean-square error - * AvgError - average error - * AvgRelError - average relative error - * NGrad - number of gradient calculations - * NHess - number of Hessian calculations - * NCholesky - number of Cholesky decompositions - -NOTE 1: RelCLSError/AvgCE are zero on regression problems. - -NOTE 2: on classification problems RMSError/AvgError/AvgRelError contain - errors in prediction of posterior probabilities -*************************************************************************/ -_mlpreport_owner::_mlpreport_owner() -{ - p_struct = (alglib_impl::mlpreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlpreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlpreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlpreport_owner::_mlpreport_owner(const _mlpreport_owner &rhs) -{ - p_struct = (alglib_impl::mlpreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlpreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlpreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlpreport_owner& _mlpreport_owner::operator=(const _mlpreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mlpreport_clear(p_struct); - if( !alglib_impl::_mlpreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mlpreport_owner::~_mlpreport_owner() -{ - alglib_impl::_mlpreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mlpreport* _mlpreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mlpreport* _mlpreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mlpreport::mlpreport() : _mlpreport_owner() ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),ngrad(p_struct->ngrad),nhess(p_struct->nhess),ncholesky(p_struct->ncholesky) -{ -} - -mlpreport::mlpreport(const mlpreport &rhs):_mlpreport_owner(rhs) ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),ngrad(p_struct->ngrad),nhess(p_struct->nhess),ncholesky(p_struct->ncholesky) -{ -} - -mlpreport& mlpreport::operator=(const mlpreport &rhs) -{ - if( this==&rhs ) - return *this; - _mlpreport_owner::operator=(rhs); - return *this; -} - -mlpreport::~mlpreport() -{ -} - - -/************************************************************************* -Cross-validation estimates of generalization error -*************************************************************************/ -_mlpcvreport_owner::_mlpcvreport_owner() -{ - p_struct = (alglib_impl::mlpcvreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlpcvreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlpcvreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlpcvreport_owner::_mlpcvreport_owner(const _mlpcvreport_owner &rhs) -{ - p_struct = (alglib_impl::mlpcvreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlpcvreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlpcvreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlpcvreport_owner& _mlpcvreport_owner::operator=(const _mlpcvreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mlpcvreport_clear(p_struct); - if( !alglib_impl::_mlpcvreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mlpcvreport_owner::~_mlpcvreport_owner() -{ - alglib_impl::_mlpcvreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mlpcvreport* _mlpcvreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mlpcvreport* _mlpcvreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mlpcvreport::mlpcvreport() : _mlpcvreport_owner() ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror) -{ -} - -mlpcvreport::mlpcvreport(const mlpcvreport &rhs):_mlpcvreport_owner(rhs) ,relclserror(p_struct->relclserror),avgce(p_struct->avgce),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror) -{ -} - -mlpcvreport& mlpcvreport::operator=(const mlpcvreport &rhs) -{ - if( this==&rhs ) - return *this; - _mlpcvreport_owner::operator=(rhs); - return *this; -} - -mlpcvreport::~mlpcvreport() -{ -} - - -/************************************************************************* -Trainer object for neural network. - -You should not try to access fields of this object directly - use ALGLIB -functions to work with this object. -*************************************************************************/ -_mlptrainer_owner::_mlptrainer_owner() -{ - p_struct = (alglib_impl::mlptrainer*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlptrainer), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlptrainer_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlptrainer_owner::_mlptrainer_owner(const _mlptrainer_owner &rhs) -{ - p_struct = (alglib_impl::mlptrainer*)alglib_impl::ae_malloc(sizeof(alglib_impl::mlptrainer), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mlptrainer_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mlptrainer_owner& _mlptrainer_owner::operator=(const _mlptrainer_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mlptrainer_clear(p_struct); - if( !alglib_impl::_mlptrainer_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mlptrainer_owner::~_mlptrainer_owner() -{ - alglib_impl::_mlptrainer_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mlptrainer* _mlptrainer_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mlptrainer* _mlptrainer_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mlptrainer::mlptrainer() : _mlptrainer_owner() -{ -} - -mlptrainer::mlptrainer(const mlptrainer &rhs):_mlptrainer_owner(rhs) -{ -} - -mlptrainer& mlptrainer::operator=(const mlptrainer &rhs) -{ - if( this==&rhs ) - return *this; - _mlptrainer_owner::operator=(rhs); - return *this; -} - -mlptrainer::~mlptrainer() -{ -} - -/************************************************************************* -Neural network training using modified Levenberg-Marquardt with exact -Hessian calculation and regularization. Subroutine trains neural network -with restarts from random positions. Algorithm is well suited for small -and medium scale problems (hundreds of weights). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts from random position, >0. - If you don't know what Restarts to choose, use 2. - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -9, if internal matrix inverse subroutine failed - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlptrainlm(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlptrainlm(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Neural network training using L-BFGS algorithm with regularization. -Subroutine trains neural network with restarts from random positions. -Algorithm is well suited for problems of any dimensionality (memory -requirements and step complexity are linear by weights number). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts from random position, >0. - If you don't know what Restarts to choose, use 2. - WStep - stopping criterion. Algorithm stops if step size is - less than WStep. Recommended value - 0.01. Zero step - size means stopping after MaxIts iterations. - MaxIts - stopping criterion. Algorithm stops after MaxIts - iterations (NOT gradient calculations). Zero MaxIts - means stopping when step is sufficiently small. - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -8, if both WStep=0 and MaxIts=0 - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlptrainlbfgs(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, ae_int_t &info, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlptrainlbfgs(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, wstep, maxits, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Neural network training using early stopping (base algorithm - L-BFGS with -regularization). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - TrnXY - training set - TrnSize - training set size, TrnSize>0 - ValXY - validation set - ValSize - validation set size, ValSize>0 - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts, either: - * strictly positive number - algorithm make specified - number of restarts from random position. - * -1, in which case algorithm makes exactly one run - from the initial state of the network (no randomization). - If you don't know what Restarts to choose, choose one - one the following: - * -1 (deterministic start) - * +1 (one random restart) - * +5 (moderate amount of random restarts) - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1, ...). - * 2, task has been solved, stopping criterion met - - sufficiently small step size. Not expected (we - use EARLY stopping) but possible and not an - error. - * 6, task has been solved, stopping criterion met - - increasing of validation set error. - Rep - training report - -NOTE: - -Algorithm stops if validation set error increases for a long enough or -step size is small enought (there are task where validation set may -decrease for eternity). In any case solution returned corresponds to the -minimum of validation set error. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlptraines(const multilayerperceptron &network, const real_2d_array &trnxy, const ae_int_t trnsize, const real_2d_array &valxy, const ae_int_t valsize, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlptraines(const_cast(network.c_ptr()), const_cast(trnxy.c_ptr()), trnsize, const_cast(valxy.c_ptr()), valsize, decay, restarts, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cross-validation estimate of generalization error. - -Base algorithm - L-BFGS. - -INPUT PARAMETERS: - Network - neural network with initialized geometry. Network is - not changed during cross-validation - it is used only - as a representative of its architecture. - XY - training set. - SSize - training set size - Decay - weight decay, same as in MLPTrainLBFGS - Restarts - number of restarts, >0. - restarts are counted for each partition separately, so - total number of restarts will be Restarts*FoldsCount. - WStep - stopping criterion, same as in MLPTrainLBFGS - MaxIts - stopping criterion, same as in MLPTrainLBFGS - FoldsCount - number of folds in k-fold cross-validation, - 2<=FoldsCount<=SSize. - recommended value: 10. - -OUTPUT PARAMETERS: - Info - return code, same as in MLPTrainLBFGS - Rep - report, same as in MLPTrainLM/MLPTrainLBFGS - CVRep - generalization error estimates - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcvlbfgs(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, const ae_int_t foldscount, ae_int_t &info, mlpreport &rep, mlpcvreport &cvrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpkfoldcvlbfgs(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, wstep, maxits, foldscount, &info, const_cast(rep.c_ptr()), const_cast(cvrep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cross-validation estimate of generalization error. - -Base algorithm - Levenberg-Marquardt. - -INPUT PARAMETERS: - Network - neural network with initialized geometry. Network is - not changed during cross-validation - it is used only - as a representative of its architecture. - XY - training set. - SSize - training set size - Decay - weight decay, same as in MLPTrainLBFGS - Restarts - number of restarts, >0. - restarts are counted for each partition separately, so - total number of restarts will be Restarts*FoldsCount. - FoldsCount - number of folds in k-fold cross-validation, - 2<=FoldsCount<=SSize. - recommended value: 10. - -OUTPUT PARAMETERS: - Info - return code, same as in MLPTrainLBFGS - Rep - report, same as in MLPTrainLM/MLPTrainLBFGS - CVRep - generalization error estimates - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcvlm(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const ae_int_t foldscount, ae_int_t &info, mlpreport &rep, mlpcvreport &cvrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpkfoldcvlm(const_cast(network.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, foldscount, &info, const_cast(rep.c_ptr()), const_cast(cvrep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function estimates generalization error using cross-validation on the -current dataset with current training settings. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * FoldsCount cross-validation rounds (always) - ! * NRestarts training sessions performed within each of - ! cross-validation rounds (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. Network is not changed during cross- - validation and is not trained - it is used only as - representative of its architecture. I.e., we estimate - generalization properties of ARCHITECTURE, not some - specific network. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that for each cross-validation - round specified number of random restarts is - performed, with best network being chosen after - training. - * NRestarts=0 is same as NRestarts=1 - FoldsCount - number of folds in k-fold cross-validation: - * 2<=FoldsCount<=size of dataset - * recommended value: 10. - * values larger than dataset size will be silently - truncated down to dataset size - -OUTPUT PARAMETERS: - Rep - structure which contains cross-validation estimates: - * Rep.RelCLSError - fraction of misclassified cases. - * Rep.AvgCE - acerage cross-entropy - * Rep.RMSError - root-mean-square error - * Rep.AvgError - average error - * Rep.AvgRelError - average relative error - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - or subset with only one point was given, zeros are returned as - estimates. - -NOTE: this method performs FoldsCount cross-validation rounds, each one - with NRestarts random starts. Thus, FoldsCount*NRestarts networks - are trained in total. - -NOTE: Rep.RelCLSError/Rep.AvgCE are zero on regression problems. - -NOTE: on classification problems Rep.RMSError/Rep.AvgError/Rep.AvgRelError - contain errors in prediction of posterior probabilities. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcv(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpkfoldcv(const_cast(s.c_ptr()), const_cast(network.c_ptr()), nrestarts, foldscount, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlpkfoldcv(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlpkfoldcv(const_cast(s.c_ptr()), const_cast(network.c_ptr()), nrestarts, foldscount, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Creation of the network trainer object for regression networks - -INPUT PARAMETERS: - NIn - number of inputs, NIn>=1 - NOut - number of outputs, NOut>=1 - -OUTPUT PARAMETERS: - S - neural network trainer object. - This structure can be used to train any regression - network with NIn inputs and NOut outputs. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatetrainer(const ae_int_t nin, const ae_int_t nout, mlptrainer &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreatetrainer(nin, nout, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Creation of the network trainer object for classification networks - -INPUT PARAMETERS: - NIn - number of inputs, NIn>=1 - NClasses - number of classes, NClasses>=2 - -OUTPUT PARAMETERS: - S - neural network trainer object. - This structure can be used to train any classification - network with NIn inputs and NOut outputs. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatetrainercls(const ae_int_t nin, const ae_int_t nclasses, mlptrainer &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpcreatetrainercls(nin, nclasses, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets "current dataset" of the trainer object to one passed -by user. - -INPUT PARAMETERS: - S - trainer object - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. - NPoints - points count, >=0. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -datasetformat is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetdataset(const mlptrainer &s, const real_2d_array &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetdataset(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets "current dataset" of the trainer object to one passed -by user (sparse matrix is used to store dataset). - -INPUT PARAMETERS: - S - trainer object - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Any sparse storage format can be used: - Hash-table, CRS... - NPoints - points count, >=0 - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -datasetformat is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetsparsedataset(const mlptrainer &s, const sparsematrix &xy, const ae_int_t npoints) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetsparsedataset(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), npoints, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets weight decay coefficient which is used for training. - -INPUT PARAMETERS: - S - trainer object - Decay - weight decay coefficient, >=0. Weight decay term - 'Decay*||Weights||^2' is added to error function. If - you don't know what Decay to choose, use 1.0E-3. - Weight decay can be set to zero, in this case network - is trained without weight decay. - -NOTE: by default network uses some small nonzero value for weight decay. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetdecay(const mlptrainer &s, const double decay) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetdecay(const_cast(s.c_ptr()), decay, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping criteria for the optimizer. - -INPUT PARAMETERS: - S - trainer object - WStep - stopping criterion. Algorithm stops if step size is - less than WStep. Recommended value - 0.01. Zero step - size means stopping after MaxIts iterations. - WStep>=0. - MaxIts - stopping criterion. Algorithm stops after MaxIts - epochs (full passes over entire dataset). Zero MaxIts - means stopping when step is sufficiently small. - MaxIts>=0. - -NOTE: by default, WStep=0.005 and MaxIts=0 are used. These values are also - used when MLPSetCond() is called with WStep=0 and MaxIts=0. - -NOTE: these stopping criteria are used for all kinds of neural training - - from "conventional" networks to early stopping ensembles. When used - for "conventional" networks, they are used as the only stopping - criteria. When combined with early stopping, they used as ADDITIONAL - stopping criteria which can terminate early stopping algorithm. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetcond(const mlptrainer &s, const double wstep, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetcond(const_cast(s.c_ptr()), wstep, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets training algorithm: batch training using L-BFGS will be -used. - -This algorithm: -* the most robust for small-scale problems, but may be too slow for large - scale ones. -* perfoms full pass through the dataset before performing step -* uses conditions specified by MLPSetCond() for stopping -* is default one used by trainer object - -INPUT PARAMETERS: - S - trainer object - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetalgobatch(const mlptrainer &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpsetalgobatch(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function trains neural network passed to this function, using current -dataset (one which was passed to MLPSetDataset() or MLPSetSparseDataset()) -and current training settings. Training from NRestarts random starting -positions is performed, best network is chosen. - -Training is performed using current training algorithm. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * NRestarts training sessions performed within each of - ! cross-validation rounds (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that specified number of random - restarts are performed, best network is chosen after - training - * NRestarts=0 means that current state of the network - is used for training. - -OUTPUT PARAMETERS: - Network - trained network - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - network is filled by zero values. Same behavior for functions - MLPStartTraining and MLPContinueTraining. - -NOTE: this method uses sum-of-squares error function for training. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlptrainnetwork(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlptrainnetwork(const_cast(s.c_ptr()), const_cast(network.c_ptr()), nrestarts, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlptrainnetwork(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlptrainnetwork(const_cast(s.c_ptr()), const_cast(network.c_ptr()), nrestarts, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -IMPORTANT: this is an "expert" version of the MLPTrain() function. We do - not recommend you to use it unless you are pretty sure that you - need ability to monitor training progress. - -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTraining() call, -and then user subsequently calls MLPContinueTraining() to perform one more -iteration of the training. - -After call to this function trainer object remembers network and is ready -to train it. However, no training is performed until first call to -MLPContinueTraining() function. Subsequent calls to MLPContinueTraining() -will advance training progress one iteration further. - -EXAMPLE: - > - > ...initialize network and trainer object.... - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > ...visualize training progress... - > - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. - RandomStart - randomize network before training or not: - * True means that network is randomized and its - initial state (one which was passed to the trainer - object) is lost. - * False means that training is started from the - current state of the network - -OUTPUT PARAMETERS: - Network - neural network which is ready to training (weights are - initialized, preprocessor is initialized using current - training set) - -NOTE: this method uses sum-of-squares error function for training. - -NOTE: it is expected that trainer object settings are NOT changed during - step-by-step training, i.e. no one changes stopping criteria or - training set during training. It is possible and there is no defense - against such actions, but algorithm behavior in such cases is - undefined and can be unpredictable. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpstarttraining(const mlptrainer &s, const multilayerperceptron &network, const bool randomstart) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpstarttraining(const_cast(s.c_ptr()), const_cast(network.c_ptr()), randomstart, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -IMPORTANT: this is an "expert" version of the MLPTrain() function. We do - not recommend you to use it unless you are pretty sure that you - need ability to monitor training progress. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTraining() call, -and then user subsequently calls MLPContinueTraining() to perform one more -iteration of the training. - -This function performs one more iteration of the training and returns -either True (training continues) or False (training stopped). In case True -was returned, Network weights are updated according to the current state -of the optimization progress. In case False was returned, no additional -updates is performed (previous update of the network weights moved us to -the final point, and no additional updates is needed). - -EXAMPLE: - > - > [initialize network and trainer object] - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > [visualize training progress] - > - -INPUT PARAMETERS: - S - trainer object - Network - neural network structure, which is used to store - current state of the training process. - -OUTPUT PARAMETERS: - Network - weights of the neural network are rewritten by the - current approximation. - -NOTE: this method uses sum-of-squares error function for training. - -NOTE: it is expected that trainer object settings are NOT changed during - step-by-step training, i.e. no one changes stopping criteria or - training set during training. It is possible and there is no defense - against such actions, but algorithm behavior in such cases is - undefined and can be unpredictable. - -NOTE: It is expected that Network is the same one which was passed to - MLPStartTraining() function. However, THIS function checks only - following: - * that number of network inputs is consistent with trainer object - settings - * that number of network outputs/classes is consistent with trainer - object settings - * that number of network weights is the same as number of weights in - the network passed to MLPStartTraining() function - Exception is thrown when these conditions are violated. - - It is also expected that you do not change state of the network on - your own - the only party who has right to change network during its - training is a trainer object. Any attempt to interfere with trainer - may lead to unpredictable results. - - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -bool mlpcontinuetraining(const mlptrainer &s, const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::mlpcontinuetraining(const_cast(s.c_ptr()), const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -bool smp_mlpcontinuetraining(const mlptrainer &s, const multilayerperceptron &network) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::_pexec_mlpcontinuetraining(const_cast(s.c_ptr()), const_cast(network.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Training neural networks ensemble using bootstrap aggregating (bagging). -Modified Levenberg-Marquardt algorithm is used as base training method. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpebagginglm(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep, mlpcvreport &ooberrors) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpebagginglm(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, &info, const_cast(rep.c_ptr()), const_cast(ooberrors.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Training neural networks ensemble using bootstrap aggregating (bagging). -L-BFGS algorithm is used as base training method. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - WStep - stopping criterion, same as in MLPTrainLBFGS - MaxIts - stopping criterion, same as in MLPTrainLBFGS - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -8, if both WStep=0 and MaxIts=0 - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpebagginglbfgs(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, ae_int_t &info, mlpreport &rep, mlpcvreport &ooberrors) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpebagginglbfgs(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, wstep, maxits, &info, const_cast(rep.c_ptr()), const_cast(ooberrors.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Training neural networks ensemble using early stopping. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 6, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpetraines(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlpetraines(const_cast(ensemble.c_ptr()), const_cast(xy.c_ptr()), npoints, decay, restarts, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function trains neural network ensemble passed to this function using -current dataset and early stopping training algorithm. Each early stopping -round performs NRestarts random restarts (thus, EnsembleSize*NRestarts -training rounds is performed in total). - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * EnsembleSize training sessions performed for each of ensemble - ! members (always parallelized) - ! * NRestarts training sessions performed within each of training - ! sessions (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object; - Ensemble - neural network ensemble. It must have same number of - inputs and outputs/classes as was specified during - creation of the trainer object. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that specified number of random - restarts are performed during each ES round; - * NRestarts=0 is silently replaced by 1. - -OUTPUT PARAMETERS: - Ensemble - trained ensemble; - Rep - it contains all type of errors. - -NOTE: this training method uses BOTH early stopping and weight decay! So, - you should select weight decay before starting training just as you - select it before training "conventional" networks. - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - or single-point dataset was passed, ensemble is filled by zero - values. - -NOTE: this method uses sum-of-squares error function for training. - - -- ALGLIB -- - Copyright 22.08.2012 by Bochkanov Sergey -*************************************************************************/ -void mlptrainensemblees(const mlptrainer &s, const mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mlptrainensemblees(const_cast(s.c_ptr()), const_cast(ensemble.c_ptr()), nrestarts, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_mlptrainensemblees(const mlptrainer &s, const mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_mlptrainensemblees(const_cast(s.c_ptr()), const_cast(ensemble.c_ptr()), nrestarts, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Principal components analysis - -Subroutine builds orthogonal basis where first axis corresponds to -direction with maximum variance, second axis maximizes variance in subspace -orthogonal to first axis and so on. - -It should be noted that, unlike LDA, PCA does not use class labels. - -INPUT PARAMETERS: - X - dataset, array[0..NPoints-1,0..NVars-1]. - matrix contains ONLY INDEPENDENT VARIABLES. - NPoints - dataset size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - -ÂÛÕÎÄÍÛÅ ÏÀÐÀÌÅÒÐÛ: - Info - return code: - * -4, if SVD subroutine haven't converged - * -1, if wrong parameters has been passed (NPoints<0, - NVars<1) - * 1, if task is solved - S2 - array[0..NVars-1]. variance values corresponding - to basis vectors. - V - array[0..NVars-1,0..NVars-1] - matrix, whose columns store basis vectors. - - -- ALGLIB -- - Copyright 25.08.2008 by Bochkanov Sergey -*************************************************************************/ -void pcabuildbasis(const real_2d_array &x, const ae_int_t npoints, const ae_int_t nvars, ae_int_t &info, real_1d_array &s2, real_2d_array &v) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pcabuildbasis(const_cast(x.c_ptr()), npoints, nvars, &info, const_cast(s2.c_ptr()), const_cast(v.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static double bdss_xlny(double x, double y, ae_state *_state); -static double bdss_getcv(/* Integer */ ae_vector* cnt, - ae_int_t nc, - ae_state *_state); -static void bdss_tieaddc(/* Integer */ ae_vector* c, - /* Integer */ ae_vector* ties, - ae_int_t ntie, - ae_int_t nc, - /* Integer */ ae_vector* cnt, - ae_state *_state); -static void bdss_tiesubc(/* Integer */ ae_vector* c, - /* Integer */ ae_vector* ties, - ae_int_t ntie, - ae_int_t nc, - /* Integer */ ae_vector* cnt, - ae_state *_state); - - -static ae_int_t clustering_parallelcomplexity = 200000; -static ae_bool clustering_selectcenterpp(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - /* Real */ ae_matrix* centers, - /* Boolean */ ae_vector* busycenters, - ae_int_t ccnt, - /* Real */ ae_vector* d2, - /* Real */ ae_vector* p, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void clustering_clusterizerrunahcinternal(clusterizerstate* s, - /* Real */ ae_matrix* d, - ahcreport* rep, - ae_state *_state); -static void clustering_evaluatedistancematrixrec(/* Real */ ae_matrix* xy, - ae_int_t nfeatures, - ae_int_t disttype, - /* Real */ ae_matrix* d, - ae_int_t i0, - ae_int_t i1, - ae_int_t j0, - ae_int_t j1, - ae_state *_state); - - - - -static ae_int_t dforest_innernodewidth = 3; -static ae_int_t dforest_leafnodewidth = 2; -static ae_int_t dforest_dfusestrongsplits = 1; -static ae_int_t dforest_dfuseevs = 2; -static ae_int_t dforest_dffirstversion = 0; -static ae_int_t dforest_dfclserror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -static void dforest_dfprocessinternal(decisionforest* df, - ae_int_t offs, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -static void dforest_dfbuildtree(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t nfeatures, - ae_int_t nvarsinpool, - ae_int_t flags, - dfinternalbuffers* bufs, - hqrndstate* rs, - ae_state *_state); -static void dforest_dfbuildtreerec(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t nfeatures, - ae_int_t nvarsinpool, - ae_int_t flags, - ae_int_t* numprocessed, - ae_int_t idx1, - ae_int_t idx2, - dfinternalbuffers* bufs, - hqrndstate* rs, - ae_state *_state); -static void dforest_dfsplitc(/* Real */ ae_vector* x, - /* Integer */ ae_vector* c, - /* Integer */ ae_vector* cntbuf, - ae_int_t n, - ae_int_t nc, - ae_int_t flags, - ae_int_t* info, - double* threshold, - double* e, - /* Real */ ae_vector* sortrbuf, - /* Integer */ ae_vector* sortibuf, - ae_state *_state); -static void dforest_dfsplitr(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t flags, - ae_int_t* info, - double* threshold, - double* e, - /* Real */ ae_vector* sortrbuf, - /* Real */ ae_vector* sortrbuf2, - ae_state *_state); - - -static ae_int_t linreg_lrvnum = 5; -static void linreg_lrinternal(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state); - - - - - - -static ae_int_t mlpbase_mlpvnum = 7; -static ae_int_t mlpbase_mlpfirstversion = 0; -static ae_int_t mlpbase_nfieldwidth = 4; -static ae_int_t mlpbase_hlconnfieldwidth = 5; -static ae_int_t mlpbase_hlnfieldwidth = 4; -static ae_int_t mlpbase_gradbasecasecost = 50000; -static ae_int_t mlpbase_microbatchsize = 64; -static void mlpbase_addinputlayer(ae_int_t ncount, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state); -static void mlpbase_addbiasedsummatorlayer(ae_int_t ncount, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state); -static void mlpbase_addactivationlayer(ae_int_t functype, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state); -static void mlpbase_addzerolayer(/* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state); -static void mlpbase_hladdinputlayer(multilayerperceptron* network, - ae_int_t* connidx, - ae_int_t* neuroidx, - ae_int_t* structinfoidx, - ae_int_t nin, - ae_state *_state); -static void mlpbase_hladdoutputlayer(multilayerperceptron* network, - ae_int_t* connidx, - ae_int_t* neuroidx, - ae_int_t* structinfoidx, - ae_int_t* weightsidx, - ae_int_t k, - ae_int_t nprev, - ae_int_t nout, - ae_bool iscls, - ae_bool islinearout, - ae_state *_state); -static void mlpbase_hladdhiddenlayer(multilayerperceptron* network, - ae_int_t* connidx, - ae_int_t* neuroidx, - ae_int_t* structinfoidx, - ae_int_t* weightsidx, - ae_int_t k, - ae_int_t nprev, - ae_int_t ncur, - ae_state *_state); -static void mlpbase_fillhighlevelinformation(multilayerperceptron* network, - ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - ae_bool iscls, - ae_bool islinearout, - ae_state *_state); -static void mlpbase_mlpcreate(ae_int_t nin, - ae_int_t nout, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t layerscount, - ae_bool isclsnet, - multilayerperceptron* network, - ae_state *_state); -static void mlpbase_mlphessianbatchinternal(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_bool naturalerr, - double* e, - /* Real */ ae_vector* grad, - /* Real */ ae_matrix* h, - ae_state *_state); -static void mlpbase_mlpinternalcalculategradient(multilayerperceptron* network, - /* Real */ ae_vector* neurons, - /* Real */ ae_vector* weights, - /* Real */ ae_vector* derror, - /* Real */ ae_vector* grad, - ae_bool naturalerrorfunc, - ae_state *_state); -static void mlpbase_mlpchunkedgradient(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - double* e, - ae_bool naturalerrorfunc, - ae_state *_state); -static void mlpbase_mlpchunkedprocess(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - ae_state *_state); -static double mlpbase_safecrossentropy(double t, - double z, - ae_state *_state); -static void mlpbase_randomizebackwardpass(multilayerperceptron* network, - ae_int_t neuronidx, - double v, - ae_state *_state); - - -static double logit_xtol = 100*ae_machineepsilon; -static double logit_ftol = 0.0001; -static double logit_gtol = 0.3; -static ae_int_t logit_maxfev = 20; -static double logit_stpmin = 1.0E-2; -static double logit_stpmax = 1.0E5; -static ae_int_t logit_logitvnum = 6; -static void logit_mnliexp(/* Real */ ae_vector* w, - /* Real */ ae_vector* x, - ae_state *_state); -static void logit_mnlallerrors(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double* relcls, - double* avgce, - double* rms, - double* avg, - double* avgrel, - ae_state *_state); -static void logit_mnlmcsrch(ae_int_t n, - /* Real */ ae_vector* x, - double* f, - /* Real */ ae_vector* g, - /* Real */ ae_vector* s, - double* stp, - ae_int_t* info, - ae_int_t* nfev, - /* Real */ ae_vector* wa, - logitmcstate* state, - ae_int_t* stage, - ae_state *_state); -static void logit_mnlmcstep(double* stx, - double* fx, - double* dx, - double* sty, - double* fy, - double* dy, - double* stp, - double fp, - double dp, - ae_bool* brackt, - double stmin, - double stmax, - ae_int_t* info, - ae_state *_state); - - -static double mcpd_xtol = 1.0E-8; -static void mcpd_mcpdinit(ae_int_t n, - ae_int_t entrystate, - ae_int_t exitstate, - mcpdstate* s, - ae_state *_state); - - -static ae_int_t mlpe_mlpefirstversion = 1; - - -static double mlptrain_mindecay = 0.001; -static ae_int_t mlptrain_defaultlbfgsfactor = 6; -static void mlptrain_mlpkfoldcvgeneral(multilayerperceptron* n, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t foldscount, - ae_bool lmalgorithm, - double wstep, - ae_int_t maxits, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* cvrep, - ae_state *_state); -static void mlptrain_mlpkfoldsplit(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nclasses, - ae_int_t foldscount, - ae_bool stratifiedsplits, - /* Integer */ ae_vector* folds, - ae_state *_state); -static void mlptrain_mthreadcv(mlptrainer* s, - ae_int_t rowsize, - ae_int_t nrestarts, - /* Integer */ ae_vector* folds, - ae_int_t fold, - ae_int_t dfold, - /* Real */ ae_matrix* cvy, - ae_shared_pool* pooldatacv, - ae_state *_state); -static void mlptrain_mlptrainnetworkx(mlptrainer* s, - ae_int_t nrestarts, - ae_int_t algokind, - /* Integer */ ae_vector* trnsubset, - ae_int_t trnsubsetsize, - /* Integer */ ae_vector* valsubset, - ae_int_t valsubsetsize, - multilayerperceptron* network, - mlpreport* rep, - ae_bool isrootcall, - ae_shared_pool* sessions, - ae_state *_state); -static void mlptrain_mlptrainensemblex(mlptrainer* s, - mlpensemble* ensemble, - ae_int_t idx0, - ae_int_t idx1, - ae_int_t nrestarts, - ae_int_t trainingmethod, - sinteger* ngrad, - ae_bool isrootcall, - ae_shared_pool* esessions, - ae_state *_state); -static void mlptrain_mlpstarttrainingx(mlptrainer* s, - ae_bool randomstart, - ae_int_t algokind, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - smlptrnsession* session, - ae_state *_state); -static ae_bool mlptrain_mlpcontinuetrainingx(mlptrainer* s, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - ae_int_t* ngradbatch, - smlptrnsession* session, - ae_state *_state); -static void mlptrain_mlpebagginginternal(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_bool lmalgorithm, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* ooberrors, - ae_state *_state); -static void mlptrain_initmlptrnsession(multilayerperceptron* networktrained, - ae_bool randomizenetwork, - mlptrainer* trainer, - smlptrnsession* session, - ae_state *_state); -static void mlptrain_initmlptrnsessions(multilayerperceptron* networktrained, - ae_bool randomizenetwork, - mlptrainer* trainer, - ae_shared_pool* sessions, - ae_state *_state); -static void mlptrain_initmlpetrnsession(multilayerperceptron* individualnetwork, - mlptrainer* trainer, - mlpetrnsession* session, - ae_state *_state); -static void mlptrain_initmlpetrnsessions(multilayerperceptron* individualnetwork, - mlptrainer* trainer, - ae_shared_pool* sessions, - ae_state *_state); - - - - - - - -/************************************************************************* -This set of routines (DSErrAllocate, DSErrAccumulate, DSErrFinish) -calculates different error functions (classification error, cross-entropy, -rms, avg, avg.rel errors). - -1. DSErrAllocate prepares buffer. -2. DSErrAccumulate accumulates individual errors: - * Y contains predicted output (posterior probabilities for classification) - * DesiredY contains desired output (class number for classification) -3. DSErrFinish outputs results: - * Buf[0] contains relative classification error (zero for regression tasks) - * Buf[1] contains avg. cross-entropy (zero for regression tasks) - * Buf[2] contains rms error (regression, classification) - * Buf[3] contains average error (regression, classification) - * Buf[4] contains average relative error (regression, classification) - -NOTES(1): - "NClasses>0" means that we have classification task. - "NClasses<0" means regression task with -NClasses real outputs. - -NOTES(2): - rms. avg, avg.rel errors for classification tasks are interpreted as - errors in posterior probabilities with respect to probabilities given - by training/test set. - - -- ALGLIB -- - Copyright 11.01.2009 by Bochkanov Sergey -*************************************************************************/ -void dserrallocate(ae_int_t nclasses, - /* Real */ ae_vector* buf, - ae_state *_state) -{ - - ae_vector_clear(buf); - - ae_vector_set_length(buf, 7+1, _state); - buf->ptr.p_double[0] = 0; - buf->ptr.p_double[1] = 0; - buf->ptr.p_double[2] = 0; - buf->ptr.p_double[3] = 0; - buf->ptr.p_double[4] = 0; - buf->ptr.p_double[5] = nclasses; - buf->ptr.p_double[6] = 0; - buf->ptr.p_double[7] = 0; -} - - -/************************************************************************* -See DSErrAllocate for comments on this routine. - - -- ALGLIB -- - Copyright 11.01.2009 by Bochkanov Sergey -*************************************************************************/ -void dserraccumulate(/* Real */ ae_vector* buf, - /* Real */ ae_vector* y, - /* Real */ ae_vector* desiredy, - ae_state *_state) -{ - ae_int_t nclasses; - ae_int_t nout; - ae_int_t offs; - ae_int_t mmax; - ae_int_t rmax; - ae_int_t j; - double v; - double ev; - - - offs = 5; - nclasses = ae_round(buf->ptr.p_double[offs], _state); - if( nclasses>0 ) - { - - /* - * Classification - */ - rmax = ae_round(desiredy->ptr.p_double[0], _state); - mmax = 0; - for(j=1; j<=nclasses-1; j++) - { - if( ae_fp_greater(y->ptr.p_double[j],y->ptr.p_double[mmax]) ) - { - mmax = j; - } - } - if( mmax!=rmax ) - { - buf->ptr.p_double[0] = buf->ptr.p_double[0]+1; - } - if( ae_fp_greater(y->ptr.p_double[rmax],0) ) - { - buf->ptr.p_double[1] = buf->ptr.p_double[1]-ae_log(y->ptr.p_double[rmax], _state); - } - else - { - buf->ptr.p_double[1] = buf->ptr.p_double[1]+ae_log(ae_maxrealnumber, _state); - } - for(j=0; j<=nclasses-1; j++) - { - v = y->ptr.p_double[j]; - if( j==rmax ) - { - ev = 1; - } - else - { - ev = 0; - } - buf->ptr.p_double[2] = buf->ptr.p_double[2]+ae_sqr(v-ev, _state); - buf->ptr.p_double[3] = buf->ptr.p_double[3]+ae_fabs(v-ev, _state); - if( ae_fp_neq(ev,0) ) - { - buf->ptr.p_double[4] = buf->ptr.p_double[4]+ae_fabs((v-ev)/ev, _state); - buf->ptr.p_double[offs+2] = buf->ptr.p_double[offs+2]+1; - } - } - buf->ptr.p_double[offs+1] = buf->ptr.p_double[offs+1]+1; - } - else - { - - /* - * Regression - */ - nout = -nclasses; - rmax = 0; - for(j=1; j<=nout-1; j++) - { - if( ae_fp_greater(desiredy->ptr.p_double[j],desiredy->ptr.p_double[rmax]) ) - { - rmax = j; - } - } - mmax = 0; - for(j=1; j<=nout-1; j++) - { - if( ae_fp_greater(y->ptr.p_double[j],y->ptr.p_double[mmax]) ) - { - mmax = j; - } - } - if( mmax!=rmax ) - { - buf->ptr.p_double[0] = buf->ptr.p_double[0]+1; - } - for(j=0; j<=nout-1; j++) - { - v = y->ptr.p_double[j]; - ev = desiredy->ptr.p_double[j]; - buf->ptr.p_double[2] = buf->ptr.p_double[2]+ae_sqr(v-ev, _state); - buf->ptr.p_double[3] = buf->ptr.p_double[3]+ae_fabs(v-ev, _state); - if( ae_fp_neq(ev,0) ) - { - buf->ptr.p_double[4] = buf->ptr.p_double[4]+ae_fabs((v-ev)/ev, _state); - buf->ptr.p_double[offs+2] = buf->ptr.p_double[offs+2]+1; - } - } - buf->ptr.p_double[offs+1] = buf->ptr.p_double[offs+1]+1; - } -} - - -/************************************************************************* -See DSErrAllocate for comments on this routine. - - -- ALGLIB -- - Copyright 11.01.2009 by Bochkanov Sergey -*************************************************************************/ -void dserrfinish(/* Real */ ae_vector* buf, ae_state *_state) -{ - ae_int_t nout; - ae_int_t offs; - - - offs = 5; - nout = ae_iabs(ae_round(buf->ptr.p_double[offs], _state), _state); - if( ae_fp_neq(buf->ptr.p_double[offs+1],0) ) - { - buf->ptr.p_double[0] = buf->ptr.p_double[0]/buf->ptr.p_double[offs+1]; - buf->ptr.p_double[1] = buf->ptr.p_double[1]/buf->ptr.p_double[offs+1]; - buf->ptr.p_double[2] = ae_sqrt(buf->ptr.p_double[2]/(nout*buf->ptr.p_double[offs+1]), _state); - buf->ptr.p_double[3] = buf->ptr.p_double[3]/(nout*buf->ptr.p_double[offs+1]); - } - if( ae_fp_neq(buf->ptr.p_double[offs+2],0) ) - { - buf->ptr.p_double[4] = buf->ptr.p_double[4]/buf->ptr.p_double[offs+2]; - } -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 19.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dsnormalize(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - /* Real */ ae_vector* means, - /* Real */ ae_vector* sigmas, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector tmp; - double mean; - double variance; - double skewness; - double kurtosis; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(means); - ae_vector_clear(sigmas); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - - /* - * Test parameters - */ - if( npoints<=0||nvars<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * Standartization - */ - ae_vector_set_length(means, nvars-1+1, _state); - ae_vector_set_length(sigmas, nvars-1+1, _state); - ae_vector_set_length(&tmp, npoints-1+1, _state); - for(j=0; j<=nvars-1; j++) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][j], xy->stride, ae_v_len(0,npoints-1)); - samplemoments(&tmp, npoints, &mean, &variance, &skewness, &kurtosis, _state); - means->ptr.p_double[j] = mean; - sigmas->ptr.p_double[j] = ae_sqrt(variance, _state); - if( ae_fp_eq(sigmas->ptr.p_double[j],0) ) - { - sigmas->ptr.p_double[j] = 1; - } - for(i=0; i<=npoints-1; i++) - { - xy->ptr.pp_double[i][j] = (xy->ptr.pp_double[i][j]-means->ptr.p_double[j])/sigmas->ptr.p_double[j]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 19.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dsnormalizec(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - /* Real */ ae_vector* means, - /* Real */ ae_vector* sigmas, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t j; - ae_vector tmp; - double mean; - double variance; - double skewness; - double kurtosis; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(means); - ae_vector_clear(sigmas); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - - /* - * Test parameters - */ - if( npoints<=0||nvars<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * Standartization - */ - ae_vector_set_length(means, nvars-1+1, _state); - ae_vector_set_length(sigmas, nvars-1+1, _state); - ae_vector_set_length(&tmp, npoints-1+1, _state); - for(j=0; j<=nvars-1; j++) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][j], xy->stride, ae_v_len(0,npoints-1)); - samplemoments(&tmp, npoints, &mean, &variance, &skewness, &kurtosis, _state); - means->ptr.p_double[j] = mean; - sigmas->ptr.p_double[j] = ae_sqrt(variance, _state); - if( ae_fp_eq(sigmas->ptr.p_double[j],0) ) - { - sigmas->ptr.p_double[j] = 1; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 19.05.2008 by Bochkanov Sergey -*************************************************************************/ -double dsgetmeanmindistance(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector tmp; - ae_vector tmp2; - double v; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp2, 0, DT_REAL, _state, ae_true); - - - /* - * Test parameters - */ - if( npoints<=0||nvars<1 ) - { - result = 0; - ae_frame_leave(_state); - return result; - } - - /* - * Process - */ - ae_vector_set_length(&tmp, npoints-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - tmp.ptr.p_double[i] = ae_maxrealnumber; - } - ae_vector_set_length(&tmp2, nvars-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - for(j=i+1; j<=npoints-1; j++) - { - ae_v_move(&tmp2.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&tmp2.ptr.p_double[0], 1, &xy->ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1)); - v = ae_v_dotproduct(&tmp2.ptr.p_double[0], 1, &tmp2.ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - v = ae_sqrt(v, _state); - tmp.ptr.p_double[i] = ae_minreal(tmp.ptr.p_double[i], v, _state); - tmp.ptr.p_double[j] = ae_minreal(tmp.ptr.p_double[j], v, _state); - } - } - result = 0; - for(i=0; i<=npoints-1; i++) - { - result = result+tmp.ptr.p_double[i]/npoints; - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 19.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dstie(/* Real */ ae_vector* a, - ae_int_t n, - /* Integer */ ae_vector* ties, - ae_int_t* tiecount, - /* Integer */ ae_vector* p1, - /* Integer */ ae_vector* p2, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t k; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(ties); - *tiecount = 0; - ae_vector_clear(p1); - ae_vector_clear(p2); - ae_vector_init(&tmp, 0, DT_INT, _state, ae_true); - - - /* - * Special case - */ - if( n<=0 ) - { - *tiecount = 0; - ae_frame_leave(_state); - return; - } - - /* - * Sort A - */ - tagsort(a, n, p1, p2, _state); - - /* - * Process ties - */ - *tiecount = 1; - for(i=1; i<=n-1; i++) - { - if( ae_fp_neq(a->ptr.p_double[i],a->ptr.p_double[i-1]) ) - { - *tiecount = *tiecount+1; - } - } - ae_vector_set_length(ties, *tiecount+1, _state); - ties->ptr.p_int[0] = 0; - k = 1; - for(i=1; i<=n-1; i++) - { - if( ae_fp_neq(a->ptr.p_double[i],a->ptr.p_double[i-1]) ) - { - ties->ptr.p_int[k] = i; - k = k+1; - } - } - ties->ptr.p_int[*tiecount] = n; - ae_frame_leave(_state); -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 11.12.2008 by Bochkanov Sergey -*************************************************************************/ -void dstiefasti(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t n, - /* Integer */ ae_vector* ties, - ae_int_t* tiecount, - /* Real */ ae_vector* bufr, - /* Integer */ ae_vector* bufi, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t k; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - *tiecount = 0; - ae_vector_init(&tmp, 0, DT_INT, _state, ae_true); - - - /* - * Special case - */ - if( n<=0 ) - { - *tiecount = 0; - ae_frame_leave(_state); - return; - } - - /* - * Sort A - */ - tagsortfasti(a, b, bufr, bufi, n, _state); - - /* - * Process ties - */ - ties->ptr.p_int[0] = 0; - k = 1; - for(i=1; i<=n-1; i++) - { - if( ae_fp_neq(a->ptr.p_double[i],a->ptr.p_double[i-1]) ) - { - ties->ptr.p_int[k] = i; - k = k+1; - } - } - ties->ptr.p_int[k] = n; - *tiecount = k; - ae_frame_leave(_state); -} - - -/************************************************************************* -Optimal binary classification - -Algorithms finds optimal (=with minimal cross-entropy) binary partition. -Internal subroutine. - -INPUT PARAMETERS: - A - array[0..N-1], variable - C - array[0..N-1], class numbers (0 or 1). - N - array size - -OUTPUT PARAMETERS: - Info - completetion code: - * -3, all values of A[] are same (partition is impossible) - * -2, one of C[] is incorrect (<0, >1) - * -1, incorrect pararemets were passed (N<=0). - * 1, OK - Threshold- partiton boundary. Left part contains values which are - strictly less than Threshold. Right part contains values - which are greater than or equal to Threshold. - PAL, PBL- probabilities P(0|v=Threshold) and P(1|v>=Threshold) - CVE - cross-validation estimate of cross-entropy - - -- ALGLIB -- - Copyright 22.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplit2(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - ae_int_t n, - ae_int_t* info, - double* threshold, - double* pal, - double* pbl, - double* par, - double* pbr, - double* cve, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _a; - ae_vector _c; - ae_int_t i; - ae_int_t t; - double s; - ae_vector ties; - ae_int_t tiecount; - ae_vector p1; - ae_vector p2; - ae_int_t k; - ae_int_t koptimal; - double pak; - double pbk; - double cvoptimal; - double cv; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init_copy(&_c, c, _state, ae_true); - c = &_c; - *info = 0; - *threshold = 0; - *pal = 0; - *pbl = 0; - *par = 0; - *pbr = 0; - *cve = 0; - ae_vector_init(&ties, 0, DT_INT, _state, ae_true); - ae_vector_init(&p1, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - - /* - * Test for errors in inputs - */ - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - if( c->ptr.p_int[i]!=0&&c->ptr.p_int[i]!=1 ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * Tie - */ - dstie(a, n, &ties, &tiecount, &p1, &p2, _state); - for(i=0; i<=n-1; i++) - { - if( p2.ptr.p_int[i]!=i ) - { - t = c->ptr.p_int[i]; - c->ptr.p_int[i] = c->ptr.p_int[p2.ptr.p_int[i]]; - c->ptr.p_int[p2.ptr.p_int[i]] = t; - } - } - - /* - * Special case: number of ties is 1. - * - * NOTE: we assume that P[i,j] equals to 0 or 1, - * intermediate values are not allowed. - */ - if( tiecount==1 ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * General case, number of ties > 1 - * - * NOTE: we assume that P[i,j] equals to 0 or 1, - * intermediate values are not allowed. - */ - *pal = 0; - *pbl = 0; - *par = 0; - *pbr = 0; - for(i=0; i<=n-1; i++) - { - if( c->ptr.p_int[i]==0 ) - { - *par = *par+1; - } - if( c->ptr.p_int[i]==1 ) - { - *pbr = *pbr+1; - } - } - koptimal = -1; - cvoptimal = ae_maxrealnumber; - for(k=0; k<=tiecount-2; k++) - { - - /* - * first, obtain information about K-th tie which is - * moved from R-part to L-part - */ - pak = 0; - pbk = 0; - for(i=ties.ptr.p_int[k]; i<=ties.ptr.p_int[k+1]-1; i++) - { - if( c->ptr.p_int[i]==0 ) - { - pak = pak+1; - } - if( c->ptr.p_int[i]==1 ) - { - pbk = pbk+1; - } - } - - /* - * Calculate cross-validation CE - */ - cv = 0; - cv = cv-bdss_xlny(*pal+pak, (*pal+pak)/(*pal+pak+(*pbl)+pbk+1), _state); - cv = cv-bdss_xlny(*pbl+pbk, (*pbl+pbk)/(*pal+pak+1+(*pbl)+pbk), _state); - cv = cv-bdss_xlny(*par-pak, (*par-pak)/(*par-pak+(*pbr)-pbk+1), _state); - cv = cv-bdss_xlny(*pbr-pbk, (*pbr-pbk)/(*par-pak+1+(*pbr)-pbk), _state); - - /* - * Compare with best - */ - if( ae_fp_less(cv,cvoptimal) ) - { - cvoptimal = cv; - koptimal = k; - } - - /* - * update - */ - *pal = *pal+pak; - *pbl = *pbl+pbk; - *par = *par-pak; - *pbr = *pbr-pbk; - } - *cve = cvoptimal; - *threshold = 0.5*(a->ptr.p_double[ties.ptr.p_int[koptimal]]+a->ptr.p_double[ties.ptr.p_int[koptimal+1]]); - *pal = 0; - *pbl = 0; - *par = 0; - *pbr = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_less(a->ptr.p_double[i],*threshold) ) - { - if( c->ptr.p_int[i]==0 ) - { - *pal = *pal+1; - } - else - { - *pbl = *pbl+1; - } - } - else - { - if( c->ptr.p_int[i]==0 ) - { - *par = *par+1; - } - else - { - *pbr = *pbr+1; - } - } - } - s = *pal+(*pbl); - *pal = *pal/s; - *pbl = *pbl/s; - s = *par+(*pbr); - *par = *par/s; - *pbr = *pbr/s; - ae_frame_leave(_state); -} - - -/************************************************************************* -Optimal partition, internal subroutine. Fast version. - -Accepts: - A array[0..N-1] array of attributes array[0..N-1] - C array[0..N-1] array of class labels - TiesBuf array[0..N] temporaries (ties) - CntBuf array[0..2*NC-1] temporaries (counts) - Alpha centering factor (0<=alpha<=1, recommended value - 0.05) - BufR array[0..N-1] temporaries - BufI array[0..N-1] temporaries - -Output: - Info error code (">0"=OK, "<0"=bad) - RMS training set RMS error - CVRMS leave-one-out RMS error - -Note: - content of all arrays is changed by subroutine; - it doesn't allocate temporaries. - - -- ALGLIB -- - Copyright 11.12.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplit2fast(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - /* Integer */ ae_vector* tiesbuf, - /* Integer */ ae_vector* cntbuf, - /* Real */ ae_vector* bufr, - /* Integer */ ae_vector* bufi, - ae_int_t n, - ae_int_t nc, - double alpha, - ae_int_t* info, - double* threshold, - double* rms, - double* cvrms, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t cl; - ae_int_t tiecount; - double cbest; - double cc; - ae_int_t koptimal; - ae_int_t sl; - ae_int_t sr; - double v; - double w; - double x; - - *info = 0; - *threshold = 0; - *rms = 0; - *cvrms = 0; - - - /* - * Test for errors in inputs - */ - if( n<=0||nc<2 ) - { - *info = -1; - return; - } - for(i=0; i<=n-1; i++) - { - if( c->ptr.p_int[i]<0||c->ptr.p_int[i]>=nc ) - { - *info = -2; - return; - } - } - *info = 1; - - /* - * Tie - */ - dstiefasti(a, c, n, tiesbuf, &tiecount, bufr, bufi, _state); - - /* - * Special case: number of ties is 1. - */ - if( tiecount==1 ) - { - *info = -3; - return; - } - - /* - * General case, number of ties > 1 - */ - for(i=0; i<=2*nc-1; i++) - { - cntbuf->ptr.p_int[i] = 0; - } - for(i=0; i<=n-1; i++) - { - cntbuf->ptr.p_int[nc+c->ptr.p_int[i]] = cntbuf->ptr.p_int[nc+c->ptr.p_int[i]]+1; - } - koptimal = -1; - *threshold = a->ptr.p_double[n-1]; - cbest = ae_maxrealnumber; - sl = 0; - sr = n; - for(k=0; k<=tiecount-2; k++) - { - - /* - * first, move Kth tie from right to left - */ - for(i=tiesbuf->ptr.p_int[k]; i<=tiesbuf->ptr.p_int[k+1]-1; i++) - { - cl = c->ptr.p_int[i]; - cntbuf->ptr.p_int[cl] = cntbuf->ptr.p_int[cl]+1; - cntbuf->ptr.p_int[nc+cl] = cntbuf->ptr.p_int[nc+cl]-1; - } - sl = sl+(tiesbuf->ptr.p_int[k+1]-tiesbuf->ptr.p_int[k]); - sr = sr-(tiesbuf->ptr.p_int[k+1]-tiesbuf->ptr.p_int[k]); - - /* - * Calculate RMS error - */ - v = 0; - for(i=0; i<=nc-1; i++) - { - w = cntbuf->ptr.p_int[i]; - v = v+w*ae_sqr(w/sl-1, _state); - v = v+(sl-w)*ae_sqr(w/sl, _state); - w = cntbuf->ptr.p_int[nc+i]; - v = v+w*ae_sqr(w/sr-1, _state); - v = v+(sr-w)*ae_sqr(w/sr, _state); - } - v = ae_sqrt(v/(nc*n), _state); - - /* - * Compare with best - */ - x = (double)(2*sl)/(double)(sl+sr)-1; - cc = v*(1-alpha+alpha*ae_sqr(x, _state)); - if( ae_fp_less(cc,cbest) ) - { - - /* - * store split - */ - *rms = v; - koptimal = k; - cbest = cc; - - /* - * calculate CVRMS error - */ - *cvrms = 0; - for(i=0; i<=nc-1; i++) - { - if( sl>1 ) - { - w = cntbuf->ptr.p_int[i]; - *cvrms = *cvrms+w*ae_sqr((w-1)/(sl-1)-1, _state); - *cvrms = *cvrms+(sl-w)*ae_sqr(w/(sl-1), _state); - } - else - { - w = cntbuf->ptr.p_int[i]; - *cvrms = *cvrms+w*ae_sqr((double)1/(double)nc-1, _state); - *cvrms = *cvrms+(sl-w)*ae_sqr((double)1/(double)nc, _state); - } - if( sr>1 ) - { - w = cntbuf->ptr.p_int[nc+i]; - *cvrms = *cvrms+w*ae_sqr((w-1)/(sr-1)-1, _state); - *cvrms = *cvrms+(sr-w)*ae_sqr(w/(sr-1), _state); - } - else - { - w = cntbuf->ptr.p_int[nc+i]; - *cvrms = *cvrms+w*ae_sqr((double)1/(double)nc-1, _state); - *cvrms = *cvrms+(sr-w)*ae_sqr((double)1/(double)nc, _state); - } - } - *cvrms = ae_sqrt(*cvrms/(nc*n), _state); - } - } - - /* - * Calculate threshold. - * Code is a bit complicated because there can be such - * numbers that 0.5(A+B) equals to A or B (if A-B=epsilon) - */ - *threshold = 0.5*(a->ptr.p_double[tiesbuf->ptr.p_int[koptimal]]+a->ptr.p_double[tiesbuf->ptr.p_int[koptimal+1]]); - if( ae_fp_less_eq(*threshold,a->ptr.p_double[tiesbuf->ptr.p_int[koptimal]]) ) - { - *threshold = a->ptr.p_double[tiesbuf->ptr.p_int[koptimal+1]]; - } -} - - -/************************************************************************* -Automatic non-optimal discretization, internal subroutine. - - -- ALGLIB -- - Copyright 22.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dssplitk(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - ae_int_t n, - ae_int_t nc, - ae_int_t kmax, - ae_int_t* info, - /* Real */ ae_vector* thresholds, - ae_int_t* ni, - double* cve, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _a; - ae_vector _c; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t k; - ae_vector ties; - ae_int_t tiecount; - ae_vector p1; - ae_vector p2; - ae_vector cnt; - double v2; - ae_int_t bestk; - double bestcve; - ae_vector bestsizes; - double curcve; - ae_vector cursizes; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init_copy(&_c, c, _state, ae_true); - c = &_c; - *info = 0; - ae_vector_clear(thresholds); - *ni = 0; - *cve = 0; - ae_vector_init(&ties, 0, DT_INT, _state, ae_true); - ae_vector_init(&p1, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - ae_vector_init(&cnt, 0, DT_INT, _state, ae_true); - ae_vector_init(&bestsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(&cursizes, 0, DT_INT, _state, ae_true); - - - /* - * Test for errors in inputs - */ - if( (n<=0||nc<2)||kmax<2 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - if( c->ptr.p_int[i]<0||c->ptr.p_int[i]>=nc ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * Tie - */ - dstie(a, n, &ties, &tiecount, &p1, &p2, _state); - for(i=0; i<=n-1; i++) - { - if( p2.ptr.p_int[i]!=i ) - { - k = c->ptr.p_int[i]; - c->ptr.p_int[i] = c->ptr.p_int[p2.ptr.p_int[i]]; - c->ptr.p_int[p2.ptr.p_int[i]] = k; - } - } - - /* - * Special cases - */ - if( tiecount==1 ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * General case: - * 0. allocate arrays - */ - kmax = ae_minint(kmax, tiecount, _state); - ae_vector_set_length(&bestsizes, kmax-1+1, _state); - ae_vector_set_length(&cursizes, kmax-1+1, _state); - ae_vector_set_length(&cnt, nc-1+1, _state); - - /* - * General case: - * 1. prepare "weak" solution (two subintervals, divided at median) - */ - v2 = ae_maxrealnumber; - j = -1; - for(i=1; i<=tiecount-1; i++) - { - if( ae_fp_less(ae_fabs(ties.ptr.p_int[i]-0.5*(n-1), _state),v2) ) - { - v2 = ae_fabs(ties.ptr.p_int[i]-0.5*n, _state); - j = i; - } - } - ae_assert(j>0, "DSSplitK: internal error #1!", _state); - bestk = 2; - bestsizes.ptr.p_int[0] = ties.ptr.p_int[j]; - bestsizes.ptr.p_int[1] = n-j; - bestcve = 0; - for(i=0; i<=nc-1; i++) - { - cnt.ptr.p_int[i] = 0; - } - for(i=0; i<=j-1; i++) - { - bdss_tieaddc(c, &ties, i, nc, &cnt, _state); - } - bestcve = bestcve+bdss_getcv(&cnt, nc, _state); - for(i=0; i<=nc-1; i++) - { - cnt.ptr.p_int[i] = 0; - } - for(i=j; i<=tiecount-1; i++) - { - bdss_tieaddc(c, &ties, i, nc, &cnt, _state); - } - bestcve = bestcve+bdss_getcv(&cnt, nc, _state); - - /* - * General case: - * 2. Use greedy algorithm to find sub-optimal split in O(KMax*N) time - */ - for(k=2; k<=kmax; k++) - { - - /* - * Prepare greedy K-interval split - */ - for(i=0; i<=k-1; i++) - { - cursizes.ptr.p_int[i] = 0; - } - i = 0; - j = 0; - while(j<=tiecount-1&&i<=k-1) - { - - /* - * Rule: I-th bin is empty, fill it - */ - if( cursizes.ptr.p_int[i]==0 ) - { - cursizes.ptr.p_int[i] = ties.ptr.p_int[j+1]-ties.ptr.p_int[j]; - j = j+1; - continue; - } - - /* - * Rule: (K-1-I) bins left, (K-1-I) ties left (1 tie per bin); next bin - */ - if( tiecount-j==k-1-i ) - { - i = i+1; - continue; - } - - /* - * Rule: last bin, always place in current - */ - if( i==k-1 ) - { - cursizes.ptr.p_int[i] = cursizes.ptr.p_int[i]+ties.ptr.p_int[j+1]-ties.ptr.p_int[j]; - j = j+1; - continue; - } - - /* - * Place J-th tie in I-th bin, or leave for I+1-th bin. - */ - if( ae_fp_less(ae_fabs(cursizes.ptr.p_int[i]+ties.ptr.p_int[j+1]-ties.ptr.p_int[j]-(double)n/(double)k, _state),ae_fabs(cursizes.ptr.p_int[i]-(double)n/(double)k, _state)) ) - { - cursizes.ptr.p_int[i] = cursizes.ptr.p_int[i]+ties.ptr.p_int[j+1]-ties.ptr.p_int[j]; - j = j+1; - } - else - { - i = i+1; - } - } - ae_assert(cursizes.ptr.p_int[k-1]!=0&&j==tiecount, "DSSplitK: internal error #1", _state); - - /* - * Calculate CVE - */ - curcve = 0; - j = 0; - for(i=0; i<=k-1; i++) - { - for(j1=0; j1<=nc-1; j1++) - { - cnt.ptr.p_int[j1] = 0; - } - for(j1=j; j1<=j+cursizes.ptr.p_int[i]-1; j1++) - { - cnt.ptr.p_int[c->ptr.p_int[j1]] = cnt.ptr.p_int[c->ptr.p_int[j1]]+1; - } - curcve = curcve+bdss_getcv(&cnt, nc, _state); - j = j+cursizes.ptr.p_int[i]; - } - - /* - * Choose best variant - */ - if( ae_fp_less(curcve,bestcve) ) - { - for(i=0; i<=k-1; i++) - { - bestsizes.ptr.p_int[i] = cursizes.ptr.p_int[i]; - } - bestcve = curcve; - bestk = k; - } - } - - /* - * Transform from sizes to thresholds - */ - *cve = bestcve; - *ni = bestk; - ae_vector_set_length(thresholds, *ni-2+1, _state); - j = bestsizes.ptr.p_int[0]; - for(i=1; i<=bestk-1; i++) - { - thresholds->ptr.p_double[i-1] = 0.5*(a->ptr.p_double[j-1]+a->ptr.p_double[j]); - j = j+bestsizes.ptr.p_int[i]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Automatic optimal discretization, internal subroutine. - - -- ALGLIB -- - Copyright 22.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplitk(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - ae_int_t n, - ae_int_t nc, - ae_int_t kmax, - ae_int_t* info, - /* Real */ ae_vector* thresholds, - ae_int_t* ni, - double* cve, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _a; - ae_vector _c; - ae_int_t i; - ae_int_t j; - ae_int_t s; - ae_int_t jl; - ae_int_t jr; - double v2; - ae_vector ties; - ae_int_t tiecount; - ae_vector p1; - ae_vector p2; - double cvtemp; - ae_vector cnt; - ae_vector cnt2; - ae_matrix cv; - ae_matrix splits; - ae_int_t k; - ae_int_t koptimal; - double cvoptimal; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init_copy(&_c, c, _state, ae_true); - c = &_c; - *info = 0; - ae_vector_clear(thresholds); - *ni = 0; - *cve = 0; - ae_vector_init(&ties, 0, DT_INT, _state, ae_true); - ae_vector_init(&p1, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - ae_vector_init(&cnt, 0, DT_INT, _state, ae_true); - ae_vector_init(&cnt2, 0, DT_INT, _state, ae_true); - ae_matrix_init(&cv, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&splits, 0, 0, DT_INT, _state, ae_true); - - - /* - * Test for errors in inputs - */ - if( (n<=0||nc<2)||kmax<2 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - if( c->ptr.p_int[i]<0||c->ptr.p_int[i]>=nc ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * Tie - */ - dstie(a, n, &ties, &tiecount, &p1, &p2, _state); - for(i=0; i<=n-1; i++) - { - if( p2.ptr.p_int[i]!=i ) - { - k = c->ptr.p_int[i]; - c->ptr.p_int[i] = c->ptr.p_int[p2.ptr.p_int[i]]; - c->ptr.p_int[p2.ptr.p_int[i]] = k; - } - } - - /* - * Special cases - */ - if( tiecount==1 ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * General case - * Use dynamic programming to find best split in O(KMax*NC*TieCount^2) time - */ - kmax = ae_minint(kmax, tiecount, _state); - ae_matrix_set_length(&cv, kmax-1+1, tiecount-1+1, _state); - ae_matrix_set_length(&splits, kmax-1+1, tiecount-1+1, _state); - ae_vector_set_length(&cnt, nc-1+1, _state); - ae_vector_set_length(&cnt2, nc-1+1, _state); - for(j=0; j<=nc-1; j++) - { - cnt.ptr.p_int[j] = 0; - } - for(j=0; j<=tiecount-1; j++) - { - bdss_tieaddc(c, &ties, j, nc, &cnt, _state); - splits.ptr.pp_int[0][j] = 0; - cv.ptr.pp_double[0][j] = bdss_getcv(&cnt, nc, _state); - } - for(k=1; k<=kmax-1; k++) - { - for(j=0; j<=nc-1; j++) - { - cnt.ptr.p_int[j] = 0; - } - - /* - * Subtask size J in [K..TieCount-1]: - * optimal K-splitting on ties from 0-th to J-th. - */ - for(j=k; j<=tiecount-1; j++) - { - - /* - * Update Cnt - let it contain classes of ties from K-th to J-th - */ - bdss_tieaddc(c, &ties, j, nc, &cnt, _state); - - /* - * Search for optimal split point S in [K..J] - */ - for(i=0; i<=nc-1; i++) - { - cnt2.ptr.p_int[i] = cnt.ptr.p_int[i]; - } - cv.ptr.pp_double[k][j] = cv.ptr.pp_double[k-1][j-1]+bdss_getcv(&cnt2, nc, _state); - splits.ptr.pp_int[k][j] = j; - for(s=k+1; s<=j; s++) - { - - /* - * Update Cnt2 - let it contain classes of ties from S-th to J-th - */ - bdss_tiesubc(c, &ties, s-1, nc, &cnt2, _state); - - /* - * Calculate CVE - */ - cvtemp = cv.ptr.pp_double[k-1][s-1]+bdss_getcv(&cnt2, nc, _state); - if( ae_fp_less(cvtemp,cv.ptr.pp_double[k][j]) ) - { - cv.ptr.pp_double[k][j] = cvtemp; - splits.ptr.pp_int[k][j] = s; - } - } - } - } - - /* - * Choose best partition, output result - */ - koptimal = -1; - cvoptimal = ae_maxrealnumber; - for(k=0; k<=kmax-1; k++) - { - if( ae_fp_less(cv.ptr.pp_double[k][tiecount-1],cvoptimal) ) - { - cvoptimal = cv.ptr.pp_double[k][tiecount-1]; - koptimal = k; - } - } - ae_assert(koptimal>=0, "DSOptimalSplitK: internal error #1!", _state); - if( koptimal==0 ) - { - - /* - * Special case: best partition is one big interval. - * Even 2-partition is not better. - * This is possible when dealing with "weak" predictor variables. - * - * Make binary split as close to the median as possible. - */ - v2 = ae_maxrealnumber; - j = -1; - for(i=1; i<=tiecount-1; i++) - { - if( ae_fp_less(ae_fabs(ties.ptr.p_int[i]-0.5*(n-1), _state),v2) ) - { - v2 = ae_fabs(ties.ptr.p_int[i]-0.5*(n-1), _state); - j = i; - } - } - ae_assert(j>0, "DSOptimalSplitK: internal error #2!", _state); - ae_vector_set_length(thresholds, 0+1, _state); - thresholds->ptr.p_double[0] = 0.5*(a->ptr.p_double[ties.ptr.p_int[j-1]]+a->ptr.p_double[ties.ptr.p_int[j]]); - *ni = 2; - *cve = 0; - for(i=0; i<=nc-1; i++) - { - cnt.ptr.p_int[i] = 0; - } - for(i=0; i<=j-1; i++) - { - bdss_tieaddc(c, &ties, i, nc, &cnt, _state); - } - *cve = *cve+bdss_getcv(&cnt, nc, _state); - for(i=0; i<=nc-1; i++) - { - cnt.ptr.p_int[i] = 0; - } - for(i=j; i<=tiecount-1; i++) - { - bdss_tieaddc(c, &ties, i, nc, &cnt, _state); - } - *cve = *cve+bdss_getcv(&cnt, nc, _state); - } - else - { - - /* - * General case: 2 or more intervals - * - * NOTE: we initialize both JL and JR (left and right bounds), - * altough algorithm needs only JL. - */ - ae_vector_set_length(thresholds, koptimal-1+1, _state); - *ni = koptimal+1; - *cve = cv.ptr.pp_double[koptimal][tiecount-1]; - jl = splits.ptr.pp_int[koptimal][tiecount-1]; - jr = tiecount-1; - for(k=koptimal; k>=1; k--) - { - thresholds->ptr.p_double[k-1] = 0.5*(a->ptr.p_double[ties.ptr.p_int[jl-1]]+a->ptr.p_double[ties.ptr.p_int[jl]]); - jr = jl-1; - jl = splits.ptr.pp_int[k-1][jl-1]; - } - touchint(&jr, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal function -*************************************************************************/ -static double bdss_xlny(double x, double y, ae_state *_state) -{ - double result; - - - if( ae_fp_eq(x,0) ) - { - result = 0; - } - else - { - result = x*ae_log(y, _state); - } - return result; -} - - -/************************************************************************* -Internal function, -returns number of samples of class I in Cnt[I] -*************************************************************************/ -static double bdss_getcv(/* Integer */ ae_vector* cnt, - ae_int_t nc, - ae_state *_state) -{ - ae_int_t i; - double s; - double result; - - - s = 0; - for(i=0; i<=nc-1; i++) - { - s = s+cnt->ptr.p_int[i]; - } - result = 0; - for(i=0; i<=nc-1; i++) - { - result = result-bdss_xlny(cnt->ptr.p_int[i], cnt->ptr.p_int[i]/(s+nc-1), _state); - } - return result; -} - - -/************************************************************************* -Internal function, adds number of samples of class I in tie NTie to Cnt[I] -*************************************************************************/ -static void bdss_tieaddc(/* Integer */ ae_vector* c, - /* Integer */ ae_vector* ties, - ae_int_t ntie, - ae_int_t nc, - /* Integer */ ae_vector* cnt, - ae_state *_state) -{ - ae_int_t i; - - - for(i=ties->ptr.p_int[ntie]; i<=ties->ptr.p_int[ntie+1]-1; i++) - { - cnt->ptr.p_int[c->ptr.p_int[i]] = cnt->ptr.p_int[c->ptr.p_int[i]]+1; - } -} - - -/************************************************************************* -Internal function, subtracts number of samples of class I in tie NTie to Cnt[I] -*************************************************************************/ -static void bdss_tiesubc(/* Integer */ ae_vector* c, - /* Integer */ ae_vector* ties, - ae_int_t ntie, - ae_int_t nc, - /* Integer */ ae_vector* cnt, - ae_state *_state) -{ - ae_int_t i; - - - for(i=ties->ptr.p_int[ntie]; i<=ties->ptr.p_int[ntie+1]-1; i++) - { - cnt->ptr.p_int[c->ptr.p_int[i]] = cnt->ptr.p_int[c->ptr.p_int[i]]-1; - } -} - - -ae_bool _cvreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - cvreport *p = (cvreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _cvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - cvreport *dst = (cvreport*)_dst; - cvreport *src = (cvreport*)_src; - dst->relclserror = src->relclserror; - dst->avgce = src->avgce; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - return ae_true; -} - - -void _cvreport_clear(void* _p) -{ - cvreport *p = (cvreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _cvreport_destroy(void* _p) -{ - cvreport *p = (cvreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -This function initializes clusterizer object. Newly initialized object is -empty, i.e. it does not contain dataset. You should use it as follows: -1. creation -2. dataset is added with ClusterizerSetPoints() -3. additional parameters are set -3. clusterization is performed with one of the clustering functions - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizercreate(clusterizerstate* s, ae_state *_state) -{ - - _clusterizerstate_clear(s); - - s->npoints = 0; - s->nfeatures = 0; - s->disttype = 2; - s->ahcalgo = 0; - s->kmeansrestarts = 1; - s->kmeansmaxits = 0; -} - - -/************************************************************************* -This function adds dataset to the clusterizer structure. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -NOTE 1: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - -NOTE 2: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric - * k-means++ clustering algorithm may be used only with Euclidean - distance function - Thus, list of specific clustering algorithms you may use depends - on distance function you specify when you set your dataset. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetpoints(clusterizerstate* s, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_int_t disttype, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert((((((((disttype==0||disttype==1)||disttype==2)||disttype==10)||disttype==11)||disttype==12)||disttype==13)||disttype==20)||disttype==21, "ClusterizerSetPoints: incorrect DistType", _state); - ae_assert(npoints>=0, "ClusterizerSetPoints: NPoints<0", _state); - ae_assert(nfeatures>=1, "ClusterizerSetPoints: NFeatures<1", _state); - ae_assert(xy->rows>=npoints, "ClusterizerSetPoints: Rows(XY)cols>=nfeatures, "ClusterizerSetPoints: Cols(XY)npoints = npoints; - s->nfeatures = nfeatures; - s->disttype = disttype; - rmatrixsetlengthatleast(&s->xy, npoints, nfeatures, _state); - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&s->xy.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nfeatures-1)); - } -} - - -/************************************************************************* -This function adds dataset given by distance matrix to the clusterizer -structure. It is important that dataset is not given explicitly - only -distance matrix is given. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - D - array[NPoints,NPoints], distance matrix given by its upper - or lower triangle (main diagonal is ignored because its - entries are expected to be zero). - NPoints - number of points - IsUpper - whether upper or lower triangle of D is given. - -NOTE 1: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric, including one which is given by - distance matrix - * k-means++ clustering algorithm may be used only with Euclidean - distance function and explicitly given points - it can not be - used with dataset given by distance matrix - Thus, if you call this function, you will be unable to use k-means - clustering algorithm to process your problem. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetdistances(clusterizerstate* s, - /* Real */ ae_matrix* d, - ae_int_t npoints, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t j0; - ae_int_t j1; - - - ae_assert(npoints>=0, "ClusterizerSetDistances: NPoints<0", _state); - ae_assert(d->rows>=npoints, "ClusterizerSetDistances: Rows(D)cols>=npoints, "ClusterizerSetDistances: Cols(D)npoints = npoints; - s->nfeatures = 0; - s->disttype = -1; - rmatrixsetlengthatleast(&s->d, npoints, npoints, _state); - for(i=0; i<=npoints-1; i++) - { - if( isupper ) - { - j0 = i+1; - j1 = npoints-1; - } - else - { - j0 = 0; - j1 = i-1; - } - for(j=j0; j<=j1; j++) - { - ae_assert(ae_isfinite(d->ptr.pp_double[i][j], _state)&&ae_fp_greater_eq(d->ptr.pp_double[i][j],0), "ClusterizerSetDistances: D contains infinite, NAN or negative elements", _state); - s->d.ptr.pp_double[i][j] = d->ptr.pp_double[i][j]; - s->d.ptr.pp_double[j][i] = d->ptr.pp_double[i][j]; - } - s->d.ptr.pp_double[i][i] = 0; - } -} - - -/************************************************************************* -This function sets agglomerative hierarchical clustering algorithm - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - Algo - algorithm type: - * 0 complete linkage (default algorithm) - * 1 single linkage - * 2 unweighted average linkage - * 3 weighted average linkage - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetahcalgo(clusterizerstate* s, - ae_int_t algo, - ae_state *_state) -{ - - - ae_assert(((algo==0||algo==1)||algo==2)||algo==3, "ClusterizerSetHCAlgo: incorrect algorithm type", _state); - s->ahcalgo = algo; -} - - -/************************************************************************* -This function sets k-means++ properties : number of restarts and maximum -number of iterations per one run. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - Restarts- restarts count, >=1. - k-means++ algorithm performs several restarts and chooses - best set of centers (one with minimum squared distance). - MaxIts - maximum number of k-means iterations performed during one - run. >=0, zero value means that algorithm performs unlimited - number of iterations. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetkmeanslimits(clusterizerstate* s, - ae_int_t restarts, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(restarts>=1, "ClusterizerSetKMeansLimits: Restarts<=0", _state); - ae_assert(maxits>=0, "ClusterizerSetKMeansLimits: MaxIts<0", _state); - s->kmeansrestarts = restarts; - s->kmeansmaxits = maxits; -} - - -/************************************************************************* -This function performs agglomerative hierarchical clustering - -FOR USERS OF SMP EDITION: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Multicore version is pretty efficient on large - ! problems which need more than 1.000.000 operations to be solved, - ! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU - ! cycles), but gives no speed-up for small problems (less than 100.000 - ! operations). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - -OUTPUT PARAMETERS: - Rep - clustering results; see description of AHCReport - structure for more information. - -NOTE 1: hierarchical clustering algorithms require large amounts of memory. - In particular, this implementation needs sizeof(double)*NPoints^2 - bytes, which are used to store distance matrix. In case we work - with user-supplied matrix, this amount is multiplied by 2 (we have - to store original matrix and to work with its copy). - - For example, problem with 10000 points would require 800M of RAM, - even when working in a 1-dimensional space. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizerrunahc(clusterizerstate* s, - ahcreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t npoints; - ae_int_t nfeatures; - ae_matrix d; - - ae_frame_make(_state, &_frame_block); - _ahcreport_clear(rep); - ae_matrix_init(&d, 0, 0, DT_REAL, _state, ae_true); - - npoints = s->npoints; - nfeatures = s->nfeatures; - - /* - * Fill Rep.NPoints, quick exit when NPoints<=1 - */ - rep->npoints = npoints; - if( npoints==0 ) - { - ae_vector_set_length(&rep->p, 0, _state); - ae_matrix_set_length(&rep->z, 0, 0, _state); - ae_matrix_set_length(&rep->pz, 0, 0, _state); - ae_matrix_set_length(&rep->pm, 0, 0, _state); - ae_vector_set_length(&rep->mergedist, 0, _state); - ae_frame_leave(_state); - return; - } - if( npoints==1 ) - { - ae_vector_set_length(&rep->p, 1, _state); - ae_matrix_set_length(&rep->z, 0, 0, _state); - ae_matrix_set_length(&rep->pz, 0, 0, _state); - ae_matrix_set_length(&rep->pm, 0, 0, _state); - ae_vector_set_length(&rep->mergedist, 0, _state); - rep->p.ptr.p_int[0] = 0; - ae_frame_leave(_state); - return; - } - - /* - * More than one point - */ - if( s->disttype==-1 ) - { - - /* - * Run clusterizer with user-supplied distance matrix - */ - clustering_clusterizerrunahcinternal(s, &s->d, rep, _state); - ae_frame_leave(_state); - return; - } - else - { - - /* - * Build distance matrix D. - */ - clusterizergetdistances(&s->xy, npoints, nfeatures, s->disttype, &d, _state); - - /* - * Run clusterizer - */ - clustering_clusterizerrunahcinternal(s, &d, rep, _state); - ae_frame_leave(_state); - return; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_clusterizerrunahc(clusterizerstate* s, - ahcreport* rep, ae_state *_state) -{ - clusterizerrunahc(s,rep, _state); -} - - -/************************************************************************* -This function performs clustering by k-means++ algorithm. - -You may change algorithm properties like number of restarts or iterations -limit by calling ClusterizerSetKMeansLimits() functions. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - K - number of clusters, K>=0. - K can be zero only when algorithm is called for empty - dataset, in this case completion code is set to - success (+1). - If K=0 and dataset size is non-zero, we can not - meaningfully assign points to some center (there are no - centers because K=0) and return -3 as completion code - (failure). - -OUTPUT PARAMETERS: - Rep - clustering results; see description of KMeansReport - structure for more information. - -NOTE 1: k-means clustering can be performed only for datasets with - Euclidean distance function. Algorithm will return negative - completion code in Rep.TerminationType in case dataset was added - to clusterizer with DistType other than Euclidean (or dataset was - specified by distance matrix instead of explicitly given points). - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizerrunkmeans(clusterizerstate* s, - ae_int_t k, - kmeansreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix dummy; - - ae_frame_make(_state, &_frame_block); - _kmeansreport_clear(rep); - ae_matrix_init(&dummy, 0, 0, DT_REAL, _state, ae_true); - - ae_assert(k>=0, "ClusterizerRunKMeans: K<0", _state); - - /* - * Incorrect distance type - */ - if( s->disttype!=2 ) - { - rep->npoints = s->npoints; - rep->terminationtype = -5; - rep->k = k; - ae_frame_leave(_state); - return; - } - - /* - * K>NPoints or (K=0 and NPoints>0) - */ - if( k>s->npoints||(k==0&&s->npoints>0) ) - { - rep->npoints = s->npoints; - rep->terminationtype = -3; - rep->k = k; - ae_frame_leave(_state); - return; - } - - /* - * No points - */ - if( s->npoints==0 ) - { - rep->npoints = 0; - rep->terminationtype = 1; - rep->k = k; - ae_frame_leave(_state); - return; - } - - /* - * Normal case: - * 1<=K<=NPoints, Euclidean distance - */ - rep->npoints = s->npoints; - rep->nfeatures = s->nfeatures; - rep->k = k; - rep->npoints = s->npoints; - rep->nfeatures = s->nfeatures; - kmeansgenerateinternal(&s->xy, s->npoints, s->nfeatures, k, s->kmeansmaxits, s->kmeansrestarts, &rep->terminationtype, &dummy, ae_false, &rep->c, ae_true, &rep->cidx, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function returns distance matrix for dataset - -FOR USERS OF SMP EDITION: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Multicore version is pretty efficient on large - ! problems which need more than 1.000.000 operations to be solved, - ! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU - ! cycles), but gives no speed-up for small problems (less than 100.000 - ! operations). - -INPUT PARAMETERS: - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -OUTPUT PARAMETERS: - D - array[NPoints,NPoints], distance matrix - (full matrix is returned, with lower and upper triangles) - -NOTES: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizergetdistances(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_int_t disttype, - /* Real */ ae_matrix* d, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double vv; - double vr; - ae_matrix tmpxy; - ae_vector tmpx; - ae_vector tmpy; - ae_vector diagbuf; - apbuffers buf; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(d); - ae_matrix_init(&tmpxy, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpy, 0, DT_REAL, _state, ae_true); - ae_vector_init(&diagbuf, 0, DT_REAL, _state, ae_true); - _apbuffers_init(&buf, _state, ae_true); - - ae_assert(nfeatures>=1, "ClusterizerGetDistances: NFeatures<1", _state); - ae_assert(npoints>=0, "ClusterizerGetDistances: NPoints<1", _state); - ae_assert((((((((disttype==0||disttype==1)||disttype==2)||disttype==10)||disttype==11)||disttype==12)||disttype==13)||disttype==20)||disttype==21, "ClusterizerGetDistances: incorrect DistType", _state); - ae_assert(xy->rows>=npoints, "ClusterizerGetDistances: Rows(XY)cols>=nfeatures, "ClusterizerGetDistances: Cols(XY)ptr.pp_double[0][0] = 0; - ae_frame_leave(_state); - return; - } - - /* - * Build distance matrix D. - */ - if( disttype==0||disttype==1 ) - { - - /* - * Chebyshev or city-block distances: - * * recursively calculate upper triangle (with main diagonal) - * * copy it to the bottom part of the matrix - */ - ae_matrix_set_length(d, npoints, npoints, _state); - clustering_evaluatedistancematrixrec(xy, nfeatures, disttype, d, 0, npoints, 0, npoints, _state); - rmatrixenforcesymmetricity(d, npoints, ae_true, _state); - ae_frame_leave(_state); - return; - } - if( disttype==2 ) - { - - /* - * Euclidean distance - * - * NOTE: parallelization is done within RMatrixSYRK - */ - ae_matrix_set_length(d, npoints, npoints, _state); - ae_matrix_set_length(&tmpxy, npoints, nfeatures, _state); - ae_vector_set_length(&tmpx, nfeatures, _state); - ae_vector_set_length(&diagbuf, npoints, _state); - for(j=0; j<=nfeatures-1; j++) - { - tmpx.ptr.p_double[j] = 0.0; - } - v = (double)1/(double)npoints; - for(i=0; i<=npoints-1; i++) - { - ae_v_addd(&tmpx.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nfeatures-1), v); - } - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&tmpxy.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nfeatures-1)); - ae_v_sub(&tmpxy.ptr.pp_double[i][0], 1, &tmpx.ptr.p_double[0], 1, ae_v_len(0,nfeatures-1)); - } - rmatrixsyrk(npoints, nfeatures, 1.0, &tmpxy, 0, 0, 0, 0.0, d, 0, 0, ae_true, _state); - for(i=0; i<=npoints-1; i++) - { - diagbuf.ptr.p_double[i] = d->ptr.pp_double[i][i]; - } - for(i=0; i<=npoints-1; i++) - { - d->ptr.pp_double[i][i] = 0.0; - for(j=i+1; j<=npoints-1; j++) - { - v = ae_sqrt(ae_maxreal(diagbuf.ptr.p_double[i]+diagbuf.ptr.p_double[j]-2*d->ptr.pp_double[i][j], 0.0, _state), _state); - d->ptr.pp_double[i][j] = v; - } - } - rmatrixenforcesymmetricity(d, npoints, ae_true, _state); - ae_frame_leave(_state); - return; - } - if( disttype==10||disttype==11 ) - { - - /* - * Absolute/nonabsolute Pearson correlation distance - * - * NOTE: parallelization is done within PearsonCorrM, which calls RMatrixSYRK internally - */ - ae_matrix_set_length(d, npoints, npoints, _state); - ae_vector_set_length(&diagbuf, npoints, _state); - ae_matrix_set_length(&tmpxy, npoints, nfeatures, _state); - for(i=0; i<=npoints-1; i++) - { - v = 0.0; - for(j=0; j<=nfeatures-1; j++) - { - v = v+xy->ptr.pp_double[i][j]; - } - v = v/nfeatures; - for(j=0; j<=nfeatures-1; j++) - { - tmpxy.ptr.pp_double[i][j] = xy->ptr.pp_double[i][j]-v; - } - } - rmatrixsyrk(npoints, nfeatures, 1.0, &tmpxy, 0, 0, 0, 0.0, d, 0, 0, ae_true, _state); - for(i=0; i<=npoints-1; i++) - { - diagbuf.ptr.p_double[i] = d->ptr.pp_double[i][i]; - } - for(i=0; i<=npoints-1; i++) - { - d->ptr.pp_double[i][i] = 0.0; - for(j=i+1; j<=npoints-1; j++) - { - v = d->ptr.pp_double[i][j]/ae_sqrt(diagbuf.ptr.p_double[i]*diagbuf.ptr.p_double[j], _state); - if( disttype==10 ) - { - v = 1-v; - } - else - { - v = 1-ae_fabs(v, _state); - } - v = ae_maxreal(v, 0.0, _state); - d->ptr.pp_double[i][j] = v; - } - } - rmatrixenforcesymmetricity(d, npoints, ae_true, _state); - ae_frame_leave(_state); - return; - } - if( disttype==12||disttype==13 ) - { - - /* - * Absolute/nonabsolute uncentered Pearson correlation distance - * - * NOTE: parallelization is done within RMatrixSYRK - */ - ae_matrix_set_length(d, npoints, npoints, _state); - ae_vector_set_length(&diagbuf, npoints, _state); - rmatrixsyrk(npoints, nfeatures, 1.0, xy, 0, 0, 0, 0.0, d, 0, 0, ae_true, _state); - for(i=0; i<=npoints-1; i++) - { - diagbuf.ptr.p_double[i] = d->ptr.pp_double[i][i]; - } - for(i=0; i<=npoints-1; i++) - { - d->ptr.pp_double[i][i] = 0.0; - for(j=i+1; j<=npoints-1; j++) - { - v = d->ptr.pp_double[i][j]/ae_sqrt(diagbuf.ptr.p_double[i]*diagbuf.ptr.p_double[j], _state); - if( disttype==13 ) - { - v = ae_fabs(v, _state); - } - v = ae_minreal(v, 1.0, _state); - d->ptr.pp_double[i][j] = 1-v; - } - } - rmatrixenforcesymmetricity(d, npoints, ae_true, _state); - ae_frame_leave(_state); - return; - } - if( disttype==20||disttype==21 ) - { - - /* - * Spearman rank correlation - * - * NOTE: parallelization of correlation matrix is done within - * PearsonCorrM, which calls RMatrixSYRK internally - */ - ae_matrix_set_length(d, npoints, npoints, _state); - ae_vector_set_length(&diagbuf, npoints, _state); - ae_matrix_set_length(&tmpxy, npoints, nfeatures, _state); - rmatrixcopy(npoints, nfeatures, xy, 0, 0, &tmpxy, 0, 0, _state); - rankdatacentered(&tmpxy, npoints, nfeatures, _state); - rmatrixsyrk(npoints, nfeatures, 1.0, &tmpxy, 0, 0, 0, 0.0, d, 0, 0, ae_true, _state); - for(i=0; i<=npoints-1; i++) - { - if( ae_fp_greater(d->ptr.pp_double[i][i],0) ) - { - diagbuf.ptr.p_double[i] = 1/ae_sqrt(d->ptr.pp_double[i][i], _state); - } - else - { - diagbuf.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=npoints-1; i++) - { - v = diagbuf.ptr.p_double[i]; - d->ptr.pp_double[i][i] = 0.0; - for(j=i+1; j<=npoints-1; j++) - { - vv = d->ptr.pp_double[i][j]*v*diagbuf.ptr.p_double[j]; - if( disttype==20 ) - { - vr = 1-vv; - } - else - { - vr = 1-ae_fabs(vv, _state); - } - if( ae_fp_less(vr,0) ) - { - vr = 0.0; - } - d->ptr.pp_double[i][j] = vr; - } - } - rmatrixenforcesymmetricity(d, npoints, ae_true, _state); - ae_frame_leave(_state); - return; - } - ae_assert(ae_false, "Assertion failed", _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_clusterizergetdistances(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_int_t disttype, - /* Real */ ae_matrix* d, ae_state *_state) -{ - clusterizergetdistances(xy,npoints,nfeatures,disttype,d, _state); -} - - -/************************************************************************* -This function takes as input clusterization report Rep, desired clusters -count K, and builds top K clusters from hierarchical clusterization tree. -It returns assignment of points to clusters (array of cluster indexes). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - K - desired number of clusters, 1<=K<=NPoints. - K can be zero only when NPoints=0. - -OUTPUT PARAMETERS: - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I]npoints; - ae_assert(npoints>=0, "ClusterizerGetKClusters: internal error in Rep integrity", _state); - ae_assert(k>=0, "ClusterizerGetKClusters: K<=0", _state); - ae_assert(k<=npoints, "ClusterizerGetKClusters: K>NPoints", _state); - ae_assert(k>0||npoints==0, "ClusterizerGetKClusters: K<=0", _state); - ae_assert(npoints==rep->npoints, "ClusterizerGetKClusters: NPoints<>Rep.NPoints", _state); - - /* - * Quick exit - */ - if( npoints==0 ) - { - ae_frame_leave(_state); - return; - } - if( npoints==1 ) - { - ae_vector_set_length(cz, 1, _state); - ae_vector_set_length(cidx, 1, _state); - cz->ptr.p_int[0] = 0; - cidx->ptr.p_int[0] = 0; - ae_frame_leave(_state); - return; - } - - /* - * Replay merges, from top to bottom, - * keep track of clusters being present at the moment - */ - ae_vector_set_length(&presentclusters, 2*npoints-1, _state); - ae_vector_set_length(&tmpidx, npoints, _state); - for(i=0; i<=2*npoints-3; i++) - { - presentclusters.ptr.p_bool[i] = ae_false; - } - presentclusters.ptr.p_bool[2*npoints-2] = ae_true; - for(i=0; i<=npoints-1; i++) - { - tmpidx.ptr.p_int[i] = 2*npoints-2; - } - for(mergeidx=npoints-2; mergeidx>=npoints-k; mergeidx--) - { - - /* - * Update information about clusters being present at the moment - */ - presentclusters.ptr.p_bool[npoints+mergeidx] = ae_false; - presentclusters.ptr.p_bool[rep->z.ptr.pp_int[mergeidx][0]] = ae_true; - presentclusters.ptr.p_bool[rep->z.ptr.pp_int[mergeidx][1]] = ae_true; - - /* - * Update TmpIdx according to the current state of the dataset - * - * NOTE: TmpIdx contains cluster indexes from [0..2*NPoints-2]; - * we will convert them to [0..K-1] later. - */ - i0 = rep->pm.ptr.pp_int[mergeidx][0]; - i1 = rep->pm.ptr.pp_int[mergeidx][1]; - t = rep->z.ptr.pp_int[mergeidx][0]; - for(i=i0; i<=i1; i++) - { - tmpidx.ptr.p_int[i] = t; - } - i0 = rep->pm.ptr.pp_int[mergeidx][2]; - i1 = rep->pm.ptr.pp_int[mergeidx][3]; - t = rep->z.ptr.pp_int[mergeidx][1]; - for(i=i0; i<=i1; i++) - { - tmpidx.ptr.p_int[i] = t; - } - } - - /* - * Fill CZ - array which allows us to convert cluster indexes - * from one system to another. - */ - ae_vector_set_length(cz, k, _state); - ae_vector_set_length(&clusterindexes, 2*npoints-1, _state); - t = 0; - for(i=0; i<=2*npoints-2; i++) - { - if( presentclusters.ptr.p_bool[i] ) - { - cz->ptr.p_int[t] = i; - clusterindexes.ptr.p_int[i] = t; - t = t+1; - } - } - ae_assert(t==k, "ClusterizerGetKClusters: internal error", _state); - - /* - * Convert indexes stored in CIdx - */ - ae_vector_set_length(cidx, npoints, _state); - for(i=0; i<=npoints-1; i++) - { - cidx->ptr.p_int[i] = clusterindexes.ptr.p_int[tmpidx.ptr.p_int[rep->p.ptr.p_int[i]]]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function accepts AHC report Rep, desired minimum intercluster -distance and returns top clusters from hierarchical clusterization tree -which are separated by distance R or HIGHER. - -It returns assignment of points to clusters (array of cluster indexes). - -There is one more function with similar name - ClusterizerSeparatedByCorr, -which returns clusters with intercluster correlation equal to R or LOWER -(note: higher for distance, lower for correlation). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - R - desired minimum intercluster distance, R>=0 - -OUTPUT PARAMETERS: - K - number of clusters, 1<=K<=NPoints - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I]npoints&&ae_fp_greater_eq(rep->mergedist.ptr.p_double[rep->npoints-1-(*k)],r)) - { - *k = *k+1; - } - clusterizergetkclusters(rep, *k, cidx, cz, _state); -} - - -/************************************************************************* -This function accepts AHC report Rep, desired maximum intercluster -correlation and returns top clusters from hierarchical clusterization tree -which are separated by correlation R or LOWER. - -It returns assignment of points to clusters (array of cluster indexes). - -There is one more function with similar name - ClusterizerSeparatedByDist, -which returns clusters with intercluster distance equal to R or HIGHER -(note: higher for distance, lower for correlation). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - R - desired maximum intercluster correlation, -1<=R<=+1 - -OUTPUT PARAMETERS: - K - number of clusters, 1<=K<=NPoints - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I]npoints&&ae_fp_greater_eq(rep->mergedist.ptr.p_double[rep->npoints-1-(*k)],1-r)) - { - *k = *k+1; - } - clusterizergetkclusters(rep, *k, cidx, cz, _state); -} - - -/************************************************************************* -K-means++ clusterization - -INPUT PARAMETERS: - XY - dataset, array [0..NPoints-1,0..NVars-1]. - NPoints - dataset size, NPoints>=K - NVars - number of variables, NVars>=1 - K - desired number of clusters, K>=1 - Restarts - number of restarts, Restarts>=1 - -OUTPUT PARAMETERS: - Info - return code: - * -3, if task is degenerate (number of distinct points is - less than K) - * -1, if incorrect NPoints/NFeatures/K/Restarts was passed - * 1, if subroutine finished successfully - CCol - array[0..NVars-1,0..K-1].matrix whose columns store - cluster's centers - NeedCCol - True in case caller requires to store result in CCol - CRow - array[0..K-1,0..NVars-1], same as CCol, but centers are - stored in rows - NeedCRow - True in case caller requires to store result in CCol - XYC - array[NPoints], which contains cluster indexes - - -- ALGLIB -- - Copyright 21.03.2009 by Bochkanov Sergey -*************************************************************************/ -void kmeansgenerateinternal(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t k, - ae_int_t maxits, - ae_int_t restarts, - ae_int_t* info, - /* Real */ ae_matrix* ccol, - ae_bool needccol, - /* Real */ ae_matrix* crow, - ae_bool needcrow, - /* Integer */ ae_vector* xyc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_matrix ct; - ae_matrix ctbest; - ae_vector xycbest; - double e; - double eprev; - double ebest; - ae_vector x; - ae_vector tmp; - ae_vector d2; - ae_vector p; - ae_vector csizes; - ae_vector cbusy; - double v; - ae_int_t cclosest; - double dclosest; - ae_vector work; - ae_bool waschanges; - ae_bool zerosizeclusters; - ae_int_t pass; - ae_int_t itcnt; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_matrix_clear(ccol); - ae_matrix_clear(crow); - ae_vector_clear(xyc); - ae_matrix_init(&ct, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ctbest, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xycbest, 0, DT_INT, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_REAL, _state, ae_true); - ae_vector_init(&csizes, 0, DT_INT, _state, ae_true); - ae_vector_init(&cbusy, 0, DT_BOOL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - _hqrndstate_init(&rs, _state, ae_true); - - - /* - * Test parameters - */ - if( ((npointsptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - cbusy.ptr.p_bool[0] = ae_true; - for(i=1; i<=k-1; i++) - { - cbusy.ptr.p_bool[i] = ae_false; - } - if( !clustering_selectcenterpp(xy, npoints, nvars, &ct, &cbusy, k, &d2, &p, &tmp, _state) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Update centers: - * 2. update center positions - */ - for(i=0; i<=npoints-1; i++) - { - xyc->ptr.p_int[i] = -1; - } - eprev = ae_maxrealnumber; - itcnt = 0; - e = 0; - while(maxits==0||itcntptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&tmp.ptr.p_double[0], 1, &ct.ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1)); - v = ae_v_dotproduct(&tmp.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - if( ae_fp_less(v,dclosest) ) - { - cclosest = j; - dclosest = v; - } - } - if( xyc->ptr.p_int[i]!=cclosest ) - { - waschanges = ae_true; - } - xyc->ptr.p_int[i] = cclosest; - } - - /* - * Update centers - */ - for(j=0; j<=k-1; j++) - { - csizes.ptr.p_int[j] = 0; - } - for(i=0; i<=k-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - ct.ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=npoints-1; i++) - { - csizes.ptr.p_int[xyc->ptr.p_int[i]] = csizes.ptr.p_int[xyc->ptr.p_int[i]]+1; - ae_v_add(&ct.ptr.pp_double[xyc->ptr.p_int[i]][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - } - zerosizeclusters = ae_false; - for(j=0; j<=k-1; j++) - { - if( csizes.ptr.p_int[j]!=0 ) - { - v = (double)1/(double)csizes.ptr.p_int[j]; - ae_v_muld(&ct.ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1), v); - } - cbusy.ptr.p_bool[j] = csizes.ptr.p_int[j]!=0; - zerosizeclusters = zerosizeclusters||csizes.ptr.p_int[j]==0; - } - if( zerosizeclusters ) - { - - /* - * Some clusters have zero size - rare, but possible. - * We'll choose new centers for such clusters using k-means++ rule - * and restart algorithm - */ - if( !clustering_selectcenterpp(xy, npoints, nvars, &ct, &cbusy, k, &d2, &p, &tmp, _state) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - continue; - } - - /* - * Stop if one of two conditions is met: - * 1. nothing has changed during iteration - * 2. energy function increased - */ - e = 0; - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&tmp.ptr.p_double[0], 1, &ct.ptr.pp_double[xyc->ptr.p_int[i]][0], 1, ae_v_len(0,nvars-1)); - v = ae_v_dotproduct(&tmp.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - e = e+v; - } - if( !waschanges||ae_fp_greater_eq(e,eprev) ) - { - break; - } - - /* - * Update EPrev - */ - eprev = e; - } - - /* - * 3. Calculate E, compare with best centers found so far - */ - if( ae_fp_less(e,ebest) ) - { - - /* - * store partition. - */ - ebest = e; - copymatrix(&ct, 0, k-1, 0, nvars-1, &ctbest, 0, k-1, 0, nvars-1, _state); - for(i=0; i<=npoints-1; i++) - { - xycbest.ptr.p_int[i] = xyc->ptr.p_int[i]; - } - } - } - - /* - * Copy and transpose - */ - if( needccol ) - { - ae_matrix_set_length(ccol, nvars, k, _state); - copyandtranspose(&ctbest, 0, k-1, 0, nvars-1, ccol, 0, nvars-1, 0, k-1, _state); - } - if( needcrow ) - { - ae_matrix_set_length(crow, k, nvars, _state); - rmatrixcopy(k, nvars, &ctbest, 0, 0, crow, 0, 0, _state); - } - for(i=0; i<=npoints-1; i++) - { - xyc->ptr.p_int[i] = xycbest.ptr.p_int[i]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Select center for a new cluster using k-means++ rule -*************************************************************************/ -static ae_bool clustering_selectcenterpp(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - /* Real */ ae_matrix* centers, - /* Boolean */ ae_vector* busycenters, - ae_int_t ccnt, - /* Real */ ae_vector* d2, - /* Real */ ae_vector* p, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t cc; - double v; - double s; - ae_bool result; - - - result = ae_true; - for(cc=0; cc<=ccnt-1; cc++) - { - if( !busycenters->ptr.p_bool[cc] ) - { - - /* - * fill D2 - */ - for(i=0; i<=npoints-1; i++) - { - d2->ptr.p_double[i] = ae_maxrealnumber; - for(j=0; j<=ccnt-1; j++) - { - if( busycenters->ptr.p_bool[j] ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&tmp->ptr.p_double[0], 1, ¢ers->ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1)); - v = ae_v_dotproduct(&tmp->ptr.p_double[0], 1, &tmp->ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - if( ae_fp_less(v,d2->ptr.p_double[i]) ) - { - d2->ptr.p_double[i] = v; - } - } - } - } - - /* - * calculate P (non-cumulative) - */ - s = 0; - for(i=0; i<=npoints-1; i++) - { - s = s+d2->ptr.p_double[i]; - } - if( ae_fp_eq(s,0) ) - { - result = ae_false; - return result; - } - s = 1/s; - ae_v_moved(&p->ptr.p_double[0], 1, &d2->ptr.p_double[0], 1, ae_v_len(0,npoints-1), s); - - /* - * choose one of points with probability P - * random number within (0,1) is generated and - * inverse empirical CDF is used to randomly choose a point. - */ - s = 0; - v = ae_randomreal(_state); - for(i=0; i<=npoints-1; i++) - { - s = s+p->ptr.p_double[i]; - if( ae_fp_less_eq(v,s)||i==npoints-1 ) - { - ae_v_move(¢ers->ptr.pp_double[cc][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - busycenters->ptr.p_bool[cc] = ae_true; - break; - } - } - } - } - return result; -} - - -/************************************************************************* -This function performs agglomerative hierarchical clustering using -precomputed distance matrix. Internal function, should not be called -directly. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - D - distance matrix, array[S.NFeatures,S.NFeatures] - Contents of the matrix is destroyed during - algorithm operation. - -OUTPUT PARAMETERS: - Rep - clustering results; see description of AHCReport - structure for more information. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -static void clustering_clusterizerrunahcinternal(clusterizerstate* s, - /* Real */ ae_matrix* d, - ahcreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double v; - ae_int_t mergeidx; - ae_int_t c0; - ae_int_t c1; - ae_int_t s0; - ae_int_t s1; - ae_int_t ar; - ae_int_t br; - ae_int_t npoints; - ae_vector cidx; - ae_vector csizes; - ae_vector nnidx; - ae_matrix cinfo; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&cidx, 0, DT_INT, _state, ae_true); - ae_vector_init(&csizes, 0, DT_INT, _state, ae_true); - ae_vector_init(&nnidx, 0, DT_INT, _state, ae_true); - ae_matrix_init(&cinfo, 0, 0, DT_INT, _state, ae_true); - - npoints = s->npoints; - - /* - * Fill Rep.NPoints, quick exit when NPoints<=1 - */ - rep->npoints = npoints; - if( npoints==0 ) - { - ae_vector_set_length(&rep->p, 0, _state); - ae_matrix_set_length(&rep->z, 0, 0, _state); - ae_matrix_set_length(&rep->pz, 0, 0, _state); - ae_matrix_set_length(&rep->pm, 0, 0, _state); - ae_vector_set_length(&rep->mergedist, 0, _state); - ae_frame_leave(_state); - return; - } - if( npoints==1 ) - { - ae_vector_set_length(&rep->p, 1, _state); - ae_matrix_set_length(&rep->z, 0, 0, _state); - ae_matrix_set_length(&rep->pz, 0, 0, _state); - ae_matrix_set_length(&rep->pm, 0, 0, _state); - ae_vector_set_length(&rep->mergedist, 0, _state); - rep->p.ptr.p_int[0] = 0; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&rep->z, npoints-1, 2, _state); - ae_vector_set_length(&rep->mergedist, npoints-1, _state); - - /* - * Build list of nearest neighbors - */ - ae_vector_set_length(&nnidx, npoints, _state); - for(i=0; i<=npoints-1; i++) - { - - /* - * Calculate index of the nearest neighbor - */ - k = -1; - v = ae_maxrealnumber; - for(j=0; j<=npoints-1; j++) - { - if( j!=i&&ae_fp_less(d->ptr.pp_double[i][j],v) ) - { - k = j; - v = d->ptr.pp_double[i][j]; - } - } - ae_assert(ae_fp_less(v,ae_maxrealnumber), "ClusterizerRunAHC: internal error", _state); - nnidx.ptr.p_int[i] = k; - } - - /* - * Distance matrix is built, perform merges. - * - * NOTE 1: CIdx is array[NPoints] which maps rows/columns of the - * distance matrix D to indexes of clusters. Values of CIdx - * from [0,NPoints) denote single-point clusters, and values - * from [NPoints,2*NPoints-1) denote ones obtained by merging - * smaller clusters. Negative calues correspond to absent clusters. - * - * Initially it contains [0...NPoints-1], after each merge - * one element of CIdx (one with index C0) is replaced by - * NPoints+MergeIdx, and another one with index C1 is - * rewritten by -1. - * - * NOTE 2: CSizes is array[NPoints] which stores sizes of clusters. - * - */ - ae_vector_set_length(&cidx, npoints, _state); - ae_vector_set_length(&csizes, npoints, _state); - for(i=0; i<=npoints-1; i++) - { - cidx.ptr.p_int[i] = i; - csizes.ptr.p_int[i] = 1; - } - for(mergeidx=0; mergeidx<=npoints-2; mergeidx++) - { - - /* - * Select pair of clusters (C0,C1) with CIdx[C0]=0 ) - { - if( ae_fp_less(d->ptr.pp_double[i][nnidx.ptr.p_int[i]],v) ) - { - c0 = i; - c1 = nnidx.ptr.p_int[i]; - v = d->ptr.pp_double[i][nnidx.ptr.p_int[i]]; - } - } - } - ae_assert(ae_fp_less(v,ae_maxrealnumber), "ClusterizerRunAHC: internal error", _state); - if( cidx.ptr.p_int[c0]>cidx.ptr.p_int[c1] ) - { - i = c1; - c1 = c0; - c0 = i; - } - - /* - * Fill one row of Rep.Z and one element of Rep.MergeDist - */ - rep->z.ptr.pp_int[mergeidx][0] = cidx.ptr.p_int[c0]; - rep->z.ptr.pp_int[mergeidx][1] = cidx.ptr.p_int[c1]; - rep->mergedist.ptr.p_double[mergeidx] = v; - - /* - * Update distance matrix: - * * row/column C0 are updated by distances to the new cluster - * * row/column C1 are considered empty (we can fill them by zeros, - * but do not want to spend time - we just ignore them) - * - * NOTE: it is important to update distance matrix BEFORE CIdx/CSizes - * are updated. - */ - ae_assert(((s->ahcalgo==0||s->ahcalgo==1)||s->ahcalgo==2)||s->ahcalgo==3, "ClusterizerRunAHC: internal error", _state); - for(i=0; i<=npoints-1; i++) - { - if( i!=c0&&i!=c1 ) - { - if( s->ahcalgo==0 ) - { - d->ptr.pp_double[i][c0] = ae_maxreal(d->ptr.pp_double[i][c0], d->ptr.pp_double[i][c1], _state); - } - if( s->ahcalgo==1 ) - { - d->ptr.pp_double[i][c0] = ae_minreal(d->ptr.pp_double[i][c0], d->ptr.pp_double[i][c1], _state); - } - if( s->ahcalgo==2 ) - { - d->ptr.pp_double[i][c0] = (csizes.ptr.p_int[c0]*d->ptr.pp_double[i][c0]+csizes.ptr.p_int[c1]*d->ptr.pp_double[i][c1])/(csizes.ptr.p_int[c0]+csizes.ptr.p_int[c1]); - } - if( s->ahcalgo==3 ) - { - d->ptr.pp_double[i][c0] = (d->ptr.pp_double[i][c0]+d->ptr.pp_double[i][c1])/2; - } - d->ptr.pp_double[c0][i] = d->ptr.pp_double[i][c0]; - } - } - - /* - * Update CIdx and CSizes - */ - cidx.ptr.p_int[c0] = npoints+mergeidx; - cidx.ptr.p_int[c1] = -1; - csizes.ptr.p_int[c0] = csizes.ptr.p_int[c0]+csizes.ptr.p_int[c1]; - csizes.ptr.p_int[c1] = 0; - - /* - * Update nearest neighbors array: - * * update nearest neighbors of everything except for C0/C1 - * * update neighbors of C0/C1 - */ - for(i=0; i<=npoints-1; i++) - { - if( (cidx.ptr.p_int[i]>=0&&i!=c0)&&(nnidx.ptr.p_int[i]==c0||nnidx.ptr.p_int[i]==c1) ) - { - - /* - * I-th cluster which is distinct from C0/C1 has former C0/C1 cluster as its nearest - * neighbor. We handle this issue depending on specific AHC algorithm being used. - */ - if( s->ahcalgo==1 ) - { - - /* - * Single linkage. Merging of two clusters together - * does NOT change distances between new cluster and - * other clusters. - * - * The only thing we have to do is to update nearest neighbor index - */ - nnidx.ptr.p_int[i] = c0; - } - else - { - - /* - * Something other than single linkage. We have to re-examine - * all the row to find nearest neighbor. - */ - k = -1; - v = ae_maxrealnumber; - for(j=0; j<=npoints-1; j++) - { - if( (cidx.ptr.p_int[j]>=0&&j!=i)&&ae_fp_less(d->ptr.pp_double[i][j],v) ) - { - k = j; - v = d->ptr.pp_double[i][j]; - } - } - ae_assert(ae_fp_less(v,ae_maxrealnumber)||mergeidx==npoints-2, "ClusterizerRunAHC: internal error", _state); - nnidx.ptr.p_int[i] = k; - } - } - } - k = -1; - v = ae_maxrealnumber; - for(j=0; j<=npoints-1; j++) - { - if( (cidx.ptr.p_int[j]>=0&&j!=c0)&&ae_fp_less(d->ptr.pp_double[c0][j],v) ) - { - k = j; - v = d->ptr.pp_double[c0][j]; - } - } - ae_assert(ae_fp_less(v,ae_maxrealnumber)||mergeidx==npoints-2, "ClusterizerRunAHC: internal error", _state); - nnidx.ptr.p_int[c0] = k; - } - - /* - * Calculate Rep.P and Rep.PM. - * - * In order to do that, we fill CInfo matrix - (2*NPoints-1)*3 matrix, - * with I-th row containing: - * * CInfo[I,0] - size of I-th cluster - * * CInfo[I,1] - beginning of I-th cluster - * * CInfo[I,2] - end of I-th cluster - * * CInfo[I,3] - height of I-th cluster - * - * We perform it as follows: - * * first NPoints clusters have unit size (CInfo[I,0]=1) and zero - * height (CInfo[I,3]=0) - * * we replay NPoints-1 merges from first to last and fill sizes of - * corresponding clusters (new size is a sum of sizes of clusters - * being merged) and height (new height is max(heights)+1). - * * now we ready to determine locations of clusters. Last cluster - * spans entire dataset, we know it. We replay merges from last to - * first, during each merge we already know location of the merge - * result, and we can position first cluster to the left part of - * the result, and second cluster to the right part. - */ - ae_vector_set_length(&rep->p, npoints, _state); - ae_matrix_set_length(&rep->pm, npoints-1, 6, _state); - ae_matrix_set_length(&cinfo, 2*npoints-1, 4, _state); - for(i=0; i<=npoints-1; i++) - { - cinfo.ptr.pp_int[i][0] = 1; - cinfo.ptr.pp_int[i][3] = 0; - } - for(i=0; i<=npoints-2; i++) - { - cinfo.ptr.pp_int[npoints+i][0] = cinfo.ptr.pp_int[rep->z.ptr.pp_int[i][0]][0]+cinfo.ptr.pp_int[rep->z.ptr.pp_int[i][1]][0]; - cinfo.ptr.pp_int[npoints+i][3] = ae_maxint(cinfo.ptr.pp_int[rep->z.ptr.pp_int[i][0]][3], cinfo.ptr.pp_int[rep->z.ptr.pp_int[i][1]][3], _state)+1; - } - cinfo.ptr.pp_int[2*npoints-2][1] = 0; - cinfo.ptr.pp_int[2*npoints-2][2] = npoints-1; - for(i=npoints-2; i>=0; i--) - { - - /* - * We merge C0 which spans [A0,B0] and C1 (spans [A1,B1]), - * with unknown A0, B0, A1, B1. However, we know that result - * is CR, which spans [AR,BR] with known AR/BR, and we know - * sizes of C0, C1, CR (denotes as S0, S1, SR). - */ - c0 = rep->z.ptr.pp_int[i][0]; - c1 = rep->z.ptr.pp_int[i][1]; - s0 = cinfo.ptr.pp_int[c0][0]; - s1 = cinfo.ptr.pp_int[c1][0]; - ar = cinfo.ptr.pp_int[npoints+i][1]; - br = cinfo.ptr.pp_int[npoints+i][2]; - cinfo.ptr.pp_int[c0][1] = ar; - cinfo.ptr.pp_int[c0][2] = ar+s0-1; - cinfo.ptr.pp_int[c1][1] = br-(s1-1); - cinfo.ptr.pp_int[c1][2] = br; - rep->pm.ptr.pp_int[i][0] = cinfo.ptr.pp_int[c0][1]; - rep->pm.ptr.pp_int[i][1] = cinfo.ptr.pp_int[c0][2]; - rep->pm.ptr.pp_int[i][2] = cinfo.ptr.pp_int[c1][1]; - rep->pm.ptr.pp_int[i][3] = cinfo.ptr.pp_int[c1][2]; - rep->pm.ptr.pp_int[i][4] = cinfo.ptr.pp_int[c0][3]; - rep->pm.ptr.pp_int[i][5] = cinfo.ptr.pp_int[c1][3]; - } - for(i=0; i<=npoints-1; i++) - { - ae_assert(cinfo.ptr.pp_int[i][1]==cinfo.ptr.pp_int[i][2], "Assertion failed", _state); - rep->p.ptr.p_int[i] = cinfo.ptr.pp_int[i][1]; - } - - /* - * Calculate Rep.PZ - */ - ae_matrix_set_length(&rep->pz, npoints-1, 2, _state); - for(i=0; i<=npoints-2; i++) - { - rep->pz.ptr.pp_int[i][0] = rep->z.ptr.pp_int[i][0]; - rep->pz.ptr.pp_int[i][1] = rep->z.ptr.pp_int[i][1]; - if( rep->pz.ptr.pp_int[i][0]pz.ptr.pp_int[i][0] = rep->p.ptr.p_int[rep->pz.ptr.pp_int[i][0]]; - } - if( rep->pz.ptr.pp_int[i][1]pz.ptr.pp_int[i][1] = rep->p.ptr.p_int[rep->pz.ptr.pp_int[i][1]]; - } - } - ae_frame_leave(_state); -} - - -static void clustering_evaluatedistancematrixrec(/* Real */ ae_matrix* xy, - ae_int_t nfeatures, - ae_int_t disttype, - /* Real */ ae_matrix* d, - ae_int_t i0, - ae_int_t i1, - ae_int_t j0, - ae_int_t j1, - ae_state *_state) -{ - double rcomplexity; - ae_int_t len0; - ae_int_t len1; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double v; - double vv; - - - ae_assert(disttype==0||disttype==1, "EvaluateDistanceMatrixRec: incorrect DistType", _state); - - /* - * Normalize J0/J1: - * * J0:=max(J0,I0) - we ignore lower triangle - * * J1:=max(J1,J0) - normalize J1 - */ - j0 = ae_maxint(j0, i0, _state); - j1 = ae_maxint(j1, j0, _state); - if( j1<=j0||i1<=i0 ) - { - return; - } - - /* - * Try to process in parallel. Two condtions must hold in order to - * activate parallel processing: - * 1. I1-I0>2 or J1-J0>2 - * 2. (I1-I0)*(J1-J0)*NFeatures>=ParallelComplexity - * - * NOTE: all quantities are converted to reals in order to avoid - * integer overflow during multiplication - * - * NOTE: strict inequality in (1) is necessary to reduce task to 2x2 - * basecases. In future versions we will be able to handle such - * basecases more efficiently than 1x1 cases. - */ - rcomplexity = i1-i0; - rcomplexity = rcomplexity*(j1-j0); - rcomplexity = rcomplexity*nfeatures; - if( ae_fp_greater_eq(rcomplexity,clustering_parallelcomplexity)&&(i1-i0>2||j1-j0>2) ) - { - - /* - * Recursive division along largest of dimensions - */ - if( i1-i0>j1-j0 ) - { - splitlengtheven(i1-i0, &len0, &len1, _state); - clustering_evaluatedistancematrixrec(xy, nfeatures, disttype, d, i0, i0+len0, j0, j1, _state); - clustering_evaluatedistancematrixrec(xy, nfeatures, disttype, d, i0+len0, i1, j0, j1, _state); - } - else - { - splitlengtheven(j1-j0, &len0, &len1, _state); - clustering_evaluatedistancematrixrec(xy, nfeatures, disttype, d, i0, i1, j0, j0+len0, _state); - clustering_evaluatedistancematrixrec(xy, nfeatures, disttype, d, i0, i1, j0+len0, j1, _state); - } - return; - } - - /* - * Sequential processing - */ - for(i=i0; i<=i1-1; i++) - { - for(j=j0; j<=j1-1; j++) - { - if( j>=i ) - { - v = 0.0; - if( disttype==0 ) - { - for(k=0; k<=nfeatures-1; k++) - { - vv = xy->ptr.pp_double[i][k]-xy->ptr.pp_double[j][k]; - if( ae_fp_less(vv,0) ) - { - vv = -vv; - } - if( ae_fp_greater(vv,v) ) - { - v = vv; - } - } - } - if( disttype==1 ) - { - for(k=0; k<=nfeatures-1; k++) - { - vv = xy->ptr.pp_double[i][k]-xy->ptr.pp_double[j][k]; - if( ae_fp_less(vv,0) ) - { - vv = -vv; - } - v = v+vv; - } - } - d->ptr.pp_double[i][j] = v; - } - } - } -} - - -ae_bool _clusterizerstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - clusterizerstate *p = (clusterizerstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->xy, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->d, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _clusterizerstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - clusterizerstate *dst = (clusterizerstate*)_dst; - clusterizerstate *src = (clusterizerstate*)_src; - dst->npoints = src->npoints; - dst->nfeatures = src->nfeatures; - dst->disttype = src->disttype; - if( !ae_matrix_init_copy(&dst->xy, &src->xy, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - dst->ahcalgo = src->ahcalgo; - dst->kmeansrestarts = src->kmeansrestarts; - dst->kmeansmaxits = src->kmeansmaxits; - return ae_true; -} - - -void _clusterizerstate_clear(void* _p) -{ - clusterizerstate *p = (clusterizerstate*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->xy); - ae_matrix_clear(&p->d); -} - - -void _clusterizerstate_destroy(void* _p) -{ - clusterizerstate *p = (clusterizerstate*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->xy); - ae_matrix_destroy(&p->d); -} - - -ae_bool _ahcreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - ahcreport *p = (ahcreport*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->p, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->z, 0, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->pz, 0, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->pm, 0, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mergedist, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _ahcreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - ahcreport *dst = (ahcreport*)_dst; - ahcreport *src = (ahcreport*)_src; - dst->npoints = src->npoints; - if( !ae_vector_init_copy(&dst->p, &src->p, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->z, &src->z, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->pz, &src->pz, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->pm, &src->pm, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mergedist, &src->mergedist, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _ahcreport_clear(void* _p) -{ - ahcreport *p = (ahcreport*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->p); - ae_matrix_clear(&p->z); - ae_matrix_clear(&p->pz); - ae_matrix_clear(&p->pm); - ae_vector_clear(&p->mergedist); -} - - -void _ahcreport_destroy(void* _p) -{ - ahcreport *p = (ahcreport*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->p); - ae_matrix_destroy(&p->z); - ae_matrix_destroy(&p->pz); - ae_matrix_destroy(&p->pm); - ae_vector_destroy(&p->mergedist); -} - - -ae_bool _kmeansreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - kmeansreport *p = (kmeansreport*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->c, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cidx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _kmeansreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - kmeansreport *dst = (kmeansreport*)_dst; - kmeansreport *src = (kmeansreport*)_src; - dst->npoints = src->npoints; - dst->nfeatures = src->nfeatures; - dst->terminationtype = src->terminationtype; - dst->k = src->k; - if( !ae_matrix_init_copy(&dst->c, &src->c, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cidx, &src->cidx, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _kmeansreport_clear(void* _p) -{ - kmeansreport *p = (kmeansreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->c); - ae_vector_clear(&p->cidx); -} - - -void _kmeansreport_destroy(void* _p) -{ - kmeansreport *p = (kmeansreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->c); - ae_vector_destroy(&p->cidx); -} - - - - -/************************************************************************* -k-means++ clusterization. -Backward compatibility function, we recommend to use CLUSTERING subpackage -as better replacement. - - -- ALGLIB -- - Copyright 21.03.2009 by Bochkanov Sergey -*************************************************************************/ -void kmeansgenerate(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t k, - ae_int_t restarts, - ae_int_t* info, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* xyc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix dummy; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_matrix_clear(c); - ae_vector_clear(xyc); - ae_matrix_init(&dummy, 0, 0, DT_REAL, _state, ae_true); - - kmeansgenerateinternal(xy, npoints, nvars, k, 0, restarts, info, c, ae_true, &dummy, ae_false, xyc, _state); - ae_frame_leave(_state); -} - - - - -/************************************************************************* -This subroutine builds random decision forest. - -INPUT PARAMETERS: - XY - training set - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - task type: - * NClasses=1 - regression task with one - dependent variable - * NClasses>1 - classification task with - NClasses classes. - NTrees - number of trees in a forest, NTrees>=1. - recommended values: 50-100. - R - percent of a training set used to build - individual trees. 01). - * 1, if task has been solved - DF - model built - Rep - training report, contains error on a training set - and out-of-bag estimates of generalization error. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfbuildrandomdecisionforest(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t ntrees, - double r, - ae_int_t* info, - decisionforest* df, - dfreport* rep, - ae_state *_state) -{ - ae_int_t samplesize; - - *info = 0; - _decisionforest_clear(df); - _dfreport_clear(rep); - - if( ae_fp_less_eq(r,0)||ae_fp_greater(r,1) ) - { - *info = -1; - return; - } - samplesize = ae_maxint(ae_round(r*npoints, _state), 1, _state); - dfbuildinternal(xy, npoints, nvars, nclasses, ntrees, samplesize, ae_maxint(nvars/2, 1, _state), dforest_dfusestrongsplits+dforest_dfuseevs, info, df, rep, _state); -} - - -/************************************************************************* -This subroutine builds random decision forest. -This function gives ability to tune number of variables used when choosing -best split. - -INPUT PARAMETERS: - XY - training set - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - task type: - * NClasses=1 - regression task with one - dependent variable - * NClasses>1 - classification task with - NClasses classes. - NTrees - number of trees in a forest, NTrees>=1. - recommended values: 50-100. - NRndVars - number of variables used when choosing best split - R - percent of a training set used to build - individual trees. 01). - * 1, if task has been solved - DF - model built - Rep - training report, contains error on a training set - and out-of-bag estimates of generalization error. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfbuildrandomdecisionforestx1(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t ntrees, - ae_int_t nrndvars, - double r, - ae_int_t* info, - decisionforest* df, - dfreport* rep, - ae_state *_state) -{ - ae_int_t samplesize; - - *info = 0; - _decisionforest_clear(df); - _dfreport_clear(rep); - - if( ae_fp_less_eq(r,0)||ae_fp_greater(r,1) ) - { - *info = -1; - return; - } - if( nrndvars<=0||nrndvars>nvars ) - { - *info = -1; - return; - } - samplesize = ae_maxint(ae_round(r*npoints, _state), 1, _state); - dfbuildinternal(xy, npoints, nvars, nclasses, ntrees, samplesize, nrndvars, dforest_dfusestrongsplits+dforest_dfuseevs, info, df, rep, _state); -} - - -void dfbuildinternal(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t ntrees, - ae_int_t samplesize, - ae_int_t nfeatures, - ae_int_t flags, - ae_int_t* info, - decisionforest* df, - dfreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t tmpi; - ae_int_t lasttreeoffs; - ae_int_t offs; - ae_int_t ooboffs; - ae_int_t treesize; - ae_int_t nvarsinpool; - ae_bool useevs; - dfinternalbuffers bufs; - ae_vector permbuf; - ae_vector oobbuf; - ae_vector oobcntbuf; - ae_matrix xys; - ae_vector x; - ae_vector y; - ae_int_t oobcnt; - ae_int_t oobrelcnt; - double v; - double vmin; - double vmax; - ae_bool bflag; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _decisionforest_clear(df); - _dfreport_clear(rep); - _dfinternalbuffers_init(&bufs, _state, ae_true); - ae_vector_init(&permbuf, 0, DT_INT, _state, ae_true); - ae_vector_init(&oobbuf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&oobcntbuf, 0, DT_INT, _state, ae_true); - ae_matrix_init(&xys, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - _hqrndstate_init(&rs, _state, ae_true); - - - /* - * Test for inputs - */ - if( (((((npoints<1||samplesize<1)||samplesize>npoints)||nvars<1)||nclasses<1)||ntrees<1)||nfeatures<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( nclasses>1 ) - { - for(i=0; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nvars], _state)<0||ae_round(xy->ptr.pp_double[i][nvars], _state)>=nclasses ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - } - *info = 1; - - /* - * Flags - */ - useevs = flags/dforest_dfuseevs%2!=0; - - /* - * Allocate data, prepare header - */ - treesize = 1+dforest_innernodewidth*(samplesize-1)+dforest_leafnodewidth*samplesize; - ae_vector_set_length(&permbuf, npoints-1+1, _state); - ae_vector_set_length(&bufs.treebuf, treesize-1+1, _state); - ae_vector_set_length(&bufs.idxbuf, npoints-1+1, _state); - ae_vector_set_length(&bufs.tmpbufr, npoints-1+1, _state); - ae_vector_set_length(&bufs.tmpbufr2, npoints-1+1, _state); - ae_vector_set_length(&bufs.tmpbufi, npoints-1+1, _state); - ae_vector_set_length(&bufs.sortrbuf, npoints, _state); - ae_vector_set_length(&bufs.sortrbuf2, npoints, _state); - ae_vector_set_length(&bufs.sortibuf, npoints, _state); - ae_vector_set_length(&bufs.varpool, nvars-1+1, _state); - ae_vector_set_length(&bufs.evsbin, nvars-1+1, _state); - ae_vector_set_length(&bufs.evssplits, nvars-1+1, _state); - ae_vector_set_length(&bufs.classibuf, 2*nclasses-1+1, _state); - ae_vector_set_length(&oobbuf, nclasses*npoints-1+1, _state); - ae_vector_set_length(&oobcntbuf, npoints-1+1, _state); - ae_vector_set_length(&df->trees, ntrees*treesize-1+1, _state); - ae_matrix_set_length(&xys, samplesize-1+1, nvars+1, _state); - ae_vector_set_length(&x, nvars-1+1, _state); - ae_vector_set_length(&y, nclasses-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - permbuf.ptr.p_int[i] = i; - } - for(i=0; i<=npoints*nclasses-1; i++) - { - oobbuf.ptr.p_double[i] = 0; - } - for(i=0; i<=npoints-1; i++) - { - oobcntbuf.ptr.p_int[i] = 0; - } - - /* - * Prepare variable pool and EVS (extended variable selection/splitting) buffers - * (whether EVS is turned on or not): - * 1. detect binary variables and pre-calculate splits for them - * 2. detect variables with non-distinct values and exclude them from pool - */ - for(i=0; i<=nvars-1; i++) - { - bufs.varpool.ptr.p_int[i] = i; - } - nvarsinpool = nvars; - if( useevs ) - { - for(j=0; j<=nvars-1; j++) - { - vmin = xy->ptr.pp_double[0][j]; - vmax = vmin; - for(i=0; i<=npoints-1; i++) - { - v = xy->ptr.pp_double[i][j]; - vmin = ae_minreal(vmin, v, _state); - vmax = ae_maxreal(vmax, v, _state); - } - if( ae_fp_eq(vmin,vmax) ) - { - - /* - * exclude variable from pool - */ - bufs.varpool.ptr.p_int[j] = bufs.varpool.ptr.p_int[nvarsinpool-1]; - bufs.varpool.ptr.p_int[nvarsinpool-1] = -1; - nvarsinpool = nvarsinpool-1; - continue; - } - bflag = ae_false; - for(i=0; i<=npoints-1; i++) - { - v = xy->ptr.pp_double[i][j]; - if( ae_fp_neq(v,vmin)&&ae_fp_neq(v,vmax) ) - { - bflag = ae_true; - break; - } - } - if( bflag ) - { - - /* - * non-binary variable - */ - bufs.evsbin.ptr.p_bool[j] = ae_false; - } - else - { - - /* - * Prepare - */ - bufs.evsbin.ptr.p_bool[j] = ae_true; - bufs.evssplits.ptr.p_double[j] = 0.5*(vmin+vmax); - if( ae_fp_less_eq(bufs.evssplits.ptr.p_double[j],vmin) ) - { - bufs.evssplits.ptr.p_double[j] = vmax; - } - } - } - } - - /* - * RANDOM FOREST FORMAT - * W[0] - size of array - * W[1] - version number - * W[2] - NVars - * W[3] - NClasses (1 for regression) - * W[4] - NTrees - * W[5] - trees offset - * - * - * TREE FORMAT - * W[Offs] - size of sub-array - * node info: - * W[K+0] - variable number (-1 for leaf mode) - * W[K+1] - threshold (class/value for leaf node) - * W[K+2] - ">=" branch index (absent for leaf node) - * - */ - df->nvars = nvars; - df->nclasses = nclasses; - df->ntrees = ntrees; - - /* - * Build forest - */ - hqrndrandomize(&rs, _state); - offs = 0; - for(i=0; i<=ntrees-1; i++) - { - - /* - * Prepare sample - */ - for(k=0; k<=samplesize-1; k++) - { - j = k+hqrnduniformi(&rs, npoints-k, _state); - tmpi = permbuf.ptr.p_int[k]; - permbuf.ptr.p_int[k] = permbuf.ptr.p_int[j]; - permbuf.ptr.p_int[j] = tmpi; - j = permbuf.ptr.p_int[k]; - ae_v_move(&xys.ptr.pp_double[k][0], 1, &xy->ptr.pp_double[j][0], 1, ae_v_len(0,nvars)); - } - - /* - * build tree, copy - */ - dforest_dfbuildtree(&xys, samplesize, nvars, nclasses, nfeatures, nvarsinpool, flags, &bufs, &rs, _state); - j = ae_round(bufs.treebuf.ptr.p_double[0], _state); - ae_v_move(&df->trees.ptr.p_double[offs], 1, &bufs.treebuf.ptr.p_double[0], 1, ae_v_len(offs,offs+j-1)); - lasttreeoffs = offs; - offs = offs+j; - - /* - * OOB estimates - */ - for(k=samplesize; k<=npoints-1; k++) - { - for(j=0; j<=nclasses-1; j++) - { - y.ptr.p_double[j] = 0; - } - j = permbuf.ptr.p_int[k]; - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1)); - dforest_dfprocessinternal(df, lasttreeoffs, &x, &y, _state); - ae_v_add(&oobbuf.ptr.p_double[j*nclasses], 1, &y.ptr.p_double[0], 1, ae_v_len(j*nclasses,(j+1)*nclasses-1)); - oobcntbuf.ptr.p_int[j] = oobcntbuf.ptr.p_int[j]+1; - } - } - df->bufsize = offs; - - /* - * Normalize OOB results - */ - for(i=0; i<=npoints-1; i++) - { - if( oobcntbuf.ptr.p_int[i]!=0 ) - { - v = (double)1/(double)oobcntbuf.ptr.p_int[i]; - ae_v_muld(&oobbuf.ptr.p_double[i*nclasses], 1, ae_v_len(i*nclasses,i*nclasses+nclasses-1), v); - } - } - - /* - * Calculate training set estimates - */ - rep->relclserror = dfrelclserror(df, xy, npoints, _state); - rep->avgce = dfavgce(df, xy, npoints, _state); - rep->rmserror = dfrmserror(df, xy, npoints, _state); - rep->avgerror = dfavgerror(df, xy, npoints, _state); - rep->avgrelerror = dfavgrelerror(df, xy, npoints, _state); - - /* - * Calculate OOB estimates. - */ - rep->oobrelclserror = 0; - rep->oobavgce = 0; - rep->oobrmserror = 0; - rep->oobavgerror = 0; - rep->oobavgrelerror = 0; - oobcnt = 0; - oobrelcnt = 0; - for(i=0; i<=npoints-1; i++) - { - if( oobcntbuf.ptr.p_int[i]!=0 ) - { - ooboffs = i*nclasses; - if( nclasses>1 ) - { - - /* - * classification-specific code - */ - k = ae_round(xy->ptr.pp_double[i][nvars], _state); - tmpi = 0; - for(j=1; j<=nclasses-1; j++) - { - if( ae_fp_greater(oobbuf.ptr.p_double[ooboffs+j],oobbuf.ptr.p_double[ooboffs+tmpi]) ) - { - tmpi = j; - } - } - if( tmpi!=k ) - { - rep->oobrelclserror = rep->oobrelclserror+1; - } - if( ae_fp_neq(oobbuf.ptr.p_double[ooboffs+k],0) ) - { - rep->oobavgce = rep->oobavgce-ae_log(oobbuf.ptr.p_double[ooboffs+k], _state); - } - else - { - rep->oobavgce = rep->oobavgce-ae_log(ae_minrealnumber, _state); - } - for(j=0; j<=nclasses-1; j++) - { - if( j==k ) - { - rep->oobrmserror = rep->oobrmserror+ae_sqr(oobbuf.ptr.p_double[ooboffs+j]-1, _state); - rep->oobavgerror = rep->oobavgerror+ae_fabs(oobbuf.ptr.p_double[ooboffs+j]-1, _state); - rep->oobavgrelerror = rep->oobavgrelerror+ae_fabs(oobbuf.ptr.p_double[ooboffs+j]-1, _state); - oobrelcnt = oobrelcnt+1; - } - else - { - rep->oobrmserror = rep->oobrmserror+ae_sqr(oobbuf.ptr.p_double[ooboffs+j], _state); - rep->oobavgerror = rep->oobavgerror+ae_fabs(oobbuf.ptr.p_double[ooboffs+j], _state); - } - } - } - else - { - - /* - * regression-specific code - */ - rep->oobrmserror = rep->oobrmserror+ae_sqr(oobbuf.ptr.p_double[ooboffs]-xy->ptr.pp_double[i][nvars], _state); - rep->oobavgerror = rep->oobavgerror+ae_fabs(oobbuf.ptr.p_double[ooboffs]-xy->ptr.pp_double[i][nvars], _state); - if( ae_fp_neq(xy->ptr.pp_double[i][nvars],0) ) - { - rep->oobavgrelerror = rep->oobavgrelerror+ae_fabs((oobbuf.ptr.p_double[ooboffs]-xy->ptr.pp_double[i][nvars])/xy->ptr.pp_double[i][nvars], _state); - oobrelcnt = oobrelcnt+1; - } - } - - /* - * update OOB estimates count. - */ - oobcnt = oobcnt+1; - } - } - if( oobcnt>0 ) - { - rep->oobrelclserror = rep->oobrelclserror/oobcnt; - rep->oobavgce = rep->oobavgce/oobcnt; - rep->oobrmserror = ae_sqrt(rep->oobrmserror/(oobcnt*nclasses), _state); - rep->oobavgerror = rep->oobavgerror/(oobcnt*nclasses); - if( oobrelcnt>0 ) - { - rep->oobavgrelerror = rep->oobavgrelerror/oobrelcnt; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - DF - decision forest model - X - input vector, array[0..NVars-1]. - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - -See also DFProcessI. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfprocess(decisionforest* df, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t offs; - ae_int_t i; - double v; - - - - /* - * Proceed - */ - if( y->cntnclasses ) - { - ae_vector_set_length(y, df->nclasses, _state); - } - offs = 0; - for(i=0; i<=df->nclasses-1; i++) - { - y->ptr.p_double[i] = 0; - } - for(i=0; i<=df->ntrees-1; i++) - { - - /* - * Process basic tree - */ - dforest_dfprocessinternal(df, offs, x, y, _state); - - /* - * Next tree - */ - offs = offs+ae_round(df->trees.ptr.p_double[offs], _state); - } - v = (double)1/(double)df->ntrees; - ae_v_muld(&y->ptr.p_double[0], 1, ae_v_len(0,df->nclasses-1), v); -} - - -/************************************************************************* -'interactive' variant of DFProcess for languages like Python which support -constructs like "Y = DFProcessI(DF,X)" and interactive mode of interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void dfprocessi(decisionforest* df, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - - ae_vector_clear(y); - - dfprocess(df, x, y, _state); -} - - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - Zero if model solves regression task. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfrelclserror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - result = (double)dforest_dfclserror(df, xy, npoints, _state)/(double)npoints; - return result; -} - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*LN(2)). - Zero if model solves regression task. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgce(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t tmpi; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&x, df->nvars-1+1, _state); - ae_vector_set_length(&y, df->nclasses-1+1, _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,df->nvars-1)); - dfprocess(df, &x, &y, _state); - if( df->nclasses>1 ) - { - - /* - * classification-specific code - */ - k = ae_round(xy->ptr.pp_double[i][df->nvars], _state); - tmpi = 0; - for(j=1; j<=df->nclasses-1; j++) - { - if( ae_fp_greater(y.ptr.p_double[j],y.ptr.p_double[tmpi]) ) - { - tmpi = j; - } - } - if( ae_fp_neq(y.ptr.p_double[k],0) ) - { - result = result-ae_log(y.ptr.p_double[k], _state); - } - else - { - result = result-ae_log(ae_minrealnumber, _state); - } - } - } - result = result/npoints; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - Its meaning for regression task is obvious. As for - classification task, RMS error means error when estimating posterior - probabilities. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfrmserror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t tmpi; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&x, df->nvars-1+1, _state); - ae_vector_set_length(&y, df->nclasses-1+1, _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,df->nvars-1)); - dfprocess(df, &x, &y, _state); - if( df->nclasses>1 ) - { - - /* - * classification-specific code - */ - k = ae_round(xy->ptr.pp_double[i][df->nvars], _state); - tmpi = 0; - for(j=1; j<=df->nclasses-1; j++) - { - if( ae_fp_greater(y.ptr.p_double[j],y.ptr.p_double[tmpi]) ) - { - tmpi = j; - } - } - for(j=0; j<=df->nclasses-1; j++) - { - if( j==k ) - { - result = result+ae_sqr(y.ptr.p_double[j]-1, _state); - } - else - { - result = result+ae_sqr(y.ptr.p_double[j], _state); - } - } - } - else - { - - /* - * regression-specific code - */ - result = result+ae_sqr(y.ptr.p_double[0]-xy->ptr.pp_double[i][df->nvars], _state); - } - } - result = ae_sqrt(result/(npoints*df->nclasses), _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for - classification task, it means average error when estimating posterior - probabilities. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgerror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&x, df->nvars-1+1, _state); - ae_vector_set_length(&y, df->nclasses-1+1, _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,df->nvars-1)); - dfprocess(df, &x, &y, _state); - if( df->nclasses>1 ) - { - - /* - * classification-specific code - */ - k = ae_round(xy->ptr.pp_double[i][df->nvars], _state); - for(j=0; j<=df->nclasses-1; j++) - { - if( j==k ) - { - result = result+ae_fabs(y.ptr.p_double[j]-1, _state); - } - else - { - result = result+ae_fabs(y.ptr.p_double[j], _state); - } - } - } - else - { - - /* - * regression-specific code - */ - result = result+ae_fabs(y.ptr.p_double[0]-xy->ptr.pp_double[i][df->nvars], _state); - } - } - result = result/(npoints*df->nclasses); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for - classification task, it means average relative error when estimating - posterior probability of belonging to the correct class. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgrelerror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_int_t relcnt; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&x, df->nvars-1+1, _state); - ae_vector_set_length(&y, df->nclasses-1+1, _state); - result = 0; - relcnt = 0; - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,df->nvars-1)); - dfprocess(df, &x, &y, _state); - if( df->nclasses>1 ) - { - - /* - * classification-specific code - */ - k = ae_round(xy->ptr.pp_double[i][df->nvars], _state); - for(j=0; j<=df->nclasses-1; j++) - { - if( j==k ) - { - result = result+ae_fabs(y.ptr.p_double[j]-1, _state); - relcnt = relcnt+1; - } - } - } - else - { - - /* - * regression-specific code - */ - if( ae_fp_neq(xy->ptr.pp_double[i][df->nvars],0) ) - { - result = result+ae_fabs((y.ptr.p_double[0]-xy->ptr.pp_double[i][df->nvars])/xy->ptr.pp_double[i][df->nvars], _state); - relcnt = relcnt+1; - } - } - } - if( relcnt>0 ) - { - result = result/relcnt; - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Copying of DecisionForest strucure - -INPUT PARAMETERS: - DF1 - original - -OUTPUT PARAMETERS: - DF2 - copy - - -- ALGLIB -- - Copyright 13.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfcopy(decisionforest* df1, decisionforest* df2, ae_state *_state) -{ - - _decisionforest_clear(df2); - - df2->nvars = df1->nvars; - df2->nclasses = df1->nclasses; - df2->ntrees = df1->ntrees; - df2->bufsize = df1->bufsize; - ae_vector_set_length(&df2->trees, df1->bufsize-1+1, _state); - ae_v_move(&df2->trees.ptr.p_double[0], 1, &df1->trees.ptr.p_double[0], 1, ae_v_len(0,df1->bufsize-1)); -} - - -/************************************************************************* -Serializer: allocation - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void dfalloc(ae_serializer* s, decisionforest* forest, ae_state *_state) -{ - - - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - allocrealarray(s, &forest->trees, forest->bufsize, _state); -} - - -/************************************************************************* -Serializer: serialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void dfserialize(ae_serializer* s, - decisionforest* forest, - ae_state *_state) -{ - - - ae_serializer_serialize_int(s, getrdfserializationcode(_state), _state); - ae_serializer_serialize_int(s, dforest_dffirstversion, _state); - ae_serializer_serialize_int(s, forest->nvars, _state); - ae_serializer_serialize_int(s, forest->nclasses, _state); - ae_serializer_serialize_int(s, forest->ntrees, _state); - ae_serializer_serialize_int(s, forest->bufsize, _state); - serializerealarray(s, &forest->trees, forest->bufsize, _state); -} - - -/************************************************************************* -Serializer: unserialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void dfunserialize(ae_serializer* s, - decisionforest* forest, - ae_state *_state) -{ - ae_int_t i0; - ae_int_t i1; - - _decisionforest_clear(forest); - - - /* - * check correctness of header - */ - ae_serializer_unserialize_int(s, &i0, _state); - ae_assert(i0==getrdfserializationcode(_state), "DFUnserialize: stream header corrupted", _state); - ae_serializer_unserialize_int(s, &i1, _state); - ae_assert(i1==dforest_dffirstversion, "DFUnserialize: stream header corrupted", _state); - - /* - * Unserialize data - */ - ae_serializer_unserialize_int(s, &forest->nvars, _state); - ae_serializer_unserialize_int(s, &forest->nclasses, _state); - ae_serializer_unserialize_int(s, &forest->ntrees, _state); - ae_serializer_unserialize_int(s, &forest->bufsize, _state); - unserializerealarray(s, &forest->trees, _state); -} - - -/************************************************************************* -Classification error -*************************************************************************/ -static ae_int_t dforest_dfclserror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t tmpi; - ae_int_t result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - if( df->nclasses<=1 ) - { - result = 0; - ae_frame_leave(_state); - return result; - } - ae_vector_set_length(&x, df->nvars-1+1, _state); - ae_vector_set_length(&y, df->nclasses-1+1, _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,df->nvars-1)); - dfprocess(df, &x, &y, _state); - k = ae_round(xy->ptr.pp_double[i][df->nvars], _state); - tmpi = 0; - for(j=1; j<=df->nclasses-1; j++) - { - if( ae_fp_greater(y.ptr.p_double[j],y.ptr.p_double[tmpi]) ) - { - tmpi = j; - } - } - if( tmpi!=k ) - { - result = result+1; - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Internal subroutine for processing one decision tree starting at Offs -*************************************************************************/ -static void dforest_dfprocessinternal(decisionforest* df, - ae_int_t offs, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t k; - ae_int_t idx; - - - - /* - * Set pointer to the root - */ - k = offs+1; - - /* - * Navigate through the tree - */ - for(;;) - { - if( ae_fp_eq(df->trees.ptr.p_double[k],-1) ) - { - if( df->nclasses==1 ) - { - y->ptr.p_double[0] = y->ptr.p_double[0]+df->trees.ptr.p_double[k+1]; - } - else - { - idx = ae_round(df->trees.ptr.p_double[k+1], _state); - y->ptr.p_double[idx] = y->ptr.p_double[idx]+1; - } - break; - } - if( ae_fp_less(x->ptr.p_double[ae_round(df->trees.ptr.p_double[k], _state)],df->trees.ptr.p_double[k+1]) ) - { - k = k+dforest_innernodewidth; - } - else - { - k = offs+ae_round(df->trees.ptr.p_double[k+2], _state); - } - } -} - - -/************************************************************************* -Builds one decision tree. Just a wrapper for the DFBuildTreeRec. -*************************************************************************/ -static void dforest_dfbuildtree(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t nfeatures, - ae_int_t nvarsinpool, - ae_int_t flags, - dfinternalbuffers* bufs, - hqrndstate* rs, - ae_state *_state) -{ - ae_int_t numprocessed; - ae_int_t i; - - - ae_assert(npoints>0, "Assertion failed", _state); - - /* - * Prepare IdxBuf. It stores indices of the training set elements. - * When training set is being split, contents of IdxBuf is - * correspondingly reordered so we can know which elements belong - * to which branch of decision tree. - */ - for(i=0; i<=npoints-1; i++) - { - bufs->idxbuf.ptr.p_int[i] = i; - } - - /* - * Recursive procedure - */ - numprocessed = 1; - dforest_dfbuildtreerec(xy, npoints, nvars, nclasses, nfeatures, nvarsinpool, flags, &numprocessed, 0, npoints-1, bufs, rs, _state); - bufs->treebuf.ptr.p_double[0] = numprocessed; -} - - -/************************************************************************* -Builds one decision tree (internal recursive subroutine) - -Parameters: - TreeBuf - large enough array, at least TreeSize - IdxBuf - at least NPoints elements - TmpBufR - at least NPoints - TmpBufR2 - at least NPoints - TmpBufI - at least NPoints - TmpBufI2 - at least NPoints+1 -*************************************************************************/ -static void dforest_dfbuildtreerec(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t nfeatures, - ae_int_t nvarsinpool, - ae_int_t flags, - ae_int_t* numprocessed, - ae_int_t idx1, - ae_int_t idx2, - dfinternalbuffers* bufs, - hqrndstate* rs, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_bool bflag; - ae_int_t i1; - ae_int_t i2; - ae_int_t info; - double sl; - double sr; - double w; - ae_int_t idxbest; - double ebest; - double tbest; - ae_int_t varcur; - double s; - double v; - double v1; - double v2; - double threshold; - ae_int_t oldnp; - double currms; - ae_bool useevs; - - - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - tbest = 0; - - /* - * Prepare - */ - ae_assert(npoints>0, "Assertion failed", _state); - ae_assert(idx2>=idx1, "Assertion failed", _state); - useevs = flags/dforest_dfuseevs%2!=0; - - /* - * Leaf node - */ - if( idx2==idx1 ) - { - bufs->treebuf.ptr.p_double[*numprocessed] = -1; - bufs->treebuf.ptr.p_double[*numprocessed+1] = xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[idx1]][nvars]; - *numprocessed = *numprocessed+dforest_leafnodewidth; - return; - } - - /* - * Non-leaf node. - * Select random variable, prepare split: - * 1. prepare default solution - no splitting, class at random - * 2. investigate possible splits, compare with default/best - */ - idxbest = -1; - if( nclasses>1 ) - { - - /* - * default solution for classification - */ - for(i=0; i<=nclasses-1; i++) - { - bufs->classibuf.ptr.p_int[i] = 0; - } - s = idx2-idx1+1; - for(i=idx1; i<=idx2; i++) - { - j = ae_round(xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[i]][nvars], _state); - bufs->classibuf.ptr.p_int[j] = bufs->classibuf.ptr.p_int[j]+1; - } - ebest = 0; - for(i=0; i<=nclasses-1; i++) - { - ebest = ebest+bufs->classibuf.ptr.p_int[i]*ae_sqr(1-bufs->classibuf.ptr.p_int[i]/s, _state)+(s-bufs->classibuf.ptr.p_int[i])*ae_sqr(bufs->classibuf.ptr.p_int[i]/s, _state); - } - ebest = ae_sqrt(ebest/(nclasses*(idx2-idx1+1)), _state); - } - else - { - - /* - * default solution for regression - */ - v = 0; - for(i=idx1; i<=idx2; i++) - { - v = v+xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[i]][nvars]; - } - v = v/(idx2-idx1+1); - ebest = 0; - for(i=idx1; i<=idx2; i++) - { - ebest = ebest+ae_sqr(xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[i]][nvars]-v, _state); - } - ebest = ae_sqrt(ebest/(idx2-idx1+1), _state); - } - i = 0; - while(i<=ae_minint(nfeatures, nvarsinpool, _state)-1) - { - - /* - * select variables from pool - */ - j = i+hqrnduniformi(rs, nvarsinpool-i, _state); - k = bufs->varpool.ptr.p_int[i]; - bufs->varpool.ptr.p_int[i] = bufs->varpool.ptr.p_int[j]; - bufs->varpool.ptr.p_int[j] = k; - varcur = bufs->varpool.ptr.p_int[i]; - - /* - * load variable values to working array - * - * apply EVS preprocessing: if all variable values are same, - * variable is excluded from pool. - * - * This is necessary for binary pre-splits (see later) to work. - */ - for(j=idx1; j<=idx2; j++) - { - bufs->tmpbufr.ptr.p_double[j-idx1] = xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[j]][varcur]; - } - if( useevs ) - { - bflag = ae_false; - v = bufs->tmpbufr.ptr.p_double[0]; - for(j=0; j<=idx2-idx1; j++) - { - if( ae_fp_neq(bufs->tmpbufr.ptr.p_double[j],v) ) - { - bflag = ae_true; - break; - } - } - if( !bflag ) - { - - /* - * exclude variable from pool, - * go to the next iteration. - * I is not increased. - */ - k = bufs->varpool.ptr.p_int[i]; - bufs->varpool.ptr.p_int[i] = bufs->varpool.ptr.p_int[nvarsinpool-1]; - bufs->varpool.ptr.p_int[nvarsinpool-1] = k; - nvarsinpool = nvarsinpool-1; - continue; - } - } - - /* - * load labels to working array - */ - if( nclasses>1 ) - { - for(j=idx1; j<=idx2; j++) - { - bufs->tmpbufi.ptr.p_int[j-idx1] = ae_round(xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[j]][nvars], _state); - } - } - else - { - for(j=idx1; j<=idx2; j++) - { - bufs->tmpbufr2.ptr.p_double[j-idx1] = xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[j]][nvars]; - } - } - - /* - * calculate split - */ - if( useevs&&bufs->evsbin.ptr.p_bool[varcur] ) - { - - /* - * Pre-calculated splits for binary variables. - * Threshold is already known, just calculate RMS error - */ - threshold = bufs->evssplits.ptr.p_double[varcur]; - if( nclasses>1 ) - { - - /* - * classification-specific code - */ - for(j=0; j<=2*nclasses-1; j++) - { - bufs->classibuf.ptr.p_int[j] = 0; - } - sl = 0; - sr = 0; - for(j=0; j<=idx2-idx1; j++) - { - k = bufs->tmpbufi.ptr.p_int[j]; - if( ae_fp_less(bufs->tmpbufr.ptr.p_double[j],threshold) ) - { - bufs->classibuf.ptr.p_int[k] = bufs->classibuf.ptr.p_int[k]+1; - sl = sl+1; - } - else - { - bufs->classibuf.ptr.p_int[k+nclasses] = bufs->classibuf.ptr.p_int[k+nclasses]+1; - sr = sr+1; - } - } - ae_assert(ae_fp_neq(sl,0)&&ae_fp_neq(sr,0), "DFBuildTreeRec: something strange!", _state); - currms = 0; - for(j=0; j<=nclasses-1; j++) - { - w = bufs->classibuf.ptr.p_int[j]; - currms = currms+w*ae_sqr(w/sl-1, _state); - currms = currms+(sl-w)*ae_sqr(w/sl, _state); - w = bufs->classibuf.ptr.p_int[nclasses+j]; - currms = currms+w*ae_sqr(w/sr-1, _state); - currms = currms+(sr-w)*ae_sqr(w/sr, _state); - } - currms = ae_sqrt(currms/(nclasses*(idx2-idx1+1)), _state); - } - else - { - - /* - * regression-specific code - */ - sl = 0; - sr = 0; - v1 = 0; - v2 = 0; - for(j=0; j<=idx2-idx1; j++) - { - if( ae_fp_less(bufs->tmpbufr.ptr.p_double[j],threshold) ) - { - v1 = v1+bufs->tmpbufr2.ptr.p_double[j]; - sl = sl+1; - } - else - { - v2 = v2+bufs->tmpbufr2.ptr.p_double[j]; - sr = sr+1; - } - } - ae_assert(ae_fp_neq(sl,0)&&ae_fp_neq(sr,0), "DFBuildTreeRec: something strange!", _state); - v1 = v1/sl; - v2 = v2/sr; - currms = 0; - for(j=0; j<=idx2-idx1; j++) - { - if( ae_fp_less(bufs->tmpbufr.ptr.p_double[j],threshold) ) - { - currms = currms+ae_sqr(v1-bufs->tmpbufr2.ptr.p_double[j], _state); - } - else - { - currms = currms+ae_sqr(v2-bufs->tmpbufr2.ptr.p_double[j], _state); - } - } - currms = ae_sqrt(currms/(idx2-idx1+1), _state); - } - info = 1; - } - else - { - - /* - * Generic splits - */ - if( nclasses>1 ) - { - dforest_dfsplitc(&bufs->tmpbufr, &bufs->tmpbufi, &bufs->classibuf, idx2-idx1+1, nclasses, dforest_dfusestrongsplits, &info, &threshold, &currms, &bufs->sortrbuf, &bufs->sortibuf, _state); - } - else - { - dforest_dfsplitr(&bufs->tmpbufr, &bufs->tmpbufr2, idx2-idx1+1, dforest_dfusestrongsplits, &info, &threshold, &currms, &bufs->sortrbuf, &bufs->sortrbuf2, _state); - } - } - if( info>0 ) - { - if( ae_fp_less_eq(currms,ebest) ) - { - ebest = currms; - idxbest = varcur; - tbest = threshold; - } - } - - /* - * Next iteration - */ - i = i+1; - } - - /* - * to split or not to split - */ - if( idxbest<0 ) - { - - /* - * All values are same, cannot split. - */ - bufs->treebuf.ptr.p_double[*numprocessed] = -1; - if( nclasses>1 ) - { - - /* - * Select random class label (randomness allows us to - * approximate distribution of the classes) - */ - bufs->treebuf.ptr.p_double[*numprocessed+1] = ae_round(xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[idx1+hqrnduniformi(rs, idx2-idx1+1, _state)]][nvars], _state); - } - else - { - - /* - * Select average (for regression task). - */ - v = 0; - for(i=idx1; i<=idx2; i++) - { - v = v+xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[i]][nvars]/(idx2-idx1+1); - } - bufs->treebuf.ptr.p_double[*numprocessed+1] = v; - } - *numprocessed = *numprocessed+dforest_leafnodewidth; - } - else - { - - /* - * we can split - */ - bufs->treebuf.ptr.p_double[*numprocessed] = idxbest; - bufs->treebuf.ptr.p_double[*numprocessed+1] = tbest; - i1 = idx1; - i2 = idx2; - while(i1<=i2) - { - - /* - * Reorder indices so that left partition is in [Idx1..I1-1], - * and right partition is in [I2+1..Idx2] - */ - if( ae_fp_less(xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[i1]][idxbest],tbest) ) - { - i1 = i1+1; - continue; - } - if( ae_fp_greater_eq(xy->ptr.pp_double[bufs->idxbuf.ptr.p_int[i2]][idxbest],tbest) ) - { - i2 = i2-1; - continue; - } - j = bufs->idxbuf.ptr.p_int[i1]; - bufs->idxbuf.ptr.p_int[i1] = bufs->idxbuf.ptr.p_int[i2]; - bufs->idxbuf.ptr.p_int[i2] = j; - i1 = i1+1; - i2 = i2-1; - } - oldnp = *numprocessed; - *numprocessed = *numprocessed+dforest_innernodewidth; - dforest_dfbuildtreerec(xy, npoints, nvars, nclasses, nfeatures, nvarsinpool, flags, numprocessed, idx1, i1-1, bufs, rs, _state); - bufs->treebuf.ptr.p_double[oldnp+2] = *numprocessed; - dforest_dfbuildtreerec(xy, npoints, nvars, nclasses, nfeatures, nvarsinpool, flags, numprocessed, i2+1, idx2, bufs, rs, _state); - } -} - - -/************************************************************************* -Makes split on attribute -*************************************************************************/ -static void dforest_dfsplitc(/* Real */ ae_vector* x, - /* Integer */ ae_vector* c, - /* Integer */ ae_vector* cntbuf, - ae_int_t n, - ae_int_t nc, - ae_int_t flags, - ae_int_t* info, - double* threshold, - double* e, - /* Real */ ae_vector* sortrbuf, - /* Integer */ ae_vector* sortibuf, - ae_state *_state) -{ - ae_int_t i; - ae_int_t neq; - ae_int_t nless; - ae_int_t ngreater; - ae_int_t q; - ae_int_t qmin; - ae_int_t qmax; - ae_int_t qcnt; - double cursplit; - ae_int_t nleft; - double v; - double cure; - double w; - double sl; - double sr; - - *info = 0; - *threshold = 0; - *e = 0; - - tagsortfasti(x, c, sortrbuf, sortibuf, n, _state); - *e = ae_maxrealnumber; - *threshold = 0.5*(x->ptr.p_double[0]+x->ptr.p_double[n-1]); - *info = -3; - if( flags/dforest_dfusestrongsplits%2==0 ) - { - - /* - * weak splits, split at half - */ - qcnt = 2; - qmin = 1; - qmax = 1; - } - else - { - - /* - * strong splits: choose best quartile - */ - qcnt = 4; - qmin = 1; - qmax = 3; - } - for(q=qmin; q<=qmax; q++) - { - cursplit = x->ptr.p_double[n*q/qcnt]; - neq = 0; - nless = 0; - ngreater = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_less(x->ptr.p_double[i],cursplit) ) - { - nless = nless+1; - } - if( ae_fp_eq(x->ptr.p_double[i],cursplit) ) - { - neq = neq+1; - } - if( ae_fp_greater(x->ptr.p_double[i],cursplit) ) - { - ngreater = ngreater+1; - } - } - ae_assert(neq!=0, "DFSplitR: NEq=0, something strange!!!", _state); - if( nless!=0||ngreater!=0 ) - { - - /* - * set threshold between two partitions, with - * some tweaking to avoid problems with floating point - * arithmetics. - * - * The problem is that when you calculates C = 0.5*(A+B) there - * can be no C which lies strictly between A and B (for example, - * there is no floating point number which is - * greater than 1 and less than 1+eps). In such situations - * we choose right side as theshold (remember that - * points which lie on threshold falls to the right side). - */ - if( nlessptr.p_double[nless+neq-1]+x->ptr.p_double[nless+neq]); - nleft = nless+neq; - if( ae_fp_less_eq(cursplit,x->ptr.p_double[nless+neq-1]) ) - { - cursplit = x->ptr.p_double[nless+neq]; - } - } - else - { - cursplit = 0.5*(x->ptr.p_double[nless-1]+x->ptr.p_double[nless]); - nleft = nless; - if( ae_fp_less_eq(cursplit,x->ptr.p_double[nless-1]) ) - { - cursplit = x->ptr.p_double[nless]; - } - } - *info = 1; - cure = 0; - for(i=0; i<=2*nc-1; i++) - { - cntbuf->ptr.p_int[i] = 0; - } - for(i=0; i<=nleft-1; i++) - { - cntbuf->ptr.p_int[c->ptr.p_int[i]] = cntbuf->ptr.p_int[c->ptr.p_int[i]]+1; - } - for(i=nleft; i<=n-1; i++) - { - cntbuf->ptr.p_int[nc+c->ptr.p_int[i]] = cntbuf->ptr.p_int[nc+c->ptr.p_int[i]]+1; - } - sl = nleft; - sr = n-nleft; - v = 0; - for(i=0; i<=nc-1; i++) - { - w = cntbuf->ptr.p_int[i]; - v = v+w*ae_sqr(w/sl-1, _state); - v = v+(sl-w)*ae_sqr(w/sl, _state); - w = cntbuf->ptr.p_int[nc+i]; - v = v+w*ae_sqr(w/sr-1, _state); - v = v+(sr-w)*ae_sqr(w/sr, _state); - } - cure = ae_sqrt(v/(nc*n), _state); - if( ae_fp_less(cure,*e) ) - { - *threshold = cursplit; - *e = cure; - } - } - } -} - - -/************************************************************************* -Makes split on attribute -*************************************************************************/ -static void dforest_dfsplitr(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t flags, - ae_int_t* info, - double* threshold, - double* e, - /* Real */ ae_vector* sortrbuf, - /* Real */ ae_vector* sortrbuf2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t neq; - ae_int_t nless; - ae_int_t ngreater; - ae_int_t q; - ae_int_t qmin; - ae_int_t qmax; - ae_int_t qcnt; - double cursplit; - ae_int_t nleft; - double v; - double cure; - - *info = 0; - *threshold = 0; - *e = 0; - - tagsortfastr(x, y, sortrbuf, sortrbuf2, n, _state); - *e = ae_maxrealnumber; - *threshold = 0.5*(x->ptr.p_double[0]+x->ptr.p_double[n-1]); - *info = -3; - if( flags/dforest_dfusestrongsplits%2==0 ) - { - - /* - * weak splits, split at half - */ - qcnt = 2; - qmin = 1; - qmax = 1; - } - else - { - - /* - * strong splits: choose best quartile - */ - qcnt = 4; - qmin = 1; - qmax = 3; - } - for(q=qmin; q<=qmax; q++) - { - cursplit = x->ptr.p_double[n*q/qcnt]; - neq = 0; - nless = 0; - ngreater = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_less(x->ptr.p_double[i],cursplit) ) - { - nless = nless+1; - } - if( ae_fp_eq(x->ptr.p_double[i],cursplit) ) - { - neq = neq+1; - } - if( ae_fp_greater(x->ptr.p_double[i],cursplit) ) - { - ngreater = ngreater+1; - } - } - ae_assert(neq!=0, "DFSplitR: NEq=0, something strange!!!", _state); - if( nless!=0||ngreater!=0 ) - { - - /* - * set threshold between two partitions, with - * some tweaking to avoid problems with floating point - * arithmetics. - * - * The problem is that when you calculates C = 0.5*(A+B) there - * can be no C which lies strictly between A and B (for example, - * there is no floating point number which is - * greater than 1 and less than 1+eps). In such situations - * we choose right side as theshold (remember that - * points which lie on threshold falls to the right side). - */ - if( nlessptr.p_double[nless+neq-1]+x->ptr.p_double[nless+neq]); - nleft = nless+neq; - if( ae_fp_less_eq(cursplit,x->ptr.p_double[nless+neq-1]) ) - { - cursplit = x->ptr.p_double[nless+neq]; - } - } - else - { - cursplit = 0.5*(x->ptr.p_double[nless-1]+x->ptr.p_double[nless]); - nleft = nless; - if( ae_fp_less_eq(cursplit,x->ptr.p_double[nless-1]) ) - { - cursplit = x->ptr.p_double[nless]; - } - } - *info = 1; - cure = 0; - v = 0; - for(i=0; i<=nleft-1; i++) - { - v = v+y->ptr.p_double[i]; - } - v = v/nleft; - for(i=0; i<=nleft-1; i++) - { - cure = cure+ae_sqr(y->ptr.p_double[i]-v, _state); - } - v = 0; - for(i=nleft; i<=n-1; i++) - { - v = v+y->ptr.p_double[i]; - } - v = v/(n-nleft); - for(i=nleft; i<=n-1; i++) - { - cure = cure+ae_sqr(y->ptr.p_double[i]-v, _state); - } - cure = ae_sqrt(cure/n, _state); - if( ae_fp_less(cure,*e) ) - { - *threshold = cursplit; - *e = cure; - } - } - } -} - - -ae_bool _decisionforest_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - decisionforest *p = (decisionforest*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->trees, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _decisionforest_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - decisionforest *dst = (decisionforest*)_dst; - decisionforest *src = (decisionforest*)_src; - dst->nvars = src->nvars; - dst->nclasses = src->nclasses; - dst->ntrees = src->ntrees; - dst->bufsize = src->bufsize; - if( !ae_vector_init_copy(&dst->trees, &src->trees, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _decisionforest_clear(void* _p) -{ - decisionforest *p = (decisionforest*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->trees); -} - - -void _decisionforest_destroy(void* _p) -{ - decisionforest *p = (decisionforest*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->trees); -} - - -ae_bool _dfreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - dfreport *p = (dfreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _dfreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - dfreport *dst = (dfreport*)_dst; - dfreport *src = (dfreport*)_src; - dst->relclserror = src->relclserror; - dst->avgce = src->avgce; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->oobrelclserror = src->oobrelclserror; - dst->oobavgce = src->oobavgce; - dst->oobrmserror = src->oobrmserror; - dst->oobavgerror = src->oobavgerror; - dst->oobavgrelerror = src->oobavgrelerror; - return ae_true; -} - - -void _dfreport_clear(void* _p) -{ - dfreport *p = (dfreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _dfreport_destroy(void* _p) -{ - dfreport *p = (dfreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _dfinternalbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - dfinternalbuffers *p = (dfinternalbuffers*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->treebuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->idxbuf, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpbufr, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpbufr2, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpbufi, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->classibuf, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->sortrbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->sortrbuf2, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->sortibuf, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->varpool, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->evsbin, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->evssplits, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _dfinternalbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - dfinternalbuffers *dst = (dfinternalbuffers*)_dst; - dfinternalbuffers *src = (dfinternalbuffers*)_src; - if( !ae_vector_init_copy(&dst->treebuf, &src->treebuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->idxbuf, &src->idxbuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpbufr, &src->tmpbufr, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpbufr2, &src->tmpbufr2, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpbufi, &src->tmpbufi, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->classibuf, &src->classibuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->sortrbuf, &src->sortrbuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->sortrbuf2, &src->sortrbuf2, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->sortibuf, &src->sortibuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->varpool, &src->varpool, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->evsbin, &src->evsbin, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->evssplits, &src->evssplits, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _dfinternalbuffers_clear(void* _p) -{ - dfinternalbuffers *p = (dfinternalbuffers*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->treebuf); - ae_vector_clear(&p->idxbuf); - ae_vector_clear(&p->tmpbufr); - ae_vector_clear(&p->tmpbufr2); - ae_vector_clear(&p->tmpbufi); - ae_vector_clear(&p->classibuf); - ae_vector_clear(&p->sortrbuf); - ae_vector_clear(&p->sortrbuf2); - ae_vector_clear(&p->sortibuf); - ae_vector_clear(&p->varpool); - ae_vector_clear(&p->evsbin); - ae_vector_clear(&p->evssplits); -} - - -void _dfinternalbuffers_destroy(void* _p) -{ - dfinternalbuffers *p = (dfinternalbuffers*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->treebuf); - ae_vector_destroy(&p->idxbuf); - ae_vector_destroy(&p->tmpbufr); - ae_vector_destroy(&p->tmpbufr2); - ae_vector_destroy(&p->tmpbufi); - ae_vector_destroy(&p->classibuf); - ae_vector_destroy(&p->sortrbuf); - ae_vector_destroy(&p->sortrbuf2); - ae_vector_destroy(&p->sortibuf); - ae_vector_destroy(&p->varpool); - ae_vector_destroy(&p->evsbin); - ae_vector_destroy(&p->evssplits); -} - - - - -/************************************************************************* -Linear regression - -Subroutine builds model: - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] + A(N) - -and model found in ALGLIB format, covariation matrix, training set errors -(rms, average, average relative) and leave-one-out cross-validation -estimate of the generalization error. CV estimate calculated using fast -algorithm with O(NPoints*NVars) complexity. - -When covariation matrix is calculated standard deviations of function -values are assumed to be equal to RMS error on the training set. - -INPUT PARAMETERS: - XY - training set, array [0..NPoints-1,0..NVars]: - * NVars columns - independent variables - * last column - dependent variable - NPoints - training set size, NPoints>NVars+1 - NVars - number of independent variables - -OUTPUT PARAMETERS: - Info - return code: - * -255, in case of unknown internal error - * -4, if internal SVD subroutine haven't converged - * -1, if incorrect parameters was passed (NPointsrmserror, _state)*npoints/(npoints-nvars-1); - for(i=0; i<=nvars; i++) - { - ae_v_muld(&ar->c.ptr.pp_double[i][0], 1, ae_v_len(0,nvars), sigma2); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Linear regression - -Variant of LRBuild which uses vector of standatd deviations (errors in -function values). - -INPUT PARAMETERS: - XY - training set, array [0..NPoints-1,0..NVars]: - * NVars columns - independent variables - * last column - dependent variable - S - standard deviations (errors in function values) - array[0..NPoints-1], S[i]>0. - NPoints - training set size, NPoints>NVars+1 - NVars - number of independent variables - -OUTPUT PARAMETERS: - Info - return code: - * -255, in case of unknown internal error - * -4, if internal SVD subroutine haven't converged - * -1, if incorrect parameters was passed (NPointsptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - xyi.ptr.pp_double[i][nvars] = 1; - xyi.ptr.pp_double[i][nvars+1] = xy->ptr.pp_double[i][nvars]; - } - - /* - * Standartization - */ - ae_vector_set_length(&x, npoints-1+1, _state); - ae_vector_set_length(&means, nvars-1+1, _state); - ae_vector_set_length(&sigmas, nvars-1+1, _state); - for(j=0; j<=nvars-1; j++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[0][j], xy->stride, ae_v_len(0,npoints-1)); - samplemoments(&x, npoints, &mean, &variance, &skewness, &kurtosis, _state); - means.ptr.p_double[j] = mean; - sigmas.ptr.p_double[j] = ae_sqrt(variance, _state); - if( ae_fp_eq(sigmas.ptr.p_double[j],0) ) - { - sigmas.ptr.p_double[j] = 1; - } - for(i=0; i<=npoints-1; i++) - { - xyi.ptr.pp_double[i][j] = (xyi.ptr.pp_double[i][j]-means.ptr.p_double[j])/sigmas.ptr.p_double[j]; - } - } - - /* - * Internal processing - */ - linreg_lrinternal(&xyi, s, npoints, nvars+1, info, lm, ar, _state); - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Un-standartization - */ - offs = ae_round(lm->w.ptr.p_double[3], _state); - for(j=0; j<=nvars-1; j++) - { - - /* - * Constant term is updated (and its covariance too, - * since it gets some variance from J-th component) - */ - lm->w.ptr.p_double[offs+nvars] = lm->w.ptr.p_double[offs+nvars]-lm->w.ptr.p_double[offs+j]*means.ptr.p_double[j]/sigmas.ptr.p_double[j]; - v = means.ptr.p_double[j]/sigmas.ptr.p_double[j]; - ae_v_subd(&ar->c.ptr.pp_double[nvars][0], 1, &ar->c.ptr.pp_double[j][0], 1, ae_v_len(0,nvars), v); - ae_v_subd(&ar->c.ptr.pp_double[0][nvars], ar->c.stride, &ar->c.ptr.pp_double[0][j], ar->c.stride, ae_v_len(0,nvars), v); - - /* - * J-th term is updated - */ - lm->w.ptr.p_double[offs+j] = lm->w.ptr.p_double[offs+j]/sigmas.ptr.p_double[j]; - v = 1/sigmas.ptr.p_double[j]; - ae_v_muld(&ar->c.ptr.pp_double[j][0], 1, ae_v_len(0,nvars), v); - ae_v_muld(&ar->c.ptr.pp_double[0][j], ar->c.stride, ae_v_len(0,nvars), v); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Like LRBuildS, but builds model - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] - -i.e. with zero constant term. - - -- ALGLIB -- - Copyright 30.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lrbuildzs(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix xyi; - ae_vector x; - ae_vector c; - ae_int_t i; - ae_int_t j; - double v; - ae_int_t offs; - double mean; - double variance; - double skewness; - double kurtosis; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _linearmodel_clear(lm); - _lrreport_clear(ar); - ae_matrix_init(&xyi, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c, 0, DT_REAL, _state, ae_true); - - - /* - * Test parameters - */ - if( npoints<=nvars+1||nvars<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - - /* - * Copy data, add one more column (constant term) - */ - ae_matrix_set_length(&xyi, npoints-1+1, nvars+1+1, _state); - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&xyi.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - xyi.ptr.pp_double[i][nvars] = 0; - xyi.ptr.pp_double[i][nvars+1] = xy->ptr.pp_double[i][nvars]; - } - - /* - * Standartization: unusual scaling - */ - ae_vector_set_length(&x, npoints-1+1, _state); - ae_vector_set_length(&c, nvars-1+1, _state); - for(j=0; j<=nvars-1; j++) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[0][j], xy->stride, ae_v_len(0,npoints-1)); - samplemoments(&x, npoints, &mean, &variance, &skewness, &kurtosis, _state); - if( ae_fp_greater(ae_fabs(mean, _state),ae_sqrt(variance, _state)) ) - { - - /* - * variation is relatively small, it is better to - * bring mean value to 1 - */ - c.ptr.p_double[j] = mean; - } - else - { - - /* - * variation is large, it is better to bring variance to 1 - */ - if( ae_fp_eq(variance,0) ) - { - variance = 1; - } - c.ptr.p_double[j] = ae_sqrt(variance, _state); - } - for(i=0; i<=npoints-1; i++) - { - xyi.ptr.pp_double[i][j] = xyi.ptr.pp_double[i][j]/c.ptr.p_double[j]; - } - } - - /* - * Internal processing - */ - linreg_lrinternal(&xyi, s, npoints, nvars+1, info, lm, ar, _state); - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Un-standartization - */ - offs = ae_round(lm->w.ptr.p_double[3], _state); - for(j=0; j<=nvars-1; j++) - { - - /* - * J-th term is updated - */ - lm->w.ptr.p_double[offs+j] = lm->w.ptr.p_double[offs+j]/c.ptr.p_double[j]; - v = 1/c.ptr.p_double[j]; - ae_v_muld(&ar->c.ptr.pp_double[j][0], 1, ae_v_len(0,nvars), v); - ae_v_muld(&ar->c.ptr.pp_double[0][j], ar->c.stride, ae_v_len(0,nvars), v); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Like LRBuild but builds model - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] - -i.e. with zero constant term. - - -- ALGLIB -- - Copyright 30.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lrbuildz(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector s; - ae_int_t i; - double sigma2; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _linearmodel_clear(lm); - _lrreport_clear(ar); - ae_vector_init(&s, 0, DT_REAL, _state, ae_true); - - if( npoints<=nvars+1||nvars<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&s, npoints-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - s.ptr.p_double[i] = 1; - } - lrbuildzs(xy, &s, npoints, nvars, info, lm, ar, _state); - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - sigma2 = ae_sqr(ar->rmserror, _state)*npoints/(npoints-nvars-1); - for(i=0; i<=nvars; i++) - { - ae_v_muld(&ar->c.ptr.pp_double[i][0], 1, ae_v_len(0,nvars), sigma2); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacks coefficients of linear model. - -INPUT PARAMETERS: - LM - linear model in ALGLIB format - -OUTPUT PARAMETERS: - V - coefficients, array[0..NVars] - constant term (intercept) is stored in the V[NVars]. - NVars - number of independent variables (one less than number - of coefficients) - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -void lrunpack(linearmodel* lm, - /* Real */ ae_vector* v, - ae_int_t* nvars, - ae_state *_state) -{ - ae_int_t offs; - - ae_vector_clear(v); - *nvars = 0; - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==linreg_lrvnum, "LINREG: Incorrect LINREG version!", _state); - *nvars = ae_round(lm->w.ptr.p_double[2], _state); - offs = ae_round(lm->w.ptr.p_double[3], _state); - ae_vector_set_length(v, *nvars+1, _state); - ae_v_move(&v->ptr.p_double[0], 1, &lm->w.ptr.p_double[offs], 1, ae_v_len(0,*nvars)); -} - - -/************************************************************************* -"Packs" coefficients and creates linear model in ALGLIB format (LRUnpack -reversed). - -INPUT PARAMETERS: - V - coefficients, array[0..NVars] - NVars - number of independent variables - -OUTPUT PAREMETERS: - LM - linear model. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -void lrpack(/* Real */ ae_vector* v, - ae_int_t nvars, - linearmodel* lm, - ae_state *_state) -{ - ae_int_t offs; - - _linearmodel_clear(lm); - - ae_vector_set_length(&lm->w, 4+nvars+1, _state); - offs = 4; - lm->w.ptr.p_double[0] = 4+nvars+1; - lm->w.ptr.p_double[1] = linreg_lrvnum; - lm->w.ptr.p_double[2] = nvars; - lm->w.ptr.p_double[3] = offs; - ae_v_move(&lm->w.ptr.p_double[offs], 1, &v->ptr.p_double[0], 1, ae_v_len(offs,offs+nvars)); -} - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - LM - linear model - X - input vector, array[0..NVars-1]. - -Result: - value of linear model regression estimate - - -- ALGLIB -- - Copyright 03.09.2008 by Bochkanov Sergey -*************************************************************************/ -double lrprocess(linearmodel* lm, - /* Real */ ae_vector* x, - ae_state *_state) -{ - double v; - ae_int_t offs; - ae_int_t nvars; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==linreg_lrvnum, "LINREG: Incorrect LINREG version!", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - offs = ae_round(lm->w.ptr.p_double[3], _state); - v = ae_v_dotproduct(&x->ptr.p_double[0], 1, &lm->w.ptr.p_double[offs], 1, ae_v_len(0,nvars-1)); - result = v+lm->w.ptr.p_double[offs+nvars]; - return result; -} - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - LM - linear model - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double lrrmserror(linearmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_int_t i; - double v; - ae_int_t offs; - ae_int_t nvars; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==linreg_lrvnum, "LINREG: Incorrect LINREG version!", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - offs = ae_round(lm->w.ptr.p_double[3], _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - v = ae_v_dotproduct(&xy->ptr.pp_double[i][0], 1, &lm->w.ptr.p_double[offs], 1, ae_v_len(0,nvars-1)); - v = v+lm->w.ptr.p_double[offs+nvars]; - result = result+ae_sqr(v-xy->ptr.pp_double[i][nvars], _state); - } - result = ae_sqrt(result/npoints, _state); - return result; -} - - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - LM - linear model - XY - test set - NPoints - test set size - -RESULT: - average error. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double lravgerror(linearmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_int_t i; - double v; - ae_int_t offs; - ae_int_t nvars; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==linreg_lrvnum, "LINREG: Incorrect LINREG version!", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - offs = ae_round(lm->w.ptr.p_double[3], _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - v = ae_v_dotproduct(&xy->ptr.pp_double[i][0], 1, &lm->w.ptr.p_double[offs], 1, ae_v_len(0,nvars-1)); - v = v+lm->w.ptr.p_double[offs+nvars]; - result = result+ae_fabs(v-xy->ptr.pp_double[i][nvars], _state); - } - result = result/npoints; - return result; -} - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - LM - linear model - XY - test set - NPoints - test set size - -RESULT: - average relative error. - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double lravgrelerror(linearmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - double v; - ae_int_t offs; - ae_int_t nvars; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==linreg_lrvnum, "LINREG: Incorrect LINREG version!", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - offs = ae_round(lm->w.ptr.p_double[3], _state); - result = 0; - k = 0; - for(i=0; i<=npoints-1; i++) - { - if( ae_fp_neq(xy->ptr.pp_double[i][nvars],0) ) - { - v = ae_v_dotproduct(&xy->ptr.pp_double[i][0], 1, &lm->w.ptr.p_double[offs], 1, ae_v_len(0,nvars-1)); - v = v+lm->w.ptr.p_double[offs+nvars]; - result = result+ae_fabs((v-xy->ptr.pp_double[i][nvars])/xy->ptr.pp_double[i][nvars], _state); - k = k+1; - } - } - if( k!=0 ) - { - result = result/k; - } - return result; -} - - -/************************************************************************* -Copying of LinearModel strucure - -INPUT PARAMETERS: - LM1 - original - -OUTPUT PARAMETERS: - LM2 - copy - - -- ALGLIB -- - Copyright 15.03.2009 by Bochkanov Sergey -*************************************************************************/ -void lrcopy(linearmodel* lm1, linearmodel* lm2, ae_state *_state) -{ - ae_int_t k; - - _linearmodel_clear(lm2); - - k = ae_round(lm1->w.ptr.p_double[0], _state); - ae_vector_set_length(&lm2->w, k-1+1, _state); - ae_v_move(&lm2->w.ptr.p_double[0], 1, &lm1->w.ptr.p_double[0], 1, ae_v_len(0,k-1)); -} - - -void lrlines(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t n, - ae_int_t* info, - double* a, - double* b, - double* vara, - double* varb, - double* covab, - double* corrab, - double* p, - ae_state *_state) -{ - ae_int_t i; - double ss; - double sx; - double sxx; - double sy; - double stt; - double e1; - double e2; - double t; - double chi2; - - *info = 0; - *a = 0; - *b = 0; - *vara = 0; - *varb = 0; - *covab = 0; - *corrab = 0; - *p = 0; - - if( n<2 ) - { - *info = -1; - return; - } - for(i=0; i<=n-1; i++) - { - if( ae_fp_less_eq(s->ptr.p_double[i],0) ) - { - *info = -2; - return; - } - } - *info = 1; - - /* - * Calculate S, SX, SY, SXX - */ - ss = 0; - sx = 0; - sy = 0; - sxx = 0; - for(i=0; i<=n-1; i++) - { - t = ae_sqr(s->ptr.p_double[i], _state); - ss = ss+1/t; - sx = sx+xy->ptr.pp_double[i][0]/t; - sy = sy+xy->ptr.pp_double[i][1]/t; - sxx = sxx+ae_sqr(xy->ptr.pp_double[i][0], _state)/t; - } - - /* - * Test for condition number - */ - t = ae_sqrt(4*ae_sqr(sx, _state)+ae_sqr(ss-sxx, _state), _state); - e1 = 0.5*(ss+sxx+t); - e2 = 0.5*(ss+sxx-t); - if( ae_fp_less_eq(ae_minreal(e1, e2, _state),1000*ae_machineepsilon*ae_maxreal(e1, e2, _state)) ) - { - *info = -3; - return; - } - - /* - * Calculate A, B - */ - *a = 0; - *b = 0; - stt = 0; - for(i=0; i<=n-1; i++) - { - t = (xy->ptr.pp_double[i][0]-sx/ss)/s->ptr.p_double[i]; - *b = *b+t*xy->ptr.pp_double[i][1]/s->ptr.p_double[i]; - stt = stt+ae_sqr(t, _state); - } - *b = *b/stt; - *a = (sy-sx*(*b))/ss; - - /* - * Calculate goodness-of-fit - */ - if( n>2 ) - { - chi2 = 0; - for(i=0; i<=n-1; i++) - { - chi2 = chi2+ae_sqr((xy->ptr.pp_double[i][1]-(*a)-*b*xy->ptr.pp_double[i][0])/s->ptr.p_double[i], _state); - } - *p = incompletegammac((double)(n-2)/(double)2, chi2/2, _state); - } - else - { - *p = 1; - } - - /* - * Calculate other parameters - */ - *vara = (1+ae_sqr(sx, _state)/(ss*stt))/ss; - *varb = 1/stt; - *covab = -sx/(ss*stt); - *corrab = *covab/ae_sqrt(*vara*(*varb), _state); -} - - -void lrline(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t* info, - double* a, - double* b, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector s; - ae_int_t i; - double vara; - double varb; - double covab; - double corrab; - double p; - - ae_frame_make(_state, &_frame_block); - *info = 0; - *a = 0; - *b = 0; - ae_vector_init(&s, 0, DT_REAL, _state, ae_true); - - if( n<2 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&s, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - s.ptr.p_double[i] = 1; - } - lrlines(xy, &s, n, info, a, b, &vara, &varb, &covab, &corrab, &p, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal linear regression subroutine -*************************************************************************/ -static void linreg_lrinternal(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix a; - ae_matrix u; - ae_matrix vt; - ae_matrix vm; - ae_matrix xym; - ae_vector b; - ae_vector sv; - ae_vector t; - ae_vector svi; - ae_vector work; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t ncv; - ae_int_t na; - ae_int_t nacv; - double r; - double p; - double epstol; - lrreport ar2; - ae_int_t offs; - linearmodel tlm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _linearmodel_clear(lm); - _lrreport_clear(ar); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&u, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vt, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xym, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sv, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&svi, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - _lrreport_init(&ar2, _state, ae_true); - _linearmodel_init(&tlm, _state, ae_true); - - epstol = 1000; - - /* - * Check for errors in data - */ - if( npointsptr.p_double[i],0) ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * Create design matrix - */ - ae_matrix_set_length(&a, npoints-1+1, nvars-1+1, _state); - ae_vector_set_length(&b, npoints-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - r = 1/s->ptr.p_double[i]; - ae_v_moved(&a.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1), r); - b.ptr.p_double[i] = xy->ptr.pp_double[i][nvars]/s->ptr.p_double[i]; - } - - /* - * Allocate W: - * W[0] array size - * W[1] version number, 0 - * W[2] NVars (minus 1, to be compatible with external representation) - * W[3] coefficients offset - */ - ae_vector_set_length(&lm->w, 4+nvars-1+1, _state); - offs = 4; - lm->w.ptr.p_double[0] = 4+nvars; - lm->w.ptr.p_double[1] = linreg_lrvnum; - lm->w.ptr.p_double[2] = nvars-1; - lm->w.ptr.p_double[3] = offs; - - /* - * Solve problem using SVD: - * - * 0. check for degeneracy (different types) - * 1. A = U*diag(sv)*V' - * 2. T = b'*U - * 3. w = SUM((T[i]/sv[i])*V[..,i]) - * 4. cov(wi,wj) = SUM(Vji*Vjk/sv[i]^2,K=1..M) - * - * see $15.4 of "Numerical Recipes in C" for more information - */ - ae_vector_set_length(&t, nvars-1+1, _state); - ae_vector_set_length(&svi, nvars-1+1, _state); - ae_matrix_set_length(&ar->c, nvars-1+1, nvars-1+1, _state); - ae_matrix_set_length(&vm, nvars-1+1, nvars-1+1, _state); - if( !rmatrixsvd(&a, npoints, nvars, 1, 1, 2, &sv, &u, &vt, _state) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - if( ae_fp_less_eq(sv.ptr.p_double[0],0) ) - { - - /* - * Degenerate case: zero design matrix. - */ - for(i=offs; i<=offs+nvars-1; i++) - { - lm->w.ptr.p_double[i] = 0; - } - ar->rmserror = lrrmserror(lm, xy, npoints, _state); - ar->avgerror = lravgerror(lm, xy, npoints, _state); - ar->avgrelerror = lravgrelerror(lm, xy, npoints, _state); - ar->cvrmserror = ar->rmserror; - ar->cvavgerror = ar->avgerror; - ar->cvavgrelerror = ar->avgrelerror; - ar->ncvdefects = 0; - ae_vector_set_length(&ar->cvdefects, nvars-1+1, _state); - ae_matrix_set_length(&ar->c, nvars-1+1, nvars-1+1, _state); - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - ar->c.ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - if( ae_fp_less_eq(sv.ptr.p_double[nvars-1],epstol*ae_machineepsilon*sv.ptr.p_double[0]) ) - { - - /* - * Degenerate case, non-zero design matrix. - * - * We can leave it and solve task in SVD least squares fashion. - * Solution and covariance matrix will be obtained correctly, - * but CV error estimates - will not. It is better to reduce - * it to non-degenerate task and to obtain correct CV estimates. - */ - for(k=nvars; k>=1; k--) - { - if( ae_fp_greater(sv.ptr.p_double[k-1],epstol*ae_machineepsilon*sv.ptr.p_double[0]) ) - { - - /* - * Reduce - */ - ae_matrix_set_length(&xym, npoints-1+1, k+1, _state); - for(i=0; i<=npoints-1; i++) - { - for(j=0; j<=k-1; j++) - { - r = ae_v_dotproduct(&xy->ptr.pp_double[i][0], 1, &vt.ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1)); - xym.ptr.pp_double[i][j] = r; - } - xym.ptr.pp_double[i][k] = xy->ptr.pp_double[i][nvars]; - } - - /* - * Solve - */ - linreg_lrinternal(&xym, s, npoints, k, info, &tlm, &ar2, _state); - if( *info!=1 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Convert back to un-reduced format - */ - for(j=0; j<=nvars-1; j++) - { - lm->w.ptr.p_double[offs+j] = 0; - } - for(j=0; j<=k-1; j++) - { - r = tlm.w.ptr.p_double[offs+j]; - ae_v_addd(&lm->w.ptr.p_double[offs], 1, &vt.ptr.pp_double[j][0], 1, ae_v_len(offs,offs+nvars-1), r); - } - ar->rmserror = ar2.rmserror; - ar->avgerror = ar2.avgerror; - ar->avgrelerror = ar2.avgrelerror; - ar->cvrmserror = ar2.cvrmserror; - ar->cvavgerror = ar2.cvavgerror; - ar->cvavgrelerror = ar2.cvavgrelerror; - ar->ncvdefects = ar2.ncvdefects; - ae_vector_set_length(&ar->cvdefects, nvars-1+1, _state); - for(j=0; j<=ar->ncvdefects-1; j++) - { - ar->cvdefects.ptr.p_int[j] = ar2.cvdefects.ptr.p_int[j]; - } - ae_matrix_set_length(&ar->c, nvars-1+1, nvars-1+1, _state); - ae_vector_set_length(&work, nvars+1, _state); - matrixmatrixmultiply(&ar2.c, 0, k-1, 0, k-1, ae_false, &vt, 0, k-1, 0, nvars-1, ae_false, 1.0, &vm, 0, k-1, 0, nvars-1, 0.0, &work, _state); - matrixmatrixmultiply(&vt, 0, k-1, 0, nvars-1, ae_true, &vm, 0, k-1, 0, nvars-1, ae_false, 1.0, &ar->c, 0, nvars-1, 0, nvars-1, 0.0, &work, _state); - ae_frame_leave(_state); - return; - } - } - *info = -255; - ae_frame_leave(_state); - return; - } - for(i=0; i<=nvars-1; i++) - { - if( ae_fp_greater(sv.ptr.p_double[i],epstol*ae_machineepsilon*sv.ptr.p_double[0]) ) - { - svi.ptr.p_double[i] = 1/sv.ptr.p_double[i]; - } - else - { - svi.ptr.p_double[i] = 0; - } - } - for(i=0; i<=nvars-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=npoints-1; i++) - { - r = b.ptr.p_double[i]; - ae_v_addd(&t.ptr.p_double[0], 1, &u.ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1), r); - } - for(i=0; i<=nvars-1; i++) - { - lm->w.ptr.p_double[offs+i] = 0; - } - for(i=0; i<=nvars-1; i++) - { - r = t.ptr.p_double[i]*svi.ptr.p_double[i]; - ae_v_addd(&lm->w.ptr.p_double[offs], 1, &vt.ptr.pp_double[i][0], 1, ae_v_len(offs,offs+nvars-1), r); - } - for(j=0; j<=nvars-1; j++) - { - r = svi.ptr.p_double[j]; - ae_v_moved(&vm.ptr.pp_double[0][j], vm.stride, &vt.ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1), r); - } - for(i=0; i<=nvars-1; i++) - { - for(j=i; j<=nvars-1; j++) - { - r = ae_v_dotproduct(&vm.ptr.pp_double[i][0], 1, &vm.ptr.pp_double[j][0], 1, ae_v_len(0,nvars-1)); - ar->c.ptr.pp_double[i][j] = r; - ar->c.ptr.pp_double[j][i] = r; - } - } - - /* - * Leave-1-out cross-validation error. - * - * NOTATIONS: - * A design matrix - * A*x = b original linear least squares task - * U*S*V' SVD of A - * ai i-th row of the A - * bi i-th element of the b - * xf solution of the original LLS task - * - * Cross-validation error of i-th element from a sample is - * calculated using following formula: - * - * ERRi = ai*xf - (ai*xf-bi*(ui*ui'))/(1-ui*ui') (1) - * - * This formula can be derived from normal equations of the - * original task - * - * (A'*A)x = A'*b (2) - * - * by applying modification (zeroing out i-th row of A) to (2): - * - * (A-ai)'*(A-ai) = (A-ai)'*b - * - * and using Sherman-Morrison formula for updating matrix inverse - * - * NOTE 1: b is not zeroed out since it is much simpler and - * does not influence final result. - * - * NOTE 2: some design matrices A have such ui that 1-ui*ui'=0. - * Formula (1) can't be applied for such cases and they are skipped - * from CV calculation (which distorts resulting CV estimate). - * But from the properties of U we can conclude that there can - * be no more than NVars such vectors. Usually - * NVars << NPoints, so in a normal case it only slightly - * influences result. - */ - ncv = 0; - na = 0; - nacv = 0; - ar->rmserror = 0; - ar->avgerror = 0; - ar->avgrelerror = 0; - ar->cvrmserror = 0; - ar->cvavgerror = 0; - ar->cvavgrelerror = 0; - ar->ncvdefects = 0; - ae_vector_set_length(&ar->cvdefects, nvars-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - - /* - * Error on a training set - */ - r = ae_v_dotproduct(&xy->ptr.pp_double[i][0], 1, &lm->w.ptr.p_double[offs], 1, ae_v_len(0,nvars-1)); - ar->rmserror = ar->rmserror+ae_sqr(r-xy->ptr.pp_double[i][nvars], _state); - ar->avgerror = ar->avgerror+ae_fabs(r-xy->ptr.pp_double[i][nvars], _state); - if( ae_fp_neq(xy->ptr.pp_double[i][nvars],0) ) - { - ar->avgrelerror = ar->avgrelerror+ae_fabs((r-xy->ptr.pp_double[i][nvars])/xy->ptr.pp_double[i][nvars], _state); - na = na+1; - } - - /* - * Error using fast leave-one-out cross-validation - */ - p = ae_v_dotproduct(&u.ptr.pp_double[i][0], 1, &u.ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - if( ae_fp_greater(p,1-epstol*ae_machineepsilon) ) - { - ar->cvdefects.ptr.p_int[ar->ncvdefects] = i; - ar->ncvdefects = ar->ncvdefects+1; - continue; - } - r = s->ptr.p_double[i]*(r/s->ptr.p_double[i]-b.ptr.p_double[i]*p)/(1-p); - ar->cvrmserror = ar->cvrmserror+ae_sqr(r-xy->ptr.pp_double[i][nvars], _state); - ar->cvavgerror = ar->cvavgerror+ae_fabs(r-xy->ptr.pp_double[i][nvars], _state); - if( ae_fp_neq(xy->ptr.pp_double[i][nvars],0) ) - { - ar->cvavgrelerror = ar->cvavgrelerror+ae_fabs((r-xy->ptr.pp_double[i][nvars])/xy->ptr.pp_double[i][nvars], _state); - nacv = nacv+1; - } - ncv = ncv+1; - } - if( ncv==0 ) - { - - /* - * Something strange: ALL ui are degenerate. - * Unexpected... - */ - *info = -255; - ae_frame_leave(_state); - return; - } - ar->rmserror = ae_sqrt(ar->rmserror/npoints, _state); - ar->avgerror = ar->avgerror/npoints; - if( na!=0 ) - { - ar->avgrelerror = ar->avgrelerror/na; - } - ar->cvrmserror = ae_sqrt(ar->cvrmserror/ncv, _state); - ar->cvavgerror = ar->cvavgerror/ncv; - if( nacv!=0 ) - { - ar->cvavgrelerror = ar->cvavgrelerror/nacv; - } - ae_frame_leave(_state); -} - - -ae_bool _linearmodel_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - linearmodel *p = (linearmodel*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->w, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _linearmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - linearmodel *dst = (linearmodel*)_dst; - linearmodel *src = (linearmodel*)_src; - if( !ae_vector_init_copy(&dst->w, &src->w, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _linearmodel_clear(void* _p) -{ - linearmodel *p = (linearmodel*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->w); -} - - -void _linearmodel_destroy(void* _p) -{ - linearmodel *p = (linearmodel*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->w); -} - - -ae_bool _lrreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - lrreport *p = (lrreport*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->c, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cvdefects, 0, DT_INT, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _lrreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - lrreport *dst = (lrreport*)_dst; - lrreport *src = (lrreport*)_src; - if( !ae_matrix_init_copy(&dst->c, &src->c, _state, make_automatic) ) - return ae_false; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->cvrmserror = src->cvrmserror; - dst->cvavgerror = src->cvavgerror; - dst->cvavgrelerror = src->cvavgrelerror; - dst->ncvdefects = src->ncvdefects; - if( !ae_vector_init_copy(&dst->cvdefects, &src->cvdefects, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _lrreport_clear(void* _p) -{ - lrreport *p = (lrreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->c); - ae_vector_clear(&p->cvdefects); -} - - -void _lrreport_destroy(void* _p) -{ - lrreport *p = (lrreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->c); - ae_vector_destroy(&p->cvdefects); -} - - - - -/************************************************************************* -Filters: simple moving averages (unsymmetric). - -This filter replaces array by results of SMA(K) filter. SMA(K) is defined -as filter which averages at most K previous points (previous - not points -AROUND central point) - or less, in case of the first K-1 points. - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filtersma(/* Real */ ae_vector* x, - ae_int_t n, - ae_int_t k, - ae_state *_state) -{ - ae_int_t i; - double runningsum; - double termsinsum; - ae_int_t zeroprefix; - double v; - - - ae_assert(n>=0, "FilterSMA: N<0", _state); - ae_assert(x->cnt>=n, "FilterSMA: Length(X)=1, "FilterSMA: K<1", _state); - - /* - * Quick exit, if necessary - */ - if( n<=1||k==1 ) - { - return; - } - - /* - * Prepare variables (see below for explanation) - */ - runningsum = 0.0; - termsinsum = 0; - for(i=ae_maxint(n-k, 0, _state); i<=n-1; i++) - { - runningsum = runningsum+x->ptr.p_double[i]; - termsinsum = termsinsum+1; - } - i = ae_maxint(n-k, 0, _state); - zeroprefix = 0; - while(i<=n-1&&ae_fp_eq(x->ptr.p_double[i],0)) - { - zeroprefix = zeroprefix+1; - i = i+1; - } - - /* - * General case: we assume that N>1 and K>1 - * - * Make one pass through all elements. At the beginning of - * the iteration we have: - * * I element being processed - * * RunningSum current value of the running sum - * (including I-th element) - * * TermsInSum number of terms in sum, 0<=TermsInSum<=K - * * ZeroPrefix length of the sequence of zero elements - * which starts at X[I-K+1] and continues towards X[I]. - * Equal to zero in case X[I-K+1] is non-zero. - * This value is used to make RunningSum exactly zero - * when it follows from the problem properties. - */ - for(i=n-1; i>=0; i--) - { - - /* - * Store new value of X[i], save old value in V - */ - v = x->ptr.p_double[i]; - x->ptr.p_double[i] = runningsum/termsinsum; - - /* - * Update RunningSum and TermsInSum - */ - if( i-k>=0 ) - { - runningsum = runningsum-v+x->ptr.p_double[i-k]; - } - else - { - runningsum = runningsum-v; - termsinsum = termsinsum-1; - } - - /* - * Update ZeroPrefix. - * In case we have ZeroPrefix=TermsInSum, - * RunningSum is reset to zero. - */ - if( i-k>=0 ) - { - if( ae_fp_neq(x->ptr.p_double[i-k],0) ) - { - zeroprefix = 0; - } - else - { - zeroprefix = ae_minint(zeroprefix+1, k, _state); - } - } - else - { - zeroprefix = ae_minint(zeroprefix, i+1, _state); - } - if( ae_fp_eq(zeroprefix,termsinsum) ) - { - runningsum = 0; - } - } -} - - -/************************************************************************* -Filters: exponential moving averages. - -This filter replaces array by results of EMA(alpha) filter. EMA(alpha) is -defined as filter which replaces X[] by S[]: - S[0] = X[0] - S[t] = alpha*X[t] + (1-alpha)*S[t-1] - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - alpha - 0=0, "FilterEMA: N<0", _state); - ae_assert(x->cnt>=n, "FilterEMA: Length(X)1", _state); - - /* - * Quick exit, if necessary - */ - if( n<=1||ae_fp_eq(alpha,1) ) - { - return; - } - - /* - * Process - */ - for(i=1; i<=n-1; i++) - { - x->ptr.p_double[i] = alpha*x->ptr.p_double[i]+(1-alpha)*x->ptr.p_double[i-1]; - } -} - - -/************************************************************************* -Filters: linear regression moving averages. - -This filter replaces array by results of LRMA(K) filter. - -LRMA(K) is defined as filter which, for each data point, builds linear -regression model using K prevous points (point itself is included in -these K points) and calculates value of this linear model at the point in -question. - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filterlrma(/* Real */ ae_vector* x, - ae_int_t n, - ae_int_t k, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t m; - ae_matrix xy; - ae_vector s; - ae_int_t info; - double a; - double b; - double vara; - double varb; - double covab; - double corrab; - double p; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init(&xy, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&s, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=0, "FilterLRMA: N<0", _state); - ae_assert(x->cnt>=n, "FilterLRMA: Length(X)=1, "FilterLRMA: K<1", _state); - - /* - * Quick exit, if necessary: - * * either N is equal to 1 (nothing to average) - * * or K is 1 (only point itself is used) or 2 (model is too simple, - * we will always get identity transformation) - */ - if( n<=1||k<=2 ) - { - ae_frame_leave(_state); - return; - } - - /* - * General case: K>2, N>1. - * We do not process points with I<2 because first two points (I=0 and I=1) will be - * left unmodified by LRMA filter in any case. - */ - ae_matrix_set_length(&xy, k, 2, _state); - ae_vector_set_length(&s, k, _state); - for(i=0; i<=k-1; i++) - { - xy.ptr.pp_double[i][0] = i; - s.ptr.p_double[i] = 1.0; - } - for(i=n-1; i>=2; i--) - { - m = ae_minint(i+1, k, _state); - ae_v_move(&xy.ptr.pp_double[0][1], xy.stride, &x->ptr.p_double[i-m+1], 1, ae_v_len(0,m-1)); - lrlines(&xy, &s, m, &info, &a, &b, &vara, &varb, &covab, &corrab, &p, _state); - ae_assert(info==1, "FilterLRMA: internal error", _state); - x->ptr.p_double[i] = a+b*(m-1); - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -Multiclass Fisher LDA - -Subroutine finds coefficients of linear combination which optimally separates -training set on classes. - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars]. - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - - -OUTPUT PARAMETERS: - Info - return code: - * -4, if internal EVD subroutine hasn't converged - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed (NPoints<0, - NVars<1, NClasses<2) - * 1, if task has been solved - * 2, if there was a multicollinearity in training set, - but task has been solved. - W - linear combination coefficients, array[0..NVars-1] - - -- ALGLIB -- - Copyright 31.05.2008 by Bochkanov Sergey -*************************************************************************/ -void fisherlda(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t* info, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix w2; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(w); - ae_matrix_init(&w2, 0, 0, DT_REAL, _state, ae_true); - - fisherldan(xy, npoints, nvars, nclasses, info, &w2, _state); - if( *info>0 ) - { - ae_vector_set_length(w, nvars-1+1, _state); - ae_v_move(&w->ptr.p_double[0], 1, &w2.ptr.pp_double[0][0], w2.stride, ae_v_len(0,nvars-1)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -N-dimensional multiclass Fisher LDA - -Subroutine finds coefficients of linear combinations which optimally separates -training set on classes. It returns N-dimensional basis whose vector are sorted -by quality of training set separation (in descending order). - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars]. - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - - -OUTPUT PARAMETERS: - Info - return code: - * -4, if internal EVD subroutine hasn't converged - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed (NPoints<0, - NVars<1, NClasses<2) - * 1, if task has been solved - * 2, if there was a multicollinearity in training set, - but task has been solved. - W - basis, array[0..NVars-1,0..NVars-1] - columns of matrix stores basis vectors, sorted by - quality of training set separation (in descending order) - - -- ALGLIB -- - Copyright 31.05.2008 by Bochkanov Sergey -*************************************************************************/ -void fisherldan(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t* info, - /* Real */ ae_matrix* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t m; - double v; - ae_vector c; - ae_vector mu; - ae_matrix muc; - ae_vector nc; - ae_matrix sw; - ae_matrix st; - ae_matrix z; - ae_matrix z2; - ae_matrix tm; - ae_matrix sbroot; - ae_matrix a; - ae_matrix xyproj; - ae_matrix wproj; - ae_vector tf; - ae_vector d; - ae_vector d2; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_matrix_clear(w); - ae_vector_init(&c, 0, DT_INT, _state, ae_true); - ae_vector_init(&mu, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&muc, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&nc, 0, DT_INT, _state, ae_true); - ae_matrix_init(&sw, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&st, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z2, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&sbroot, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xyproj, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&wproj, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - - /* - * Test data - */ - if( (npoints<0||nvars<1)||nclasses<2 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nvars], _state)<0||ae_round(xy->ptr.pp_double[i][nvars], _state)>=nclasses ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * Special case: NPoints<=1 - * Degenerate task. - */ - if( npoints<=1 ) - { - *info = 2; - ae_matrix_set_length(w, nvars-1+1, nvars-1+1, _state); - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - if( i==j ) - { - w->ptr.pp_double[i][j] = 1; - } - else - { - w->ptr.pp_double[i][j] = 0; - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * Prepare temporaries - */ - ae_vector_set_length(&tf, nvars-1+1, _state); - ae_vector_set_length(&work, ae_maxint(nvars, npoints, _state)+1, _state); - - /* - * Convert class labels from reals to integers (just for convenience) - */ - ae_vector_set_length(&c, npoints-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - c.ptr.p_int[i] = ae_round(xy->ptr.pp_double[i][nvars], _state); - } - - /* - * Calculate class sizes and means - */ - ae_vector_set_length(&mu, nvars-1+1, _state); - ae_matrix_set_length(&muc, nclasses-1+1, nvars-1+1, _state); - ae_vector_set_length(&nc, nclasses-1+1, _state); - for(j=0; j<=nvars-1; j++) - { - mu.ptr.p_double[j] = 0; - } - for(i=0; i<=nclasses-1; i++) - { - nc.ptr.p_int[i] = 0; - for(j=0; j<=nvars-1; j++) - { - muc.ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=npoints-1; i++) - { - ae_v_add(&mu.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - ae_v_add(&muc.ptr.pp_double[c.ptr.p_int[i]][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - nc.ptr.p_int[c.ptr.p_int[i]] = nc.ptr.p_int[c.ptr.p_int[i]]+1; - } - for(i=0; i<=nclasses-1; i++) - { - v = (double)1/(double)nc.ptr.p_int[i]; - ae_v_muld(&muc.ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1), v); - } - v = (double)1/(double)npoints; - ae_v_muld(&mu.ptr.p_double[0], 1, ae_v_len(0,nvars-1), v); - - /* - * Create ST matrix - */ - ae_matrix_set_length(&st, nvars-1+1, nvars-1+1, _state); - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - st.ptr.pp_double[i][j] = 0; - } - } - for(k=0; k<=npoints-1; k++) - { - ae_v_move(&tf.ptr.p_double[0], 1, &xy->ptr.pp_double[k][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&tf.ptr.p_double[0], 1, &mu.ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - for(i=0; i<=nvars-1; i++) - { - v = tf.ptr.p_double[i]; - ae_v_addd(&st.ptr.pp_double[i][0], 1, &tf.ptr.p_double[0], 1, ae_v_len(0,nvars-1), v); - } - } - - /* - * Create SW matrix - */ - ae_matrix_set_length(&sw, nvars-1+1, nvars-1+1, _state); - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - sw.ptr.pp_double[i][j] = 0; - } - } - for(k=0; k<=npoints-1; k++) - { - ae_v_move(&tf.ptr.p_double[0], 1, &xy->ptr.pp_double[k][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&tf.ptr.p_double[0], 1, &muc.ptr.pp_double[c.ptr.p_int[k]][0], 1, ae_v_len(0,nvars-1)); - for(i=0; i<=nvars-1; i++) - { - v = tf.ptr.p_double[i]; - ae_v_addd(&sw.ptr.pp_double[i][0], 1, &tf.ptr.p_double[0], 1, ae_v_len(0,nvars-1), v); - } - } - - /* - * Maximize ratio J=(w'*ST*w)/(w'*SW*w). - * - * First, make transition from w to v such that w'*ST*w becomes v'*v: - * v = root(ST)*w = R*w - * R = root(D)*Z' - * w = (root(ST)^-1)*v = RI*v - * RI = Z*inv(root(D)) - * J = (v'*v)/(v'*(RI'*SW*RI)*v) - * ST = Z*D*Z' - * - * so we have - * - * J = (v'*v) / (v'*(inv(root(D))*Z'*SW*Z*inv(root(D)))*v) = - * = (v'*v) / (v'*A*v) - */ - if( !smatrixevd(&st, nvars, 1, ae_true, &d, &z, _state) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(w, nvars-1+1, nvars-1+1, _state); - if( ae_fp_less_eq(d.ptr.p_double[nvars-1],0)||ae_fp_less_eq(d.ptr.p_double[0],1000*ae_machineepsilon*d.ptr.p_double[nvars-1]) ) - { - - /* - * Special case: D[NVars-1]<=0 - * Degenerate task (all variables takes the same value). - */ - if( ae_fp_less_eq(d.ptr.p_double[nvars-1],0) ) - { - *info = 2; - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - if( i==j ) - { - w->ptr.pp_double[i][j] = 1; - } - else - { - w->ptr.pp_double[i][j] = 0; - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * Special case: degenerate ST matrix, multicollinearity found. - * Since we know ST eigenvalues/vectors we can translate task to - * non-degenerate form. - * - * Let WG is orthogonal basis of the non zero variance subspace - * of the ST and let WZ is orthogonal basis of the zero variance - * subspace. - * - * Projection on WG allows us to use LDA on reduced M-dimensional - * subspace, N-M vectors of WZ allows us to update reduced LDA - * factors to full N-dimensional subspace. - */ - m = 0; - for(k=0; k<=nvars-1; k++) - { - if( ae_fp_less_eq(d.ptr.p_double[k],1000*ae_machineepsilon*d.ptr.p_double[nvars-1]) ) - { - m = k+1; - } - } - ae_assert(m!=0, "FisherLDAN: internal error #1", _state); - ae_matrix_set_length(&xyproj, npoints-1+1, nvars-m+1, _state); - matrixmatrixmultiply(xy, 0, npoints-1, 0, nvars-1, ae_false, &z, 0, nvars-1, m, nvars-1, ae_false, 1.0, &xyproj, 0, npoints-1, 0, nvars-m-1, 0.0, &work, _state); - for(i=0; i<=npoints-1; i++) - { - xyproj.ptr.pp_double[i][nvars-m] = xy->ptr.pp_double[i][nvars]; - } - fisherldan(&xyproj, npoints, nvars-m, nclasses, info, &wproj, _state); - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - matrixmatrixmultiply(&z, 0, nvars-1, m, nvars-1, ae_false, &wproj, 0, nvars-m-1, 0, nvars-m-1, ae_false, 1.0, w, 0, nvars-1, 0, nvars-m-1, 0.0, &work, _state); - for(k=nvars-m; k<=nvars-1; k++) - { - ae_v_move(&w->ptr.pp_double[0][k], w->stride, &z.ptr.pp_double[0][k-(nvars-m)], z.stride, ae_v_len(0,nvars-1)); - } - *info = 2; - } - else - { - - /* - * General case: no multicollinearity - */ - ae_matrix_set_length(&tm, nvars-1+1, nvars-1+1, _state); - ae_matrix_set_length(&a, nvars-1+1, nvars-1+1, _state); - matrixmatrixmultiply(&sw, 0, nvars-1, 0, nvars-1, ae_false, &z, 0, nvars-1, 0, nvars-1, ae_false, 1.0, &tm, 0, nvars-1, 0, nvars-1, 0.0, &work, _state); - matrixmatrixmultiply(&z, 0, nvars-1, 0, nvars-1, ae_true, &tm, 0, nvars-1, 0, nvars-1, ae_false, 1.0, &a, 0, nvars-1, 0, nvars-1, 0.0, &work, _state); - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - a.ptr.pp_double[i][j] = a.ptr.pp_double[i][j]/ae_sqrt(d.ptr.p_double[i]*d.ptr.p_double[j], _state); - } - } - if( !smatrixevd(&a, nvars, 1, ae_true, &d2, &z2, _state) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - for(k=0; k<=nvars-1; k++) - { - for(i=0; i<=nvars-1; i++) - { - tf.ptr.p_double[i] = z2.ptr.pp_double[i][k]/ae_sqrt(d.ptr.p_double[i], _state); - } - for(i=0; i<=nvars-1; i++) - { - v = ae_v_dotproduct(&z.ptr.pp_double[i][0], 1, &tf.ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - w->ptr.pp_double[i][k] = v; - } - } - } - - /* - * Post-processing: - * * normalization - * * converting to non-negative form, if possible - */ - for(k=0; k<=nvars-1; k++) - { - v = ae_v_dotproduct(&w->ptr.pp_double[0][k], w->stride, &w->ptr.pp_double[0][k], w->stride, ae_v_len(0,nvars-1)); - v = 1/ae_sqrt(v, _state); - ae_v_muld(&w->ptr.pp_double[0][k], w->stride, ae_v_len(0,nvars-1), v); - v = 0; - for(i=0; i<=nvars-1; i++) - { - v = v+w->ptr.pp_double[i][k]; - } - if( ae_fp_less(v,0) ) - { - ae_v_muld(&w->ptr.pp_double[0][k], w->stride, ae_v_len(0,nvars-1), -1); - } - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -This function returns number of weights updates which is required for -gradient calculation problem to be splitted. -*************************************************************************/ -ae_int_t mlpgradsplitcost(ae_state *_state) -{ - ae_int_t result; - - - result = mlpbase_gradbasecasecost; - return result; -} - - -/************************************************************************* -This function returns number of elements in subset of dataset which is -required for gradient calculation problem to be splitted. -*************************************************************************/ -ae_int_t mlpgradsplitsize(ae_state *_state) -{ - ae_int_t result; - - - result = mlpbase_microbatchsize; - return result; -} - - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers, with linear output layer. Network weights are filled with small -random values. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate0(ae_int_t nin, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(-5, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, 0, 0, nout, ae_false, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreate0, but with one hidden layer (NHid neurons) with -non-linear activation function. Output layer is linear. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3+3; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(-5, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid, 0, nout, ae_false, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreate0, but with two hidden layers (NHid1 and NHid2 neurons) -with non-linear activation function. Output layer is linear. - $ALL - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3+3+3; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid2, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(-5, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid1, nhid2, nout, ae_false, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers with non-linear output layer. Network weights are filled with small -random values. - -Activation function of the output layer takes values: - - (B, +INF), if D>=0 - -or - - (-INF, B), if D<0. - - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb0(ae_int_t nin, - ae_int_t nout, - double b, - double d, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3; - if( ae_fp_greater_eq(d,0) ) - { - d = 1; - } - else - { - d = -1; - } - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(3, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, 0, 0, nout, ae_false, ae_false, _state); - - /* - * Turn on ouputs shift/scaling. - */ - for(i=nin; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = b; - network->columnsigmas.ptr.p_double[i] = d; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreateB0 but with non-linear hidden layer. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double b, - double d, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3+3; - if( ae_fp_greater_eq(d,0) ) - { - d = 1; - } - else - { - d = -1; - } - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(3, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid, 0, nout, ae_false, ae_false, _state); - - /* - * Turn on ouputs shift/scaling. - */ - for(i=nin; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = b; - network->columnsigmas.ptr.p_double[i] = d; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreateB0 but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double b, - double d, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3+3+3; - if( ae_fp_greater_eq(d,0) ) - { - d = 1; - } - else - { - d = -1; - } - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid2, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(3, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid1, nhid2, nout, ae_false, ae_false, _state); - - /* - * Turn on ouputs shift/scaling. - */ - for(i=nin; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = b; - network->columnsigmas.ptr.p_double[i] = d; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers with non-linear output layer. Network weights are filled with small -random values. Activation function of the output layer takes values [A,B]. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater0(ae_int_t nin, - ae_int_t nout, - double a, - double b, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, 0, 0, nout, ae_false, ae_false, _state); - - /* - * Turn on outputs shift/scaling. - */ - for(i=nin; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = 0.5*(a+b); - network->columnsigmas.ptr.p_double[i] = 0.5*(a-b); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreateR0, but with non-linear hidden layer. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double a, - double b, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3+3; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid, 0, nout, ae_false, ae_false, _state); - - /* - * Turn on outputs shift/scaling. - */ - for(i=nin; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = 0.5*(a+b); - network->columnsigmas.ptr.p_double[i] = 0.5*(a-b); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreateR0, but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double a, - double b, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - layerscount = 1+3+3+3; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid2, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_false, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid1, nhid2, nout, ae_false, ae_false, _state); - - /* - * Turn on outputs shift/scaling. - */ - for(i=nin; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = 0.5*(a+b); - network->columnsigmas.ptr.p_double[i] = 0.5*(a-b); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Creates classifier network with NIn inputs and NOut possible classes. -Network contains no hidden layers and linear output layer with SOFTMAX- -normalization (so outputs sums up to 1.0 and converge to posterior -probabilities). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec0(ae_int_t nin, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - ae_assert(nout>=2, "MLPCreateC0: NOut<2!", _state); - layerscount = 1+2+1; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout-1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addzerolayer(&lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_true, network, _state); - mlpbase_fillhighlevelinformation(network, nin, 0, 0, nout, ae_true, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreateC0, but with one non-linear hidden layer. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - ae_assert(nout>=2, "MLPCreateC1: NOut<2!", _state); - layerscount = 1+3+2+1; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout-1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addzerolayer(&lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_true, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid, 0, nout, ae_true, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Same as MLPCreateC0, but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector lsizes; - ae_vector ltypes; - ae_vector lconnfirst; - ae_vector lconnlast; - ae_int_t layerscount; - ae_int_t lastproc; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&lsizes, 0, DT_INT, _state, ae_true); - ae_vector_init(<ypes, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lconnlast, 0, DT_INT, _state, ae_true); - - ae_assert(nout>=2, "MLPCreateC2: NOut<2!", _state); - layerscount = 1+3+3+2+1; - - /* - * Allocate arrays - */ - ae_vector_set_length(&lsizes, layerscount-1+1, _state); - ae_vector_set_length(<ypes, layerscount-1+1, _state); - ae_vector_set_length(&lconnfirst, layerscount-1+1, _state); - ae_vector_set_length(&lconnlast, layerscount-1+1, _state); - - /* - * Layers - */ - mlpbase_addinputlayer(nin, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nhid2, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addactivationlayer(1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addbiasedsummatorlayer(nout-1, &lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - mlpbase_addzerolayer(&lsizes, <ypes, &lconnfirst, &lconnlast, &lastproc, _state); - - /* - * Create - */ - mlpbase_mlpcreate(nin, nout, &lsizes, <ypes, &lconnfirst, &lconnlast, layerscount, ae_true, network, _state); - mlpbase_fillhighlevelinformation(network, nin, nhid1, nhid2, nout, ae_true, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Copying of neural network - -INPUT PARAMETERS: - Network1 - original - -OUTPUT PARAMETERS: - Network2 - copy - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcopy(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state) -{ - - _multilayerperceptron_clear(network2); - - mlpcopyshared(network1, network2, _state); -} - - -/************************************************************************* -Copying of neural network (second parameter is passed as shared object). - -INPUT PARAMETERS: - Network1 - original - -OUTPUT PARAMETERS: - Network2 - copy - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcopyshared(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t wcount; - ae_int_t i; - mlpbuffers buf; - smlpgrad sgrad; - - ae_frame_make(_state, &_frame_block); - _mlpbuffers_init(&buf, _state, ae_true); - _smlpgrad_init(&sgrad, _state, ae_true); - - - /* - * Copy scalar and array fields - */ - network2->hlnetworktype = network1->hlnetworktype; - network2->hlnormtype = network1->hlnormtype; - copyintegerarray(&network1->hllayersizes, &network2->hllayersizes, _state); - copyintegerarray(&network1->hlconnections, &network2->hlconnections, _state); - copyintegerarray(&network1->hlneurons, &network2->hlneurons, _state); - copyintegerarray(&network1->structinfo, &network2->structinfo, _state); - copyrealarray(&network1->weights, &network2->weights, _state); - copyrealarray(&network1->columnmeans, &network2->columnmeans, _state); - copyrealarray(&network1->columnsigmas, &network2->columnsigmas, _state); - copyrealarray(&network1->neurons, &network2->neurons, _state); - copyrealarray(&network1->dfdnet, &network2->dfdnet, _state); - copyrealarray(&network1->derror, &network2->derror, _state); - copyrealarray(&network1->x, &network2->x, _state); - copyrealarray(&network1->y, &network2->y, _state); - copyrealarray(&network1->nwbuf, &network2->nwbuf, _state); - copyintegerarray(&network1->integerbuf, &network2->integerbuf, _state); - - /* - * copy buffers - */ - wcount = mlpgetweightscount(network1, _state); - ae_shared_pool_set_seed(&network2->buf, &buf, sizeof(buf), _mlpbuffers_init, _mlpbuffers_init_copy, _mlpbuffers_destroy, _state); - ae_vector_set_length(&sgrad.g, wcount, _state); - sgrad.f = 0.0; - for(i=0; i<=wcount-1; i++) - { - sgrad.g.ptr.p_double[i] = 0.0; - } - ae_shared_pool_set_seed(&network2->gradbuf, &sgrad, sizeof(sgrad), _smlpgrad_init, _smlpgrad_init_copy, _smlpgrad_destroy, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function compares architectures of neural networks. Only geometries -are compared, weights and other parameters are not tested. - - -- ALGLIB -- - Copyright 20.06.2013 by Bochkanov Sergey -*************************************************************************/ -ae_bool mlpsamearchitecture(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ninfo; - ae_bool result; - - - ae_assert(network1->structinfo.cnt>0&&network1->structinfo.cnt>=network1->structinfo.ptr.p_int[0], "MLPSameArchitecture: Network1 is uninitialized", _state); - ae_assert(network2->structinfo.cnt>0&&network2->structinfo.cnt>=network2->structinfo.ptr.p_int[0], "MLPSameArchitecture: Network2 is uninitialized", _state); - result = ae_false; - if( network1->structinfo.ptr.p_int[0]!=network2->structinfo.ptr.p_int[0] ) - { - return result; - } - ninfo = network1->structinfo.ptr.p_int[0]; - for(i=0; i<=ninfo-1; i++) - { - if( network1->structinfo.ptr.p_int[i]!=network2->structinfo.ptr.p_int[i] ) - { - return result; - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function copies tunable parameters (weights/means/sigmas) from one -network to another with same architecture. It performs some rudimentary -checks that architectures are same, and throws exception if check fails. - -It is intended for fast copying of states between two network which are -known to have same geometry. - -INPUT PARAMETERS: - Network1 - source, must be correctly initialized - Network2 - target, must have same architecture - -OUTPUT PARAMETERS: - Network2 - network state is copied from source to target - - -- ALGLIB -- - Copyright 20.06.2013 by Bochkanov Sergey -*************************************************************************/ -void mlpcopytunableparameters(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ninfo; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - - - ae_assert(network1->structinfo.cnt>0&&network1->structinfo.cnt>=network1->structinfo.ptr.p_int[0], "MLPCopyTunableParameters: Network1 is uninitialized", _state); - ae_assert(network2->structinfo.cnt>0&&network2->structinfo.cnt>=network2->structinfo.ptr.p_int[0], "MLPCopyTunableParameters: Network2 is uninitialized", _state); - ae_assert(network1->structinfo.ptr.p_int[0]==network2->structinfo.ptr.p_int[0], "MLPCopyTunableParameters: Network1 geometry differs from that of Network2", _state); - ninfo = network1->structinfo.ptr.p_int[0]; - for(i=0; i<=ninfo-1; i++) - { - ae_assert(network1->structinfo.ptr.p_int[i]==network2->structinfo.ptr.p_int[i], "MLPCopyTunableParameters: Network1 geometry differs from that of Network2", _state); - } - mlpproperties(network1, &nin, &nout, &wcount, _state); - for(i=0; i<=wcount-1; i++) - { - network2->weights.ptr.p_double[i] = network1->weights.ptr.p_double[i]; - } - if( mlpissoftmax(network1, _state) ) - { - for(i=0; i<=nin-1; i++) - { - network2->columnmeans.ptr.p_double[i] = network1->columnmeans.ptr.p_double[i]; - network2->columnsigmas.ptr.p_double[i] = network1->columnsigmas.ptr.p_double[i]; - } - } - else - { - for(i=0; i<=nin+nout-1; i++) - { - network2->columnmeans.ptr.p_double[i] = network1->columnmeans.ptr.p_double[i]; - network2->columnsigmas.ptr.p_double[i] = network1->columnsigmas.ptr.p_double[i]; - } - } -} - - -/************************************************************************* -This function exports tunable parameters (weights/means/sigmas) from -network to contiguous array. Nothing is guaranteed about array format, the -only thing you can count for is that MLPImportTunableParameters() will be -able to parse it. - -It is intended for fast copying of states between network and backup array - -INPUT PARAMETERS: - Network - source, must be correctly initialized - P - array to use. If its size is enough to store data, it - is reused. - -OUTPUT PARAMETERS: - P - array which stores network parameters, resized if needed - PCount - number of parameters stored in array. - - -- ALGLIB -- - Copyright 20.06.2013 by Bochkanov Sergey -*************************************************************************/ -void mlpexporttunableparameters(multilayerperceptron* network, - /* Real */ ae_vector* p, - ae_int_t* pcount, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - - *pcount = 0; - - ae_assert(network->structinfo.cnt>0&&network->structinfo.cnt>=network->structinfo.ptr.p_int[0], "MLPExportTunableParameters: Network is uninitialized", _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - if( mlpissoftmax(network, _state) ) - { - *pcount = wcount+2*nin; - rvectorsetlengthatleast(p, *pcount, _state); - k = 0; - for(i=0; i<=wcount-1; i++) - { - p->ptr.p_double[k] = network->weights.ptr.p_double[i]; - k = k+1; - } - for(i=0; i<=nin-1; i++) - { - p->ptr.p_double[k] = network->columnmeans.ptr.p_double[i]; - k = k+1; - p->ptr.p_double[k] = network->columnsigmas.ptr.p_double[i]; - k = k+1; - } - } - else - { - *pcount = wcount+2*(nin+nout); - rvectorsetlengthatleast(p, *pcount, _state); - k = 0; - for(i=0; i<=wcount-1; i++) - { - p->ptr.p_double[k] = network->weights.ptr.p_double[i]; - k = k+1; - } - for(i=0; i<=nin+nout-1; i++) - { - p->ptr.p_double[k] = network->columnmeans.ptr.p_double[i]; - k = k+1; - p->ptr.p_double[k] = network->columnsigmas.ptr.p_double[i]; - k = k+1; - } - } -} - - -/************************************************************************* -This function imports tunable parameters (weights/means/sigmas) which -were exported by MLPExportTunableParameters(). - -It is intended for fast copying of states between network and backup array - -INPUT PARAMETERS: - Network - target: - * must be correctly initialized - * must have same geometry as network used to export params - P - array with parameters - - -- ALGLIB -- - Copyright 20.06.2013 by Bochkanov Sergey -*************************************************************************/ -void mlpimporttunableparameters(multilayerperceptron* network, - /* Real */ ae_vector* p, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - - - ae_assert(network->structinfo.cnt>0&&network->structinfo.cnt>=network->structinfo.ptr.p_int[0], "MLPImportTunableParameters: Network is uninitialized", _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - if( mlpissoftmax(network, _state) ) - { - k = 0; - for(i=0; i<=wcount-1; i++) - { - network->weights.ptr.p_double[i] = p->ptr.p_double[k]; - k = k+1; - } - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = p->ptr.p_double[k]; - k = k+1; - network->columnsigmas.ptr.p_double[i] = p->ptr.p_double[k]; - k = k+1; - } - } - else - { - k = 0; - for(i=0; i<=wcount-1; i++) - { - network->weights.ptr.p_double[i] = p->ptr.p_double[k]; - k = k+1; - } - for(i=0; i<=nin+nout-1; i++) - { - network->columnmeans.ptr.p_double[i] = p->ptr.p_double[k]; - k = k+1; - network->columnsigmas.ptr.p_double[i] = p->ptr.p_double[k]; - k = k+1; - } - } -} - - -/************************************************************************* -Serialization of MultiLayerPerceptron strucure - -INPUT PARAMETERS: - Network - original - -OUTPUT PARAMETERS: - RA - array of real numbers which stores network, - array[0..RLen-1] - RLen - RA lenght - - -- ALGLIB -- - Copyright 29.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpserializeold(multilayerperceptron* network, - /* Real */ ae_vector* ra, - ae_int_t* rlen, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ssize; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t sigmalen; - ae_int_t offs; - - ae_vector_clear(ra); - *rlen = 0; - - - /* - * Unload info - */ - ssize = network->structinfo.ptr.p_int[0]; - nin = network->structinfo.ptr.p_int[1]; - nout = network->structinfo.ptr.p_int[2]; - wcount = network->structinfo.ptr.p_int[4]; - if( mlpissoftmax(network, _state) ) - { - sigmalen = nin; - } - else - { - sigmalen = nin+nout; - } - - /* - * RA format: - * LEN DESRC. - * 1 RLen - * 1 version (MLPVNum) - * 1 StructInfo size - * SSize StructInfo - * WCount Weights - * SigmaLen ColumnMeans - * SigmaLen ColumnSigmas - */ - *rlen = 3+ssize+wcount+2*sigmalen; - ae_vector_set_length(ra, *rlen-1+1, _state); - ra->ptr.p_double[0] = *rlen; - ra->ptr.p_double[1] = mlpbase_mlpvnum; - ra->ptr.p_double[2] = ssize; - offs = 3; - for(i=0; i<=ssize-1; i++) - { - ra->ptr.p_double[offs+i] = network->structinfo.ptr.p_int[i]; - } - offs = offs+ssize; - ae_v_move(&ra->ptr.p_double[offs], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(offs,offs+wcount-1)); - offs = offs+wcount; - ae_v_move(&ra->ptr.p_double[offs], 1, &network->columnmeans.ptr.p_double[0], 1, ae_v_len(offs,offs+sigmalen-1)); - offs = offs+sigmalen; - ae_v_move(&ra->ptr.p_double[offs], 1, &network->columnsigmas.ptr.p_double[0], 1, ae_v_len(offs,offs+sigmalen-1)); - offs = offs+sigmalen; -} - - -/************************************************************************* -Unserialization of MultiLayerPerceptron strucure - -INPUT PARAMETERS: - RA - real array which stores network - -OUTPUT PARAMETERS: - Network - restored network - - -- ALGLIB -- - Copyright 29.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpunserializeold(/* Real */ ae_vector* ra, - multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ssize; - ae_int_t ntotal; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t sigmalen; - ae_int_t offs; - - _multilayerperceptron_clear(network); - - ae_assert(ae_round(ra->ptr.p_double[1], _state)==mlpbase_mlpvnum, "MLPUnserialize: incorrect array!", _state); - - /* - * Unload StructInfo from IA - */ - offs = 3; - ssize = ae_round(ra->ptr.p_double[2], _state); - ae_vector_set_length(&network->structinfo, ssize-1+1, _state); - for(i=0; i<=ssize-1; i++) - { - network->structinfo.ptr.p_int[i] = ae_round(ra->ptr.p_double[offs+i], _state); - } - offs = offs+ssize; - - /* - * Unload info from StructInfo - */ - ssize = network->structinfo.ptr.p_int[0]; - nin = network->structinfo.ptr.p_int[1]; - nout = network->structinfo.ptr.p_int[2]; - ntotal = network->structinfo.ptr.p_int[3]; - wcount = network->structinfo.ptr.p_int[4]; - if( network->structinfo.ptr.p_int[6]==0 ) - { - sigmalen = nin+nout; - } - else - { - sigmalen = nin; - } - - /* - * Allocate space for other fields - */ - ae_vector_set_length(&network->weights, wcount-1+1, _state); - ae_vector_set_length(&network->columnmeans, sigmalen-1+1, _state); - ae_vector_set_length(&network->columnsigmas, sigmalen-1+1, _state); - ae_vector_set_length(&network->neurons, ntotal-1+1, _state); - ae_vector_set_length(&network->nwbuf, ae_maxint(wcount, 2*nout, _state)-1+1, _state); - ae_vector_set_length(&network->dfdnet, ntotal-1+1, _state); - ae_vector_set_length(&network->x, nin-1+1, _state); - ae_vector_set_length(&network->y, nout-1+1, _state); - ae_vector_set_length(&network->derror, ntotal-1+1, _state); - - /* - * Copy parameters from RA - */ - ae_v_move(&network->weights.ptr.p_double[0], 1, &ra->ptr.p_double[offs], 1, ae_v_len(0,wcount-1)); - offs = offs+wcount; - ae_v_move(&network->columnmeans.ptr.p_double[0], 1, &ra->ptr.p_double[offs], 1, ae_v_len(0,sigmalen-1)); - offs = offs+sigmalen; - ae_v_move(&network->columnsigmas.ptr.p_double[0], 1, &ra->ptr.p_double[offs], 1, ae_v_len(0,sigmalen-1)); - offs = offs+sigmalen; -} - - -/************************************************************************* -Randomization of neural network weights - - -- ALGLIB -- - Copyright 06.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlprandomize(multilayerperceptron* network, ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - hqrndstate r; - ae_int_t entrysize; - ae_int_t entryoffs; - ae_int_t neuronidx; - ae_int_t neurontype; - double vmean; - double vvar; - ae_int_t i; - ae_int_t n1; - ae_int_t n2; - double desiredsigma; - ae_int_t montecarlocnt; - double ef; - double ef2; - double v; - double wscale; - - ae_frame_make(_state, &_frame_block); - _hqrndstate_init(&r, _state, ae_true); - - hqrndrandomize(&r, _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - desiredsigma = 0.5; - montecarlocnt = 20; - - /* - * Stage 1: - * * Network.Weights is filled by standard deviation of weights - * * default values: sigma=1 - */ - for(i=0; i<=wcount-1; i++) - { - network->weights.ptr.p_double[i] = 1.0; - } - - /* - * Stage 2: - * * assume that input neurons have zero mean and unit standard deviation - * * assume that constant neurons have zero standard deviation - * * perform forward pass along neurons - * * for each non-input non-constant neuron: - * * calculate mean and standard deviation of neuron's output - * assuming that we know means/deviations of neurons which feed it - * and assuming that weights has unit variance and zero mean. - * * for each nonlinear neuron additionally we perform backward pass: - * * scale variances of weights which feed it in such way that neuron's - * input has unit standard deviation - * - * NOTE: this algorithm assumes that each connection feeds at most one - * non-linear neuron. This assumption can be incorrect in upcoming - * architectures with strong neurons. However, algorithm should - * work smoothly even in this case. - * - * During this stage we use Network.RndBuf, which is grouped into NTotal - * entries, each of them having following format: - * - * Buf[Offset+0] mean value of neuron's output - * Buf[Offset+1] standard deviation of neuron's output - * - * - */ - entrysize = 2; - rvectorsetlengthatleast(&network->rndbuf, entrysize*ntotal, _state); - for(neuronidx=0; neuronidx<=ntotal-1; neuronidx++) - { - neurontype = network->structinfo.ptr.p_int[istart+neuronidx*mlpbase_nfieldwidth+0]; - entryoffs = entrysize*neuronidx; - if( neurontype==-2 ) - { - - /* - * Input neuron: zero mean, unit variance. - */ - network->rndbuf.ptr.p_double[entryoffs+0] = 0.0; - network->rndbuf.ptr.p_double[entryoffs+1] = 1.0; - continue; - } - if( neurontype==-3 ) - { - - /* - * "-1" neuron: mean=-1, zero variance. - */ - network->rndbuf.ptr.p_double[entryoffs+0] = -1.0; - network->rndbuf.ptr.p_double[entryoffs+1] = 0.0; - continue; - } - if( neurontype==-4 ) - { - - /* - * "0" neuron: mean=0, zero variance. - */ - network->rndbuf.ptr.p_double[entryoffs+0] = 0.0; - network->rndbuf.ptr.p_double[entryoffs+1] = 0.0; - continue; - } - if( neurontype==0 ) - { - - /* - * Adaptive summator neuron: - * * calculate its mean and variance. - * * we assume that weights of this neuron have unit variance and zero mean. - * * thus, neuron's output is always have zero mean - * * as for variance, it is a bit more interesting: - * * let n[i] is i-th input neuron - * * let w[i] is i-th weight - * * we assume that n[i] and w[i] are independently distributed - * * Var(n0*w0+n1*w1+...) = Var(n0*w0)+Var(n1*w1)+... - * * Var(X*Y) = mean(X)^2*Var(Y) + mean(Y)^2*Var(X) + Var(X)*Var(Y) - * * mean(w[i])=0, var(w[i])=1 - * * Var(n[i]*w[i]) = mean(n[i])^2 + Var(n[i]) - */ - n1 = network->structinfo.ptr.p_int[istart+neuronidx*mlpbase_nfieldwidth+2]; - n2 = n1+network->structinfo.ptr.p_int[istart+neuronidx*mlpbase_nfieldwidth+1]-1; - vmean = 0.0; - vvar = 0.0; - for(i=n1; i<=n2; i++) - { - vvar = vvar+ae_sqr(network->rndbuf.ptr.p_double[entrysize*i+0], _state)+ae_sqr(network->rndbuf.ptr.p_double[entrysize*i+1], _state); - } - network->rndbuf.ptr.p_double[entryoffs+0] = vmean; - network->rndbuf.ptr.p_double[entryoffs+1] = ae_sqrt(vvar, _state); - continue; - } - if( neurontype==-5 ) - { - - /* - * Linear activation function - */ - i = network->structinfo.ptr.p_int[istart+neuronidx*mlpbase_nfieldwidth+2]; - vmean = network->rndbuf.ptr.p_double[entrysize*i+0]; - vvar = ae_sqr(network->rndbuf.ptr.p_double[entrysize*i+1], _state); - if( ae_fp_greater(vvar,0) ) - { - wscale = desiredsigma/ae_sqrt(vvar, _state); - } - else - { - wscale = 1.0; - } - mlpbase_randomizebackwardpass(network, i, wscale, _state); - network->rndbuf.ptr.p_double[entryoffs+0] = vmean*wscale; - network->rndbuf.ptr.p_double[entryoffs+1] = desiredsigma; - continue; - } - if( neurontype>0 ) - { - - /* - * Nonlinear activation function: - * * scale its inputs - * * estimate mean/sigma of its output using Monte-Carlo method - * (we simulate different inputs with unit deviation and - * sample activation function output on such inputs) - */ - i = network->structinfo.ptr.p_int[istart+neuronidx*mlpbase_nfieldwidth+2]; - vmean = network->rndbuf.ptr.p_double[entrysize*i+0]; - vvar = ae_sqr(network->rndbuf.ptr.p_double[entrysize*i+1], _state); - if( ae_fp_greater(vvar,0) ) - { - wscale = desiredsigma/ae_sqrt(vvar, _state); - } - else - { - wscale = 1.0; - } - mlpbase_randomizebackwardpass(network, i, wscale, _state); - ef = 0.0; - ef2 = 0.0; - vmean = vmean*wscale; - for(i=0; i<=montecarlocnt-1; i++) - { - v = vmean+desiredsigma*hqrndnormal(&r, _state); - ef = ef+v; - ef2 = ef2+v*v; - } - ef = ef/montecarlocnt; - ef2 = ef2/montecarlocnt; - network->rndbuf.ptr.p_double[entryoffs+0] = ef; - network->rndbuf.ptr.p_double[entryoffs+1] = ae_maxreal(ef2-ef*ef, 0.0, _state); - continue; - } - ae_assert(ae_false, "MLPRandomize: unexpected neuron type", _state); - } - - /* - * Stage 3: generate weights. - */ - for(i=0; i<=wcount-1; i++) - { - network->weights.ptr.p_double[i] = network->weights.ptr.p_double[i]*hqrndnormal(&r, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Randomization of neural network weights and standartisator - - -- ALGLIB -- - Copyright 10.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlprandomizefull(multilayerperceptron* network, ae_state *_state) -{ - ae_int_t i; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t offs; - ae_int_t ntype; - - - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Process network - */ - mlprandomize(network, _state); - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = ae_randomreal(_state)-0.5; - network->columnsigmas.ptr.p_double[i] = ae_randomreal(_state)+0.5; - } - if( !mlpissoftmax(network, _state) ) - { - for(i=0; i<=nout-1; i++) - { - offs = istart+(ntotal-nout+i)*mlpbase_nfieldwidth; - ntype = network->structinfo.ptr.p_int[offs+0]; - if( ntype==0 ) - { - - /* - * Shifts are changed only for linear outputs neurons - */ - network->columnmeans.ptr.p_double[nin+i] = 2*ae_randomreal(_state)-1; - } - if( ntype==0||ntype==3 ) - { - - /* - * Scales are changed only for linear or bounded outputs neurons. - * Note that scale randomization preserves sign. - */ - network->columnsigmas.ptr.p_double[nin+i] = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state)*(1.5*ae_randomreal(_state)+0.5); - } - } - } -} - - -/************************************************************************* -Internal subroutine. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpinitpreprocessor(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t jmax; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t offs; - ae_int_t ntype; - ae_vector means; - ae_vector sigmas; - double s; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&means, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sigmas, 0, DT_REAL, _state, ae_true); - - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Means/Sigmas - */ - if( mlpissoftmax(network, _state) ) - { - jmax = nin-1; - } - else - { - jmax = nin+nout-1; - } - ae_vector_set_length(&means, jmax+1, _state); - ae_vector_set_length(&sigmas, jmax+1, _state); - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = 0; - sigmas.ptr.p_double[i] = 0; - } - for(i=0; i<=ssize-1; i++) - { - for(j=0; j<=jmax; j++) - { - means.ptr.p_double[j] = means.ptr.p_double[j]+xy->ptr.pp_double[i][j]; - } - } - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = means.ptr.p_double[i]/ssize; - } - for(i=0; i<=ssize-1; i++) - { - for(j=0; j<=jmax; j++) - { - sigmas.ptr.p_double[j] = sigmas.ptr.p_double[j]+ae_sqr(xy->ptr.pp_double[i][j]-means.ptr.p_double[j], _state); - } - } - for(i=0; i<=jmax; i++) - { - sigmas.ptr.p_double[i] = ae_sqrt(sigmas.ptr.p_double[i]/ssize, _state); - } - - /* - * Inputs - */ - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = means.ptr.p_double[i]; - network->columnsigmas.ptr.p_double[i] = sigmas.ptr.p_double[i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[i],0) ) - { - network->columnsigmas.ptr.p_double[i] = 1; - } - } - - /* - * Outputs - */ - if( !mlpissoftmax(network, _state) ) - { - for(i=0; i<=nout-1; i++) - { - offs = istart+(ntotal-nout+i)*mlpbase_nfieldwidth; - ntype = network->structinfo.ptr.p_int[offs+0]; - - /* - * Linear outputs - */ - if( ntype==0 ) - { - network->columnmeans.ptr.p_double[nin+i] = means.ptr.p_double[nin+i]; - network->columnsigmas.ptr.p_double[nin+i] = sigmas.ptr.p_double[nin+i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - - /* - * Bounded outputs (half-interval) - */ - if( ntype==3 ) - { - s = means.ptr.p_double[nin+i]-network->columnmeans.ptr.p_double[nin+i]; - if( ae_fp_eq(s,0) ) - { - s = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state); - } - if( ae_fp_eq(s,0) ) - { - s = 1.0; - } - network->columnsigmas.ptr.p_double[nin+i] = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state)*ae_fabs(s, _state); - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. -Initialization for preprocessor based on a sample. - -INPUT - Network - initialized neural network; - XY - sample, given by sparse matrix; - SSize - sample size. - -OUTPUT - Network - neural network with initialised preprocessor. - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpinitpreprocessorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t ssize, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t jmax; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t offs; - ae_int_t ntype; - ae_vector means; - ae_vector sigmas; - double s; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&means, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sigmas, 0, DT_REAL, _state, ae_true); - - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Means/Sigmas - */ - if( mlpissoftmax(network, _state) ) - { - jmax = nin-1; - } - else - { - jmax = nin+nout-1; - } - ae_vector_set_length(&means, jmax+1, _state); - ae_vector_set_length(&sigmas, jmax+1, _state); - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = 0; - sigmas.ptr.p_double[i] = 0; - } - for(i=0; i<=ssize-1; i++) - { - sparsegetrow(xy, i, &network->xyrow, _state); - for(j=0; j<=jmax; j++) - { - means.ptr.p_double[j] = means.ptr.p_double[j]+network->xyrow.ptr.p_double[j]; - } - } - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = means.ptr.p_double[i]/ssize; - } - for(i=0; i<=ssize-1; i++) - { - sparsegetrow(xy, i, &network->xyrow, _state); - for(j=0; j<=jmax; j++) - { - sigmas.ptr.p_double[j] = sigmas.ptr.p_double[j]+ae_sqr(network->xyrow.ptr.p_double[j]-means.ptr.p_double[j], _state); - } - } - for(i=0; i<=jmax; i++) - { - sigmas.ptr.p_double[i] = ae_sqrt(sigmas.ptr.p_double[i]/ssize, _state); - } - - /* - * Inputs - */ - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = means.ptr.p_double[i]; - network->columnsigmas.ptr.p_double[i] = sigmas.ptr.p_double[i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[i],0) ) - { - network->columnsigmas.ptr.p_double[i] = 1; - } - } - - /* - * Outputs - */ - if( !mlpissoftmax(network, _state) ) - { - for(i=0; i<=nout-1; i++) - { - offs = istart+(ntotal-nout+i)*mlpbase_nfieldwidth; - ntype = network->structinfo.ptr.p_int[offs+0]; - - /* - * Linear outputs - */ - if( ntype==0 ) - { - network->columnmeans.ptr.p_double[nin+i] = means.ptr.p_double[nin+i]; - network->columnsigmas.ptr.p_double[nin+i] = sigmas.ptr.p_double[nin+i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - - /* - * Bounded outputs (half-interval) - */ - if( ntype==3 ) - { - s = means.ptr.p_double[nin+i]-network->columnmeans.ptr.p_double[nin+i]; - if( ae_fp_eq(s,0) ) - { - s = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state); - } - if( ae_fp_eq(s,0) ) - { - s = 1.0; - } - network->columnsigmas.ptr.p_double[nin+i] = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state)*ae_fabs(s, _state); - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. -Initialization for preprocessor based on a subsample. - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array. - -OUTPUT: - Network - neural network with initialised preprocessor. - -NOTE: when SubsetSize<0 is used full dataset by call MLPInitPreprocessor - function. - - -- ALGLIB -- - Copyright 23.08.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpinitpreprocessorsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t jmax; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t offs; - ae_int_t ntype; - ae_vector means; - ae_vector sigmas; - double s; - ae_int_t npoints; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&means, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sigmas, 0, DT_REAL, _state, ae_true); - - ae_assert(setsize>=0, "MLPInitPreprocessorSubset: SetSize<0", _state); - if( subsetsize<0 ) - { - mlpinitpreprocessor(network, xy, setsize, _state); - ae_frame_leave(_state); - return; - } - ae_assert(subsetsize<=idx->cnt, "MLPInitPreprocessorSubset: SubsetSize>Length(Idx)", _state); - npoints = setsize; - for(i=0; i<=subsetsize-1; i++) - { - ae_assert(idx->ptr.p_int[i]>=0, "MLPInitPreprocessorSubset: incorrect index of XY row(Idx[I]<0)", _state); - ae_assert(idx->ptr.p_int[i]<=npoints-1, "MLPInitPreprocessorSubset: incorrect index of XY row(Idx[I]>Rows(XY)-1)", _state); - } - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Means/Sigmas - */ - if( mlpissoftmax(network, _state) ) - { - jmax = nin-1; - } - else - { - jmax = nin+nout-1; - } - ae_vector_set_length(&means, jmax+1, _state); - ae_vector_set_length(&sigmas, jmax+1, _state); - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = 0; - sigmas.ptr.p_double[i] = 0; - } - for(i=0; i<=subsetsize-1; i++) - { - for(j=0; j<=jmax; j++) - { - means.ptr.p_double[j] = means.ptr.p_double[j]+xy->ptr.pp_double[idx->ptr.p_int[i]][j]; - } - } - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = means.ptr.p_double[i]/subsetsize; - } - for(i=0; i<=subsetsize-1; i++) - { - for(j=0; j<=jmax; j++) - { - sigmas.ptr.p_double[j] = sigmas.ptr.p_double[j]+ae_sqr(xy->ptr.pp_double[idx->ptr.p_int[i]][j]-means.ptr.p_double[j], _state); - } - } - for(i=0; i<=jmax; i++) - { - sigmas.ptr.p_double[i] = ae_sqrt(sigmas.ptr.p_double[i]/subsetsize, _state); - } - - /* - * Inputs - */ - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = means.ptr.p_double[i]; - network->columnsigmas.ptr.p_double[i] = sigmas.ptr.p_double[i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[i],0) ) - { - network->columnsigmas.ptr.p_double[i] = 1; - } - } - - /* - * Outputs - */ - if( !mlpissoftmax(network, _state) ) - { - for(i=0; i<=nout-1; i++) - { - offs = istart+(ntotal-nout+i)*mlpbase_nfieldwidth; - ntype = network->structinfo.ptr.p_int[offs+0]; - - /* - * Linear outputs - */ - if( ntype==0 ) - { - network->columnmeans.ptr.p_double[nin+i] = means.ptr.p_double[nin+i]; - network->columnsigmas.ptr.p_double[nin+i] = sigmas.ptr.p_double[nin+i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - - /* - * Bounded outputs (half-interval) - */ - if( ntype==3 ) - { - s = means.ptr.p_double[nin+i]-network->columnmeans.ptr.p_double[nin+i]; - if( ae_fp_eq(s,0) ) - { - s = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state); - } - if( ae_fp_eq(s,0) ) - { - s = 1.0; - } - network->columnsigmas.ptr.p_double[nin+i] = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state)*ae_fabs(s, _state); - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. -Initialization for preprocessor based on a subsample. - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset, given by sparse matrix; - one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array. - -OUTPUT: - Network - neural network with initialised preprocessor. - -NOTE: when SubsetSize<0 is used full dataset by call - MLPInitPreprocessorSparse function. - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpinitpreprocessorsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t jmax; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t offs; - ae_int_t ntype; - ae_vector means; - ae_vector sigmas; - double s; - ae_int_t npoints; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&means, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sigmas, 0, DT_REAL, _state, ae_true); - - ae_assert(setsize>=0, "MLPInitPreprocessorSparseSubset: SetSize<0", _state); - if( subsetsize<0 ) - { - mlpinitpreprocessorsparse(network, xy, setsize, _state); - ae_frame_leave(_state); - return; - } - ae_assert(subsetsize<=idx->cnt, "MLPInitPreprocessorSparseSubset: SubsetSize>Length(Idx)", _state); - npoints = setsize; - for(i=0; i<=subsetsize-1; i++) - { - ae_assert(idx->ptr.p_int[i]>=0, "MLPInitPreprocessorSparseSubset: incorrect index of XY row(Idx[I]<0)", _state); - ae_assert(idx->ptr.p_int[i]<=npoints-1, "MLPInitPreprocessorSparseSubset: incorrect index of XY row(Idx[I]>Rows(XY)-1)", _state); - } - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Means/Sigmas - */ - if( mlpissoftmax(network, _state) ) - { - jmax = nin-1; - } - else - { - jmax = nin+nout-1; - } - ae_vector_set_length(&means, jmax+1, _state); - ae_vector_set_length(&sigmas, jmax+1, _state); - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = 0; - sigmas.ptr.p_double[i] = 0; - } - for(i=0; i<=subsetsize-1; i++) - { - sparsegetrow(xy, idx->ptr.p_int[i], &network->xyrow, _state); - for(j=0; j<=jmax; j++) - { - means.ptr.p_double[j] = means.ptr.p_double[j]+network->xyrow.ptr.p_double[j]; - } - } - for(i=0; i<=jmax; i++) - { - means.ptr.p_double[i] = means.ptr.p_double[i]/subsetsize; - } - for(i=0; i<=subsetsize-1; i++) - { - sparsegetrow(xy, idx->ptr.p_int[i], &network->xyrow, _state); - for(j=0; j<=jmax; j++) - { - sigmas.ptr.p_double[j] = sigmas.ptr.p_double[j]+ae_sqr(network->xyrow.ptr.p_double[j]-means.ptr.p_double[j], _state); - } - } - for(i=0; i<=jmax; i++) - { - sigmas.ptr.p_double[i] = ae_sqrt(sigmas.ptr.p_double[i]/subsetsize, _state); - } - - /* - * Inputs - */ - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = means.ptr.p_double[i]; - network->columnsigmas.ptr.p_double[i] = sigmas.ptr.p_double[i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[i],0) ) - { - network->columnsigmas.ptr.p_double[i] = 1; - } - } - - /* - * Outputs - */ - if( !mlpissoftmax(network, _state) ) - { - for(i=0; i<=nout-1; i++) - { - offs = istart+(ntotal-nout+i)*mlpbase_nfieldwidth; - ntype = network->structinfo.ptr.p_int[offs+0]; - - /* - * Linear outputs - */ - if( ntype==0 ) - { - network->columnmeans.ptr.p_double[nin+i] = means.ptr.p_double[nin+i]; - network->columnsigmas.ptr.p_double[nin+i] = sigmas.ptr.p_double[nin+i]; - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - - /* - * Bounded outputs (half-interval) - */ - if( ntype==3 ) - { - s = means.ptr.p_double[nin+i]-network->columnmeans.ptr.p_double[nin+i]; - if( ae_fp_eq(s,0) ) - { - s = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state); - } - if( ae_fp_eq(s,0) ) - { - s = 1.0; - } - network->columnsigmas.ptr.p_double[nin+i] = ae_sign(network->columnsigmas.ptr.p_double[nin+i], _state)*ae_fabs(s, _state); - if( ae_fp_eq(network->columnsigmas.ptr.p_double[nin+i],0) ) - { - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns information about initialized network: number of inputs, outputs, -weights. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpproperties(multilayerperceptron* network, - ae_int_t* nin, - ae_int_t* nout, - ae_int_t* wcount, - ae_state *_state) -{ - - *nin = 0; - *nout = 0; - *wcount = 0; - - *nin = network->structinfo.ptr.p_int[1]; - *nout = network->structinfo.ptr.p_int[2]; - *wcount = network->structinfo.ptr.p_int[4]; -} - - -/************************************************************************* -Returns number of "internal", low-level neurons in the network (one which -is stored in StructInfo). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpntotal(multilayerperceptron* network, ae_state *_state) -{ - ae_int_t result; - - - result = network->structinfo.ptr.p_int[3]; - return result; -} - - -/************************************************************************* -Returns number of inputs. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetinputscount(multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t result; - - - result = network->structinfo.ptr.p_int[1]; - return result; -} - - -/************************************************************************* -Returns number of outputs. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetoutputscount(multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t result; - - - result = network->structinfo.ptr.p_int[2]; - return result; -} - - -/************************************************************************* -Returns number of weights. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetweightscount(multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t result; - - - result = network->structinfo.ptr.p_int[4]; - return result; -} - - -/************************************************************************* -Tells whether network is SOFTMAX-normalized (i.e. classifier) or not. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -ae_bool mlpissoftmax(multilayerperceptron* network, ae_state *_state) -{ - ae_bool result; - - - result = network->structinfo.ptr.p_int[6]==1; - return result; -} - - -/************************************************************************* -This function returns total number of layers (including input, hidden and -output layers). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetlayerscount(multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t result; - - - result = network->hllayersizes.cnt; - return result; -} - - -/************************************************************************* -This function returns size of K-th layer. - -K=0 corresponds to input layer, K=CNT-1 corresponds to output layer. - -Size of the output layer is always equal to the number of outputs, although -when we have softmax-normalized network, last neuron doesn't have any -connections - it is just zero. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetlayersize(multilayerperceptron* network, - ae_int_t k, - ae_state *_state) -{ - ae_int_t result; - - - ae_assert(k>=0&&khllayersizes.cnt, "MLPGetLayerSize: incorrect layer index", _state); - result = network->hllayersizes.ptr.p_int[k]; - return result; -} - - -/************************************************************************* -This function returns offset/scaling coefficients for I-th input of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - -OUTPUT PARAMETERS: - Mean - mean term - Sigma - sigma term, guaranteed to be nonzero. - -I-th input is passed through linear transformation - IN[i] = (IN[i]-Mean)/Sigma -before feeding to the network - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetinputscaling(multilayerperceptron* network, - ae_int_t i, - double* mean, - double* sigma, - ae_state *_state) -{ - - *mean = 0; - *sigma = 0; - - ae_assert(i>=0&&ihllayersizes.ptr.p_int[0], "MLPGetInputScaling: incorrect (nonexistent) I", _state); - *mean = network->columnmeans.ptr.p_double[i]; - *sigma = network->columnsigmas.ptr.p_double[i]; - if( ae_fp_eq(*sigma,0) ) - { - *sigma = 1; - } -} - - -/************************************************************************* -This function returns offset/scaling coefficients for I-th output of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - -OUTPUT PARAMETERS: - Mean - mean term - Sigma - sigma term, guaranteed to be nonzero. - -I-th output is passed through linear transformation - OUT[i] = OUT[i]*Sigma+Mean -before returning it to user. In case we have SOFTMAX-normalized network, -we return (Mean,Sigma)=(0.0,1.0). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetoutputscaling(multilayerperceptron* network, - ae_int_t i, - double* mean, - double* sigma, - ae_state *_state) -{ - - *mean = 0; - *sigma = 0; - - ae_assert(i>=0&&ihllayersizes.ptr.p_int[network->hllayersizes.cnt-1], "MLPGetOutputScaling: incorrect (nonexistent) I", _state); - if( network->structinfo.ptr.p_int[6]==1 ) - { - *mean = 0; - *sigma = 1; - } - else - { - *mean = network->columnmeans.ptr.p_double[network->hllayersizes.ptr.p_int[0]+i]; - *sigma = network->columnsigmas.ptr.p_double[network->hllayersizes.ptr.p_int[0]+i]; - } -} - - -/************************************************************************* -This function returns information about Ith neuron of Kth layer - -INPUT PARAMETERS: - Network - network - K - layer index - I - neuron index (within layer) - -OUTPUT PARAMETERS: - FKind - activation function type (used by MLPActivationFunction()) - this value is zero for input or linear neurons - Threshold - also called offset, bias - zero for input neurons - -NOTE: this function throws exception if layer or neuron with given index -do not exists. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetneuroninfo(multilayerperceptron* network, - ae_int_t k, - ae_int_t i, - ae_int_t* fkind, - double* threshold, - ae_state *_state) -{ - ae_int_t ncnt; - ae_int_t istart; - ae_int_t highlevelidx; - ae_int_t activationoffset; - - *fkind = 0; - *threshold = 0; - - ncnt = network->hlneurons.cnt/mlpbase_hlnfieldwidth; - istart = network->structinfo.ptr.p_int[5]; - - /* - * search - */ - network->integerbuf.ptr.p_int[0] = k; - network->integerbuf.ptr.p_int[1] = i; - highlevelidx = recsearch(&network->hlneurons, mlpbase_hlnfieldwidth, 2, 0, ncnt, &network->integerbuf, _state); - ae_assert(highlevelidx>=0, "MLPGetNeuronInfo: incorrect (nonexistent) layer or neuron index", _state); - - /* - * 1. find offset of the activation function record in the - */ - if( network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+2]>=0 ) - { - activationoffset = istart+network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+2]*mlpbase_nfieldwidth; - *fkind = network->structinfo.ptr.p_int[activationoffset+0]; - } - else - { - *fkind = 0; - } - if( network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+3]>=0 ) - { - *threshold = network->weights.ptr.p_double[network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+3]]; - } - else - { - *threshold = 0; - } -} - - -/************************************************************************* -This function returns information about connection from I0-th neuron of -K0-th layer to I1-th neuron of K1-th layer. - -INPUT PARAMETERS: - Network - network - K0 - layer index - I0 - neuron index (within layer) - K1 - layer index - I1 - neuron index (within layer) - -RESULT: - connection weight (zero for non-existent connections) - -This function: -1. throws exception if layer or neuron with given index do not exists. -2. returns zero if neurons exist, but there is no connection between them - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -double mlpgetweight(multilayerperceptron* network, - ae_int_t k0, - ae_int_t i0, - ae_int_t k1, - ae_int_t i1, - ae_state *_state) -{ - ae_int_t ccnt; - ae_int_t highlevelidx; - double result; - - - ccnt = network->hlconnections.cnt/mlpbase_hlconnfieldwidth; - - /* - * check params - */ - ae_assert(k0>=0&&k0hllayersizes.cnt, "MLPGetWeight: incorrect (nonexistent) K0", _state); - ae_assert(i0>=0&&i0hllayersizes.ptr.p_int[k0], "MLPGetWeight: incorrect (nonexistent) I0", _state); - ae_assert(k1>=0&&k1hllayersizes.cnt, "MLPGetWeight: incorrect (nonexistent) K1", _state); - ae_assert(i1>=0&&i1hllayersizes.ptr.p_int[k1], "MLPGetWeight: incorrect (nonexistent) I1", _state); - - /* - * search - */ - network->integerbuf.ptr.p_int[0] = k0; - network->integerbuf.ptr.p_int[1] = i0; - network->integerbuf.ptr.p_int[2] = k1; - network->integerbuf.ptr.p_int[3] = i1; - highlevelidx = recsearch(&network->hlconnections, mlpbase_hlconnfieldwidth, 4, 0, ccnt, &network->integerbuf, _state); - if( highlevelidx>=0 ) - { - result = network->weights.ptr.p_double[network->hlconnections.ptr.p_int[highlevelidx*mlpbase_hlconnfieldwidth+4]]; - } - else - { - result = 0; - } - return result; -} - - -/************************************************************************* -This function sets offset/scaling coefficients for I-th input of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - Mean - mean term - Sigma - sigma term (if zero, will be replaced by 1.0) - -NTE: I-th input is passed through linear transformation - IN[i] = (IN[i]-Mean)/Sigma -before feeding to the network. This function sets Mean and Sigma. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetinputscaling(multilayerperceptron* network, - ae_int_t i, - double mean, - double sigma, - ae_state *_state) -{ - - - ae_assert(i>=0&&ihllayersizes.ptr.p_int[0], "MLPSetInputScaling: incorrect (nonexistent) I", _state); - ae_assert(ae_isfinite(mean, _state), "MLPSetInputScaling: infinite or NAN Mean", _state); - ae_assert(ae_isfinite(sigma, _state), "MLPSetInputScaling: infinite or NAN Sigma", _state); - if( ae_fp_eq(sigma,0) ) - { - sigma = 1; - } - network->columnmeans.ptr.p_double[i] = mean; - network->columnsigmas.ptr.p_double[i] = sigma; -} - - -/************************************************************************* -This function sets offset/scaling coefficients for I-th output of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - Mean - mean term - Sigma - sigma term (if zero, will be replaced by 1.0) - -OUTPUT PARAMETERS: - -NOTE: I-th output is passed through linear transformation - OUT[i] = OUT[i]*Sigma+Mean -before returning it to user. This function sets Sigma/Mean. In case we -have SOFTMAX-normalized network, you can not set (Sigma,Mean) to anything -other than(0.0,1.0) - this function will throw exception. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetoutputscaling(multilayerperceptron* network, - ae_int_t i, - double mean, - double sigma, - ae_state *_state) -{ - - - ae_assert(i>=0&&ihllayersizes.ptr.p_int[network->hllayersizes.cnt-1], "MLPSetOutputScaling: incorrect (nonexistent) I", _state); - ae_assert(ae_isfinite(mean, _state), "MLPSetOutputScaling: infinite or NAN Mean", _state); - ae_assert(ae_isfinite(sigma, _state), "MLPSetOutputScaling: infinite or NAN Sigma", _state); - if( network->structinfo.ptr.p_int[6]==1 ) - { - ae_assert(ae_fp_eq(mean,0), "MLPSetOutputScaling: you can not set non-zero Mean term for classifier network", _state); - ae_assert(ae_fp_eq(sigma,1), "MLPSetOutputScaling: you can not set non-unit Sigma term for classifier network", _state); - } - else - { - if( ae_fp_eq(sigma,0) ) - { - sigma = 1; - } - network->columnmeans.ptr.p_double[network->hllayersizes.ptr.p_int[0]+i] = mean; - network->columnsigmas.ptr.p_double[network->hllayersizes.ptr.p_int[0]+i] = sigma; - } -} - - -/************************************************************************* -This function modifies information about Ith neuron of Kth layer - -INPUT PARAMETERS: - Network - network - K - layer index - I - neuron index (within layer) - FKind - activation function type (used by MLPActivationFunction()) - this value must be zero for input neurons - (you can not set activation function for input neurons) - Threshold - also called offset, bias - this value must be zero for input neurons - (you can not set threshold for input neurons) - -NOTES: -1. this function throws exception if layer or neuron with given index do - not exists. -2. this function also throws exception when you try to set non-linear - activation function for input neurons (any kind of network) or for output - neurons of classifier network. -3. this function throws exception when you try to set non-zero threshold for - input neurons (any kind of network). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetneuroninfo(multilayerperceptron* network, - ae_int_t k, - ae_int_t i, - ae_int_t fkind, - double threshold, - ae_state *_state) -{ - ae_int_t ncnt; - ae_int_t istart; - ae_int_t highlevelidx; - ae_int_t activationoffset; - - - ae_assert(ae_isfinite(threshold, _state), "MLPSetNeuronInfo: infinite or NAN Threshold", _state); - - /* - * convenience vars - */ - ncnt = network->hlneurons.cnt/mlpbase_hlnfieldwidth; - istart = network->structinfo.ptr.p_int[5]; - - /* - * search - */ - network->integerbuf.ptr.p_int[0] = k; - network->integerbuf.ptr.p_int[1] = i; - highlevelidx = recsearch(&network->hlneurons, mlpbase_hlnfieldwidth, 2, 0, ncnt, &network->integerbuf, _state); - ae_assert(highlevelidx>=0, "MLPSetNeuronInfo: incorrect (nonexistent) layer or neuron index", _state); - - /* - * activation function - */ - if( network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+2]>=0 ) - { - activationoffset = istart+network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+2]*mlpbase_nfieldwidth; - network->structinfo.ptr.p_int[activationoffset+0] = fkind; - } - else - { - ae_assert(fkind==0, "MLPSetNeuronInfo: you try to set activation function for neuron which can not have one", _state); - } - - /* - * Threshold - */ - if( network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+3]>=0 ) - { - network->weights.ptr.p_double[network->hlneurons.ptr.p_int[highlevelidx*mlpbase_hlnfieldwidth+3]] = threshold; - } - else - { - ae_assert(ae_fp_eq(threshold,0), "MLPSetNeuronInfo: you try to set non-zero threshold for neuron which can not have one", _state); - } -} - - -/************************************************************************* -This function modifies information about connection from I0-th neuron of -K0-th layer to I1-th neuron of K1-th layer. - -INPUT PARAMETERS: - Network - network - K0 - layer index - I0 - neuron index (within layer) - K1 - layer index - I1 - neuron index (within layer) - W - connection weight (must be zero for non-existent - connections) - -This function: -1. throws exception if layer or neuron with given index do not exists. -2. throws exception if you try to set non-zero weight for non-existent - connection - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetweight(multilayerperceptron* network, - ae_int_t k0, - ae_int_t i0, - ae_int_t k1, - ae_int_t i1, - double w, - ae_state *_state) -{ - ae_int_t ccnt; - ae_int_t highlevelidx; - - - ccnt = network->hlconnections.cnt/mlpbase_hlconnfieldwidth; - - /* - * check params - */ - ae_assert(k0>=0&&k0hllayersizes.cnt, "MLPSetWeight: incorrect (nonexistent) K0", _state); - ae_assert(i0>=0&&i0hllayersizes.ptr.p_int[k0], "MLPSetWeight: incorrect (nonexistent) I0", _state); - ae_assert(k1>=0&&k1hllayersizes.cnt, "MLPSetWeight: incorrect (nonexistent) K1", _state); - ae_assert(i1>=0&&i1hllayersizes.ptr.p_int[k1], "MLPSetWeight: incorrect (nonexistent) I1", _state); - ae_assert(ae_isfinite(w, _state), "MLPSetWeight: infinite or NAN weight", _state); - - /* - * search - */ - network->integerbuf.ptr.p_int[0] = k0; - network->integerbuf.ptr.p_int[1] = i0; - network->integerbuf.ptr.p_int[2] = k1; - network->integerbuf.ptr.p_int[3] = i1; - highlevelidx = recsearch(&network->hlconnections, mlpbase_hlconnfieldwidth, 4, 0, ccnt, &network->integerbuf, _state); - if( highlevelidx>=0 ) - { - network->weights.ptr.p_double[network->hlconnections.ptr.p_int[highlevelidx*mlpbase_hlconnfieldwidth+4]] = w; - } - else - { - ae_assert(ae_fp_eq(w,0), "MLPSetWeight: you try to set non-zero weight for non-existent connection", _state); - } -} - - -/************************************************************************* -Neural network activation function - -INPUT PARAMETERS: - NET - neuron input - K - function index (zero for linear function) - -OUTPUT PARAMETERS: - F - function - DF - its derivative - D2F - its second derivative - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpactivationfunction(double net, - ae_int_t k, - double* f, - double* df, - double* d2f, - ae_state *_state) -{ - double net2; - double arg; - double root; - double r; - - *f = 0; - *df = 0; - *d2f = 0; - - if( k==0||k==-5 ) - { - *f = net; - *df = 1; - *d2f = 0; - return; - } - if( k==1 ) - { - - /* - * TanH activation function - */ - if( ae_fp_less(ae_fabs(net, _state),100) ) - { - *f = ae_tanh(net, _state); - } - else - { - *f = ae_sign(net, _state); - } - *df = 1-*f*(*f); - *d2f = -2*(*f)*(*df); - return; - } - if( k==3 ) - { - - /* - * EX activation function - */ - if( ae_fp_greater_eq(net,0) ) - { - net2 = net*net; - arg = net2+1; - root = ae_sqrt(arg, _state); - *f = net+root; - r = net/root; - *df = 1+r; - *d2f = (root-net*r)/arg; - } - else - { - *f = ae_exp(net, _state); - *df = *f; - *d2f = *f; - } - return; - } - if( k==2 ) - { - *f = ae_exp(-ae_sqr(net, _state), _state); - *df = -2*net*(*f); - *d2f = -2*(*f+*df*net); - return; - } - *f = 0; - *df = 0; - *d2f = 0; -} - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - Network - neural network - X - input vector, array[0..NIn-1]. - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - -See also MLPProcessI - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpprocess(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - - - if( y->cntstructinfo.ptr.p_int[2] ) - { - ae_vector_set_length(y, network->structinfo.ptr.p_int[2], _state); - } - mlpinternalprocessvector(&network->structinfo, &network->weights, &network->columnmeans, &network->columnsigmas, &network->neurons, &network->dfdnet, x, y, _state); -} - - -/************************************************************************* -'interactive' variant of MLPProcess for languages like Python which -support constructs like "Y = MLPProcess(NN,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 21.09.2010 by Bochkanov Sergey -*************************************************************************/ -void mlpprocessi(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - - ae_vector_clear(y); - - mlpprocess(network, x, y, _state); -} - - -/************************************************************************* -Error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x, depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlperror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(xy->rows>=npoints, "MLPError: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPError: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPError: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, xy, &network->dummysxy, npoints, 0, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = ae_sqr(network->err.rmserror, _state)*npoints*mlpgetoutputscount(network, _state)/2; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlperror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlperror(network,xy,npoints, _state); -} - - -/************************************************************************* -Error of the neural network on dataset given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x, depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0 - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPErrorSparse: XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=npoints, "MLPErrorSparse: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPErrorSparse: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPErrorSparse: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, &network->dummydxy, xy, npoints, 1, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = ae_sqr(network->err.rmserror, _state)*npoints*mlpgetoutputscount(network, _state)/2; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlperrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlperrorsparse(network,xy,npoints, _state); -} - - -/************************************************************************* -Natural error function for neural network, internal subroutine. - -NOTE: this function is single-threaded. Unlike other error function, it -receives no speed-up from being executed in SMP mode. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlperrorn(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - double e; - double result; - - - mlpproperties(network, &nin, &nout, &wcount, _state); - result = 0; - for(i=0; i<=ssize-1; i++) - { - - /* - * Process vector - */ - ae_v_move(&network->x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nin-1)); - mlpprocess(network, &network->x, &network->y, _state); - - /* - * Update error function - */ - if( network->structinfo.ptr.p_int[6]==0 ) - { - - /* - * Least squares error function - */ - ae_v_sub(&network->y.ptr.p_double[0], 1, &xy->ptr.pp_double[i][nin], 1, ae_v_len(0,nout-1)); - e = ae_v_dotproduct(&network->y.ptr.p_double[0], 1, &network->y.ptr.p_double[0], 1, ae_v_len(0,nout-1)); - result = result+e/2; - } - else - { - - /* - * Cross-entropy error function - */ - k = ae_round(xy->ptr.pp_double[i][nin], _state); - if( k>=0&&ky.ptr.p_double[k], _state); - } - } - } - return result; -} - - -/************************************************************************* -Classification error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: - classification error (number of misclassified cases) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_int_t result; - - - ae_assert(xy->rows>=npoints, "MLPClsError: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPClsError: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPClsError: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, xy, &network->dummysxy, npoints, 0, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = ae_round(npoints*network->err.relclserror, _state); - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -ae_int_t _pexec_mlpclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpclserror(network,xy,npoints, _state); -} - - -/************************************************************************* -Relative classification error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Percent of incorrectly classified cases. Works both for classifier -networks and general purpose networks used as classifiers. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 25.12.2008 by Bochkanov Sergey -*************************************************************************/ -double mlprelclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(xy->rows>=npoints, "MLPRelClsError: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPRelClsError: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPRelClsError: XY has less than NIn+NOut columns", _state); - } - } - if( npoints>0 ) - { - result = (double)mlpclserror(network, xy, npoints, _state)/(double)npoints; - } - else - { - result = 0.0; - } - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlprelclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlprelclserror(network,xy,npoints, _state); -} - - -/************************************************************************* -Relative classification error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. Sparse matrix must use CRS format - for storage. - NPoints - points count, >=0. - -RESULT: -Percent of incorrectly classified cases. Works both for classifier -networks and general purpose networks used as classifiers. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlprelclserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPRelClsErrorSparse: sparse matrix XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=npoints, "MLPRelClsErrorSparse: sparse matrix XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPRelClsErrorSparse: sparse matrix XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPRelClsErrorSparse: sparse matrix XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, &network->dummydxy, xy, npoints, 1, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.relclserror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlprelclserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlprelclserrorsparse(network,xy,npoints, _state); -} - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -CrossEntropy/(NPoints*LN(2)). -Zero if network solves regression task. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 08.01.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpavgce(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(xy->rows>=npoints, "MLPAvgCE: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPAvgCE: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAvgCE: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, xy, &network->dummysxy, npoints, 0, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.avgce; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlpavgce(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpavgce(network,xy,npoints, _state); -} - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set given by -sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -CrossEntropy/(NPoints*LN(2)). -Zero if network solves regression task. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 9.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgcesparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPAvgCESparse: sparse matrix XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=npoints, "MLPAvgCESparse: sparse matrix XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPAvgCESparse: sparse matrix XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAvgCESparse: sparse matrix XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, &network->dummydxy, xy, npoints, 1, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.avgce; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlpavgcesparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpavgcesparse(network,xy,npoints, _state); -} - - -/************************************************************************* -RMS error on the test set given. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Root mean square error. Its meaning for regression task is obvious. As for -classification task, RMS error means error when estimating posterior -probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlprmserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(xy->rows>=npoints, "MLPRMSError: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPRMSError: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPRMSError: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, xy, &network->dummysxy, npoints, 0, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.rmserror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlprmserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlprmserror(network,xy,npoints, _state); -} - - -/************************************************************************* -RMS error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Root mean square error. Its meaning for regression task is obvious. As for -classification task, RMS error means error when estimating posterior -probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlprmserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPRMSErrorSparse: sparse matrix XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=npoints, "MLPRMSErrorSparse: sparse matrix XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPRMSErrorSparse: sparse matrix XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPRMSErrorSparse: sparse matrix XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, &network->dummydxy, xy, npoints, 1, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.rmserror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlprmserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlprmserrorsparse(network,xy,npoints, _state); -} - - -/************************************************************************* -Average absolute error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average error when estimating posterior probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 11.03.2008 by Bochkanov Sergey -*************************************************************************/ -double mlpavgerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(xy->rows>=npoints, "MLPAvgError: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPAvgError: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAvgError: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, xy, &network->dummysxy, npoints, 0, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.avgerror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlpavgerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpavgerror(network,xy,npoints, _state); -} - - -/************************************************************************* -Average absolute error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average error when estimating posterior probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPAvgErrorSparse: XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=npoints, "MLPAvgErrorSparse: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPAvgErrorSparse: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAvgErrorSparse: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, &network->dummydxy, xy, npoints, 1, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.avgerror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlpavgerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpavgerrorsparse(network,xy,npoints, _state); -} - - -/************************************************************************* -Average relative error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average relative error when estimating posterior probability of -belonging to the correct class. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 11.03.2008 by Bochkanov Sergey -*************************************************************************/ -double mlpavgrelerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(xy->rows>=npoints, "MLPAvgRelError: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPAvgRelError: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAvgRelError: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, xy, &network->dummysxy, npoints, 0, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.avgrelerror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlpavgrelerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpavgrelerror(network,xy,npoints, _state); -} - - -/************************************************************************* -Average relative error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average relative error when estimating posterior probability of -belonging to the correct class. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgrelerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPAvgRelErrorSparse: XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=npoints, "MLPAvgRelErrorSparse: XY has less than NPoints rows", _state); - if( npoints>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPAvgRelErrorSparse: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAvgRelErrorSparse: XY has less than NIn+NOut columns", _state); - } - } - mlpallerrorsx(network, &network->dummydxy, xy, npoints, 1, &network->dummyidx, 0, npoints, 0, &network->buf, &network->err, _state); - result = network->err.avgrelerror; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlpavgrelerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state) -{ - return mlpavgrelerrorsparse(network,xy,npoints, _state); -} - - -/************************************************************************* -Gradient calculation - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - X - input vector, length of array must be at least NIn - DesiredY- desired outputs, length of array must be at least NOut - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgrad(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* desiredy, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_int_t i; - ae_int_t nout; - ae_int_t ntotal; - - *e = 0; - - - /* - * Alloc - */ - rvectorsetlengthatleast(grad, network->structinfo.ptr.p_int[4], _state); - - /* - * Prepare dError/dOut, internal structures - */ - mlpprocess(network, x, &network->y, _state); - nout = network->structinfo.ptr.p_int[2]; - ntotal = network->structinfo.ptr.p_int[3]; - *e = 0; - for(i=0; i<=ntotal-1; i++) - { - network->derror.ptr.p_double[i] = 0; - } - for(i=0; i<=nout-1; i++) - { - network->derror.ptr.p_double[ntotal-nout+i] = network->y.ptr.p_double[i]-desiredy->ptr.p_double[i]; - *e = *e+ae_sqr(network->y.ptr.p_double[i]-desiredy->ptr.p_double[i], _state)/2; - } - - /* - * gradient - */ - mlpbase_mlpinternalcalculategradient(network, &network->neurons, &network->weights, &network->derror, grad, ae_false, _state); -} - - -/************************************************************************* -Gradient calculation (natural error function is used) - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - X - input vector, length of array must be at least NIn - DesiredY- desired outputs, length of array must be at least NOut - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, sum-of-squares for regression networks, - cross-entropy for classification networks. - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradn(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* desiredy, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - double s; - ae_int_t i; - ae_int_t nout; - ae_int_t ntotal; - - *e = 0; - - - /* - * Alloc - */ - rvectorsetlengthatleast(grad, network->structinfo.ptr.p_int[4], _state); - - /* - * Prepare dError/dOut, internal structures - */ - mlpprocess(network, x, &network->y, _state); - nout = network->structinfo.ptr.p_int[2]; - ntotal = network->structinfo.ptr.p_int[3]; - for(i=0; i<=ntotal-1; i++) - { - network->derror.ptr.p_double[i] = 0; - } - *e = 0; - if( network->structinfo.ptr.p_int[6]==0 ) - { - - /* - * Regression network, least squares - */ - for(i=0; i<=nout-1; i++) - { - network->derror.ptr.p_double[ntotal-nout+i] = network->y.ptr.p_double[i]-desiredy->ptr.p_double[i]; - *e = *e+ae_sqr(network->y.ptr.p_double[i]-desiredy->ptr.p_double[i], _state)/2; - } - } - else - { - - /* - * Classification network, cross-entropy - */ - s = 0; - for(i=0; i<=nout-1; i++) - { - s = s+desiredy->ptr.p_double[i]; - } - for(i=0; i<=nout-1; i++) - { - network->derror.ptr.p_double[ntotal-nout+i] = s*network->y.ptr.p_double[i]-desiredy->ptr.p_double[i]; - *e = *e+mlpbase_safecrossentropy(desiredy->ptr.p_double[i], network->y.ptr.p_double[i], _state); - } - } - - /* - * gradient - */ - mlpbase_mlpinternalcalculategradient(network, &network->neurons, &network->weights, &network->derror, grad, ae_true, _state); -} - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in dense format; one sample = one row: - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t subset0; - ae_int_t subset1; - ae_int_t subsettype; - smlpgrad *sgrad; - ae_smart_ptr _sgrad; - - ae_frame_make(_state, &_frame_block); - *e = 0; - ae_smart_ptr_init(&_sgrad, (void**)&sgrad, _state, ae_true); - - ae_assert(ssize>=0, "MLPGradBatchSparse: SSize<0", _state); - subset0 = 0; - subset1 = ssize; - subsettype = 0; - mlpproperties(network, &nin, &nout, &wcount, _state); - rvectorsetlengthatleast(grad, wcount, _state); - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - sgrad->f = 0.0; - for(i=0; i<=wcount-1; i++) - { - sgrad->g.ptr.p_double[i] = 0.0; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - mlpgradbatchx(network, xy, &network->dummysxy, ssize, 0, &network->dummyidx, subset0, subset1, subsettype, &network->buf, &network->gradbuf, _state); - *e = 0.0; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = 0.0; - } - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - *e = *e+sgrad->f; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = grad->ptr.p_double[i]+sgrad->g.ptr.p_double[i]; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpgradbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state) -{ - mlpgradbatch(network,xy,ssize,e,grad, _state); -} - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs given by sparse -matrices - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in sparse format; one sample = one row: - * MATRIX MUST BE STORED IN CRS FORMAT - * first NIn columns contain inputs. - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t subset0; - ae_int_t subset1; - ae_int_t subsettype; - smlpgrad *sgrad; - ae_smart_ptr _sgrad; - - ae_frame_make(_state, &_frame_block); - *e = 0; - ae_smart_ptr_init(&_sgrad, (void**)&sgrad, _state, ae_true); - - ae_assert(ssize>=0, "MLPGradBatchSparse: SSize<0", _state); - ae_assert(sparseiscrs(xy, _state), "MLPGradBatchSparse: sparse matrix XY must be in CRS format.", _state); - subset0 = 0; - subset1 = ssize; - subsettype = 0; - mlpproperties(network, &nin, &nout, &wcount, _state); - rvectorsetlengthatleast(grad, wcount, _state); - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - sgrad->f = 0.0; - for(i=0; i<=wcount-1; i++) - { - sgrad->g.ptr.p_double[i] = 0.0; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - mlpgradbatchx(network, &network->dummydxy, xy, ssize, 1, &network->dummyidx, subset0, subset1, subsettype, &network->buf, &network->gradbuf, _state); - *e = 0.0; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = 0.0; - } - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - *e = *e+sgrad->f; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = grad->ptr.p_double[i]+sgrad->g.ptr.p_double[i]; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpgradbatchsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state) -{ - mlpgradbatchsparse(network,xy,ssize,e,grad, _state); -} - - -/************************************************************************* -Batch gradient calculation for a subset of dataset - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in dense format; one sample = one row: - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array: - * positive value means that subset given by Idx[] is processed - * zero value results in zero gradient - * negative value means that full dataset is processed - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, - array[WCount] - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t npoints; - ae_int_t subset0; - ae_int_t subset1; - ae_int_t subsettype; - smlpgrad *sgrad; - ae_smart_ptr _sgrad; - - ae_frame_make(_state, &_frame_block); - *e = 0; - ae_smart_ptr_init(&_sgrad, (void**)&sgrad, _state, ae_true); - - ae_assert(setsize>=0, "MLPGradBatchSubset: SetSize<0", _state); - ae_assert(subsetsize<=idx->cnt, "MLPGradBatchSubset: SubsetSize>Length(Idx)", _state); - npoints = setsize; - if( subsetsize<0 ) - { - subset0 = 0; - subset1 = setsize; - subsettype = 0; - } - else - { - subset0 = 0; - subset1 = subsetsize; - subsettype = 1; - for(i=0; i<=subsetsize-1; i++) - { - ae_assert(idx->ptr.p_int[i]>=0, "MLPGradBatchSubset: incorrect index of XY row(Idx[I]<0)", _state); - ae_assert(idx->ptr.p_int[i]<=npoints-1, "MLPGradBatchSubset: incorrect index of XY row(Idx[I]>Rows(XY)-1)", _state); - } - } - mlpproperties(network, &nin, &nout, &wcount, _state); - rvectorsetlengthatleast(grad, wcount, _state); - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - sgrad->f = 0.0; - for(i=0; i<=wcount-1; i++) - { - sgrad->g.ptr.p_double[i] = 0.0; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - mlpgradbatchx(network, xy, &network->dummysxy, setsize, 0, idx, subset0, subset1, subsettype, &network->buf, &network->gradbuf, _state); - *e = 0.0; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = 0.0; - } - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - *e = *e+sgrad->f; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = grad->ptr.p_double[i]+sgrad->g.ptr.p_double[i]; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpgradbatchsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state) -{ - mlpgradbatchsubset(network,xy,setsize,idx,subsetsize,e,grad, _state); -} - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs for a subset of -dataset given by set of indexes. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in sparse format; one sample = one row: - * MATRIX MUST BE STORED IN CRS FORMAT - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array: - * positive value means that subset given by Idx[] is processed - * zero value results in zero gradient - * negative value means that full dataset is processed - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, - array[WCount] - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatchSparse - function. - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t npoints; - ae_int_t subset0; - ae_int_t subset1; - ae_int_t subsettype; - smlpgrad *sgrad; - ae_smart_ptr _sgrad; - - ae_frame_make(_state, &_frame_block); - *e = 0; - ae_smart_ptr_init(&_sgrad, (void**)&sgrad, _state, ae_true); - - ae_assert(setsize>=0, "MLPGradBatchSparseSubset: SetSize<0", _state); - ae_assert(subsetsize<=idx->cnt, "MLPGradBatchSparseSubset: SubsetSize>Length(Idx)", _state); - ae_assert(sparseiscrs(xy, _state), "MLPGradBatchSparseSubset: sparse matrix XY must be in CRS format.", _state); - npoints = setsize; - if( subsetsize<0 ) - { - subset0 = 0; - subset1 = setsize; - subsettype = 0; - } - else - { - subset0 = 0; - subset1 = subsetsize; - subsettype = 1; - for(i=0; i<=subsetsize-1; i++) - { - ae_assert(idx->ptr.p_int[i]>=0, "MLPGradBatchSparseSubset: incorrect index of XY row(Idx[I]<0)", _state); - ae_assert(idx->ptr.p_int[i]<=npoints-1, "MLPGradBatchSparseSubset: incorrect index of XY row(Idx[I]>Rows(XY)-1)", _state); - } - } - mlpproperties(network, &nin, &nout, &wcount, _state); - rvectorsetlengthatleast(grad, wcount, _state); - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - sgrad->f = 0.0; - for(i=0; i<=wcount-1; i++) - { - sgrad->g.ptr.p_double[i] = 0.0; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - mlpgradbatchx(network, &network->dummydxy, xy, setsize, 1, idx, subset0, subset1, subsettype, &network->buf, &network->gradbuf, _state); - *e = 0.0; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = 0.0; - } - ae_shared_pool_first_recycled(&network->gradbuf, &_sgrad, _state); - while(sgrad!=NULL) - { - *e = *e+sgrad->f; - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = grad->ptr.p_double[i]+sgrad->g.ptr.p_double[i]; - } - ae_shared_pool_next_recycled(&network->gradbuf, &_sgrad, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpgradbatchsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state) -{ - mlpgradbatchsparsesubset(network,xy,setsize,idx,subsetsize,e,grad, _state); -} - - -void mlpgradbatchx(multilayerperceptron* network, - /* Real */ ae_matrix* densexy, - sparsematrix* sparsexy, - ae_int_t datasetsize, - ae_int_t datasettype, - /* Integer */ ae_vector* idx, - ae_int_t subset0, - ae_int_t subset1, - ae_int_t subsettype, - ae_shared_pool* buf, - ae_shared_pool* gradbuf, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t rowsize; - ae_int_t srcidx; - ae_int_t cstart; - ae_int_t csize; - ae_int_t j; - double problemcost; - mlpbuffers *buf2; - ae_smart_ptr _buf2; - ae_int_t len0; - ae_int_t len1; - mlpbuffers *pbuf; - ae_smart_ptr _pbuf; - smlpgrad *sgrad; - ae_smart_ptr _sgrad; - - ae_frame_make(_state, &_frame_block); - ae_smart_ptr_init(&_buf2, (void**)&buf2, _state, ae_true); - ae_smart_ptr_init(&_pbuf, (void**)&pbuf, _state, ae_true); - ae_smart_ptr_init(&_sgrad, (void**)&sgrad, _state, ae_true); - - ae_assert(datasetsize>=0, "MLPGradBatchX: SetSize<0", _state); - ae_assert(datasettype==0||datasettype==1, "MLPGradBatchX: DatasetType is incorrect", _state); - ae_assert(subsettype==0||subsettype==1, "MLPGradBatchX: SubsetType is incorrect", _state); - - /* - * Determine network and dataset properties - */ - mlpproperties(network, &nin, &nout, &wcount, _state); - if( mlpissoftmax(network, _state) ) - { - rowsize = nin+1; - } - else - { - rowsize = nin+nout; - } - - /* - * Split problem. - * - * Splitting problem allows us to reduce effect of single-precision - * arithmetics (SSE-optimized version of MLPChunkedGradient uses single - * precision internally, but converts them to double precision after - * results are exported from HPC buffer to network). Small batches are - * calculated in single precision, results are aggregated in double - * precision, and it allows us to avoid accumulation of errors when - * we process very large batches (tens of thousands of items). - * - * NOTE: it is important to use real arithmetics for ProblemCost - * because ProblemCost may be larger than MAXINT. - */ - problemcost = subset1-subset0; - problemcost = problemcost*wcount; - if( subset1-subset0>=2*mlpbase_microbatchsize&&ae_fp_greater(problemcost,mlpbase_gradbasecasecost) ) - { - splitlength(subset1-subset0, mlpbase_microbatchsize, &len0, &len1, _state); - mlpgradbatchx(network, densexy, sparsexy, datasetsize, datasettype, idx, subset0, subset0+len0, subsettype, buf, gradbuf, _state); - mlpgradbatchx(network, densexy, sparsexy, datasetsize, datasettype, idx, subset0+len0, subset1, subsettype, buf, gradbuf, _state); - ae_frame_leave(_state); - return; - } - - /* - * Chunked processing - */ - ae_shared_pool_retrieve(gradbuf, &_sgrad, _state); - ae_shared_pool_retrieve(buf, &_pbuf, _state); - hpcpreparechunkedgradient(&network->weights, wcount, mlpntotal(network, _state), nin, nout, pbuf, _state); - cstart = subset0; - while(cstartchunksize, _state)-cstart; - for(j=0; j<=csize-1; j++) - { - srcidx = -1; - if( subsettype==0 ) - { - srcidx = cstart+j; - } - if( subsettype==1 ) - { - srcidx = idx->ptr.p_int[cstart+j]; - } - ae_assert(srcidx>=0, "MLPGradBatchX: internal error", _state); - if( datasettype==0 ) - { - ae_v_move(&pbuf->xy.ptr.pp_double[j][0], 1, &densexy->ptr.pp_double[srcidx][0], 1, ae_v_len(0,rowsize-1)); - } - if( datasettype==1 ) - { - sparsegetrow(sparsexy, srcidx, &pbuf->xyrow, _state); - ae_v_move(&pbuf->xy.ptr.pp_double[j][0], 1, &pbuf->xyrow.ptr.p_double[0], 1, ae_v_len(0,rowsize-1)); - } - } - - /* - * Process chunk and advance line pointer - */ - mlpbase_mlpchunkedgradient(network, &pbuf->xy, 0, csize, &pbuf->batch4buf, &pbuf->hpcbuf, &sgrad->f, ae_false, _state); - cstart = cstart+pbuf->chunksize; - } - hpcfinalizechunkedgradient(pbuf, &sgrad->g, _state); - ae_shared_pool_recycle(buf, &_pbuf, _state); - ae_shared_pool_recycle(gradbuf, &_sgrad, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs -(natural error function is used) - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - set of inputs/outputs; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, sum-of-squares for regression networks, - cross-entropy for classification networks. - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradnbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - mlpbuffers *pbuf; - ae_smart_ptr _pbuf; - - ae_frame_make(_state, &_frame_block); - *e = 0; - ae_smart_ptr_init(&_pbuf, (void**)&pbuf, _state, ae_true); - - - /* - * Alloc - */ - mlpproperties(network, &nin, &nout, &wcount, _state); - ae_shared_pool_retrieve(&network->buf, &_pbuf, _state); - hpcpreparechunkedgradient(&network->weights, wcount, mlpntotal(network, _state), nin, nout, pbuf, _state); - rvectorsetlengthatleast(grad, wcount, _state); - for(i=0; i<=wcount-1; i++) - { - grad->ptr.p_double[i] = 0; - } - *e = 0; - i = 0; - while(i<=ssize-1) - { - mlpbase_mlpchunkedgradient(network, xy, i, ae_minint(ssize, i+pbuf->chunksize, _state)-i, &pbuf->batch4buf, &pbuf->hpcbuf, e, ae_true, _state); - i = i+pbuf->chunksize; - } - hpcfinalizechunkedgradient(pbuf, grad, _state); - ae_shared_pool_recycle(&network->buf, &_pbuf, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Batch Hessian calculation (natural error function) using R-algorithm. -Internal subroutine. - - -- ALGLIB -- - Copyright 26.01.2008 by Bochkanov Sergey. - - Hessian calculation based on R-algorithm described in - "Fast Exact Multiplication by the Hessian", - B. A. Pearlmutter, - Neural Computation, 1994. -*************************************************************************/ -void mlphessiannbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - /* Real */ ae_matrix* h, - ae_state *_state) -{ - - *e = 0; - - mlpbase_mlphessianbatchinternal(network, xy, ssize, ae_true, e, grad, h, _state); -} - - -/************************************************************************* -Batch Hessian calculation using R-algorithm. -Internal subroutine. - - -- ALGLIB -- - Copyright 26.01.2008 by Bochkanov Sergey. - - Hessian calculation based on R-algorithm described in - "Fast Exact Multiplication by the Hessian", - B. A. Pearlmutter, - Neural Computation, 1994. -*************************************************************************/ -void mlphessianbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - /* Real */ ae_matrix* h, - ae_state *_state) -{ - - *e = 0; - - mlpbase_mlphessianbatchinternal(network, xy, ssize, ae_false, e, grad, h, _state); -} - - -/************************************************************************* -Internal subroutine, shouldn't be called by user. -*************************************************************************/ -void mlpinternalprocessvector(/* Integer */ ae_vector* structinfo, - /* Real */ ae_vector* weights, - /* Real */ ae_vector* columnmeans, - /* Real */ ae_vector* columnsigmas, - /* Real */ ae_vector* neurons, - /* Real */ ae_vector* dfdnet, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n1; - ae_int_t n2; - ae_int_t w1; - ae_int_t w2; - ae_int_t ntotal; - ae_int_t nin; - ae_int_t nout; - ae_int_t istart; - ae_int_t offs; - double net; - double f; - double df; - double d2f; - double mx; - ae_bool perr; - - - - /* - * Read network geometry - */ - nin = structinfo->ptr.p_int[1]; - nout = structinfo->ptr.p_int[2]; - ntotal = structinfo->ptr.p_int[3]; - istart = structinfo->ptr.p_int[5]; - - /* - * Inputs standartisation and putting in the network - */ - for(i=0; i<=nin-1; i++) - { - if( ae_fp_neq(columnsigmas->ptr.p_double[i],0) ) - { - neurons->ptr.p_double[i] = (x->ptr.p_double[i]-columnmeans->ptr.p_double[i])/columnsigmas->ptr.p_double[i]; - } - else - { - neurons->ptr.p_double[i] = x->ptr.p_double[i]-columnmeans->ptr.p_double[i]; - } - } - - /* - * Process network - */ - for(i=0; i<=ntotal-1; i++) - { - offs = istart+i*mlpbase_nfieldwidth; - if( structinfo->ptr.p_int[offs+0]>0||structinfo->ptr.p_int[offs+0]==-5 ) - { - - /* - * Activation function - */ - mlpactivationfunction(neurons->ptr.p_double[structinfo->ptr.p_int[offs+2]], structinfo->ptr.p_int[offs+0], &f, &df, &d2f, _state); - neurons->ptr.p_double[i] = f; - dfdnet->ptr.p_double[i] = df; - continue; - } - if( structinfo->ptr.p_int[offs+0]==0 ) - { - - /* - * Adaptive summator - */ - n1 = structinfo->ptr.p_int[offs+2]; - n2 = n1+structinfo->ptr.p_int[offs+1]-1; - w1 = structinfo->ptr.p_int[offs+3]; - w2 = w1+structinfo->ptr.p_int[offs+1]-1; - net = ae_v_dotproduct(&weights->ptr.p_double[w1], 1, &neurons->ptr.p_double[n1], 1, ae_v_len(w1,w2)); - neurons->ptr.p_double[i] = net; - dfdnet->ptr.p_double[i] = 1.0; - touchint(&n2, _state); - continue; - } - if( structinfo->ptr.p_int[offs+0]<0 ) - { - perr = ae_true; - if( structinfo->ptr.p_int[offs+0]==-2 ) - { - - /* - * input neuron, left unchanged - */ - perr = ae_false; - } - if( structinfo->ptr.p_int[offs+0]==-3 ) - { - - /* - * "-1" neuron - */ - neurons->ptr.p_double[i] = -1; - perr = ae_false; - } - if( structinfo->ptr.p_int[offs+0]==-4 ) - { - - /* - * "0" neuron - */ - neurons->ptr.p_double[i] = 0; - perr = ae_false; - } - ae_assert(!perr, "MLPInternalProcessVector: internal error - unknown neuron type!", _state); - continue; - } - } - - /* - * Extract result - */ - ae_v_move(&y->ptr.p_double[0], 1, &neurons->ptr.p_double[ntotal-nout], 1, ae_v_len(0,nout-1)); - - /* - * Softmax post-processing or standardisation if needed - */ - ae_assert(structinfo->ptr.p_int[6]==0||structinfo->ptr.p_int[6]==1, "MLPInternalProcessVector: unknown normalization type!", _state); - if( structinfo->ptr.p_int[6]==1 ) - { - - /* - * Softmax - */ - mx = y->ptr.p_double[0]; - for(i=1; i<=nout-1; i++) - { - mx = ae_maxreal(mx, y->ptr.p_double[i], _state); - } - net = 0; - for(i=0; i<=nout-1; i++) - { - y->ptr.p_double[i] = ae_exp(y->ptr.p_double[i]-mx, _state); - net = net+y->ptr.p_double[i]; - } - for(i=0; i<=nout-1; i++) - { - y->ptr.p_double[i] = y->ptr.p_double[i]/net; - } - } - else - { - - /* - * Standardisation - */ - for(i=0; i<=nout-1; i++) - { - y->ptr.p_double[i] = y->ptr.p_double[i]*columnsigmas->ptr.p_double[nin+i]+columnmeans->ptr.p_double[nin+i]; - } - } -} - - -/************************************************************************* -Serializer: allocation - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpalloc(ae_serializer* s, - multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t fkind; - double threshold; - double v0; - double v1; - ae_int_t nin; - ae_int_t nout; - - - nin = network->hllayersizes.ptr.p_int[0]; - nout = network->hllayersizes.ptr.p_int[network->hllayersizes.cnt-1]; - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - allocintegerarray(s, &network->hllayersizes, -1, _state); - for(i=1; i<=network->hllayersizes.cnt-1; i++) - { - for(j=0; j<=network->hllayersizes.ptr.p_int[i]-1; j++) - { - mlpgetneuroninfo(network, i, j, &fkind, &threshold, _state); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - for(k=0; k<=network->hllayersizes.ptr.p_int[i-1]-1; k++) - { - ae_serializer_alloc_entry(s); - } - } - } - for(j=0; j<=nin-1; j++) - { - mlpgetinputscaling(network, j, &v0, &v1, _state); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - } - for(j=0; j<=nout-1; j++) - { - mlpgetoutputscaling(network, j, &v0, &v1, _state); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - } -} - - -/************************************************************************* -Serializer: serialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpserialize(ae_serializer* s, - multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t fkind; - double threshold; - double v0; - double v1; - ae_int_t nin; - ae_int_t nout; - - - nin = network->hllayersizes.ptr.p_int[0]; - nout = network->hllayersizes.ptr.p_int[network->hllayersizes.cnt-1]; - ae_serializer_serialize_int(s, getmlpserializationcode(_state), _state); - ae_serializer_serialize_int(s, mlpbase_mlpfirstversion, _state); - ae_serializer_serialize_bool(s, mlpissoftmax(network, _state), _state); - serializeintegerarray(s, &network->hllayersizes, -1, _state); - for(i=1; i<=network->hllayersizes.cnt-1; i++) - { - for(j=0; j<=network->hllayersizes.ptr.p_int[i]-1; j++) - { - mlpgetneuroninfo(network, i, j, &fkind, &threshold, _state); - ae_serializer_serialize_int(s, fkind, _state); - ae_serializer_serialize_double(s, threshold, _state); - for(k=0; k<=network->hllayersizes.ptr.p_int[i-1]-1; k++) - { - ae_serializer_serialize_double(s, mlpgetweight(network, i-1, k, i, j, _state), _state); - } - } - } - for(j=0; j<=nin-1; j++) - { - mlpgetinputscaling(network, j, &v0, &v1, _state); - ae_serializer_serialize_double(s, v0, _state); - ae_serializer_serialize_double(s, v1, _state); - } - for(j=0; j<=nout-1; j++) - { - mlpgetoutputscaling(network, j, &v0, &v1, _state); - ae_serializer_serialize_double(s, v0, _state); - ae_serializer_serialize_double(s, v1, _state); - } -} - - -/************************************************************************* -Serializer: unserialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpunserialize(ae_serializer* s, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i0; - ae_int_t i1; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t fkind; - double threshold; - double v0; - double v1; - ae_int_t nin; - ae_int_t nout; - ae_bool issoftmax; - ae_vector layersizes; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&layersizes, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of header - */ - ae_serializer_unserialize_int(s, &i0, _state); - ae_assert(i0==getmlpserializationcode(_state), "MLPUnserialize: stream header corrupted", _state); - ae_serializer_unserialize_int(s, &i1, _state); - ae_assert(i1==mlpbase_mlpfirstversion, "MLPUnserialize: stream header corrupted", _state); - - /* - * Create network - */ - ae_serializer_unserialize_bool(s, &issoftmax, _state); - unserializeintegerarray(s, &layersizes, _state); - ae_assert((layersizes.cnt==2||layersizes.cnt==3)||layersizes.cnt==4, "MLPUnserialize: too many hidden layers!", _state); - nin = layersizes.ptr.p_int[0]; - nout = layersizes.ptr.p_int[layersizes.cnt-1]; - if( layersizes.cnt==2 ) - { - if( issoftmax ) - { - mlpcreatec0(layersizes.ptr.p_int[0], layersizes.ptr.p_int[1], network, _state); - } - else - { - mlpcreate0(layersizes.ptr.p_int[0], layersizes.ptr.p_int[1], network, _state); - } - } - if( layersizes.cnt==3 ) - { - if( issoftmax ) - { - mlpcreatec1(layersizes.ptr.p_int[0], layersizes.ptr.p_int[1], layersizes.ptr.p_int[2], network, _state); - } - else - { - mlpcreate1(layersizes.ptr.p_int[0], layersizes.ptr.p_int[1], layersizes.ptr.p_int[2], network, _state); - } - } - if( layersizes.cnt==4 ) - { - if( issoftmax ) - { - mlpcreatec2(layersizes.ptr.p_int[0], layersizes.ptr.p_int[1], layersizes.ptr.p_int[2], layersizes.ptr.p_int[3], network, _state); - } - else - { - mlpcreate2(layersizes.ptr.p_int[0], layersizes.ptr.p_int[1], layersizes.ptr.p_int[2], layersizes.ptr.p_int[3], network, _state); - } - } - - /* - * Load neurons and weights - */ - for(i=1; i<=layersizes.cnt-1; i++) - { - for(j=0; j<=layersizes.ptr.p_int[i]-1; j++) - { - ae_serializer_unserialize_int(s, &fkind, _state); - ae_serializer_unserialize_double(s, &threshold, _state); - mlpsetneuroninfo(network, i, j, fkind, threshold, _state); - for(k=0; k<=layersizes.ptr.p_int[i-1]-1; k++) - { - ae_serializer_unserialize_double(s, &v0, _state); - mlpsetweight(network, i-1, k, i, j, v0, _state); - } - } - } - - /* - * Load standartizator - */ - for(j=0; j<=nin-1; j++) - { - ae_serializer_unserialize_double(s, &v0, _state); - ae_serializer_unserialize_double(s, &v1, _state); - mlpsetinputscaling(network, j, v0, v1, _state); - } - for(j=0; j<=nout-1; j++) - { - ae_serializer_unserialize_double(s, &v0, _state); - ae_serializer_unserialize_double(s, &v1, _state); - mlpsetoutputscaling(network, j, v0, v1, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Calculation of all types of errors. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -OUTPUT PARAMETERS: - Rep - it contains all type of errors. - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function. - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpallerrorssubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, - ae_state *_state) -{ - ae_int_t idx0; - ae_int_t idx1; - ae_int_t idxtype; - - _modelerrors_clear(rep); - - ae_assert(xy->rows>=setsize, "MLPAllErrorsSubset: XY has less than SetSize rows", _state); - if( setsize>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPAllErrorsSubset: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAllErrorsSubset: XY has less than NIn+NOut columns", _state); - } - } - if( subsetsize>=0 ) - { - idx0 = 0; - idx1 = subsetsize; - idxtype = 1; - } - else - { - idx0 = 0; - idx1 = setsize; - idxtype = 0; - } - mlpallerrorsx(network, xy, &network->dummysxy, setsize, 0, subset, idx0, idx1, idxtype, &network->buf, rep, _state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpallerrorssubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, ae_state *_state) -{ - mlpallerrorssubset(network,xy,setsize,subset,subsetsize,rep, _state); -} - - -/************************************************************************* -Calculation of all types of errors on sparse dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset given by sparse matrix; - one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -OUTPUT PARAMETERS: - Rep - it contains all type of errors. - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function. - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpallerrorssparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, - ae_state *_state) -{ - ae_int_t idx0; - ae_int_t idx1; - ae_int_t idxtype; - - _modelerrors_clear(rep); - - ae_assert(sparseiscrs(xy, _state), "MLPAllErrorsSparseSubset: XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=setsize, "MLPAllErrorsSparseSubset: XY has less than SetSize rows", _state); - if( setsize>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPAllErrorsSparseSubset: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPAllErrorsSparseSubset: XY has less than NIn+NOut columns", _state); - } - } - if( subsetsize>=0 ) - { - idx0 = 0; - idx1 = subsetsize; - idxtype = 1; - } - else - { - idx0 = 0; - idx1 = setsize; - idxtype = 0; - } - mlpallerrorsx(network, &network->dummydxy, xy, setsize, 1, subset, idx0, idx1, idxtype, &network->buf, rep, _state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpallerrorssparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, ae_state *_state) -{ - mlpallerrorssparsesubset(network,xy,setsize,subset,subsetsize,rep, _state); -} - - -/************************************************************************* -Error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - ae_state *_state) -{ - ae_int_t idx0; - ae_int_t idx1; - ae_int_t idxtype; - double result; - - - ae_assert(xy->rows>=setsize, "MLPErrorSubset: XY has less than SetSize rows", _state); - if( setsize>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+1, "MLPErrorSubset: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(xy->cols>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPErrorSubset: XY has less than NIn+NOut columns", _state); - } - } - if( subsetsize>=0 ) - { - idx0 = 0; - idx1 = subsetsize; - idxtype = 1; - } - else - { - idx0 = 0; - idx1 = setsize; - idxtype = 0; - } - mlpallerrorsx(network, xy, &network->dummysxy, setsize, 0, subset, idx0, idx1, idxtype, &network->buf, &network->err, _state); - result = ae_sqr(network->err.rmserror, _state)*(idx1-idx0)*mlpgetoutputscount(network, _state)/2; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlperrorsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, ae_state *_state) -{ - return mlperrorsubset(network,xy,setsize,subset,subsetsize, _state); -} - - -/************************************************************************* -Error of the neural network on sparse dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - SetSize - real size of XY, SetSize>=0; - it is used when SubsetSize<0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - ae_state *_state) -{ - ae_int_t idx0; - ae_int_t idx1; - ae_int_t idxtype; - double result; - - - ae_assert(sparseiscrs(xy, _state), "MLPErrorSparseSubset: XY is not in CRS format.", _state); - ae_assert(sparsegetnrows(xy, _state)>=setsize, "MLPErrorSparseSubset: XY has less than SetSize rows", _state); - if( setsize>0 ) - { - if( mlpissoftmax(network, _state) ) - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+1, "MLPErrorSparseSubset: XY has less than NIn+1 columns", _state); - } - else - { - ae_assert(sparsegetncols(xy, _state)>=mlpgetinputscount(network, _state)+mlpgetoutputscount(network, _state), "MLPErrorSparseSubset: XY has less than NIn+NOut columns", _state); - } - } - if( subsetsize>=0 ) - { - idx0 = 0; - idx1 = subsetsize; - idxtype = 1; - } - else - { - idx0 = 0; - idx1 = setsize; - idxtype = 0; - } - mlpallerrorsx(network, &network->dummydxy, xy, setsize, 1, subset, idx0, idx1, idxtype, &network->buf, &network->err, _state); - result = ae_sqr(network->err.rmserror, _state)*(idx1-idx0)*mlpgetoutputscount(network, _state)/2; - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -double _pexec_mlperrorsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, ae_state *_state) -{ - return mlperrorsparsesubset(network,xy,setsize,subset,subsetsize, _state); -} - - -void mlpallerrorsx(multilayerperceptron* network, - /* Real */ ae_matrix* densexy, - sparsematrix* sparsexy, - ae_int_t datasetsize, - ae_int_t datasettype, - /* Integer */ ae_vector* idx, - ae_int_t subset0, - ae_int_t subset1, - ae_int_t subsettype, - ae_shared_pool* buf, - modelerrors* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t rowsize; - ae_bool iscls; - ae_int_t srcidx; - ae_int_t cstart; - ae_int_t csize; - ae_int_t j; - mlpbuffers *pbuf; - ae_smart_ptr _pbuf; - ae_int_t len0; - ae_int_t len1; - modelerrors rep0; - modelerrors rep1; - - ae_frame_make(_state, &_frame_block); - ae_smart_ptr_init(&_pbuf, (void**)&pbuf, _state, ae_true); - _modelerrors_init(&rep0, _state, ae_true); - _modelerrors_init(&rep1, _state, ae_true); - - ae_assert(datasetsize>=0, "MLPAllErrorsX: SetSize<0", _state); - ae_assert(datasettype==0||datasettype==1, "MLPAllErrorsX: DatasetType is incorrect", _state); - ae_assert(subsettype==0||subsettype==1, "MLPAllErrorsX: SubsetType is incorrect", _state); - - /* - * Determine network properties - */ - mlpproperties(network, &nin, &nout, &wcount, _state); - iscls = mlpissoftmax(network, _state); - - /* - * Split problem. - * - * Splitting problem allows us to reduce effect of single-precision - * arithmetics (SSE-optimized version of MLPChunkedProcess uses single - * precision internally, but converts them to double precision after - * results are exported from HPC buffer to network). Small batches are - * calculated in single precision, results are aggregated in double - * precision, and it allows us to avoid accumulation of errors when - * we process very large batches (tens of thousands of items). - * - * NOTE: it is important to use real arithmetics for ProblemCost - * because ProblemCost may be larger than MAXINT. - */ - if( subset1-subset0>=2*mlpbase_microbatchsize&&ae_fp_greater(inttoreal(subset1-subset0, _state)*inttoreal(wcount, _state),mlpbase_gradbasecasecost) ) - { - splitlength(subset1-subset0, mlpbase_microbatchsize, &len0, &len1, _state); - mlpallerrorsx(network, densexy, sparsexy, datasetsize, datasettype, idx, subset0, subset0+len0, subsettype, buf, &rep0, _state); - mlpallerrorsx(network, densexy, sparsexy, datasetsize, datasettype, idx, subset0+len0, subset1, subsettype, buf, &rep1, _state); - rep->relclserror = (len0*rep0.relclserror+len1*rep1.relclserror)/(len0+len1); - rep->avgce = (len0*rep0.avgce+len1*rep1.avgce)/(len0+len1); - rep->rmserror = ae_sqrt((len0*ae_sqr(rep0.rmserror, _state)+len1*ae_sqr(rep1.rmserror, _state))/(len0+len1), _state); - rep->avgerror = (len0*rep0.avgerror+len1*rep1.avgerror)/(len0+len1); - rep->avgrelerror = (len0*rep0.avgrelerror+len1*rep1.avgrelerror)/(len0+len1); - ae_frame_leave(_state); - return; - } - - /* - * Retrieve and prepare - */ - ae_shared_pool_retrieve(buf, &_pbuf, _state); - if( iscls ) - { - rowsize = nin+1; - dserrallocate(nout, &pbuf->tmp0, _state); - } - else - { - rowsize = nin+nout; - dserrallocate(-nout, &pbuf->tmp0, _state); - } - - /* - * Processing - */ - hpcpreparechunkedgradient(&network->weights, wcount, mlpntotal(network, _state), nin, nout, pbuf, _state); - cstart = subset0; - while(cstartchunksize, _state)-cstart; - for(j=0; j<=csize-1; j++) - { - srcidx = -1; - if( subsettype==0 ) - { - srcidx = cstart+j; - } - if( subsettype==1 ) - { - srcidx = idx->ptr.p_int[cstart+j]; - } - ae_assert(srcidx>=0, "MLPAllErrorsX: internal error", _state); - if( datasettype==0 ) - { - ae_v_move(&pbuf->xy.ptr.pp_double[j][0], 1, &densexy->ptr.pp_double[srcidx][0], 1, ae_v_len(0,rowsize-1)); - } - if( datasettype==1 ) - { - sparsegetrow(sparsexy, srcidx, &pbuf->xyrow, _state); - ae_v_move(&pbuf->xy.ptr.pp_double[j][0], 1, &pbuf->xyrow.ptr.p_double[0], 1, ae_v_len(0,rowsize-1)); - } - } - - /* - * Unpack XY and process (temporary code, to be replaced by chunked processing) - */ - for(j=0; j<=csize-1; j++) - { - ae_v_move(&pbuf->xy2.ptr.pp_double[j][0], 1, &pbuf->xy.ptr.pp_double[j][0], 1, ae_v_len(0,rowsize-1)); - } - mlpbase_mlpchunkedprocess(network, &pbuf->xy2, 0, csize, &pbuf->batch4buf, &pbuf->hpcbuf, _state); - for(j=0; j<=csize-1; j++) - { - ae_v_move(&pbuf->x.ptr.p_double[0], 1, &pbuf->xy2.ptr.pp_double[j][0], 1, ae_v_len(0,nin-1)); - ae_v_move(&pbuf->y.ptr.p_double[0], 1, &pbuf->xy2.ptr.pp_double[j][nin], 1, ae_v_len(0,nout-1)); - if( iscls ) - { - pbuf->desiredy.ptr.p_double[0] = pbuf->xy.ptr.pp_double[j][nin]; - } - else - { - ae_v_move(&pbuf->desiredy.ptr.p_double[0], 1, &pbuf->xy.ptr.pp_double[j][nin], 1, ae_v_len(0,nout-1)); - } - dserraccumulate(&pbuf->tmp0, &pbuf->y, &pbuf->desiredy, _state); - } - - /* - * Process chunk and advance line pointer - */ - cstart = cstart+pbuf->chunksize; - } - dserrfinish(&pbuf->tmp0, _state); - rep->relclserror = pbuf->tmp0.ptr.p_double[0]; - rep->avgce = pbuf->tmp0.ptr.p_double[1]/ae_log(2, _state); - rep->rmserror = pbuf->tmp0.ptr.p_double[2]; - rep->avgerror = pbuf->tmp0.ptr.p_double[3]; - rep->avgrelerror = pbuf->tmp0.ptr.p_double[4]; - - /* - * Recycle - */ - ae_shared_pool_recycle(buf, &_pbuf, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine: adding new input layer to network -*************************************************************************/ -static void mlpbase_addinputlayer(ae_int_t ncount, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state) -{ - - - lsizes->ptr.p_int[0] = ncount; - ltypes->ptr.p_int[0] = -2; - lconnfirst->ptr.p_int[0] = 0; - lconnlast->ptr.p_int[0] = 0; - *lastproc = 0; -} - - -/************************************************************************* -Internal subroutine: adding new summator layer to network -*************************************************************************/ -static void mlpbase_addbiasedsummatorlayer(ae_int_t ncount, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state) -{ - - - lsizes->ptr.p_int[*lastproc+1] = 1; - ltypes->ptr.p_int[*lastproc+1] = -3; - lconnfirst->ptr.p_int[*lastproc+1] = 0; - lconnlast->ptr.p_int[*lastproc+1] = 0; - lsizes->ptr.p_int[*lastproc+2] = ncount; - ltypes->ptr.p_int[*lastproc+2] = 0; - lconnfirst->ptr.p_int[*lastproc+2] = *lastproc; - lconnlast->ptr.p_int[*lastproc+2] = *lastproc+1; - *lastproc = *lastproc+2; -} - - -/************************************************************************* -Internal subroutine: adding new summator layer to network -*************************************************************************/ -static void mlpbase_addactivationlayer(ae_int_t functype, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state) -{ - - - ae_assert(functype>0||functype==-5, "AddActivationLayer: incorrect function type", _state); - lsizes->ptr.p_int[*lastproc+1] = lsizes->ptr.p_int[*lastproc]; - ltypes->ptr.p_int[*lastproc+1] = functype; - lconnfirst->ptr.p_int[*lastproc+1] = *lastproc; - lconnlast->ptr.p_int[*lastproc+1] = *lastproc; - *lastproc = *lastproc+1; -} - - -/************************************************************************* -Internal subroutine: adding new zero layer to network -*************************************************************************/ -static void mlpbase_addzerolayer(/* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t* lastproc, - ae_state *_state) -{ - - - lsizes->ptr.p_int[*lastproc+1] = 1; - ltypes->ptr.p_int[*lastproc+1] = -4; - lconnfirst->ptr.p_int[*lastproc+1] = 0; - lconnlast->ptr.p_int[*lastproc+1] = 0; - *lastproc = *lastproc+1; -} - - -/************************************************************************* -This routine adds input layer to the high-level description of the network. - -It modifies Network.HLConnections and Network.HLNeurons and assumes that -these arrays have enough place to store data. It accepts following -parameters: - Network - network - ConnIdx - index of the first free entry in the HLConnections - NeuroIdx - index of the first free entry in the HLNeurons - StructInfoIdx- index of the first entry in the low level description - of the current layer (in the StructInfo array) - NIn - number of inputs - -It modified Network and indices. -*************************************************************************/ -static void mlpbase_hladdinputlayer(multilayerperceptron* network, - ae_int_t* connidx, - ae_int_t* neuroidx, - ae_int_t* structinfoidx, - ae_int_t nin, - ae_state *_state) -{ - ae_int_t i; - ae_int_t offs; - - - offs = mlpbase_hlnfieldwidth*(*neuroidx); - for(i=0; i<=nin-1; i++) - { - network->hlneurons.ptr.p_int[offs+0] = 0; - network->hlneurons.ptr.p_int[offs+1] = i; - network->hlneurons.ptr.p_int[offs+2] = -1; - network->hlneurons.ptr.p_int[offs+3] = -1; - offs = offs+mlpbase_hlnfieldwidth; - } - *neuroidx = *neuroidx+nin; - *structinfoidx = *structinfoidx+nin; -} - - -/************************************************************************* -This routine adds output layer to the high-level description of -the network. - -It modifies Network.HLConnections and Network.HLNeurons and assumes that -these arrays have enough place to store data. It accepts following -parameters: - Network - network - ConnIdx - index of the first free entry in the HLConnections - NeuroIdx - index of the first free entry in the HLNeurons - StructInfoIdx- index of the first entry in the low level description - of the current layer (in the StructInfo array) - WeightsIdx - index of the first entry in the Weights array which - corresponds to the current layer - K - current layer index - NPrev - number of neurons in the previous layer - NOut - number of outputs - IsCls - is it classifier network? - IsLinear - is it network with linear output? - -It modified Network and ConnIdx/NeuroIdx/StructInfoIdx/WeightsIdx. -*************************************************************************/ -static void mlpbase_hladdoutputlayer(multilayerperceptron* network, - ae_int_t* connidx, - ae_int_t* neuroidx, - ae_int_t* structinfoidx, - ae_int_t* weightsidx, - ae_int_t k, - ae_int_t nprev, - ae_int_t nout, - ae_bool iscls, - ae_bool islinearout, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t neurooffs; - ae_int_t connoffs; - - - ae_assert((iscls&&islinearout)||!iscls, "HLAddOutputLayer: internal error", _state); - neurooffs = mlpbase_hlnfieldwidth*(*neuroidx); - connoffs = mlpbase_hlconnfieldwidth*(*connidx); - if( !iscls ) - { - - /* - * Regression network - */ - for(i=0; i<=nout-1; i++) - { - network->hlneurons.ptr.p_int[neurooffs+0] = k; - network->hlneurons.ptr.p_int[neurooffs+1] = i; - network->hlneurons.ptr.p_int[neurooffs+2] = *structinfoidx+1+nout+i; - network->hlneurons.ptr.p_int[neurooffs+3] = *weightsidx+nprev+(nprev+1)*i; - neurooffs = neurooffs+mlpbase_hlnfieldwidth; - } - for(i=0; i<=nprev-1; i++) - { - for(j=0; j<=nout-1; j++) - { - network->hlconnections.ptr.p_int[connoffs+0] = k-1; - network->hlconnections.ptr.p_int[connoffs+1] = i; - network->hlconnections.ptr.p_int[connoffs+2] = k; - network->hlconnections.ptr.p_int[connoffs+3] = j; - network->hlconnections.ptr.p_int[connoffs+4] = *weightsidx+i+j*(nprev+1); - connoffs = connoffs+mlpbase_hlconnfieldwidth; - } - } - *connidx = *connidx+nprev*nout; - *neuroidx = *neuroidx+nout; - *structinfoidx = *structinfoidx+2*nout+1; - *weightsidx = *weightsidx+nout*(nprev+1); - } - else - { - - /* - * Classification network - */ - for(i=0; i<=nout-2; i++) - { - network->hlneurons.ptr.p_int[neurooffs+0] = k; - network->hlneurons.ptr.p_int[neurooffs+1] = i; - network->hlneurons.ptr.p_int[neurooffs+2] = -1; - network->hlneurons.ptr.p_int[neurooffs+3] = *weightsidx+nprev+(nprev+1)*i; - neurooffs = neurooffs+mlpbase_hlnfieldwidth; - } - network->hlneurons.ptr.p_int[neurooffs+0] = k; - network->hlneurons.ptr.p_int[neurooffs+1] = i; - network->hlneurons.ptr.p_int[neurooffs+2] = -1; - network->hlneurons.ptr.p_int[neurooffs+3] = -1; - for(i=0; i<=nprev-1; i++) - { - for(j=0; j<=nout-2; j++) - { - network->hlconnections.ptr.p_int[connoffs+0] = k-1; - network->hlconnections.ptr.p_int[connoffs+1] = i; - network->hlconnections.ptr.p_int[connoffs+2] = k; - network->hlconnections.ptr.p_int[connoffs+3] = j; - network->hlconnections.ptr.p_int[connoffs+4] = *weightsidx+i+j*(nprev+1); - connoffs = connoffs+mlpbase_hlconnfieldwidth; - } - } - *connidx = *connidx+nprev*(nout-1); - *neuroidx = *neuroidx+nout; - *structinfoidx = *structinfoidx+nout+2; - *weightsidx = *weightsidx+(nout-1)*(nprev+1); - } -} - - -/************************************************************************* -This routine adds hidden layer to the high-level description of -the network. - -It modifies Network.HLConnections and Network.HLNeurons and assumes that -these arrays have enough place to store data. It accepts following -parameters: - Network - network - ConnIdx - index of the first free entry in the HLConnections - NeuroIdx - index of the first free entry in the HLNeurons - StructInfoIdx- index of the first entry in the low level description - of the current layer (in the StructInfo array) - WeightsIdx - index of the first entry in the Weights array which - corresponds to the current layer - K - current layer index - NPrev - number of neurons in the previous layer - NCur - number of neurons in the current layer - -It modified Network and ConnIdx/NeuroIdx/StructInfoIdx/WeightsIdx. -*************************************************************************/ -static void mlpbase_hladdhiddenlayer(multilayerperceptron* network, - ae_int_t* connidx, - ae_int_t* neuroidx, - ae_int_t* structinfoidx, - ae_int_t* weightsidx, - ae_int_t k, - ae_int_t nprev, - ae_int_t ncur, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t neurooffs; - ae_int_t connoffs; - - - neurooffs = mlpbase_hlnfieldwidth*(*neuroidx); - connoffs = mlpbase_hlconnfieldwidth*(*connidx); - for(i=0; i<=ncur-1; i++) - { - network->hlneurons.ptr.p_int[neurooffs+0] = k; - network->hlneurons.ptr.p_int[neurooffs+1] = i; - network->hlneurons.ptr.p_int[neurooffs+2] = *structinfoidx+1+ncur+i; - network->hlneurons.ptr.p_int[neurooffs+3] = *weightsidx+nprev+(nprev+1)*i; - neurooffs = neurooffs+mlpbase_hlnfieldwidth; - } - for(i=0; i<=nprev-1; i++) - { - for(j=0; j<=ncur-1; j++) - { - network->hlconnections.ptr.p_int[connoffs+0] = k-1; - network->hlconnections.ptr.p_int[connoffs+1] = i; - network->hlconnections.ptr.p_int[connoffs+2] = k; - network->hlconnections.ptr.p_int[connoffs+3] = j; - network->hlconnections.ptr.p_int[connoffs+4] = *weightsidx+i+j*(nprev+1); - connoffs = connoffs+mlpbase_hlconnfieldwidth; - } - } - *connidx = *connidx+nprev*ncur; - *neuroidx = *neuroidx+ncur; - *structinfoidx = *structinfoidx+2*ncur+1; - *weightsidx = *weightsidx+ncur*(nprev+1); -} - - -/************************************************************************* -This function fills high level information about network created using -internal MLPCreate() function. - -This function does NOT examine StructInfo for low level information, it -just expects that network has following structure: - - input neuron \ - ... | input layer - input neuron / - - "-1" neuron \ - biased summator | - ... | - biased summator | hidden layer(s), if there are exists any - activation function | - ... | - activation function / - - "-1" neuron \ - biased summator | output layer: - ... | - biased summator | * we have NOut summators/activators for regression networks - activation function | * we have only NOut-1 summators and no activators for classifiers - ... | * we have "0" neuron only when we have classifier - activation function | - "0" neuron / - - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -static void mlpbase_fillhighlevelinformation(multilayerperceptron* network, - ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - ae_bool iscls, - ae_bool islinearout, - ae_state *_state) -{ - ae_int_t idxweights; - ae_int_t idxstruct; - ae_int_t idxneuro; - ae_int_t idxconn; - - - ae_assert((iscls&&islinearout)||!iscls, "FillHighLevelInformation: internal error", _state); - - /* - * Preparations common to all types of networks - */ - idxweights = 0; - idxneuro = 0; - idxstruct = 0; - idxconn = 0; - network->hlnetworktype = 0; - - /* - * network without hidden layers - */ - if( nhid1==0 ) - { - ae_vector_set_length(&network->hllayersizes, 2, _state); - network->hllayersizes.ptr.p_int[0] = nin; - network->hllayersizes.ptr.p_int[1] = nout; - if( !iscls ) - { - ae_vector_set_length(&network->hlconnections, mlpbase_hlconnfieldwidth*nin*nout, _state); - ae_vector_set_length(&network->hlneurons, mlpbase_hlnfieldwidth*(nin+nout), _state); - network->hlnormtype = 0; - } - else - { - ae_vector_set_length(&network->hlconnections, mlpbase_hlconnfieldwidth*nin*(nout-1), _state); - ae_vector_set_length(&network->hlneurons, mlpbase_hlnfieldwidth*(nin+nout), _state); - network->hlnormtype = 1; - } - mlpbase_hladdinputlayer(network, &idxconn, &idxneuro, &idxstruct, nin, _state); - mlpbase_hladdoutputlayer(network, &idxconn, &idxneuro, &idxstruct, &idxweights, 1, nin, nout, iscls, islinearout, _state); - return; - } - - /* - * network with one hidden layers - */ - if( nhid2==0 ) - { - ae_vector_set_length(&network->hllayersizes, 3, _state); - network->hllayersizes.ptr.p_int[0] = nin; - network->hllayersizes.ptr.p_int[1] = nhid1; - network->hllayersizes.ptr.p_int[2] = nout; - if( !iscls ) - { - ae_vector_set_length(&network->hlconnections, mlpbase_hlconnfieldwidth*(nin*nhid1+nhid1*nout), _state); - ae_vector_set_length(&network->hlneurons, mlpbase_hlnfieldwidth*(nin+nhid1+nout), _state); - network->hlnormtype = 0; - } - else - { - ae_vector_set_length(&network->hlconnections, mlpbase_hlconnfieldwidth*(nin*nhid1+nhid1*(nout-1)), _state); - ae_vector_set_length(&network->hlneurons, mlpbase_hlnfieldwidth*(nin+nhid1+nout), _state); - network->hlnormtype = 1; - } - mlpbase_hladdinputlayer(network, &idxconn, &idxneuro, &idxstruct, nin, _state); - mlpbase_hladdhiddenlayer(network, &idxconn, &idxneuro, &idxstruct, &idxweights, 1, nin, nhid1, _state); - mlpbase_hladdoutputlayer(network, &idxconn, &idxneuro, &idxstruct, &idxweights, 2, nhid1, nout, iscls, islinearout, _state); - return; - } - - /* - * Two hidden layers - */ - ae_vector_set_length(&network->hllayersizes, 4, _state); - network->hllayersizes.ptr.p_int[0] = nin; - network->hllayersizes.ptr.p_int[1] = nhid1; - network->hllayersizes.ptr.p_int[2] = nhid2; - network->hllayersizes.ptr.p_int[3] = nout; - if( !iscls ) - { - ae_vector_set_length(&network->hlconnections, mlpbase_hlconnfieldwidth*(nin*nhid1+nhid1*nhid2+nhid2*nout), _state); - ae_vector_set_length(&network->hlneurons, mlpbase_hlnfieldwidth*(nin+nhid1+nhid2+nout), _state); - network->hlnormtype = 0; - } - else - { - ae_vector_set_length(&network->hlconnections, mlpbase_hlconnfieldwidth*(nin*nhid1+nhid1*nhid2+nhid2*(nout-1)), _state); - ae_vector_set_length(&network->hlneurons, mlpbase_hlnfieldwidth*(nin+nhid1+nhid2+nout), _state); - network->hlnormtype = 1; - } - mlpbase_hladdinputlayer(network, &idxconn, &idxneuro, &idxstruct, nin, _state); - mlpbase_hladdhiddenlayer(network, &idxconn, &idxneuro, &idxstruct, &idxweights, 1, nin, nhid1, _state); - mlpbase_hladdhiddenlayer(network, &idxconn, &idxneuro, &idxstruct, &idxweights, 2, nhid1, nhid2, _state); - mlpbase_hladdoutputlayer(network, &idxconn, &idxneuro, &idxstruct, &idxweights, 3, nhid2, nout, iscls, islinearout, _state); -} - - -/************************************************************************* -Internal subroutine. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -static void mlpbase_mlpcreate(ae_int_t nin, - ae_int_t nout, - /* Integer */ ae_vector* lsizes, - /* Integer */ ae_vector* ltypes, - /* Integer */ ae_vector* lconnfirst, - /* Integer */ ae_vector* lconnlast, - ae_int_t layerscount, - ae_bool isclsnet, - multilayerperceptron* network, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t ssize; - ae_int_t ntotal; - ae_int_t wcount; - ae_int_t offs; - ae_int_t nprocessed; - ae_int_t wallocated; - ae_vector localtemp; - ae_vector lnfirst; - ae_vector lnsyn; - mlpbuffers buf; - smlpgrad sgrad; - - ae_frame_make(_state, &_frame_block); - _multilayerperceptron_clear(network); - ae_vector_init(&localtemp, 0, DT_INT, _state, ae_true); - ae_vector_init(&lnfirst, 0, DT_INT, _state, ae_true); - ae_vector_init(&lnsyn, 0, DT_INT, _state, ae_true); - _mlpbuffers_init(&buf, _state, ae_true); - _smlpgrad_init(&sgrad, _state, ae_true); - - - /* - * Check - */ - ae_assert(layerscount>0, "MLPCreate: wrong parameters!", _state); - ae_assert(ltypes->ptr.p_int[0]==-2, "MLPCreate: wrong LTypes[0] (must be -2)!", _state); - for(i=0; i<=layerscount-1; i++) - { - ae_assert(lsizes->ptr.p_int[i]>0, "MLPCreate: wrong LSizes!", _state); - ae_assert(lconnfirst->ptr.p_int[i]>=0&&(lconnfirst->ptr.p_int[i]ptr.p_int[i]>=lconnfirst->ptr.p_int[i]&&(lconnlast->ptr.p_int[i]ptr.p_int[i]>=0||ltypes->ptr.p_int[i]==-5 ) - { - lnsyn.ptr.p_int[i] = 0; - for(j=lconnfirst->ptr.p_int[i]; j<=lconnlast->ptr.p_int[i]; j++) - { - lnsyn.ptr.p_int[i] = lnsyn.ptr.p_int[i]+lsizes->ptr.p_int[j]; - } - } - else - { - if( (ltypes->ptr.p_int[i]==-2||ltypes->ptr.p_int[i]==-3)||ltypes->ptr.p_int[i]==-4 ) - { - lnsyn.ptr.p_int[i] = 0; - } - } - ae_assert(lnsyn.ptr.p_int[i]>=0, "MLPCreate: internal error #0!", _state); - - /* - * Other info - */ - lnfirst.ptr.p_int[i] = ntotal; - ntotal = ntotal+lsizes->ptr.p_int[i]; - if( ltypes->ptr.p_int[i]==0 ) - { - wcount = wcount+lnsyn.ptr.p_int[i]*lsizes->ptr.p_int[i]; - } - } - ssize = 7+ntotal*mlpbase_nfieldwidth; - - /* - * Allocate - */ - ae_vector_set_length(&network->structinfo, ssize-1+1, _state); - ae_vector_set_length(&network->weights, wcount-1+1, _state); - if( isclsnet ) - { - ae_vector_set_length(&network->columnmeans, nin-1+1, _state); - ae_vector_set_length(&network->columnsigmas, nin-1+1, _state); - } - else - { - ae_vector_set_length(&network->columnmeans, nin+nout-1+1, _state); - ae_vector_set_length(&network->columnsigmas, nin+nout-1+1, _state); - } - ae_vector_set_length(&network->neurons, ntotal-1+1, _state); - ae_vector_set_length(&network->nwbuf, ae_maxint(wcount, 2*nout, _state)-1+1, _state); - ae_vector_set_length(&network->integerbuf, 3+1, _state); - ae_vector_set_length(&network->dfdnet, ntotal-1+1, _state); - ae_vector_set_length(&network->x, nin-1+1, _state); - ae_vector_set_length(&network->y, nout-1+1, _state); - ae_vector_set_length(&network->derror, ntotal-1+1, _state); - - /* - * Fill structure: global info - */ - network->structinfo.ptr.p_int[0] = ssize; - network->structinfo.ptr.p_int[1] = nin; - network->structinfo.ptr.p_int[2] = nout; - network->structinfo.ptr.p_int[3] = ntotal; - network->structinfo.ptr.p_int[4] = wcount; - network->structinfo.ptr.p_int[5] = 7; - if( isclsnet ) - { - network->structinfo.ptr.p_int[6] = 1; - } - else - { - network->structinfo.ptr.p_int[6] = 0; - } - - /* - * Fill structure: neuron connections - */ - nprocessed = 0; - wallocated = 0; - for(i=0; i<=layerscount-1; i++) - { - for(j=0; j<=lsizes->ptr.p_int[i]-1; j++) - { - offs = network->structinfo.ptr.p_int[5]+nprocessed*mlpbase_nfieldwidth; - network->structinfo.ptr.p_int[offs+0] = ltypes->ptr.p_int[i]; - if( ltypes->ptr.p_int[i]==0 ) - { - - /* - * Adaptive summator: - * * connections with weights to previous neurons - */ - network->structinfo.ptr.p_int[offs+1] = lnsyn.ptr.p_int[i]; - network->structinfo.ptr.p_int[offs+2] = lnfirst.ptr.p_int[lconnfirst->ptr.p_int[i]]; - network->structinfo.ptr.p_int[offs+3] = wallocated; - wallocated = wallocated+lnsyn.ptr.p_int[i]; - nprocessed = nprocessed+1; - } - if( ltypes->ptr.p_int[i]>0||ltypes->ptr.p_int[i]==-5 ) - { - - /* - * Activation layer: - * * each neuron connected to one (only one) of previous neurons. - * * no weights - */ - network->structinfo.ptr.p_int[offs+1] = 1; - network->structinfo.ptr.p_int[offs+2] = lnfirst.ptr.p_int[lconnfirst->ptr.p_int[i]]+j; - network->structinfo.ptr.p_int[offs+3] = -1; - nprocessed = nprocessed+1; - } - if( (ltypes->ptr.p_int[i]==-2||ltypes->ptr.p_int[i]==-3)||ltypes->ptr.p_int[i]==-4 ) - { - nprocessed = nprocessed+1; - } - } - } - ae_assert(wallocated==wcount, "MLPCreate: internal error #1!", _state); - ae_assert(nprocessed==ntotal, "MLPCreate: internal error #2!", _state); - - /* - * Fill weights by small random values - * Initialize means and sigmas - */ - for(i=0; i<=nin-1; i++) - { - network->columnmeans.ptr.p_double[i] = 0; - network->columnsigmas.ptr.p_double[i] = 1; - } - if( !isclsnet ) - { - for(i=0; i<=nout-1; i++) - { - network->columnmeans.ptr.p_double[nin+i] = 0; - network->columnsigmas.ptr.p_double[nin+i] = 1; - } - } - mlprandomize(network, _state); - - /* - * Seed buffers - */ - ae_shared_pool_set_seed(&network->buf, &buf, sizeof(buf), _mlpbuffers_init, _mlpbuffers_init_copy, _mlpbuffers_destroy, _state); - ae_vector_set_length(&sgrad.g, wcount, _state); - sgrad.f = 0.0; - for(i=0; i<=wcount-1; i++) - { - sgrad.g.ptr.p_double[i] = 0.0; - } - ae_shared_pool_set_seed(&network->gradbuf, &sgrad, sizeof(sgrad), _smlpgrad_init, _smlpgrad_init_copy, _smlpgrad_destroy, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine for Hessian calculation. - -WARNING! Unspeakable math far beyong human capabilities :) -*************************************************************************/ -static void mlpbase_mlphessianbatchinternal(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_bool naturalerr, - double* e, - /* Real */ ae_vector* grad, - /* Real */ ae_matrix* h, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t kl; - ae_int_t offs; - ae_int_t n1; - ae_int_t n2; - ae_int_t w1; - ae_int_t w2; - double s; - double t; - double v; - double et; - ae_bool bflag; - double f; - double df; - double d2f; - double deidyj; - double mx; - double q; - double z; - double s2; - double expi; - double expj; - ae_vector x; - ae_vector desiredy; - ae_vector gt; - ae_vector zeros; - ae_matrix rx; - ae_matrix ry; - ae_matrix rdx; - ae_matrix rdy; - - ae_frame_make(_state, &_frame_block); - *e = 0; - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&desiredy, 0, DT_REAL, _state, ae_true); - ae_vector_init(>, 0, DT_REAL, _state, ae_true); - ae_vector_init(&zeros, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&rx, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ry, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&rdx, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&rdy, 0, 0, DT_REAL, _state, ae_true); - - mlpproperties(network, &nin, &nout, &wcount, _state); - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Prepare - */ - ae_vector_set_length(&x, nin-1+1, _state); - ae_vector_set_length(&desiredy, nout-1+1, _state); - ae_vector_set_length(&zeros, wcount-1+1, _state); - ae_vector_set_length(>, wcount-1+1, _state); - ae_matrix_set_length(&rx, ntotal+nout-1+1, wcount-1+1, _state); - ae_matrix_set_length(&ry, ntotal+nout-1+1, wcount-1+1, _state); - ae_matrix_set_length(&rdx, ntotal+nout-1+1, wcount-1+1, _state); - ae_matrix_set_length(&rdy, ntotal+nout-1+1, wcount-1+1, _state); - *e = 0; - for(i=0; i<=wcount-1; i++) - { - zeros.ptr.p_double[i] = 0; - } - ae_v_move(&grad->ptr.p_double[0], 1, &zeros.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - for(i=0; i<=wcount-1; i++) - { - ae_v_move(&h->ptr.pp_double[i][0], 1, &zeros.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - } - - /* - * Process - */ - for(k=0; k<=ssize-1; k++) - { - - /* - * Process vector with MLPGradN. - * Now Neurons, DFDNET and DError contains results of the last run. - */ - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[k][0], 1, ae_v_len(0,nin-1)); - if( mlpissoftmax(network, _state) ) - { - - /* - * class labels outputs - */ - kl = ae_round(xy->ptr.pp_double[k][nin], _state); - for(i=0; i<=nout-1; i++) - { - if( i==kl ) - { - desiredy.ptr.p_double[i] = 1; - } - else - { - desiredy.ptr.p_double[i] = 0; - } - } - } - else - { - - /* - * real outputs - */ - ae_v_move(&desiredy.ptr.p_double[0], 1, &xy->ptr.pp_double[k][nin], 1, ae_v_len(0,nout-1)); - } - if( naturalerr ) - { - mlpgradn(network, &x, &desiredy, &et, >, _state); - } - else - { - mlpgrad(network, &x, &desiredy, &et, >, _state); - } - - /* - * grad, error - */ - *e = *e+et; - ae_v_add(&grad->ptr.p_double[0], 1, >.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - - /* - * Hessian. - * Forward pass of the R-algorithm - */ - for(i=0; i<=ntotal-1; i++) - { - offs = istart+i*mlpbase_nfieldwidth; - ae_v_move(&rx.ptr.pp_double[i][0], 1, &zeros.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - ae_v_move(&ry.ptr.pp_double[i][0], 1, &zeros.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - if( network->structinfo.ptr.p_int[offs+0]>0||network->structinfo.ptr.p_int[offs+0]==-5 ) - { - - /* - * Activation function - */ - n1 = network->structinfo.ptr.p_int[offs+2]; - ae_v_move(&rx.ptr.pp_double[i][0], 1, &ry.ptr.pp_double[n1][0], 1, ae_v_len(0,wcount-1)); - v = network->dfdnet.ptr.p_double[i]; - ae_v_moved(&ry.ptr.pp_double[i][0], 1, &rx.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1), v); - continue; - } - if( network->structinfo.ptr.p_int[offs+0]==0 ) - { - - /* - * Adaptive summator - */ - n1 = network->structinfo.ptr.p_int[offs+2]; - n2 = n1+network->structinfo.ptr.p_int[offs+1]-1; - w1 = network->structinfo.ptr.p_int[offs+3]; - w2 = w1+network->structinfo.ptr.p_int[offs+1]-1; - for(j=n1; j<=n2; j++) - { - v = network->weights.ptr.p_double[w1+j-n1]; - ae_v_addd(&rx.ptr.pp_double[i][0], 1, &ry.ptr.pp_double[j][0], 1, ae_v_len(0,wcount-1), v); - rx.ptr.pp_double[i][w1+j-n1] = rx.ptr.pp_double[i][w1+j-n1]+network->neurons.ptr.p_double[j]; - } - ae_v_move(&ry.ptr.pp_double[i][0], 1, &rx.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1)); - continue; - } - if( network->structinfo.ptr.p_int[offs+0]<0 ) - { - bflag = ae_true; - if( network->structinfo.ptr.p_int[offs+0]==-2 ) - { - - /* - * input neuron, left unchanged - */ - bflag = ae_false; - } - if( network->structinfo.ptr.p_int[offs+0]==-3 ) - { - - /* - * "-1" neuron, left unchanged - */ - bflag = ae_false; - } - if( network->structinfo.ptr.p_int[offs+0]==-4 ) - { - - /* - * "0" neuron, left unchanged - */ - bflag = ae_false; - } - ae_assert(!bflag, "MLPHessianNBatch: internal error - unknown neuron type!", _state); - continue; - } - } - - /* - * Hessian. Backward pass of the R-algorithm. - * - * Stage 1. Initialize RDY - */ - for(i=0; i<=ntotal+nout-1; i++) - { - ae_v_move(&rdy.ptr.pp_double[i][0], 1, &zeros.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - } - if( network->structinfo.ptr.p_int[6]==0 ) - { - - /* - * Standardisation. - * - * In context of the Hessian calculation standardisation - * is considered as additional layer with weightless - * activation function: - * - * F(NET) := Sigma*NET - * - * So we add one more layer to forward pass, and - * make forward/backward pass through this layer. - */ - for(i=0; i<=nout-1; i++) - { - n1 = ntotal-nout+i; - n2 = ntotal+i; - - /* - * Forward pass from N1 to N2 - */ - ae_v_move(&rx.ptr.pp_double[n2][0], 1, &ry.ptr.pp_double[n1][0], 1, ae_v_len(0,wcount-1)); - v = network->columnsigmas.ptr.p_double[nin+i]; - ae_v_moved(&ry.ptr.pp_double[n2][0], 1, &rx.ptr.pp_double[n2][0], 1, ae_v_len(0,wcount-1), v); - - /* - * Initialization of RDY - */ - ae_v_move(&rdy.ptr.pp_double[n2][0], 1, &ry.ptr.pp_double[n2][0], 1, ae_v_len(0,wcount-1)); - - /* - * Backward pass from N2 to N1: - * 1. Calculate R(dE/dX). - * 2. No R(dE/dWij) is needed since weight of activation neuron - * is fixed to 1. So we can update R(dE/dY) for - * the connected neuron (note that Vij=0, Wij=1) - */ - df = network->columnsigmas.ptr.p_double[nin+i]; - ae_v_moved(&rdx.ptr.pp_double[n2][0], 1, &rdy.ptr.pp_double[n2][0], 1, ae_v_len(0,wcount-1), df); - ae_v_add(&rdy.ptr.pp_double[n1][0], 1, &rdx.ptr.pp_double[n2][0], 1, ae_v_len(0,wcount-1)); - } - } - else - { - - /* - * Softmax. - * - * Initialize RDY using generalized expression for ei'(yi) - * (see expression (9) from p. 5 of "Fast Exact Multiplication by the Hessian"). - * - * When we are working with softmax network, generalized - * expression for ei'(yi) is used because softmax - * normalization leads to ei, which depends on all y's - */ - if( naturalerr ) - { - - /* - * softmax + cross-entropy. - * We have: - * - * S = sum(exp(yk)), - * ei = sum(trn)*exp(yi)/S-trn_i - * - * j=i: d(ei)/d(yj) = T*exp(yi)*(S-exp(yi))/S^2 - * j<>i: d(ei)/d(yj) = -T*exp(yi)*exp(yj)/S^2 - */ - t = 0; - for(i=0; i<=nout-1; i++) - { - t = t+desiredy.ptr.p_double[i]; - } - mx = network->neurons.ptr.p_double[ntotal-nout]; - for(i=0; i<=nout-1; i++) - { - mx = ae_maxreal(mx, network->neurons.ptr.p_double[ntotal-nout+i], _state); - } - s = 0; - for(i=0; i<=nout-1; i++) - { - network->nwbuf.ptr.p_double[i] = ae_exp(network->neurons.ptr.p_double[ntotal-nout+i]-mx, _state); - s = s+network->nwbuf.ptr.p_double[i]; - } - for(i=0; i<=nout-1; i++) - { - for(j=0; j<=nout-1; j++) - { - if( j==i ) - { - deidyj = t*network->nwbuf.ptr.p_double[i]*(s-network->nwbuf.ptr.p_double[i])/ae_sqr(s, _state); - ae_v_addd(&rdy.ptr.pp_double[ntotal-nout+i][0], 1, &ry.ptr.pp_double[ntotal-nout+i][0], 1, ae_v_len(0,wcount-1), deidyj); - } - else - { - deidyj = -t*network->nwbuf.ptr.p_double[i]*network->nwbuf.ptr.p_double[j]/ae_sqr(s, _state); - ae_v_addd(&rdy.ptr.pp_double[ntotal-nout+i][0], 1, &ry.ptr.pp_double[ntotal-nout+j][0], 1, ae_v_len(0,wcount-1), deidyj); - } - } - } - } - else - { - - /* - * For a softmax + squared error we have expression - * far beyond human imagination so we dont even try - * to comment on it. Just enjoy the code... - * - * P.S. That's why "natural error" is called "natural" - - * compact beatiful expressions, fast code.... - */ - mx = network->neurons.ptr.p_double[ntotal-nout]; - for(i=0; i<=nout-1; i++) - { - mx = ae_maxreal(mx, network->neurons.ptr.p_double[ntotal-nout+i], _state); - } - s = 0; - s2 = 0; - for(i=0; i<=nout-1; i++) - { - network->nwbuf.ptr.p_double[i] = ae_exp(network->neurons.ptr.p_double[ntotal-nout+i]-mx, _state); - s = s+network->nwbuf.ptr.p_double[i]; - s2 = s2+ae_sqr(network->nwbuf.ptr.p_double[i], _state); - } - q = 0; - for(i=0; i<=nout-1; i++) - { - q = q+(network->y.ptr.p_double[i]-desiredy.ptr.p_double[i])*network->nwbuf.ptr.p_double[i]; - } - for(i=0; i<=nout-1; i++) - { - z = -q+(network->y.ptr.p_double[i]-desiredy.ptr.p_double[i])*s; - expi = network->nwbuf.ptr.p_double[i]; - for(j=0; j<=nout-1; j++) - { - expj = network->nwbuf.ptr.p_double[j]; - if( j==i ) - { - deidyj = expi/ae_sqr(s, _state)*((z+expi)*(s-2*expi)/s+expi*s2/ae_sqr(s, _state)); - } - else - { - deidyj = expi*expj/ae_sqr(s, _state)*(s2/ae_sqr(s, _state)-2*z/s-(expi+expj)/s+(network->y.ptr.p_double[i]-desiredy.ptr.p_double[i])-(network->y.ptr.p_double[j]-desiredy.ptr.p_double[j])); - } - ae_v_addd(&rdy.ptr.pp_double[ntotal-nout+i][0], 1, &ry.ptr.pp_double[ntotal-nout+j][0], 1, ae_v_len(0,wcount-1), deidyj); - } - } - } - } - - /* - * Hessian. Backward pass of the R-algorithm - * - * Stage 2. Process. - */ - for(i=ntotal-1; i>=0; i--) - { - - /* - * Possible variants: - * 1. Activation function - * 2. Adaptive summator - * 3. Special neuron - */ - offs = istart+i*mlpbase_nfieldwidth; - if( network->structinfo.ptr.p_int[offs+0]>0||network->structinfo.ptr.p_int[offs+0]==-5 ) - { - n1 = network->structinfo.ptr.p_int[offs+2]; - - /* - * First, calculate R(dE/dX). - */ - mlpactivationfunction(network->neurons.ptr.p_double[n1], network->structinfo.ptr.p_int[offs+0], &f, &df, &d2f, _state); - v = d2f*network->derror.ptr.p_double[i]; - ae_v_moved(&rdx.ptr.pp_double[i][0], 1, &rdy.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1), df); - ae_v_addd(&rdx.ptr.pp_double[i][0], 1, &rx.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1), v); - - /* - * No R(dE/dWij) is needed since weight of activation neuron - * is fixed to 1. - * - * So we can update R(dE/dY) for the connected neuron. - * (note that Vij=0, Wij=1) - */ - ae_v_add(&rdy.ptr.pp_double[n1][0], 1, &rdx.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1)); - continue; - } - if( network->structinfo.ptr.p_int[offs+0]==0 ) - { - - /* - * Adaptive summator - */ - n1 = network->structinfo.ptr.p_int[offs+2]; - n2 = n1+network->structinfo.ptr.p_int[offs+1]-1; - w1 = network->structinfo.ptr.p_int[offs+3]; - w2 = w1+network->structinfo.ptr.p_int[offs+1]-1; - - /* - * First, calculate R(dE/dX). - */ - ae_v_move(&rdx.ptr.pp_double[i][0], 1, &rdy.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1)); - - /* - * Then, calculate R(dE/dWij) - */ - for(j=w1; j<=w2; j++) - { - v = network->neurons.ptr.p_double[n1+j-w1]; - ae_v_addd(&h->ptr.pp_double[j][0], 1, &rdx.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1), v); - v = network->derror.ptr.p_double[i]; - ae_v_addd(&h->ptr.pp_double[j][0], 1, &ry.ptr.pp_double[n1+j-w1][0], 1, ae_v_len(0,wcount-1), v); - } - - /* - * And finally, update R(dE/dY) for connected neurons. - */ - for(j=w1; j<=w2; j++) - { - v = network->weights.ptr.p_double[j]; - ae_v_addd(&rdy.ptr.pp_double[n1+j-w1][0], 1, &rdx.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1), v); - rdy.ptr.pp_double[n1+j-w1][j] = rdy.ptr.pp_double[n1+j-w1][j]+network->derror.ptr.p_double[i]; - } - continue; - } - if( network->structinfo.ptr.p_int[offs+0]<0 ) - { - bflag = ae_false; - if( (network->structinfo.ptr.p_int[offs+0]==-2||network->structinfo.ptr.p_int[offs+0]==-3)||network->structinfo.ptr.p_int[offs+0]==-4 ) - { - - /* - * Special neuron type, no back-propagation required - */ - bflag = ae_true; - } - ae_assert(bflag, "MLPHessianNBatch: unknown neuron type!", _state); - continue; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine - -Network must be processed by MLPProcess on X -*************************************************************************/ -static void mlpbase_mlpinternalcalculategradient(multilayerperceptron* network, - /* Real */ ae_vector* neurons, - /* Real */ ae_vector* weights, - /* Real */ ae_vector* derror, - /* Real */ ae_vector* grad, - ae_bool naturalerrorfunc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n1; - ae_int_t n2; - ae_int_t w1; - ae_int_t w2; - ae_int_t ntotal; - ae_int_t istart; - ae_int_t nin; - ae_int_t nout; - ae_int_t offs; - double dedf; - double dfdnet; - double v; - double fown; - double deown; - double net; - double mx; - ae_bool bflag; - - - - /* - * Read network geometry - */ - nin = network->structinfo.ptr.p_int[1]; - nout = network->structinfo.ptr.p_int[2]; - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - - /* - * Pre-processing of dError/dOut: - * from dError/dOut(normalized) to dError/dOut(non-normalized) - */ - ae_assert(network->structinfo.ptr.p_int[6]==0||network->structinfo.ptr.p_int[6]==1, "MLPInternalCalculateGradient: unknown normalization type!", _state); - if( network->structinfo.ptr.p_int[6]==1 ) - { - - /* - * Softmax - */ - if( !naturalerrorfunc ) - { - mx = network->neurons.ptr.p_double[ntotal-nout]; - for(i=0; i<=nout-1; i++) - { - mx = ae_maxreal(mx, network->neurons.ptr.p_double[ntotal-nout+i], _state); - } - net = 0; - for(i=0; i<=nout-1; i++) - { - network->nwbuf.ptr.p_double[i] = ae_exp(network->neurons.ptr.p_double[ntotal-nout+i]-mx, _state); - net = net+network->nwbuf.ptr.p_double[i]; - } - v = ae_v_dotproduct(&network->derror.ptr.p_double[ntotal-nout], 1, &network->nwbuf.ptr.p_double[0], 1, ae_v_len(ntotal-nout,ntotal-1)); - for(i=0; i<=nout-1; i++) - { - fown = network->nwbuf.ptr.p_double[i]; - deown = network->derror.ptr.p_double[ntotal-nout+i]; - network->nwbuf.ptr.p_double[nout+i] = (-v+deown*fown+deown*(net-fown))*fown/ae_sqr(net, _state); - } - for(i=0; i<=nout-1; i++) - { - network->derror.ptr.p_double[ntotal-nout+i] = network->nwbuf.ptr.p_double[nout+i]; - } - } - } - else - { - - /* - * Un-standardisation - */ - for(i=0; i<=nout-1; i++) - { - network->derror.ptr.p_double[ntotal-nout+i] = network->derror.ptr.p_double[ntotal-nout+i]*network->columnsigmas.ptr.p_double[nin+i]; - } - } - - /* - * Backpropagation - */ - for(i=ntotal-1; i>=0; i--) - { - - /* - * Extract info - */ - offs = istart+i*mlpbase_nfieldwidth; - if( network->structinfo.ptr.p_int[offs+0]>0||network->structinfo.ptr.p_int[offs+0]==-5 ) - { - - /* - * Activation function - */ - dedf = network->derror.ptr.p_double[i]; - dfdnet = network->dfdnet.ptr.p_double[i]; - derror->ptr.p_double[network->structinfo.ptr.p_int[offs+2]] = derror->ptr.p_double[network->structinfo.ptr.p_int[offs+2]]+dedf*dfdnet; - continue; - } - if( network->structinfo.ptr.p_int[offs+0]==0 ) - { - - /* - * Adaptive summator - */ - n1 = network->structinfo.ptr.p_int[offs+2]; - n2 = n1+network->structinfo.ptr.p_int[offs+1]-1; - w1 = network->structinfo.ptr.p_int[offs+3]; - w2 = w1+network->structinfo.ptr.p_int[offs+1]-1; - dedf = network->derror.ptr.p_double[i]; - dfdnet = 1.0; - v = dedf*dfdnet; - ae_v_moved(&grad->ptr.p_double[w1], 1, &neurons->ptr.p_double[n1], 1, ae_v_len(w1,w2), v); - ae_v_addd(&derror->ptr.p_double[n1], 1, &weights->ptr.p_double[w1], 1, ae_v_len(n1,n2), v); - continue; - } - if( network->structinfo.ptr.p_int[offs+0]<0 ) - { - bflag = ae_false; - if( (network->structinfo.ptr.p_int[offs+0]==-2||network->structinfo.ptr.p_int[offs+0]==-3)||network->structinfo.ptr.p_int[offs+0]==-4 ) - { - - /* - * Special neuron type, no back-propagation required - */ - bflag = ae_true; - } - ae_assert(bflag, "MLPInternalCalculateGradient: unknown neuron type!", _state); - continue; - } - } -} - - -static void mlpbase_mlpchunkedgradient(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - double* e, - ae_bool naturalerrorfunc, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t kl; - ae_int_t ntotal; - ae_int_t nin; - ae_int_t nout; - ae_int_t offs; - double f; - double df; - double d2f; - double v; - double vv; - double s; - double fown; - double deown; - ae_bool bflag; - ae_int_t istart; - ae_int_t entrysize; - ae_int_t dfoffs; - ae_int_t derroroffs; - ae_int_t entryoffs; - ae_int_t neuronidx; - ae_int_t srcentryoffs; - ae_int_t srcneuronidx; - ae_int_t srcweightidx; - ae_int_t neurontype; - ae_int_t nweights; - ae_int_t offs0; - ae_int_t offs1; - ae_int_t offs2; - double v0; - double v1; - double v2; - double v3; - double s0; - double s1; - double s2; - double s3; - ae_int_t chunksize; - - - chunksize = 4; - ae_assert(csize<=chunksize, "MLPChunkedGradient: internal error (CSize>ChunkSize)", _state); - - /* - * Try to use HPC core, if possible - */ - if( hpcchunkedgradient(&network->weights, &network->structinfo, &network->columnmeans, &network->columnsigmas, xy, cstart, csize, batch4buf, hpcbuf, e, naturalerrorfunc, _state) ) - { - return; - } - - /* - * Read network geometry, prepare data - */ - nin = network->structinfo.ptr.p_int[1]; - nout = network->structinfo.ptr.p_int[2]; - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - entrysize = 12; - dfoffs = 4; - derroroffs = 8; - - /* - * Fill Batch4Buf by zeros. - * - * THIS STAGE IS VERY IMPORTANT! - * - * We fill all components of entry - neuron values, dF/dNET, dError/dF. - * It allows us to easily handle situations when CSizeptr.p_double[i] = 0; - } - - /* - * Forward pass: - * 1. Load data into Batch4Buf. If CSizecolumnsigmas.ptr.p_double[i],0) ) - { - batch4buf->ptr.p_double[entryoffs+j] = (xy->ptr.pp_double[cstart+j][i]-network->columnmeans.ptr.p_double[i])/network->columnsigmas.ptr.p_double[i]; - } - else - { - batch4buf->ptr.p_double[entryoffs+j] = xy->ptr.pp_double[cstart+j][i]-network->columnmeans.ptr.p_double[i]; - } - } - } - for(neuronidx=0; neuronidx<=ntotal-1; neuronidx++) - { - entryoffs = entrysize*neuronidx; - offs = istart+neuronidx*mlpbase_nfieldwidth; - neurontype = network->structinfo.ptr.p_int[offs+0]; - if( neurontype>0||neurontype==-5 ) - { - - /* - * "activation function" neuron, which takes value of neuron SrcNeuronIdx - * and applies activation function to it. - * - * This neuron has no weights and no tunable parameters. - */ - srcneuronidx = network->structinfo.ptr.p_int[offs+2]; - srcentryoffs = entrysize*srcneuronidx; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+0], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+0] = f; - batch4buf->ptr.p_double[entryoffs+0+dfoffs] = df; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+1], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+1] = f; - batch4buf->ptr.p_double[entryoffs+1+dfoffs] = df; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+2], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+2] = f; - batch4buf->ptr.p_double[entryoffs+2+dfoffs] = df; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+3], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+3] = f; - batch4buf->ptr.p_double[entryoffs+3+dfoffs] = df; - continue; - } - if( neurontype==0 ) - { - - /* - * "adaptive summator" neuron, whose output is a weighted sum of inputs. - * It has weights, but has no activation function. - */ - nweights = network->structinfo.ptr.p_int[offs+1]; - srcneuronidx = network->structinfo.ptr.p_int[offs+2]; - srcentryoffs = entrysize*srcneuronidx; - srcweightidx = network->structinfo.ptr.p_int[offs+3]; - v0 = 0; - v1 = 0; - v2 = 0; - v3 = 0; - for(j=0; j<=nweights-1; j++) - { - v = network->weights.ptr.p_double[srcweightidx]; - srcweightidx = srcweightidx+1; - v0 = v0+v*batch4buf->ptr.p_double[srcentryoffs+0]; - v1 = v1+v*batch4buf->ptr.p_double[srcentryoffs+1]; - v2 = v2+v*batch4buf->ptr.p_double[srcentryoffs+2]; - v3 = v3+v*batch4buf->ptr.p_double[srcentryoffs+3]; - srcentryoffs = srcentryoffs+entrysize; - } - batch4buf->ptr.p_double[entryoffs+0] = v0; - batch4buf->ptr.p_double[entryoffs+1] = v1; - batch4buf->ptr.p_double[entryoffs+2] = v2; - batch4buf->ptr.p_double[entryoffs+3] = v3; - batch4buf->ptr.p_double[entryoffs+0+dfoffs] = 1; - batch4buf->ptr.p_double[entryoffs+1+dfoffs] = 1; - batch4buf->ptr.p_double[entryoffs+2+dfoffs] = 1; - batch4buf->ptr.p_double[entryoffs+3+dfoffs] = 1; - continue; - } - if( neurontype<0 ) - { - bflag = ae_false; - if( neurontype==-2 ) - { - - /* - * Input neuron, left unchanged - */ - bflag = ae_true; - } - if( neurontype==-3 ) - { - - /* - * "-1" neuron - */ - batch4buf->ptr.p_double[entryoffs+0] = -1; - batch4buf->ptr.p_double[entryoffs+1] = -1; - batch4buf->ptr.p_double[entryoffs+2] = -1; - batch4buf->ptr.p_double[entryoffs+3] = -1; - batch4buf->ptr.p_double[entryoffs+0+dfoffs] = 0; - batch4buf->ptr.p_double[entryoffs+1+dfoffs] = 0; - batch4buf->ptr.p_double[entryoffs+2+dfoffs] = 0; - batch4buf->ptr.p_double[entryoffs+3+dfoffs] = 0; - bflag = ae_true; - } - if( neurontype==-4 ) - { - - /* - * "0" neuron - */ - batch4buf->ptr.p_double[entryoffs+0] = 0; - batch4buf->ptr.p_double[entryoffs+1] = 0; - batch4buf->ptr.p_double[entryoffs+2] = 0; - batch4buf->ptr.p_double[entryoffs+3] = 0; - batch4buf->ptr.p_double[entryoffs+0+dfoffs] = 0; - batch4buf->ptr.p_double[entryoffs+1+dfoffs] = 0; - batch4buf->ptr.p_double[entryoffs+2+dfoffs] = 0; - batch4buf->ptr.p_double[entryoffs+3+dfoffs] = 0; - bflag = ae_true; - } - ae_assert(bflag, "MLPChunkedGradient: internal error - unknown neuron type!", _state); - continue; - } - } - - /* - * Intermediate phase between forward and backward passes. - * - * For regression networks: - * * forward pass is completely done (no additional post-processing is - * needed). - * * before starting backward pass, we have to calculate dError/dOut - * for output neurons. We also update error at this phase. - * - * For classification networks: - * * in addition to forward pass we apply SOFTMAX normalization to - * output neurons. - * * after applying normalization, we have to calculate dError/dOut, - * which is calculated in two steps: - * * first, we calculate derivative of error with respect to SOFTMAX - * normalized outputs (normalized dError) - * * then, we calculate derivative of error with respect to values - * of outputs BEFORE normalization was applied to them - */ - ae_assert(network->structinfo.ptr.p_int[6]==0||network->structinfo.ptr.p_int[6]==1, "MLPChunkedGradient: unknown normalization type!", _state); - if( network->structinfo.ptr.p_int[6]==1 ) - { - - /* - * SOFTMAX-normalized network. - * - * First, calculate (V0,V1,V2,V3) - component-wise maximum - * of output neurons. This vector of maximum values will be - * used for normalization of outputs prior to calculating - * exponentials. - * - * NOTE: the only purpose of this stage is to prevent overflow - * during calculation of exponentials. With this stage - * we make sure that all exponentials are calculated - * with non-positive argument. If you load (0,0,0,0) to - * (V0,V1,V2,V3), your program will continue working - - * although with less robustness. - */ - entryoffs = entrysize*(ntotal-nout); - v0 = batch4buf->ptr.p_double[entryoffs+0]; - v1 = batch4buf->ptr.p_double[entryoffs+1]; - v2 = batch4buf->ptr.p_double[entryoffs+2]; - v3 = batch4buf->ptr.p_double[entryoffs+3]; - entryoffs = entryoffs+entrysize; - for(i=1; i<=nout-1; i++) - { - v = batch4buf->ptr.p_double[entryoffs+0]; - if( v>v0 ) - { - v0 = v; - } - v = batch4buf->ptr.p_double[entryoffs+1]; - if( v>v1 ) - { - v1 = v; - } - v = batch4buf->ptr.p_double[entryoffs+2]; - if( v>v2 ) - { - v2 = v; - } - v = batch4buf->ptr.p_double[entryoffs+3]; - if( v>v3 ) - { - v3 = v; - } - entryoffs = entryoffs+entrysize; - } - - /* - * Then, calculate exponentials and place them to part of the - * array which is located past the last entry. We also - * calculate sum of exponentials which will be stored past the - * exponentials. - */ - entryoffs = entrysize*(ntotal-nout); - offs0 = entrysize*ntotal; - s0 = 0; - s1 = 0; - s2 = 0; - s3 = 0; - for(i=0; i<=nout-1; i++) - { - v = ae_exp(batch4buf->ptr.p_double[entryoffs+0]-v0, _state); - s0 = s0+v; - batch4buf->ptr.p_double[offs0+0] = v; - v = ae_exp(batch4buf->ptr.p_double[entryoffs+1]-v1, _state); - s1 = s1+v; - batch4buf->ptr.p_double[offs0+1] = v; - v = ae_exp(batch4buf->ptr.p_double[entryoffs+2]-v2, _state); - s2 = s2+v; - batch4buf->ptr.p_double[offs0+2] = v; - v = ae_exp(batch4buf->ptr.p_double[entryoffs+3]-v3, _state); - s3 = s3+v; - batch4buf->ptr.p_double[offs0+3] = v; - entryoffs = entryoffs+entrysize; - offs0 = offs0+chunksize; - } - offs0 = entrysize*ntotal+2*nout*chunksize; - batch4buf->ptr.p_double[offs0+0] = s0; - batch4buf->ptr.p_double[offs0+1] = s1; - batch4buf->ptr.p_double[offs0+2] = s2; - batch4buf->ptr.p_double[offs0+3] = s3; - - /* - * Now we have: - * * Batch4Buf[0...EntrySize*NTotal-1] stores: - * * NTotal*ChunkSize neuron output values (SOFTMAX normalization - * was not applied to these values), - * * NTotal*ChunkSize values of dF/dNET (derivative of neuron - * output with respect to its input) - * * NTotal*ChunkSize zeros in the elements which correspond to - * dError/dOut (derivative of error with respect to neuron output). - * * Batch4Buf[EntrySize*NTotal...EntrySize*NTotal+ChunkSize*NOut-1] - - * stores exponentials of last NOut neurons. - * * Batch4Buf[EntrySize*NTotal+ChunkSize*NOut-1...EntrySize*NTotal+ChunkSize*2*NOut-1] - * - can be used for temporary calculations - * * Batch4Buf[EntrySize*NTotal+ChunkSize*2*NOut...EntrySize*NTotal+ChunkSize*2*NOut+ChunkSize-1] - * - stores sum-of-exponentials - * - * Block below calculates derivatives of error function with respect - * to non-SOFTMAX-normalized output values of last NOut neurons. - * - * It is quite complicated; we do not describe algebra behind it, - * but if you want you may check it yourself :) - */ - if( naturalerrorfunc ) - { - - /* - * Calculate derivative of error with respect to values of - * output neurons PRIOR TO SOFTMAX NORMALIZATION. Because we - * use natural error function (cross-entropy), we can do so - * very easy. - */ - offs0 = entrysize*ntotal+2*nout*chunksize; - for(k=0; k<=csize-1; k++) - { - s = batch4buf->ptr.p_double[offs0+k]; - kl = ae_round(xy->ptr.pp_double[cstart+k][nin], _state); - offs1 = (ntotal-nout)*entrysize+derroroffs+k; - offs2 = entrysize*ntotal+k; - for(i=0; i<=nout-1; i++) - { - if( i==kl ) - { - v = 1; - } - else - { - v = 0; - } - vv = batch4buf->ptr.p_double[offs2]; - batch4buf->ptr.p_double[offs1] = vv/s-v; - *e = *e+mlpbase_safecrossentropy(v, vv/s, _state); - offs1 = offs1+entrysize; - offs2 = offs2+chunksize; - } - } - } - else - { - - /* - * SOFTMAX normalization makes things very difficult. - * Sorry, we do not dare to describe this esoteric math - * in details. - */ - offs0 = entrysize*ntotal+chunksize*2*nout; - for(k=0; k<=csize-1; k++) - { - s = batch4buf->ptr.p_double[offs0+k]; - kl = ae_round(xy->ptr.pp_double[cstart+k][nin], _state); - vv = 0; - offs1 = entrysize*ntotal+k; - offs2 = entrysize*ntotal+nout*chunksize+k; - for(i=0; i<=nout-1; i++) - { - fown = batch4buf->ptr.p_double[offs1]; - if( i==kl ) - { - deown = fown/s-1; - } - else - { - deown = fown/s; - } - batch4buf->ptr.p_double[offs2] = deown; - vv = vv+deown*fown; - *e = *e+deown*deown/2; - offs1 = offs1+chunksize; - offs2 = offs2+chunksize; - } - offs1 = entrysize*ntotal+k; - offs2 = entrysize*ntotal+nout*chunksize+k; - for(i=0; i<=nout-1; i++) - { - fown = batch4buf->ptr.p_double[offs1]; - deown = batch4buf->ptr.p_double[offs2]; - batch4buf->ptr.p_double[(ntotal-nout+i)*entrysize+derroroffs+k] = (-vv+deown*fown+deown*(s-fown))*fown/ae_sqr(s, _state); - offs1 = offs1+chunksize; - offs2 = offs2+chunksize; - } - } - } - } - else - { - - /* - * Regression network with sum-of-squares function. - * - * For each NOut of last neurons: - * * calculate difference between actual and desired output - * * calculate dError/dOut for this neuron (proportional to difference) - * * store in in last 4 components of entry (these values are used - * to start backpropagation) - * * update error - */ - for(i=0; i<=nout-1; i++) - { - v0 = network->columnsigmas.ptr.p_double[nin+i]; - v1 = network->columnmeans.ptr.p_double[nin+i]; - entryoffs = entrysize*(ntotal-nout+i); - offs0 = entryoffs; - offs1 = entryoffs+derroroffs; - for(j=0; j<=csize-1; j++) - { - v = batch4buf->ptr.p_double[offs0+j]*v0+v1-xy->ptr.pp_double[cstart+j][nin+i]; - batch4buf->ptr.p_double[offs1+j] = v*v0; - *e = *e+v*v/2; - } - } - } - - /* - * Backpropagation - */ - for(neuronidx=ntotal-1; neuronidx>=0; neuronidx--) - { - entryoffs = entrysize*neuronidx; - offs = istart+neuronidx*mlpbase_nfieldwidth; - neurontype = network->structinfo.ptr.p_int[offs+0]; - if( neurontype>0||neurontype==-5 ) - { - - /* - * Activation function - */ - srcneuronidx = network->structinfo.ptr.p_int[offs+2]; - srcentryoffs = entrysize*srcneuronidx; - offs0 = srcentryoffs+derroroffs; - offs1 = entryoffs+derroroffs; - offs2 = entryoffs+dfoffs; - batch4buf->ptr.p_double[offs0+0] = batch4buf->ptr.p_double[offs0+0]+batch4buf->ptr.p_double[offs1+0]*batch4buf->ptr.p_double[offs2+0]; - batch4buf->ptr.p_double[offs0+1] = batch4buf->ptr.p_double[offs0+1]+batch4buf->ptr.p_double[offs1+1]*batch4buf->ptr.p_double[offs2+1]; - batch4buf->ptr.p_double[offs0+2] = batch4buf->ptr.p_double[offs0+2]+batch4buf->ptr.p_double[offs1+2]*batch4buf->ptr.p_double[offs2+2]; - batch4buf->ptr.p_double[offs0+3] = batch4buf->ptr.p_double[offs0+3]+batch4buf->ptr.p_double[offs1+3]*batch4buf->ptr.p_double[offs2+3]; - continue; - } - if( neurontype==0 ) - { - - /* - * Adaptive summator - */ - nweights = network->structinfo.ptr.p_int[offs+1]; - srcneuronidx = network->structinfo.ptr.p_int[offs+2]; - srcentryoffs = entrysize*srcneuronidx; - srcweightidx = network->structinfo.ptr.p_int[offs+3]; - v0 = batch4buf->ptr.p_double[entryoffs+derroroffs+0]; - v1 = batch4buf->ptr.p_double[entryoffs+derroroffs+1]; - v2 = batch4buf->ptr.p_double[entryoffs+derroroffs+2]; - v3 = batch4buf->ptr.p_double[entryoffs+derroroffs+3]; - for(j=0; j<=nweights-1; j++) - { - offs0 = srcentryoffs; - offs1 = srcentryoffs+derroroffs; - v = network->weights.ptr.p_double[srcweightidx]; - hpcbuf->ptr.p_double[srcweightidx] = hpcbuf->ptr.p_double[srcweightidx]+batch4buf->ptr.p_double[offs0+0]*v0+batch4buf->ptr.p_double[offs0+1]*v1+batch4buf->ptr.p_double[offs0+2]*v2+batch4buf->ptr.p_double[offs0+3]*v3; - batch4buf->ptr.p_double[offs1+0] = batch4buf->ptr.p_double[offs1+0]+v*v0; - batch4buf->ptr.p_double[offs1+1] = batch4buf->ptr.p_double[offs1+1]+v*v1; - batch4buf->ptr.p_double[offs1+2] = batch4buf->ptr.p_double[offs1+2]+v*v2; - batch4buf->ptr.p_double[offs1+3] = batch4buf->ptr.p_double[offs1+3]+v*v3; - srcentryoffs = srcentryoffs+entrysize; - srcweightidx = srcweightidx+1; - } - continue; - } - if( neurontype<0 ) - { - bflag = ae_false; - if( (neurontype==-2||neurontype==-3)||neurontype==-4 ) - { - - /* - * Special neuron type, no back-propagation required - */ - bflag = ae_true; - } - ae_assert(bflag, "MLPInternalCalculateGradient: unknown neuron type!", _state); - continue; - } - } -} - - -static void mlpbase_mlpchunkedprocess(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t cstart, - ae_int_t csize, - /* Real */ ae_vector* batch4buf, - /* Real */ ae_vector* hpcbuf, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t ntotal; - ae_int_t nin; - ae_int_t nout; - ae_int_t offs; - double f; - double df; - double d2f; - double v; - ae_bool bflag; - ae_int_t istart; - ae_int_t entrysize; - ae_int_t entryoffs; - ae_int_t neuronidx; - ae_int_t srcentryoffs; - ae_int_t srcneuronidx; - ae_int_t srcweightidx; - ae_int_t neurontype; - ae_int_t nweights; - ae_int_t offs0; - double v0; - double v1; - double v2; - double v3; - double s0; - double s1; - double s2; - double s3; - ae_int_t chunksize; - - - chunksize = 4; - ae_assert(csize<=chunksize, "MLPChunkedProcess: internal error (CSize>ChunkSize)", _state); - - /* - * Try to use HPC core, if possible - */ - if( hpcchunkedprocess(&network->weights, &network->structinfo, &network->columnmeans, &network->columnsigmas, xy, cstart, csize, batch4buf, hpcbuf, _state) ) - { - return; - } - - /* - * Read network geometry, prepare data - */ - nin = network->structinfo.ptr.p_int[1]; - nout = network->structinfo.ptr.p_int[2]; - ntotal = network->structinfo.ptr.p_int[3]; - istart = network->structinfo.ptr.p_int[5]; - entrysize = 4; - - /* - * Fill Batch4Buf by zeros. - * - * THIS STAGE IS VERY IMPORTANT! - * - * We fill all components of entry - neuron values, dF/dNET, dError/dF. - * It allows us to easily handle situations when CSizeptr.p_double[i] = 0; - } - - /* - * Forward pass: - * 1. Load data into Batch4Buf. If CSizecolumnsigmas.ptr.p_double[i],0) ) - { - batch4buf->ptr.p_double[entryoffs+j] = (xy->ptr.pp_double[cstart+j][i]-network->columnmeans.ptr.p_double[i])/network->columnsigmas.ptr.p_double[i]; - } - else - { - batch4buf->ptr.p_double[entryoffs+j] = xy->ptr.pp_double[cstart+j][i]-network->columnmeans.ptr.p_double[i]; - } - } - } - for(neuronidx=0; neuronidx<=ntotal-1; neuronidx++) - { - entryoffs = entrysize*neuronidx; - offs = istart+neuronidx*mlpbase_nfieldwidth; - neurontype = network->structinfo.ptr.p_int[offs+0]; - if( neurontype>0||neurontype==-5 ) - { - - /* - * "activation function" neuron, which takes value of neuron SrcNeuronIdx - * and applies activation function to it. - * - * This neuron has no weights and no tunable parameters. - */ - srcneuronidx = network->structinfo.ptr.p_int[offs+2]; - srcentryoffs = entrysize*srcneuronidx; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+0], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+0] = f; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+1], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+1] = f; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+2], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+2] = f; - mlpactivationfunction(batch4buf->ptr.p_double[srcentryoffs+3], neurontype, &f, &df, &d2f, _state); - batch4buf->ptr.p_double[entryoffs+3] = f; - continue; - } - if( neurontype==0 ) - { - - /* - * "adaptive summator" neuron, whose output is a weighted sum of inputs. - * It has weights, but has no activation function. - */ - nweights = network->structinfo.ptr.p_int[offs+1]; - srcneuronidx = network->structinfo.ptr.p_int[offs+2]; - srcentryoffs = entrysize*srcneuronidx; - srcweightidx = network->structinfo.ptr.p_int[offs+3]; - v0 = 0; - v1 = 0; - v2 = 0; - v3 = 0; - for(j=0; j<=nweights-1; j++) - { - v = network->weights.ptr.p_double[srcweightidx]; - srcweightidx = srcweightidx+1; - v0 = v0+v*batch4buf->ptr.p_double[srcentryoffs+0]; - v1 = v1+v*batch4buf->ptr.p_double[srcentryoffs+1]; - v2 = v2+v*batch4buf->ptr.p_double[srcentryoffs+2]; - v3 = v3+v*batch4buf->ptr.p_double[srcentryoffs+3]; - srcentryoffs = srcentryoffs+entrysize; - } - batch4buf->ptr.p_double[entryoffs+0] = v0; - batch4buf->ptr.p_double[entryoffs+1] = v1; - batch4buf->ptr.p_double[entryoffs+2] = v2; - batch4buf->ptr.p_double[entryoffs+3] = v3; - continue; - } - if( neurontype<0 ) - { - bflag = ae_false; - if( neurontype==-2 ) - { - - /* - * Input neuron, left unchanged - */ - bflag = ae_true; - } - if( neurontype==-3 ) - { - - /* - * "-1" neuron - */ - batch4buf->ptr.p_double[entryoffs+0] = -1; - batch4buf->ptr.p_double[entryoffs+1] = -1; - batch4buf->ptr.p_double[entryoffs+2] = -1; - batch4buf->ptr.p_double[entryoffs+3] = -1; - bflag = ae_true; - } - if( neurontype==-4 ) - { - - /* - * "0" neuron - */ - batch4buf->ptr.p_double[entryoffs+0] = 0; - batch4buf->ptr.p_double[entryoffs+1] = 0; - batch4buf->ptr.p_double[entryoffs+2] = 0; - batch4buf->ptr.p_double[entryoffs+3] = 0; - bflag = ae_true; - } - ae_assert(bflag, "MLPChunkedProcess: internal error - unknown neuron type!", _state); - continue; - } - } - - /* - * SOFTMAX normalization or scaling. - */ - ae_assert(network->structinfo.ptr.p_int[6]==0||network->structinfo.ptr.p_int[6]==1, "MLPChunkedProcess: unknown normalization type!", _state); - if( network->structinfo.ptr.p_int[6]==1 ) - { - - /* - * SOFTMAX-normalized network. - * - * First, calculate (V0,V1,V2,V3) - component-wise maximum - * of output neurons. This vector of maximum values will be - * used for normalization of outputs prior to calculating - * exponentials. - * - * NOTE: the only purpose of this stage is to prevent overflow - * during calculation of exponentials. With this stage - * we make sure that all exponentials are calculated - * with non-positive argument. If you load (0,0,0,0) to - * (V0,V1,V2,V3), your program will continue working - - * although with less robustness. - */ - entryoffs = entrysize*(ntotal-nout); - v0 = batch4buf->ptr.p_double[entryoffs+0]; - v1 = batch4buf->ptr.p_double[entryoffs+1]; - v2 = batch4buf->ptr.p_double[entryoffs+2]; - v3 = batch4buf->ptr.p_double[entryoffs+3]; - entryoffs = entryoffs+entrysize; - for(i=1; i<=nout-1; i++) - { - v = batch4buf->ptr.p_double[entryoffs+0]; - if( v>v0 ) - { - v0 = v; - } - v = batch4buf->ptr.p_double[entryoffs+1]; - if( v>v1 ) - { - v1 = v; - } - v = batch4buf->ptr.p_double[entryoffs+2]; - if( v>v2 ) - { - v2 = v; - } - v = batch4buf->ptr.p_double[entryoffs+3]; - if( v>v3 ) - { - v3 = v; - } - entryoffs = entryoffs+entrysize; - } - - /* - * Then, calculate exponentials and place them to part of the - * array which is located past the last entry. We also - * calculate sum of exponentials. - */ - entryoffs = entrysize*(ntotal-nout); - offs0 = entrysize*ntotal; - s0 = 0; - s1 = 0; - s2 = 0; - s3 = 0; - for(i=0; i<=nout-1; i++) - { - v = ae_exp(batch4buf->ptr.p_double[entryoffs+0]-v0, _state); - s0 = s0+v; - batch4buf->ptr.p_double[offs0+0] = v; - v = ae_exp(batch4buf->ptr.p_double[entryoffs+1]-v1, _state); - s1 = s1+v; - batch4buf->ptr.p_double[offs0+1] = v; - v = ae_exp(batch4buf->ptr.p_double[entryoffs+2]-v2, _state); - s2 = s2+v; - batch4buf->ptr.p_double[offs0+2] = v; - v = ae_exp(batch4buf->ptr.p_double[entryoffs+3]-v3, _state); - s3 = s3+v; - batch4buf->ptr.p_double[offs0+3] = v; - entryoffs = entryoffs+entrysize; - offs0 = offs0+chunksize; - } - - /* - * Write SOFTMAX-normalized values to the output array. - */ - offs0 = entrysize*ntotal; - for(i=0; i<=nout-1; i++) - { - if( csize>0 ) - { - xy->ptr.pp_double[cstart+0][nin+i] = batch4buf->ptr.p_double[offs0+0]/s0; - } - if( csize>1 ) - { - xy->ptr.pp_double[cstart+1][nin+i] = batch4buf->ptr.p_double[offs0+1]/s1; - } - if( csize>2 ) - { - xy->ptr.pp_double[cstart+2][nin+i] = batch4buf->ptr.p_double[offs0+2]/s2; - } - if( csize>3 ) - { - xy->ptr.pp_double[cstart+3][nin+i] = batch4buf->ptr.p_double[offs0+3]/s3; - } - offs0 = offs0+chunksize; - } - } - else - { - - /* - * Regression network with sum-of-squares function. - * - * For each NOut of last neurons: - * * calculate difference between actual and desired output - * * calculate dError/dOut for this neuron (proportional to difference) - * * store in in last 4 components of entry (these values are used - * to start backpropagation) - * * update error - */ - for(i=0; i<=nout-1; i++) - { - v0 = network->columnsigmas.ptr.p_double[nin+i]; - v1 = network->columnmeans.ptr.p_double[nin+i]; - entryoffs = entrysize*(ntotal-nout+i); - for(j=0; j<=csize-1; j++) - { - xy->ptr.pp_double[cstart+j][nin+i] = batch4buf->ptr.p_double[entryoffs+j]*v0+v1; - } - } - } -} - - -/************************************************************************* -Returns T*Ln(T/Z), guarded against overflow/underflow. -Internal subroutine. -*************************************************************************/ -static double mlpbase_safecrossentropy(double t, - double z, - ae_state *_state) -{ - double r; - double result; - - - if( ae_fp_eq(t,0) ) - { - result = 0; - } - else - { - if( ae_fp_greater(ae_fabs(z, _state),1) ) - { - - /* - * Shouldn't be the case with softmax, - * but we just want to be sure. - */ - if( ae_fp_eq(t/z,0) ) - { - r = ae_minrealnumber; - } - else - { - r = t/z; - } - } - else - { - - /* - * Normal case - */ - if( ae_fp_eq(z,0)||ae_fp_greater_eq(ae_fabs(t, _state),ae_maxrealnumber*ae_fabs(z, _state)) ) - { - r = ae_maxrealnumber; - } - else - { - r = t/z; - } - } - result = t*ae_log(r, _state); - } - return result; -} - - -/************************************************************************* -This function performs backward pass of neural network randimization: -* it assumes that Network.Weights stores standard deviation of weights - (weights are not generated yet, only their deviations are present) -* it sets deviations of weights which feed NeuronIdx-th neuron to specified value -* it recursively passes to deeper neuron and modifies their weights -* it stops after encountering nonlinear neurons, linear activation function, - input neurons, "0" and "-1" neurons - - -- ALGLIB -- - Copyright 27.06.2013 by Bochkanov Sergey -*************************************************************************/ -static void mlpbase_randomizebackwardpass(multilayerperceptron* network, - ae_int_t neuronidx, - double v, - ae_state *_state) -{ - ae_int_t istart; - ae_int_t neurontype; - ae_int_t n1; - ae_int_t n2; - ae_int_t w1; - ae_int_t w2; - ae_int_t offs; - ae_int_t i; - - - istart = network->structinfo.ptr.p_int[5]; - neurontype = network->structinfo.ptr.p_int[istart+neuronidx*mlpbase_nfieldwidth+0]; - if( neurontype==-2 ) - { - - /* - * Input neuron - stop - */ - return; - } - if( neurontype==-3 ) - { - - /* - * "-1" neuron: stop - */ - return; - } - if( neurontype==-4 ) - { - - /* - * "0" neuron: stop - */ - return; - } - if( neurontype==0 ) - { - - /* - * Adaptive summator neuron: - * * modify deviations of its weights - * * recursively call this function for its inputs - */ - offs = istart+neuronidx*mlpbase_nfieldwidth; - n1 = network->structinfo.ptr.p_int[offs+2]; - n2 = n1+network->structinfo.ptr.p_int[offs+1]-1; - w1 = network->structinfo.ptr.p_int[offs+3]; - w2 = w1+network->structinfo.ptr.p_int[offs+1]-1; - for(i=w1; i<=w2; i++) - { - network->weights.ptr.p_double[i] = v; - } - for(i=n1; i<=n2; i++) - { - mlpbase_randomizebackwardpass(network, i, v, _state); - } - return; - } - if( neurontype==-5 ) - { - - /* - * Linear activation function: stop - */ - return; - } - if( neurontype>0 ) - { - - /* - * Nonlinear activation function: stop - */ - return; - } - ae_assert(ae_false, "RandomizeBackwardPass: unexpected neuron type", _state); -} - - -ae_bool _modelerrors_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - modelerrors *p = (modelerrors*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _modelerrors_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - modelerrors *dst = (modelerrors*)_dst; - modelerrors *src = (modelerrors*)_src; - dst->relclserror = src->relclserror; - dst->avgce = src->avgce; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - return ae_true; -} - - -void _modelerrors_clear(void* _p) -{ - modelerrors *p = (modelerrors*)_p; - ae_touch_ptr((void*)p); -} - - -void _modelerrors_destroy(void* _p) -{ - modelerrors *p = (modelerrors*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _smlpgrad_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - smlpgrad *p = (smlpgrad*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _smlpgrad_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - smlpgrad *dst = (smlpgrad*)_dst; - smlpgrad *src = (smlpgrad*)_src; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _smlpgrad_clear(void* _p) -{ - smlpgrad *p = (smlpgrad*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->g); -} - - -void _smlpgrad_destroy(void* _p) -{ - smlpgrad *p = (smlpgrad*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->g); -} - - -ae_bool _multilayerperceptron_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - multilayerperceptron *p = (multilayerperceptron*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->hllayersizes, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hlconnections, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hlneurons, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->structinfo, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->weights, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->columnmeans, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->columnsigmas, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->neurons, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dfdnet, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->derror, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->xy, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xyrow, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->nwbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->integerbuf, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !_modelerrors_init(&p->err, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rndbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init(&p->buf, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init(&p->gradbuf, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->dummydxy, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_sparsematrix_init(&p->dummysxy, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dummyidx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init(&p->dummypool, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _multilayerperceptron_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - multilayerperceptron *dst = (multilayerperceptron*)_dst; - multilayerperceptron *src = (multilayerperceptron*)_src; - dst->hlnetworktype = src->hlnetworktype; - dst->hlnormtype = src->hlnormtype; - if( !ae_vector_init_copy(&dst->hllayersizes, &src->hllayersizes, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->hlconnections, &src->hlconnections, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->hlneurons, &src->hlneurons, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->structinfo, &src->structinfo, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->weights, &src->weights, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->columnmeans, &src->columnmeans, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->columnsigmas, &src->columnsigmas, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->neurons, &src->neurons, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dfdnet, &src->dfdnet, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->derror, &src->derror, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->xy, &src->xy, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xyrow, &src->xyrow, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->nwbuf, &src->nwbuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->integerbuf, &src->integerbuf, _state, make_automatic) ) - return ae_false; - if( !_modelerrors_init_copy(&dst->err, &src->err, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rndbuf, &src->rndbuf, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init_copy(&dst->buf, &src->buf, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init_copy(&dst->gradbuf, &src->gradbuf, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->dummydxy, &src->dummydxy, _state, make_automatic) ) - return ae_false; - if( !_sparsematrix_init_copy(&dst->dummysxy, &src->dummysxy, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dummyidx, &src->dummyidx, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init_copy(&dst->dummypool, &src->dummypool, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _multilayerperceptron_clear(void* _p) -{ - multilayerperceptron *p = (multilayerperceptron*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->hllayersizes); - ae_vector_clear(&p->hlconnections); - ae_vector_clear(&p->hlneurons); - ae_vector_clear(&p->structinfo); - ae_vector_clear(&p->weights); - ae_vector_clear(&p->columnmeans); - ae_vector_clear(&p->columnsigmas); - ae_vector_clear(&p->neurons); - ae_vector_clear(&p->dfdnet); - ae_vector_clear(&p->derror); - ae_vector_clear(&p->x); - ae_vector_clear(&p->y); - ae_matrix_clear(&p->xy); - ae_vector_clear(&p->xyrow); - ae_vector_clear(&p->nwbuf); - ae_vector_clear(&p->integerbuf); - _modelerrors_clear(&p->err); - ae_vector_clear(&p->rndbuf); - ae_shared_pool_clear(&p->buf); - ae_shared_pool_clear(&p->gradbuf); - ae_matrix_clear(&p->dummydxy); - _sparsematrix_clear(&p->dummysxy); - ae_vector_clear(&p->dummyidx); - ae_shared_pool_clear(&p->dummypool); -} - - -void _multilayerperceptron_destroy(void* _p) -{ - multilayerperceptron *p = (multilayerperceptron*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->hllayersizes); - ae_vector_destroy(&p->hlconnections); - ae_vector_destroy(&p->hlneurons); - ae_vector_destroy(&p->structinfo); - ae_vector_destroy(&p->weights); - ae_vector_destroy(&p->columnmeans); - ae_vector_destroy(&p->columnsigmas); - ae_vector_destroy(&p->neurons); - ae_vector_destroy(&p->dfdnet); - ae_vector_destroy(&p->derror); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->y); - ae_matrix_destroy(&p->xy); - ae_vector_destroy(&p->xyrow); - ae_vector_destroy(&p->nwbuf); - ae_vector_destroy(&p->integerbuf); - _modelerrors_destroy(&p->err); - ae_vector_destroy(&p->rndbuf); - ae_shared_pool_destroy(&p->buf); - ae_shared_pool_destroy(&p->gradbuf); - ae_matrix_destroy(&p->dummydxy); - _sparsematrix_destroy(&p->dummysxy); - ae_vector_destroy(&p->dummyidx); - ae_shared_pool_destroy(&p->dummypool); -} - - - - -/************************************************************************* -This subroutine trains logit model. - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars] - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - -OUTPUT PARAMETERS: - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPointsptr.pp_double[i][nvars], _state)<0||ae_round(xy->ptr.pp_double[i][nvars], _state)>=nclasses ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * Initialize data - */ - rep->ngrad = 0; - rep->nhess = 0; - - /* - * Allocate array - */ - offs = 5; - ssize = 5+(nvars+1)*(nclasses-1)+nclasses; - ae_vector_set_length(&lm->w, ssize-1+1, _state); - lm->w.ptr.p_double[0] = ssize; - lm->w.ptr.p_double[1] = logit_logitvnum; - lm->w.ptr.p_double[2] = nvars; - lm->w.ptr.p_double[3] = nclasses; - lm->w.ptr.p_double[4] = offs; - - /* - * Degenerate case: all outputs are equal - */ - allsame = ae_true; - for(i=1; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nvars], _state)!=ae_round(xy->ptr.pp_double[i-1][nvars], _state) ) - { - allsame = ae_false; - } - } - if( allsame ) - { - for(i=0; i<=(nvars+1)*(nclasses-1)-1; i++) - { - lm->w.ptr.p_double[offs+i] = 0; - } - v = -2*ae_log(ae_minrealnumber, _state); - k = ae_round(xy->ptr.pp_double[0][nvars], _state); - if( k==nclasses-1 ) - { - for(i=0; i<=nclasses-2; i++) - { - lm->w.ptr.p_double[offs+i*(nvars+1)+nvars] = -v; - } - } - else - { - for(i=0; i<=nclasses-2; i++) - { - if( i==k ) - { - lm->w.ptr.p_double[offs+i*(nvars+1)+nvars] = v; - } - else - { - lm->w.ptr.p_double[offs+i*(nvars+1)+nvars] = 0; - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * General case. - * Prepare task and network. Allocate space. - */ - mlpcreatec0(nvars, nclasses, &network, _state); - mlpinitpreprocessor(&network, xy, npoints, _state); - mlpproperties(&network, &nin, &nout, &wcount, _state); - for(i=0; i<=wcount-1; i++) - { - network.weights.ptr.p_double[i] = (2*ae_randomreal(_state)-1)/nvars; - } - ae_vector_set_length(&g, wcount-1+1, _state); - ae_matrix_set_length(&h, wcount-1+1, wcount-1+1, _state); - ae_vector_set_length(&wbase, wcount-1+1, _state); - ae_vector_set_length(&wdir, wcount-1+1, _state); - ae_vector_set_length(&work, wcount-1+1, _state); - - /* - * First stage: optimize in gradient direction. - */ - for(k=0; k<=wcount/3+10; k++) - { - - /* - * Calculate gradient in starting point - */ - mlpgradnbatch(&network, xy, npoints, &e, &g, _state); - v = ae_v_dotproduct(&network.weights.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = e+0.5*decay*v; - ae_v_addd(&g.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - rep->ngrad = rep->ngrad+1; - - /* - * Setup optimization scheme - */ - ae_v_moveneg(&wdir.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - v = ae_v_dotproduct(&wdir.ptr.p_double[0], 1, &wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - wstep = ae_sqrt(v, _state); - v = 1/ae_sqrt(v, _state); - ae_v_muld(&wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1), v); - mcstage = 0; - logit_mnlmcsrch(wcount, &network.weights, &e, &g, &wdir, &wstep, &mcinfo, &mcnfev, &work, &mcstate, &mcstage, _state); - while(mcstage!=0) - { - mlpgradnbatch(&network, xy, npoints, &e, &g, _state); - v = ae_v_dotproduct(&network.weights.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = e+0.5*decay*v; - ae_v_addd(&g.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - rep->ngrad = rep->ngrad+1; - logit_mnlmcsrch(wcount, &network.weights, &e, &g, &wdir, &wstep, &mcinfo, &mcnfev, &work, &mcstate, &mcstage, _state); - } - } - - /* - * Second stage: use Hessian when we are close to the minimum - */ - for(;;) - { - - /* - * Calculate and update E/G/H - */ - mlphessiannbatch(&network, xy, npoints, &e, &g, &h, _state); - v = ae_v_dotproduct(&network.weights.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = e+0.5*decay*v; - ae_v_addd(&g.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - for(k=0; k<=wcount-1; k++) - { - h.ptr.pp_double[k][k] = h.ptr.pp_double[k][k]+decay; - } - rep->nhess = rep->nhess+1; - - /* - * Select step direction - * NOTE: it is important to use lower-triangle Cholesky - * factorization since it is much faster than higher-triangle version. - */ - spd = spdmatrixcholesky(&h, wcount, ae_false, _state); - spdmatrixcholeskysolve(&h, wcount, ae_false, &g, &solverinfo, &solverrep, &wdir, _state); - spd = solverinfo>0; - if( spd ) - { - - /* - * H is positive definite. - * Step in Newton direction. - */ - ae_v_muld(&wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1), -1); - spd = ae_true; - } - else - { - - /* - * H is indefinite. - * Step in gradient direction. - */ - ae_v_moveneg(&wdir.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - spd = ae_false; - } - - /* - * Optimize in WDir direction - */ - v = ae_v_dotproduct(&wdir.ptr.p_double[0], 1, &wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - wstep = ae_sqrt(v, _state); - v = 1/ae_sqrt(v, _state); - ae_v_muld(&wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1), v); - mcstage = 0; - logit_mnlmcsrch(wcount, &network.weights, &e, &g, &wdir, &wstep, &mcinfo, &mcnfev, &work, &mcstate, &mcstage, _state); - while(mcstage!=0) - { - mlpgradnbatch(&network, xy, npoints, &e, &g, _state); - v = ae_v_dotproduct(&network.weights.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = e+0.5*decay*v; - ae_v_addd(&g.ptr.p_double[0], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - rep->ngrad = rep->ngrad+1; - logit_mnlmcsrch(wcount, &network.weights, &e, &g, &wdir, &wstep, &mcinfo, &mcnfev, &work, &mcstate, &mcstage, _state); - } - if( spd&&((mcinfo==2||mcinfo==4)||mcinfo==6) ) - { - break; - } - } - - /* - * Convert from NN format to MNL format - */ - ae_v_move(&lm->w.ptr.p_double[offs], 1, &network.weights.ptr.p_double[0], 1, ae_v_len(offs,offs+wcount-1)); - for(k=0; k<=nvars-1; k++) - { - for(i=0; i<=nclasses-2; i++) - { - s = network.columnsigmas.ptr.p_double[k]; - if( ae_fp_eq(s,0) ) - { - s = 1; - } - j = offs+(nvars+1)*i; - v = lm->w.ptr.p_double[j+k]; - lm->w.ptr.p_double[j+k] = v/s; - lm->w.ptr.p_double[j+nvars] = lm->w.ptr.p_double[j+nvars]+v*network.columnmeans.ptr.p_double[k]/s; - } - } - for(k=0; k<=nclasses-2; k++) - { - lm->w.ptr.p_double[offs+(nvars+1)*k+nvars] = -lm->w.ptr.p_double[offs+(nvars+1)*k+nvars]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - LM - logit model, passed by non-constant reference - (some fields of structure are used as temporaries - when calculating model output). - X - input vector, array[0..NVars-1]. - Y - (possibly) preallocated buffer; if size of Y is less than - NClasses, it will be reallocated.If it is large enough, it - is NOT reallocated, so we can save some time on reallocation. - -OUTPUT PARAMETERS: - Y - result, array[0..NClasses-1] - Vector of posterior probabilities for classification task. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -void mnlprocess(logitmodel* lm, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t nvars; - ae_int_t nclasses; - ae_int_t offs; - ae_int_t i; - ae_int_t i1; - double s; - - - ae_assert(ae_fp_eq(lm->w.ptr.p_double[1],logit_logitvnum), "MNLProcess: unexpected model version", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - nclasses = ae_round(lm->w.ptr.p_double[3], _state); - offs = ae_round(lm->w.ptr.p_double[4], _state); - logit_mnliexp(&lm->w, x, _state); - s = 0; - i1 = offs+(nvars+1)*(nclasses-1); - for(i=i1; i<=i1+nclasses-1; i++) - { - s = s+lm->w.ptr.p_double[i]; - } - if( y->cntptr.p_double[i] = lm->w.ptr.p_double[i1+i]/s; - } -} - - -/************************************************************************* -'interactive' variant of MNLProcess for languages like Python which -support constructs like "Y = MNLProcess(LM,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -void mnlprocessi(logitmodel* lm, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - - ae_vector_clear(y); - - mnlprocess(lm, x, y, _state); -} - - -/************************************************************************* -Unpacks coefficients of logit model. Logit model have form: - - P(class=i) = S(i) / (S(0) + S(1) + ... +S(M-1)) - S(i) = Exp(A[i,0]*X[0] + ... + A[i,N-1]*X[N-1] + A[i,N]), when iw.ptr.p_double[1],logit_logitvnum), "MNLUnpack: unexpected model version", _state); - *nvars = ae_round(lm->w.ptr.p_double[2], _state); - *nclasses = ae_round(lm->w.ptr.p_double[3], _state); - offs = ae_round(lm->w.ptr.p_double[4], _state); - ae_matrix_set_length(a, *nclasses-2+1, *nvars+1, _state); - for(i=0; i<=*nclasses-2; i++) - { - ae_v_move(&a->ptr.pp_double[i][0], 1, &lm->w.ptr.p_double[offs+i*(*nvars+1)], 1, ae_v_len(0,*nvars)); - } -} - - -/************************************************************************* -"Packs" coefficients and creates logit model in ALGLIB format (MNLUnpack -reversed). - -INPUT PARAMETERS: - A - model (see MNLUnpack) - NVars - number of independent variables - NClasses - number of classes - -OUTPUT PARAMETERS: - LM - logit model. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -void mnlpack(/* Real */ ae_matrix* a, - ae_int_t nvars, - ae_int_t nclasses, - logitmodel* lm, - ae_state *_state) -{ - ae_int_t offs; - ae_int_t i; - ae_int_t ssize; - - _logitmodel_clear(lm); - - offs = 5; - ssize = 5+(nvars+1)*(nclasses-1)+nclasses; - ae_vector_set_length(&lm->w, ssize-1+1, _state); - lm->w.ptr.p_double[0] = ssize; - lm->w.ptr.p_double[1] = logit_logitvnum; - lm->w.ptr.p_double[2] = nvars; - lm->w.ptr.p_double[3] = nclasses; - lm->w.ptr.p_double[4] = offs; - for(i=0; i<=nclasses-2; i++) - { - ae_v_move(&lm->w.ptr.p_double[offs+i*(nvars+1)], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(offs+i*(nvars+1),offs+i*(nvars+1)+nvars)); - } -} - - -/************************************************************************* -Copying of LogitModel strucure - -INPUT PARAMETERS: - LM1 - original - -OUTPUT PARAMETERS: - LM2 - copy - - -- ALGLIB -- - Copyright 15.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mnlcopy(logitmodel* lm1, logitmodel* lm2, ae_state *_state) -{ - ae_int_t k; - - _logitmodel_clear(lm2); - - k = ae_round(lm1->w.ptr.p_double[0], _state); - ae_vector_set_length(&lm2->w, k-1+1, _state); - ae_v_move(&lm2->w.ptr.p_double[0], 1, &lm1->w.ptr.p_double[0], 1, ae_v_len(0,k-1)); -} - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*ln(2)). - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlavgce(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nvars; - ae_int_t nclasses; - ae_int_t i; - ae_vector workx; - ae_vector worky; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&workx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&worky, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_fp_eq(lm->w.ptr.p_double[1],logit_logitvnum), "MNLClsError: unexpected model version", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - nclasses = ae_round(lm->w.ptr.p_double[3], _state); - ae_vector_set_length(&workx, nvars-1+1, _state); - ae_vector_set_length(&worky, nclasses-1+1, _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - ae_assert(ae_round(xy->ptr.pp_double[i][nvars], _state)>=0&&ae_round(xy->ptr.pp_double[i][nvars], _state)ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - mnlprocess(lm, &workx, &worky, _state); - if( ae_fp_greater(worky.ptr.p_double[ae_round(xy->ptr.pp_double[i][nvars], _state)],0) ) - { - result = result-ae_log(worky.ptr.p_double[ae_round(xy->ptr.pp_double[i][nvars], _state)], _state); - } - else - { - result = result-ae_log(ae_minrealnumber, _state); - } - } - result = result/(npoints*ae_log(2, _state)); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlrelclserror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double result; - - - result = (double)mnlclserror(lm, xy, npoints, _state)/(double)npoints; - return result; -} - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - root mean square error (error when estimating posterior probabilities). - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlrmserror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double relcls; - double avgce; - double rms; - double avg; - double avgrel; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==logit_logitvnum, "MNLRMSError: Incorrect MNL version!", _state); - logit_mnlallerrors(lm, xy, npoints, &relcls, &avgce, &rms, &avg, &avgrel, _state); - result = rms; - return result; -} - - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - average error (error when estimating posterior probabilities). - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlavgerror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double relcls; - double avgce; - double rms; - double avg; - double avgrel; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==logit_logitvnum, "MNLRMSError: Incorrect MNL version!", _state); - logit_mnlallerrors(lm, xy, npoints, &relcls, &avgce, &rms, &avg, &avgrel, _state); - result = avg; - return result; -} - - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - LM - logit model - XY - test set - NPoints - test set size - -RESULT: - average relative error (error when estimating posterior probabilities). - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -double mnlavgrelerror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_state *_state) -{ - double relcls; - double avgce; - double rms; - double avg; - double avgrel; - double result; - - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==logit_logitvnum, "MNLRMSError: Incorrect MNL version!", _state); - logit_mnlallerrors(lm, xy, ssize, &relcls, &avgce, &rms, &avg, &avgrel, _state); - result = avgrel; - return result; -} - - -/************************************************************************* -Classification error on test set = MNLRelClsError*NPoints - - -- ALGLIB -- - Copyright 10.09.2008 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mnlclserror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nvars; - ae_int_t nclasses; - ae_int_t i; - ae_int_t j; - ae_vector workx; - ae_vector worky; - ae_int_t nmax; - ae_int_t result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&workx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&worky, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_fp_eq(lm->w.ptr.p_double[1],logit_logitvnum), "MNLClsError: unexpected model version", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - nclasses = ae_round(lm->w.ptr.p_double[3], _state); - ae_vector_set_length(&workx, nvars-1+1, _state); - ae_vector_set_length(&worky, nclasses-1+1, _state); - result = 0; - for(i=0; i<=npoints-1; i++) - { - - /* - * Process - */ - ae_v_move(&workx.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - mnlprocess(lm, &workx, &worky, _state); - - /* - * Logit version of the answer - */ - nmax = 0; - for(j=0; j<=nclasses-1; j++) - { - if( ae_fp_greater(worky.ptr.p_double[j],worky.ptr.p_double[nmax]) ) - { - nmax = j; - } - } - - /* - * compare - */ - if( nmax!=ae_round(xy->ptr.pp_double[i][nvars], _state) ) - { - result = result+1; - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Internal subroutine. Places exponents of the anti-overflow shifted -internal linear outputs into the service part of the W array. -*************************************************************************/ -static void logit_mnliexp(/* Real */ ae_vector* w, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t nvars; - ae_int_t nclasses; - ae_int_t offs; - ae_int_t i; - ae_int_t i1; - double v; - double mx; - - - ae_assert(ae_fp_eq(w->ptr.p_double[1],logit_logitvnum), "LOGIT: unexpected model version", _state); - nvars = ae_round(w->ptr.p_double[2], _state); - nclasses = ae_round(w->ptr.p_double[3], _state); - offs = ae_round(w->ptr.p_double[4], _state); - i1 = offs+(nvars+1)*(nclasses-1); - for(i=0; i<=nclasses-2; i++) - { - v = ae_v_dotproduct(&w->ptr.p_double[offs+i*(nvars+1)], 1, &x->ptr.p_double[0], 1, ae_v_len(offs+i*(nvars+1),offs+i*(nvars+1)+nvars-1)); - w->ptr.p_double[i1+i] = v+w->ptr.p_double[offs+i*(nvars+1)+nvars]; - } - w->ptr.p_double[i1+nclasses-1] = 0; - mx = 0; - for(i=i1; i<=i1+nclasses-1; i++) - { - mx = ae_maxreal(mx, w->ptr.p_double[i], _state); - } - for(i=i1; i<=i1+nclasses-1; i++) - { - w->ptr.p_double[i] = ae_exp(w->ptr.p_double[i]-mx, _state); - } -} - - -/************************************************************************* -Calculation of all types of errors - - -- ALGLIB -- - Copyright 30.08.2008 by Bochkanov Sergey -*************************************************************************/ -static void logit_mnlallerrors(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double* relcls, - double* avgce, - double* rms, - double* avg, - double* avgrel, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nvars; - ae_int_t nclasses; - ae_int_t i; - ae_vector buf; - ae_vector workx; - ae_vector y; - ae_vector dy; - - ae_frame_make(_state, &_frame_block); - *relcls = 0; - *avgce = 0; - *rms = 0; - *avg = 0; - *avgrel = 0; - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&workx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dy, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_round(lm->w.ptr.p_double[1], _state)==logit_logitvnum, "MNL unit: Incorrect MNL version!", _state); - nvars = ae_round(lm->w.ptr.p_double[2], _state); - nclasses = ae_round(lm->w.ptr.p_double[3], _state); - ae_vector_set_length(&workx, nvars-1+1, _state); - ae_vector_set_length(&y, nclasses-1+1, _state); - ae_vector_set_length(&dy, 0+1, _state); - dserrallocate(nclasses, &buf, _state); - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&workx.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - mnlprocess(lm, &workx, &y, _state); - dy.ptr.p_double[0] = xy->ptr.pp_double[i][nvars]; - dserraccumulate(&buf, &y, &dy, _state); - } - dserrfinish(&buf, _state); - *relcls = buf.ptr.p_double[0]; - *avgce = buf.ptr.p_double[1]; - *rms = buf.ptr.p_double[2]; - *avg = buf.ptr.p_double[3]; - *avgrel = buf.ptr.p_double[4]; - ae_frame_leave(_state); -} - - -/************************************************************************* -THE PURPOSE OF MCSRCH IS TO FIND A STEP WHICH SATISFIES A SUFFICIENT -DECREASE CONDITION AND A CURVATURE CONDITION. - -AT EACH STAGE THE SUBROUTINE UPDATES AN INTERVAL OF UNCERTAINTY WITH -ENDPOINTS STX AND STY. THE INTERVAL OF UNCERTAINTY IS INITIALLY CHOSEN -SO THAT IT CONTAINS A MINIMIZER OF THE MODIFIED FUNCTION - - F(X+STP*S) - F(X) - FTOL*STP*(GRADF(X)'S). - -IF A STEP IS OBTAINED FOR WHICH THE MODIFIED FUNCTION HAS A NONPOSITIVE -FUNCTION VALUE AND NONNEGATIVE DERIVATIVE, THEN THE INTERVAL OF -UNCERTAINTY IS CHOSEN SO THAT IT CONTAINS A MINIMIZER OF F(X+STP*S). - -THE ALGORITHM IS DESIGNED TO FIND A STEP WHICH SATISFIES THE SUFFICIENT -DECREASE CONDITION - - F(X+STP*S) .LE. F(X) + FTOL*STP*(GRADF(X)'S), - -AND THE CURVATURE CONDITION - - ABS(GRADF(X+STP*S)'S)) .LE. GTOL*ABS(GRADF(X)'S). - -IF FTOL IS LESS THAN GTOL AND IF, FOR EXAMPLE, THE FUNCTION IS BOUNDED -BELOW, THEN THERE IS ALWAYS A STEP WHICH SATISFIES BOTH CONDITIONS. -IF NO STEP CAN BE FOUND WHICH SATISFIES BOTH CONDITIONS, THEN THE -ALGORITHM USUALLY STOPS WHEN ROUNDING ERRORS PREVENT FURTHER PROGRESS. -IN THIS CASE STP ONLY SATISFIES THE SUFFICIENT DECREASE CONDITION. - -PARAMETERS DESCRIPRION - -N IS A POSITIVE INTEGER INPUT VARIABLE SET TO THE NUMBER OF VARIABLES. - -X IS AN ARRAY OF LENGTH N. ON INPUT IT MUST CONTAIN THE BASE POINT FOR -THE LINE SEARCH. ON OUTPUT IT CONTAINS X+STP*S. - -F IS A VARIABLE. ON INPUT IT MUST CONTAIN THE VALUE OF F AT X. ON OUTPUT -IT CONTAINS THE VALUE OF F AT X + STP*S. - -G IS AN ARRAY OF LENGTH N. ON INPUT IT MUST CONTAIN THE GRADIENT OF F AT X. -ON OUTPUT IT CONTAINS THE GRADIENT OF F AT X + STP*S. - -S IS AN INPUT ARRAY OF LENGTH N WHICH SPECIFIES THE SEARCH DIRECTION. - -STP IS A NONNEGATIVE VARIABLE. ON INPUT STP CONTAINS AN INITIAL ESTIMATE -OF A SATISFACTORY STEP. ON OUTPUT STP CONTAINS THE FINAL ESTIMATE. - -FTOL AND GTOL ARE NONNEGATIVE INPUT VARIABLES. TERMINATION OCCURS WHEN THE -SUFFICIENT DECREASE CONDITION AND THE DIRECTIONAL DERIVATIVE CONDITION ARE -SATISFIED. - -XTOL IS A NONNEGATIVE INPUT VARIABLE. TERMINATION OCCURS WHEN THE RELATIVE -WIDTH OF THE INTERVAL OF UNCERTAINTY IS AT MOST XTOL. - -STPMIN AND STPMAX ARE NONNEGATIVE INPUT VARIABLES WHICH SPECIFY LOWER AND -UPPER BOUNDS FOR THE STEP. - -MAXFEV IS A POSITIVE INTEGER INPUT VARIABLE. TERMINATION OCCURS WHEN THE -NUMBER OF CALLS TO FCN IS AT LEAST MAXFEV BY THE END OF AN ITERATION. - -INFO IS AN INTEGER OUTPUT VARIABLE SET AS FOLLOWS: - INFO = 0 IMPROPER INPUT PARAMETERS. - - INFO = 1 THE SUFFICIENT DECREASE CONDITION AND THE - DIRECTIONAL DERIVATIVE CONDITION HOLD. - - INFO = 2 RELATIVE WIDTH OF THE INTERVAL OF UNCERTAINTY - IS AT MOST XTOL. - - INFO = 3 NUMBER OF CALLS TO FCN HAS REACHED MAXFEV. - - INFO = 4 THE STEP IS AT THE LOWER BOUND STPMIN. - - INFO = 5 THE STEP IS AT THE UPPER BOUND STPMAX. - - INFO = 6 ROUNDING ERRORS PREVENT FURTHER PROGRESS. - THERE MAY NOT BE A STEP WHICH SATISFIES THE - SUFFICIENT DECREASE AND CURVATURE CONDITIONS. - TOLERANCES MAY BE TOO SMALL. - -NFEV IS AN INTEGER OUTPUT VARIABLE SET TO THE NUMBER OF CALLS TO FCN. - -WA IS A WORK ARRAY OF LENGTH N. - -ARGONNE NATIONAL LABORATORY. MINPACK PROJECT. JUNE 1983 -JORGE J. MORE', DAVID J. THUENTE -*************************************************************************/ -static void logit_mnlmcsrch(ae_int_t n, - /* Real */ ae_vector* x, - double* f, - /* Real */ ae_vector* g, - /* Real */ ae_vector* s, - double* stp, - ae_int_t* info, - ae_int_t* nfev, - /* Real */ ae_vector* wa, - logitmcstate* state, - ae_int_t* stage, - ae_state *_state) -{ - double v; - double p5; - double p66; - double zero; - - - - /* - * init - */ - p5 = 0.5; - p66 = 0.66; - state->xtrapf = 4.0; - zero = 0; - - /* - * Main cycle - */ - for(;;) - { - if( *stage==0 ) - { - - /* - * NEXT - */ - *stage = 2; - continue; - } - if( *stage==2 ) - { - state->infoc = 1; - *info = 0; - - /* - * CHECK THE INPUT PARAMETERS FOR ERRORS. - */ - if( ((((((n<=0||ae_fp_less_eq(*stp,0))||ae_fp_less(logit_ftol,0))||ae_fp_less(logit_gtol,zero))||ae_fp_less(logit_xtol,zero))||ae_fp_less(logit_stpmin,zero))||ae_fp_less(logit_stpmax,logit_stpmin))||logit_maxfev<=0 ) - { - *stage = 0; - return; - } - - /* - * COMPUTE THE INITIAL GRADIENT IN THE SEARCH DIRECTION - * AND CHECK THAT S IS A DESCENT DIRECTION. - */ - v = ae_v_dotproduct(&g->ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->dginit = v; - if( ae_fp_greater_eq(state->dginit,0) ) - { - *stage = 0; - return; - } - - /* - * INITIALIZE LOCAL VARIABLES. - */ - state->brackt = ae_false; - state->stage1 = ae_true; - *nfev = 0; - state->finit = *f; - state->dgtest = logit_ftol*state->dginit; - state->width = logit_stpmax-logit_stpmin; - state->width1 = state->width/p5; - ae_v_move(&wa->ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * THE VARIABLES STX, FX, DGX CONTAIN THE VALUES OF THE STEP, - * FUNCTION, AND DIRECTIONAL DERIVATIVE AT THE BEST STEP. - * THE VARIABLES STY, FY, DGY CONTAIN THE VALUE OF THE STEP, - * FUNCTION, AND DERIVATIVE AT THE OTHER ENDPOINT OF - * THE INTERVAL OF UNCERTAINTY. - * THE VARIABLES STP, F, DG CONTAIN THE VALUES OF THE STEP, - * FUNCTION, AND DERIVATIVE AT THE CURRENT STEP. - */ - state->stx = 0; - state->fx = state->finit; - state->dgx = state->dginit; - state->sty = 0; - state->fy = state->finit; - state->dgy = state->dginit; - - /* - * NEXT - */ - *stage = 3; - continue; - } - if( *stage==3 ) - { - - /* - * START OF ITERATION. - * - * SET THE MINIMUM AND MAXIMUM STEPS TO CORRESPOND - * TO THE PRESENT INTERVAL OF UNCERTAINTY. - */ - if( state->brackt ) - { - if( ae_fp_less(state->stx,state->sty) ) - { - state->stmin = state->stx; - state->stmax = state->sty; - } - else - { - state->stmin = state->sty; - state->stmax = state->stx; - } - } - else - { - state->stmin = state->stx; - state->stmax = *stp+state->xtrapf*(*stp-state->stx); - } - - /* - * FORCE THE STEP TO BE WITHIN THE BOUNDS STPMAX AND STPMIN. - */ - if( ae_fp_greater(*stp,logit_stpmax) ) - { - *stp = logit_stpmax; - } - if( ae_fp_less(*stp,logit_stpmin) ) - { - *stp = logit_stpmin; - } - - /* - * IF AN UNUSUAL TERMINATION IS TO OCCUR THEN LET - * STP BE THE LOWEST POINT OBTAINED SO FAR. - */ - if( (((state->brackt&&(ae_fp_less_eq(*stp,state->stmin)||ae_fp_greater_eq(*stp,state->stmax)))||*nfev>=logit_maxfev-1)||state->infoc==0)||(state->brackt&&ae_fp_less_eq(state->stmax-state->stmin,logit_xtol*state->stmax)) ) - { - *stp = state->stx; - } - - /* - * EVALUATE THE FUNCTION AND GRADIENT AT STP - * AND COMPUTE THE DIRECTIONAL DERIVATIVE. - */ - ae_v_move(&x->ptr.p_double[0], 1, &wa->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&x->ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1), *stp); - - /* - * NEXT - */ - *stage = 4; - return; - } - if( *stage==4 ) - { - *info = 0; - *nfev = *nfev+1; - v = ae_v_dotproduct(&g->ptr.p_double[0], 1, &s->ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->dg = v; - state->ftest1 = state->finit+*stp*state->dgtest; - - /* - * TEST FOR CONVERGENCE. - */ - if( (state->brackt&&(ae_fp_less_eq(*stp,state->stmin)||ae_fp_greater_eq(*stp,state->stmax)))||state->infoc==0 ) - { - *info = 6; - } - if( (ae_fp_eq(*stp,logit_stpmax)&&ae_fp_less_eq(*f,state->ftest1))&&ae_fp_less_eq(state->dg,state->dgtest) ) - { - *info = 5; - } - if( ae_fp_eq(*stp,logit_stpmin)&&(ae_fp_greater(*f,state->ftest1)||ae_fp_greater_eq(state->dg,state->dgtest)) ) - { - *info = 4; - } - if( *nfev>=logit_maxfev ) - { - *info = 3; - } - if( state->brackt&&ae_fp_less_eq(state->stmax-state->stmin,logit_xtol*state->stmax) ) - { - *info = 2; - } - if( ae_fp_less_eq(*f,state->ftest1)&&ae_fp_less_eq(ae_fabs(state->dg, _state),-logit_gtol*state->dginit) ) - { - *info = 1; - } - - /* - * CHECK FOR TERMINATION. - */ - if( *info!=0 ) - { - *stage = 0; - return; - } - - /* - * IN THE FIRST STAGE WE SEEK A STEP FOR WHICH THE MODIFIED - * FUNCTION HAS A NONPOSITIVE VALUE AND NONNEGATIVE DERIVATIVE. - */ - if( (state->stage1&&ae_fp_less_eq(*f,state->ftest1))&&ae_fp_greater_eq(state->dg,ae_minreal(logit_ftol, logit_gtol, _state)*state->dginit) ) - { - state->stage1 = ae_false; - } - - /* - * A MODIFIED FUNCTION IS USED TO PREDICT THE STEP ONLY IF - * WE HAVE NOT OBTAINED A STEP FOR WHICH THE MODIFIED - * FUNCTION HAS A NONPOSITIVE FUNCTION VALUE AND NONNEGATIVE - * DERIVATIVE, AND IF A LOWER FUNCTION VALUE HAS BEEN - * OBTAINED BUT THE DECREASE IS NOT SUFFICIENT. - */ - if( (state->stage1&&ae_fp_less_eq(*f,state->fx))&&ae_fp_greater(*f,state->ftest1) ) - { - - /* - * DEFINE THE MODIFIED FUNCTION AND DERIVATIVE VALUES. - */ - state->fm = *f-*stp*state->dgtest; - state->fxm = state->fx-state->stx*state->dgtest; - state->fym = state->fy-state->sty*state->dgtest; - state->dgm = state->dg-state->dgtest; - state->dgxm = state->dgx-state->dgtest; - state->dgym = state->dgy-state->dgtest; - - /* - * CALL CSTEP TO UPDATE THE INTERVAL OF UNCERTAINTY - * AND TO COMPUTE THE NEW STEP. - */ - logit_mnlmcstep(&state->stx, &state->fxm, &state->dgxm, &state->sty, &state->fym, &state->dgym, stp, state->fm, state->dgm, &state->brackt, state->stmin, state->stmax, &state->infoc, _state); - - /* - * RESET THE FUNCTION AND GRADIENT VALUES FOR F. - */ - state->fx = state->fxm+state->stx*state->dgtest; - state->fy = state->fym+state->sty*state->dgtest; - state->dgx = state->dgxm+state->dgtest; - state->dgy = state->dgym+state->dgtest; - } - else - { - - /* - * CALL MCSTEP TO UPDATE THE INTERVAL OF UNCERTAINTY - * AND TO COMPUTE THE NEW STEP. - */ - logit_mnlmcstep(&state->stx, &state->fx, &state->dgx, &state->sty, &state->fy, &state->dgy, stp, *f, state->dg, &state->brackt, state->stmin, state->stmax, &state->infoc, _state); - } - - /* - * FORCE A SUFFICIENT DECREASE IN THE SIZE OF THE - * INTERVAL OF UNCERTAINTY. - */ - if( state->brackt ) - { - if( ae_fp_greater_eq(ae_fabs(state->sty-state->stx, _state),p66*state->width1) ) - { - *stp = state->stx+p5*(state->sty-state->stx); - } - state->width1 = state->width; - state->width = ae_fabs(state->sty-state->stx, _state); - } - - /* - * NEXT. - */ - *stage = 3; - continue; - } - } -} - - -static void logit_mnlmcstep(double* stx, - double* fx, - double* dx, - double* sty, - double* fy, - double* dy, - double* stp, - double fp, - double dp, - ae_bool* brackt, - double stmin, - double stmax, - ae_int_t* info, - ae_state *_state) -{ - ae_bool bound; - double gamma; - double p; - double q; - double r; - double s; - double sgnd; - double stpc; - double stpf; - double stpq; - double theta; - - - *info = 0; - - /* - * CHECK THE INPUT PARAMETERS FOR ERRORS. - */ - if( ((*brackt&&(ae_fp_less_eq(*stp,ae_minreal(*stx, *sty, _state))||ae_fp_greater_eq(*stp,ae_maxreal(*stx, *sty, _state))))||ae_fp_greater_eq(*dx*(*stp-(*stx)),0))||ae_fp_less(stmax,stmin) ) - { - return; - } - - /* - * DETERMINE IF THE DERIVATIVES HAVE OPPOSITE SIGN. - */ - sgnd = dp*(*dx/ae_fabs(*dx, _state)); - - /* - * FIRST CASE. A HIGHER FUNCTION VALUE. - * THE MINIMUM IS BRACKETED. IF THE CUBIC STEP IS CLOSER - * TO STX THAN THE QUADRATIC STEP, THE CUBIC STEP IS TAKEN, - * ELSE THE AVERAGE OF THE CUBIC AND QUADRATIC STEPS IS TAKEN. - */ - if( ae_fp_greater(fp,*fx) ) - { - *info = 1; - bound = ae_true; - theta = 3*(*fx-fp)/(*stp-(*stx))+(*dx)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dx, _state), ae_fabs(dp, _state), _state), _state); - gamma = s*ae_sqrt(ae_sqr(theta/s, _state)-*dx/s*(dp/s), _state); - if( ae_fp_less(*stp,*stx) ) - { - gamma = -gamma; - } - p = gamma-(*dx)+theta; - q = gamma-(*dx)+gamma+dp; - r = p/q; - stpc = *stx+r*(*stp-(*stx)); - stpq = *stx+*dx/((*fx-fp)/(*stp-(*stx))+(*dx))/2*(*stp-(*stx)); - if( ae_fp_less(ae_fabs(stpc-(*stx), _state),ae_fabs(stpq-(*stx), _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpc+(stpq-stpc)/2; - } - *brackt = ae_true; - } - else - { - if( ae_fp_less(sgnd,0) ) - { - - /* - * SECOND CASE. A LOWER FUNCTION VALUE AND DERIVATIVES OF - * OPPOSITE SIGN. THE MINIMUM IS BRACKETED. IF THE CUBIC - * STEP IS CLOSER TO STX THAN THE QUADRATIC (SECANT) STEP, - * THE CUBIC STEP IS TAKEN, ELSE THE QUADRATIC STEP IS TAKEN. - */ - *info = 2; - bound = ae_false; - theta = 3*(*fx-fp)/(*stp-(*stx))+(*dx)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dx, _state), ae_fabs(dp, _state), _state), _state); - gamma = s*ae_sqrt(ae_sqr(theta/s, _state)-*dx/s*(dp/s), _state); - if( ae_fp_greater(*stp,*stx) ) - { - gamma = -gamma; - } - p = gamma-dp+theta; - q = gamma-dp+gamma+(*dx); - r = p/q; - stpc = *stp+r*(*stx-(*stp)); - stpq = *stp+dp/(dp-(*dx))*(*stx-(*stp)); - if( ae_fp_greater(ae_fabs(stpc-(*stp), _state),ae_fabs(stpq-(*stp), _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpq; - } - *brackt = ae_true; - } - else - { - if( ae_fp_less(ae_fabs(dp, _state),ae_fabs(*dx, _state)) ) - { - - /* - * THIRD CASE. A LOWER FUNCTION VALUE, DERIVATIVES OF THE - * SAME SIGN, AND THE MAGNITUDE OF THE DERIVATIVE DECREASES. - * THE CUBIC STEP IS ONLY USED IF THE CUBIC TENDS TO INFINITY - * IN THE DIRECTION OF THE STEP OR IF THE MINIMUM OF THE CUBIC - * IS BEYOND STP. OTHERWISE THE CUBIC STEP IS DEFINED TO BE - * EITHER STPMIN OR STPMAX. THE QUADRATIC (SECANT) STEP IS ALSO - * COMPUTED AND IF THE MINIMUM IS BRACKETED THEN THE THE STEP - * CLOSEST TO STX IS TAKEN, ELSE THE STEP FARTHEST AWAY IS TAKEN. - */ - *info = 3; - bound = ae_true; - theta = 3*(*fx-fp)/(*stp-(*stx))+(*dx)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dx, _state), ae_fabs(dp, _state), _state), _state); - - /* - * THE CASE GAMMA = 0 ONLY ARISES IF THE CUBIC DOES NOT TEND - * TO INFINITY IN THE DIRECTION OF THE STEP. - */ - gamma = s*ae_sqrt(ae_maxreal(0, ae_sqr(theta/s, _state)-*dx/s*(dp/s), _state), _state); - if( ae_fp_greater(*stp,*stx) ) - { - gamma = -gamma; - } - p = gamma-dp+theta; - q = gamma+(*dx-dp)+gamma; - r = p/q; - if( ae_fp_less(r,0)&&ae_fp_neq(gamma,0) ) - { - stpc = *stp+r*(*stx-(*stp)); - } - else - { - if( ae_fp_greater(*stp,*stx) ) - { - stpc = stmax; - } - else - { - stpc = stmin; - } - } - stpq = *stp+dp/(dp-(*dx))*(*stx-(*stp)); - if( *brackt ) - { - if( ae_fp_less(ae_fabs(*stp-stpc, _state),ae_fabs(*stp-stpq, _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpq; - } - } - else - { - if( ae_fp_greater(ae_fabs(*stp-stpc, _state),ae_fabs(*stp-stpq, _state)) ) - { - stpf = stpc; - } - else - { - stpf = stpq; - } - } - } - else - { - - /* - * FOURTH CASE. A LOWER FUNCTION VALUE, DERIVATIVES OF THE - * SAME SIGN, AND THE MAGNITUDE OF THE DERIVATIVE DOES - * NOT DECREASE. IF THE MINIMUM IS NOT BRACKETED, THE STEP - * IS EITHER STPMIN OR STPMAX, ELSE THE CUBIC STEP IS TAKEN. - */ - *info = 4; - bound = ae_false; - if( *brackt ) - { - theta = 3*(fp-(*fy))/(*sty-(*stp))+(*dy)+dp; - s = ae_maxreal(ae_fabs(theta, _state), ae_maxreal(ae_fabs(*dy, _state), ae_fabs(dp, _state), _state), _state); - gamma = s*ae_sqrt(ae_sqr(theta/s, _state)-*dy/s*(dp/s), _state); - if( ae_fp_greater(*stp,*sty) ) - { - gamma = -gamma; - } - p = gamma-dp+theta; - q = gamma-dp+gamma+(*dy); - r = p/q; - stpc = *stp+r*(*sty-(*stp)); - stpf = stpc; - } - else - { - if( ae_fp_greater(*stp,*stx) ) - { - stpf = stmax; - } - else - { - stpf = stmin; - } - } - } - } - } - - /* - * UPDATE THE INTERVAL OF UNCERTAINTY. THIS UPDATE DOES NOT - * DEPEND ON THE NEW STEP OR THE CASE ANALYSIS ABOVE. - */ - if( ae_fp_greater(fp,*fx) ) - { - *sty = *stp; - *fy = fp; - *dy = dp; - } - else - { - if( ae_fp_less(sgnd,0.0) ) - { - *sty = *stx; - *fy = *fx; - *dy = *dx; - } - *stx = *stp; - *fx = fp; - *dx = dp; - } - - /* - * COMPUTE THE NEW STEP AND SAFEGUARD IT. - */ - stpf = ae_minreal(stmax, stpf, _state); - stpf = ae_maxreal(stmin, stpf, _state); - *stp = stpf; - if( *brackt&&bound ) - { - if( ae_fp_greater(*sty,*stx) ) - { - *stp = ae_minreal(*stx+0.66*(*sty-(*stx)), *stp, _state); - } - else - { - *stp = ae_maxreal(*stx+0.66*(*sty-(*stx)), *stp, _state); - } - } -} - - -ae_bool _logitmodel_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - logitmodel *p = (logitmodel*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->w, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _logitmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - logitmodel *dst = (logitmodel*)_dst; - logitmodel *src = (logitmodel*)_src; - if( !ae_vector_init_copy(&dst->w, &src->w, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _logitmodel_clear(void* _p) -{ - logitmodel *p = (logitmodel*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->w); -} - - -void _logitmodel_destroy(void* _p) -{ - logitmodel *p = (logitmodel*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->w); -} - - -ae_bool _logitmcstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - logitmcstate *p = (logitmcstate*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _logitmcstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - logitmcstate *dst = (logitmcstate*)_dst; - logitmcstate *src = (logitmcstate*)_src; - dst->brackt = src->brackt; - dst->stage1 = src->stage1; - dst->infoc = src->infoc; - dst->dg = src->dg; - dst->dgm = src->dgm; - dst->dginit = src->dginit; - dst->dgtest = src->dgtest; - dst->dgx = src->dgx; - dst->dgxm = src->dgxm; - dst->dgy = src->dgy; - dst->dgym = src->dgym; - dst->finit = src->finit; - dst->ftest1 = src->ftest1; - dst->fm = src->fm; - dst->fx = src->fx; - dst->fxm = src->fxm; - dst->fy = src->fy; - dst->fym = src->fym; - dst->stx = src->stx; - dst->sty = src->sty; - dst->stmin = src->stmin; - dst->stmax = src->stmax; - dst->width = src->width; - dst->width1 = src->width1; - dst->xtrapf = src->xtrapf; - return ae_true; -} - - -void _logitmcstate_clear(void* _p) -{ - logitmcstate *p = (logitmcstate*)_p; - ae_touch_ptr((void*)p); -} - - -void _logitmcstate_destroy(void* _p) -{ - logitmcstate *p = (logitmcstate*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _mnlreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mnlreport *p = (mnlreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _mnlreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mnlreport *dst = (mnlreport*)_dst; - mnlreport *src = (mnlreport*)_src; - dst->ngrad = src->ngrad; - dst->nhess = src->nhess; - return ae_true; -} - - -void _mnlreport_clear(void* _p) -{ - mnlreport *p = (mnlreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _mnlreport_destroy(void* _p) -{ - mnlreport *p = (mnlreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -DESCRIPTION: - -This function creates MCPD (Markov Chains for Population Data) solver. - -This solver can be used to find transition matrix P for N-dimensional -prediction problem where transition from X[i] to X[i+1] is modelled as - X[i+1] = P*X[i] -where X[i] and X[i+1] are N-dimensional population vectors (components of -each X are non-negative), and P is a N*N transition matrix (elements of P -are non-negative, each column sums to 1.0). - -Such models arise when when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is constant, i.e. there is no new individuals and no one - leaves population -* you want to model transitions of individuals from one state into another - -USAGE: - -Here we give very brief outline of the MCPD. We strongly recommend you to -read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide -on data analysis which is available at http://www.alglib.net/dataanalysis/ - -1. User initializes algorithm state with MCPDCreate() call - -2. User adds one or more tracks - sequences of states which describe - evolution of a system being modelled from different starting conditions - -3. User may add optional boundary, equality and/or linear constraints on - the coefficients of P by calling one of the following functions: - * MCPDSetEC() to set equality constraints - * MCPDSetBC() to set bound constraints - * MCPDSetLC() to set linear constraints - -4. Optionally, user may set custom weights for prediction errors (by - default, algorithm assigns non-equal, automatically chosen weights for - errors in the prediction of different components of X). It can be done - with a call of MCPDSetPredictionWeights() function. - -5. User calls MCPDSolve() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. - -6. User calls MCPDResults() to get solution - -INPUT PARAMETERS: - N - problem dimension, N>=1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreate(ae_int_t n, mcpdstate* s, ae_state *_state) -{ - - _mcpdstate_clear(s); - - ae_assert(n>=1, "MCPDCreate: N<1", _state); - mcpd_mcpdinit(n, -1, -1, s, _state); -} - - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Entry-state" model, i.e. model where transition from X[i] to X[i+1] -is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -and one selected component of X[] is called "entry" state and is treated -in a special way: - system state always transits from "entry" state to some another state - system state can not transit from any state into "entry" state -Such conditions basically mean that row of P which corresponds to "entry" -state is zero. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant - at every moment of time there is some - (unpredictable) amount of "new" individuals, which can transit into one - of the states at the next turn, but still no one leaves population -* you want to model transitions of individuals from one state into another -* but you do NOT want to predict amount of "new" individuals because it - does not depends on individuals already present (hence system can not - transit INTO entry state - it can only transit FROM it). - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - EntryState- index of entry state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateentry(ae_int_t n, - ae_int_t entrystate, - mcpdstate* s, - ae_state *_state) -{ - - _mcpdstate_clear(s); - - ae_assert(n>=2, "MCPDCreateEntry: N<2", _state); - ae_assert(entrystate>=0, "MCPDCreateEntry: EntryState<0", _state); - ae_assert(entrystate=N", _state); - mcpd_mcpdinit(n, entrystate, -1, s, _state); -} - - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Exit-state" model, i.e. model where transition from X[i] to X[i+1] -is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -and one selected component of X[] is called "exit" state and is treated -in a special way: - system state can transit from any state into "exit" state - system state can not transit from "exit" state into any other state - transition operator discards "exit" state (makes it zero at each turn) -Such conditions basically mean that column of P which corresponds to -"exit" state is zero. Multiplication by such P may decrease sum of vector -components. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant - individuals can move into "exit" state - and leave population at the next turn, but there are no new individuals -* amount of individuals which leave population can be predicted -* you want to model transitions of individuals from one state into another - (including transitions into the "exit" state) - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - ExitState- index of exit state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateexit(ae_int_t n, - ae_int_t exitstate, - mcpdstate* s, - ae_state *_state) -{ - - _mcpdstate_clear(s); - - ae_assert(n>=2, "MCPDCreateExit: N<2", _state); - ae_assert(exitstate>=0, "MCPDCreateExit: ExitState<0", _state); - ae_assert(exitstate=N", _state); - mcpd_mcpdinit(n, -1, exitstate, s, _state); -} - - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Entry-Exit-states" model, i.e. model where transition from X[i] to -X[i+1] is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -one selected component of X[] is called "entry" state and is treated in a -special way: - system state always transits from "entry" state to some another state - system state can not transit from any state into "entry" state -and another one component of X[] is called "exit" state and is treated in -a special way too: - system state can transit from any state into "exit" state - system state can not transit from "exit" state into any other state - transition operator discards "exit" state (makes it zero at each turn) -Such conditions basically mean that: - row of P which corresponds to "entry" state is zero - column of P which corresponds to "exit" state is zero -Multiplication by such P may decrease sum of vector components. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant -* at every moment of time there is some (unpredictable) amount of "new" - individuals, which can transit into one of the states at the next turn -* some individuals can move (predictably) into "exit" state and leave - population at the next turn -* you want to model transitions of individuals from one state into another, - including transitions from the "entry" state and into the "exit" state. -* but you do NOT want to predict amount of "new" individuals because it - does not depends on individuals already present (hence system can not - transit INTO entry state - it can only transit FROM it). - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - EntryState- index of entry state, in 0..N-1 - ExitState- index of exit state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateentryexit(ae_int_t n, - ae_int_t entrystate, - ae_int_t exitstate, - mcpdstate* s, - ae_state *_state) -{ - - _mcpdstate_clear(s); - - ae_assert(n>=2, "MCPDCreateEntryExit: N<2", _state); - ae_assert(entrystate>=0, "MCPDCreateEntryExit: EntryState<0", _state); - ae_assert(entrystate=N", _state); - ae_assert(exitstate>=0, "MCPDCreateEntryExit: ExitState<0", _state); - ae_assert(exitstate=N", _state); - ae_assert(entrystate!=exitstate, "MCPDCreateEntryExit: EntryState=ExitState", _state); - mcpd_mcpdinit(n, entrystate, exitstate, s, _state); -} - - -/************************************************************************* -This function is used to add a track - sequence of system states at the -different moments of its evolution. - -You may add one or several tracks to the MCPD solver. In case you have -several tracks, they won't overwrite each other. For example, if you pass -two tracks, A1-A2-A3 (system at t=A+1, t=A+2 and t=A+3) and B1-B2-B3, then -solver will try to model transitions from t=A+1 to t=A+2, t=A+2 to t=A+3, -t=B+1 to t=B+2, t=B+2 to t=B+3. But it WONT mix these two tracks - i.e. it -wont try to model transition from t=A+3 to t=B+1. - -INPUT PARAMETERS: - S - solver - XY - track, array[K,N]: - * I-th row is a state at t=I - * elements of XY must be non-negative (exception will be - thrown on negative elements) - K - number of points in a track - * if given, only leading K rows of XY are used - * if not given, automatically determined from size of XY - -NOTES: - -1. Track may contain either proportional or population data: - * with proportional data all rows of XY must sum to 1.0, i.e. we have - proportions instead of absolute population values - * with population data rows of XY contain population counts and generally - do not sum to 1.0 (although they still must be non-negative) - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddtrack(mcpdstate* s, - /* Real */ ae_matrix* xy, - ae_int_t k, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - double s0; - double s1; - - - n = s->n; - ae_assert(k>=0, "MCPDAddTrack: K<0", _state); - ae_assert(xy->cols>=n, "MCPDAddTrack: Cols(XY)rows>=k, "MCPDAddTrack: Rows(XY)ptr.pp_double[i][j],0), "MCPDAddTrack: XY contains negative elements", _state); - } - } - if( k<2 ) - { - return; - } - if( s->data.rowsnpairs+k-1 ) - { - rmatrixresize(&s->data, ae_maxint(2*s->data.rows, s->npairs+k-1, _state), 2*n, _state); - } - for(i=0; i<=k-2; i++) - { - s0 = 0; - s1 = 0; - for(j=0; j<=n-1; j++) - { - if( s->states.ptr.p_int[j]>=0 ) - { - s0 = s0+xy->ptr.pp_double[i][j]; - } - if( s->states.ptr.p_int[j]<=0 ) - { - s1 = s1+xy->ptr.pp_double[i+1][j]; - } - } - if( ae_fp_greater(s0,0)&&ae_fp_greater(s1,0) ) - { - for(j=0; j<=n-1; j++) - { - if( s->states.ptr.p_int[j]>=0 ) - { - s->data.ptr.pp_double[s->npairs][j] = xy->ptr.pp_double[i][j]/s0; - } - else - { - s->data.ptr.pp_double[s->npairs][j] = 0.0; - } - if( s->states.ptr.p_int[j]<=0 ) - { - s->data.ptr.pp_double[s->npairs][n+j] = xy->ptr.pp_double[i+1][j]/s1; - } - else - { - s->data.ptr.pp_double[s->npairs][n+j] = 0.0; - } - } - s->npairs = s->npairs+1; - } - } -} - - -/************************************************************************* -This function is used to add equality constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to place equality constraints on arbitrary -subset of elements of P. Set of constraints is specified by EC, which may -contain either NAN's or finite numbers from [0,1]. NAN denotes absence of -constraint, finite number denotes equality constraint on specific element -of P. - -You can also use MCPDAddEC() function which allows to ADD equality -constraint for one element of P without changing constraints for other -elements. - -These functions (MCPDSetEC and MCPDAddEC) interact as follows: -* there is internal matrix of equality constraints which is stored in the - MCPD solver -* MCPDSetEC() replaces this matrix by another one (SET) -* MCPDAddEC() modifies one element of this matrix and leaves other ones - unchanged (ADD) -* thus MCPDAddEC() call preserves all modifications done by previous - calls, while MCPDSetEC() completely discards all changes done to the - equality constraints. - -INPUT PARAMETERS: - S - solver - EC - equality constraints, array[N,N]. Elements of EC can be - either NAN's or finite numbers from [0,1]. NAN denotes - absence of constraints, while finite value denotes - equality constraint on the corresponding element of P. - -NOTES: - -1. infinite values of EC will lead to exception being thrown. Values less -than 0.0 or greater than 1.0 will lead to error code being returned after -call to MCPDSolve(). - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetec(mcpdstate* s, - /* Real */ ae_matrix* ec, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - - - n = s->n; - ae_assert(ec->cols>=n, "MCPDSetEC: Cols(EC)rows>=n, "MCPDSetEC: Rows(EC)ptr.pp_double[i][j], _state)||ae_isnan(ec->ptr.pp_double[i][j], _state), "MCPDSetEC: EC containts infinite elements", _state); - s->ec.ptr.pp_double[i][j] = ec->ptr.pp_double[i][j]; - } - } -} - - -/************************************************************************* -This function is used to add equality constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to ADD equality constraint for one element of P -without changing constraints for other elements. - -You can also use MCPDSetEC() function which allows you to specify -arbitrary set of equality constraints in one call. - -These functions (MCPDSetEC and MCPDAddEC) interact as follows: -* there is internal matrix of equality constraints which is stored in the - MCPD solver -* MCPDSetEC() replaces this matrix by another one (SET) -* MCPDAddEC() modifies one element of this matrix and leaves other ones - unchanged (ADD) -* thus MCPDAddEC() call preserves all modifications done by previous - calls, while MCPDSetEC() completely discards all changes done to the - equality constraints. - -INPUT PARAMETERS: - S - solver - I - row index of element being constrained - J - column index of element being constrained - C - value (constraint for P[I,J]). Can be either NAN (no - constraint) or finite value from [0,1]. - -NOTES: - -1. infinite values of C will lead to exception being thrown. Values less -than 0.0 or greater than 1.0 will lead to error code being returned after -call to MCPDSolve(). - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddec(mcpdstate* s, - ae_int_t i, - ae_int_t j, - double c, - ae_state *_state) -{ - - - ae_assert(i>=0, "MCPDAddEC: I<0", _state); - ae_assert(in, "MCPDAddEC: I>=N", _state); - ae_assert(j>=0, "MCPDAddEC: J<0", _state); - ae_assert(jn, "MCPDAddEC: J>=N", _state); - ae_assert(ae_isnan(c, _state)||ae_isfinite(c, _state), "MCPDAddEC: C is not finite number or NAN", _state); - s->ec.ptr.pp_double[i][j] = c; -} - - -/************************************************************************* -This function is used to add bound constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to place bound constraints on arbitrary -subset of elements of P. Set of constraints is specified by BndL/BndU -matrices, which may contain arbitrary combination of finite numbers or -infinities (like -INFn; - ae_assert(bndl->cols>=n, "MCPDSetBC: Cols(BndL)rows>=n, "MCPDSetBC: Rows(BndL)cols>=n, "MCPDSetBC: Cols(BndU)rows>=n, "MCPDSetBC: Rows(BndU)ptr.pp_double[i][j], _state)||ae_isneginf(bndl->ptr.pp_double[i][j], _state), "MCPDSetBC: BndL containts NAN or +INF", _state); - ae_assert(ae_isfinite(bndu->ptr.pp_double[i][j], _state)||ae_isposinf(bndu->ptr.pp_double[i][j], _state), "MCPDSetBC: BndU containts NAN or -INF", _state); - s->bndl.ptr.pp_double[i][j] = bndl->ptr.pp_double[i][j]; - s->bndu.ptr.pp_double[i][j] = bndu->ptr.pp_double[i][j]; - } - } -} - - -/************************************************************************* -This function is used to add bound constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to ADD bound constraint for one element of P -without changing constraints for other elements. - -You can also use MCPDSetBC() function which allows to place bound -constraints on arbitrary subset of elements of P. Set of constraints is -specified by BndL/BndU matrices, which may contain arbitrary combination -of finite numbers or infinities (like -INF=0, "MCPDAddBC: I<0", _state); - ae_assert(in, "MCPDAddBC: I>=N", _state); - ae_assert(j>=0, "MCPDAddBC: J<0", _state); - ae_assert(jn, "MCPDAddBC: J>=N", _state); - ae_assert(ae_isfinite(bndl, _state)||ae_isneginf(bndl, _state), "MCPDAddBC: BndL is NAN or +INF", _state); - ae_assert(ae_isfinite(bndu, _state)||ae_isposinf(bndu, _state), "MCPDAddBC: BndU is NAN or -INF", _state); - s->bndl.ptr.pp_double[i][j] = bndl; - s->bndu.ptr.pp_double[i][j] = bndu; -} - - -/************************************************************************* -This function is used to set linear equality/inequality constraints on the -elements of the transition matrix P. - -This function can be used to set one or several general linear constraints -on the elements of P. Two types of constraints are supported: -* equality constraints -* inequality constraints (both less-or-equal and greater-or-equal) - -Coefficients of constraints are specified by matrix C (one of the -parameters). One row of C corresponds to one constraint. Because -transition matrix P has N*N elements, we need N*N columns to store all -coefficients (they are stored row by row), and one more column to store -right part - hence C has N*N+1 columns. Constraint kind is stored in the -CT array. - -Thus, I-th linear constraint is - P[0,0]*C[I,0] + P[0,1]*C[I,1] + .. + P[0,N-1]*C[I,N-1] + - + P[1,0]*C[I,N] + P[1,1]*C[I,N+1] + ... + - + P[N-1,N-1]*C[I,N*N-1] ?=? C[I,N*N] -where ?=? can be either "=" (CT[i]=0), "<=" (CT[i]<0) or ">=" (CT[i]>0). - -Your constraint may involve only some subset of P (less than N*N elements). -For example it can be something like - P[0,0] + P[0,1] = 0.5 -In this case you still should pass matrix with N*N+1 columns, but all its -elements (except for C[0,0], C[0,1] and C[0,N*N-1]) will be zero. - -INPUT PARAMETERS: - S - solver - C - array[K,N*N+1] - coefficients of constraints - (see above for complete description) - CT - array[K] - constraint types - (see above for complete description) - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetlc(mcpdstate* s, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - - - n = s->n; - ae_assert(c->cols>=n*n+1, "MCPDSetLC: Cols(C)rows>=k, "MCPDSetLC: Rows(C)cnt>=k, "MCPDSetLC: Len(CT)c, k, n*n+1, _state); - ivectorsetlengthatleast(&s->ct, k, _state); - for(i=0; i<=k-1; i++) - { - for(j=0; j<=n*n; j++) - { - s->c.ptr.pp_double[i][j] = c->ptr.pp_double[i][j]; - } - s->ct.ptr.p_int[i] = ct->ptr.p_int[i]; - } - s->ccnt = k; -} - - -/************************************************************************* -This function allows to tune amount of Tikhonov regularization being -applied to your problem. - -By default, regularizing term is equal to r*||P-prior_P||^2, where r is a -small non-zero value, P is transition matrix, prior_P is identity matrix, -||X||^2 is a sum of squared elements of X. - -This function allows you to change coefficient r. You can also change -prior values with MCPDSetPrior() function. - -INPUT PARAMETERS: - S - solver - V - regularization coefficient, finite non-negative value. It - is not recommended to specify zero value unless you are - pretty sure that you want it. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsettikhonovregularizer(mcpdstate* s, double v, ae_state *_state) -{ - - - ae_assert(ae_isfinite(v, _state), "MCPDSetTikhonovRegularizer: V is infinite or NAN", _state); - ae_assert(ae_fp_greater_eq(v,0.0), "MCPDSetTikhonovRegularizer: V is less than zero", _state); - s->regterm = v; -} - - -/************************************************************************* -This function allows to set prior values used for regularization of your -problem. - -By default, regularizing term is equal to r*||P-prior_P||^2, where r is a -small non-zero value, P is transition matrix, prior_P is identity matrix, -||X||^2 is a sum of squared elements of X. - -This function allows you to change prior values prior_P. You can also -change r with MCPDSetTikhonovRegularizer() function. - -INPUT PARAMETERS: - S - solver - PP - array[N,N], matrix of prior values: - 1. elements must be real numbers from [0,1] - 2. columns must sum to 1.0. - First property is checked (exception is thrown otherwise), - while second one is not checked/enforced. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetprior(mcpdstate* s, - /* Real */ ae_matrix* pp, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _pp; - ae_int_t i; - ae_int_t j; - ae_int_t n; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_pp, pp, _state, ae_true); - pp = &_pp; - - n = s->n; - ae_assert(pp->cols>=n, "MCPDSetPrior: Cols(PP)rows>=n, "MCPDSetPrior: Rows(PP)ptr.pp_double[i][j], _state), "MCPDSetPrior: PP containts infinite elements", _state); - ae_assert(ae_fp_greater_eq(pp->ptr.pp_double[i][j],0.0)&&ae_fp_less_eq(pp->ptr.pp_double[i][j],1.0), "MCPDSetPrior: PP[i,j] is less than 0.0 or greater than 1.0", _state); - s->priorp.ptr.pp_double[i][j] = pp->ptr.pp_double[i][j]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function is used to change prediction weights - -MCPD solver scales prediction errors as follows - Error(P) = ||W*(y-P*x)||^2 -where - x is a system state at time t - y is a system state at time t+1 - P is a transition matrix - W is a diagonal scaling matrix - -By default, weights are chosen in order to minimize relative prediction -error instead of absolute one. For example, if one component of state is -about 0.5 in magnitude and another one is about 0.05, then algorithm will -make corresponding weights equal to 2.0 and 20.0. - -INPUT PARAMETERS: - S - solver - PW - array[N], weights: - * must be non-negative values (exception will be thrown otherwise) - * zero values will be replaced by automatically chosen values - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetpredictionweights(mcpdstate* s, - /* Real */ ae_vector* pw, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - - - n = s->n; - ae_assert(pw->cnt>=n, "MCPDSetPredictionWeights: Length(PW)ptr.p_double[i], _state), "MCPDSetPredictionWeights: PW containts infinite or NAN elements", _state); - ae_assert(ae_fp_greater_eq(pw->ptr.p_double[i],0), "MCPDSetPredictionWeights: PW containts negative elements", _state); - s->pw.ptr.p_double[i] = pw->ptr.p_double[i]; - } -} - - -/************************************************************************* -This function is used to start solution of the MCPD problem. - -After return from this function, you can use MCPDResults() to get solution -and completion code. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsolve(mcpdstate* s, ae_state *_state) -{ - ae_int_t n; - ae_int_t npairs; - ae_int_t ccnt; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t k2; - double v; - double vv; - - - n = s->n; - npairs = s->npairs; - - /* - * init fields of S - */ - s->repterminationtype = 0; - s->repinneriterationscount = 0; - s->repouteriterationscount = 0; - s->repnfev = 0; - for(k=0; k<=n-1; k++) - { - for(k2=0; k2<=n-1; k2++) - { - s->p.ptr.pp_double[k][k2] = _state->v_nan; - } - } - - /* - * Generate "effective" weights for prediction and calculate preconditioner - */ - for(i=0; i<=n-1; i++) - { - if( ae_fp_eq(s->pw.ptr.p_double[i],0) ) - { - v = 0; - k = 0; - for(j=0; j<=npairs-1; j++) - { - if( ae_fp_neq(s->data.ptr.pp_double[j][n+i],0) ) - { - v = v+s->data.ptr.pp_double[j][n+i]; - k = k+1; - } - } - if( k!=0 ) - { - s->effectivew.ptr.p_double[i] = k/v; - } - else - { - s->effectivew.ptr.p_double[i] = 1.0; - } - } - else - { - s->effectivew.ptr.p_double[i] = s->pw.ptr.p_double[i]; - } - } - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->h.ptr.p_double[i*n+j] = 2*s->regterm; - } - } - for(k=0; k<=npairs-1; k++) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->h.ptr.p_double[i*n+j] = s->h.ptr.p_double[i*n+j]+2*ae_sqr(s->effectivew.ptr.p_double[i], _state)*ae_sqr(s->data.ptr.pp_double[k][j], _state); - } - } - } - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( ae_fp_eq(s->h.ptr.p_double[i*n+j],0) ) - { - s->h.ptr.p_double[i*n+j] = 1; - } - } - } - - /* - * Generate "effective" BndL/BndU - */ - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - - /* - * Set default boundary constraints. - * Lower bound is always zero, upper bound is calculated - * with respect to entry/exit states. - */ - s->effectivebndl.ptr.p_double[i*n+j] = 0.0; - if( s->states.ptr.p_int[i]>0||s->states.ptr.p_int[j]<0 ) - { - s->effectivebndu.ptr.p_double[i*n+j] = 0.0; - } - else - { - s->effectivebndu.ptr.p_double[i*n+j] = 1.0; - } - - /* - * Calculate intersection of the default and user-specified bound constraints. - * This code checks consistency of such combination. - */ - if( ae_isfinite(s->bndl.ptr.pp_double[i][j], _state)&&ae_fp_greater(s->bndl.ptr.pp_double[i][j],s->effectivebndl.ptr.p_double[i*n+j]) ) - { - s->effectivebndl.ptr.p_double[i*n+j] = s->bndl.ptr.pp_double[i][j]; - } - if( ae_isfinite(s->bndu.ptr.pp_double[i][j], _state)&&ae_fp_less(s->bndu.ptr.pp_double[i][j],s->effectivebndu.ptr.p_double[i*n+j]) ) - { - s->effectivebndu.ptr.p_double[i*n+j] = s->bndu.ptr.pp_double[i][j]; - } - if( ae_fp_greater(s->effectivebndl.ptr.p_double[i*n+j],s->effectivebndu.ptr.p_double[i*n+j]) ) - { - s->repterminationtype = -3; - return; - } - - /* - * Calculate intersection of the effective bound constraints - * and user-specified equality constraints. - * This code checks consistency of such combination. - */ - if( ae_isfinite(s->ec.ptr.pp_double[i][j], _state) ) - { - if( ae_fp_less(s->ec.ptr.pp_double[i][j],s->effectivebndl.ptr.p_double[i*n+j])||ae_fp_greater(s->ec.ptr.pp_double[i][j],s->effectivebndu.ptr.p_double[i*n+j]) ) - { - s->repterminationtype = -3; - return; - } - s->effectivebndl.ptr.p_double[i*n+j] = s->ec.ptr.pp_double[i][j]; - s->effectivebndu.ptr.p_double[i*n+j] = s->ec.ptr.pp_double[i][j]; - } - } - } - - /* - * Generate linear constraints: - * * "default" sums-to-one constraints (not generated for "exit" states) - */ - rmatrixsetlengthatleast(&s->effectivec, s->ccnt+n, n*n+1, _state); - ivectorsetlengthatleast(&s->effectivect, s->ccnt+n, _state); - ccnt = s->ccnt; - for(i=0; i<=s->ccnt-1; i++) - { - for(j=0; j<=n*n; j++) - { - s->effectivec.ptr.pp_double[i][j] = s->c.ptr.pp_double[i][j]; - } - s->effectivect.ptr.p_int[i] = s->ct.ptr.p_int[i]; - } - for(i=0; i<=n-1; i++) - { - if( s->states.ptr.p_int[i]>=0 ) - { - for(k=0; k<=n*n-1; k++) - { - s->effectivec.ptr.pp_double[ccnt][k] = 0; - } - for(k=0; k<=n-1; k++) - { - s->effectivec.ptr.pp_double[ccnt][k*n+i] = 1; - } - s->effectivec.ptr.pp_double[ccnt][n*n] = 1.0; - s->effectivect.ptr.p_int[ccnt] = 0; - ccnt = ccnt+1; - } - } - - /* - * create optimizer - */ - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->tmpp.ptr.p_double[i*n+j] = (double)1/(double)n; - } - } - minbleicrestartfrom(&s->bs, &s->tmpp, _state); - minbleicsetbc(&s->bs, &s->effectivebndl, &s->effectivebndu, _state); - minbleicsetlc(&s->bs, &s->effectivec, &s->effectivect, ccnt, _state); - minbleicsetcond(&s->bs, 0.0, 0.0, mcpd_xtol, 0, _state); - minbleicsetprecdiag(&s->bs, &s->h, _state); - - /* - * solve problem - */ - while(minbleiciteration(&s->bs, _state)) - { - ae_assert(s->bs.needfg, "MCPDSolve: internal error", _state); - if( s->bs.needfg ) - { - - /* - * Calculate regularization term - */ - s->bs.f = 0.0; - vv = s->regterm; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->bs.f = s->bs.f+vv*ae_sqr(s->bs.x.ptr.p_double[i*n+j]-s->priorp.ptr.pp_double[i][j], _state); - s->bs.g.ptr.p_double[i*n+j] = 2*vv*(s->bs.x.ptr.p_double[i*n+j]-s->priorp.ptr.pp_double[i][j]); - } - } - - /* - * calculate prediction error/gradient for K-th pair - */ - for(k=0; k<=npairs-1; k++) - { - for(i=0; i<=n-1; i++) - { - v = ae_v_dotproduct(&s->bs.x.ptr.p_double[i*n], 1, &s->data.ptr.pp_double[k][0], 1, ae_v_len(i*n,i*n+n-1)); - vv = s->effectivew.ptr.p_double[i]; - s->bs.f = s->bs.f+ae_sqr(vv*(v-s->data.ptr.pp_double[k][n+i]), _state); - for(j=0; j<=n-1; j++) - { - s->bs.g.ptr.p_double[i*n+j] = s->bs.g.ptr.p_double[i*n+j]+2*vv*vv*(v-s->data.ptr.pp_double[k][n+i])*s->data.ptr.pp_double[k][j]; - } - } - } - - /* - * continue - */ - continue; - } - } - minbleicresultsbuf(&s->bs, &s->tmpp, &s->br, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->p.ptr.pp_double[i][j] = s->tmpp.ptr.p_double[i*n+j]; - } - } - s->repterminationtype = s->br.terminationtype; - s->repinneriterationscount = s->br.inneriterationscount; - s->repouteriterationscount = s->br.outeriterationscount; - s->repnfev = s->br.nfev; -} - - -/************************************************************************* -MCPD results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - P - array[N,N], transition matrix - Rep - optimization report. You should check Rep.TerminationType - in order to distinguish successful termination from - unsuccessful one. Speaking short, positive values denote - success, negative ones are failures. - More information about fields of this structure can be - found in the comments on MCPDReport datatype. - - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdresults(mcpdstate* s, - /* Real */ ae_matrix* p, - mcpdreport* rep, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(p); - _mcpdreport_clear(rep); - - ae_matrix_set_length(p, s->n, s->n, _state); - for(i=0; i<=s->n-1; i++) - { - for(j=0; j<=s->n-1; j++) - { - p->ptr.pp_double[i][j] = s->p.ptr.pp_double[i][j]; - } - } - rep->terminationtype = s->repterminationtype; - rep->inneriterationscount = s->repinneriterationscount; - rep->outeriterationscount = s->repouteriterationscount; - rep->nfev = s->repnfev; -} - - -/************************************************************************* -Internal initialization function - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -static void mcpd_mcpdinit(ae_int_t n, - ae_int_t entrystate, - ae_int_t exitstate, - mcpdstate* s, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - ae_assert(n>=1, "MCPDCreate: N<1", _state); - s->n = n; - ae_vector_set_length(&s->states, n, _state); - for(i=0; i<=n-1; i++) - { - s->states.ptr.p_int[i] = 0; - } - if( entrystate>=0 ) - { - s->states.ptr.p_int[entrystate] = 1; - } - if( exitstate>=0 ) - { - s->states.ptr.p_int[exitstate] = -1; - } - s->npairs = 0; - s->regterm = 1.0E-8; - s->ccnt = 0; - ae_matrix_set_length(&s->p, n, n, _state); - ae_matrix_set_length(&s->ec, n, n, _state); - ae_matrix_set_length(&s->bndl, n, n, _state); - ae_matrix_set_length(&s->bndu, n, n, _state); - ae_vector_set_length(&s->pw, n, _state); - ae_matrix_set_length(&s->priorp, n, n, _state); - ae_vector_set_length(&s->tmpp, n*n, _state); - ae_vector_set_length(&s->effectivew, n, _state); - ae_vector_set_length(&s->effectivebndl, n*n, _state); - ae_vector_set_length(&s->effectivebndu, n*n, _state); - ae_vector_set_length(&s->h, n*n, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->p.ptr.pp_double[i][j] = 0.0; - s->priorp.ptr.pp_double[i][j] = 0.0; - s->bndl.ptr.pp_double[i][j] = _state->v_neginf; - s->bndu.ptr.pp_double[i][j] = _state->v_posinf; - s->ec.ptr.pp_double[i][j] = _state->v_nan; - } - s->pw.ptr.p_double[i] = 0.0; - s->priorp.ptr.pp_double[i][i] = 1.0; - } - ae_matrix_set_length(&s->data, 1, 2*n, _state); - for(i=0; i<=2*n-1; i++) - { - s->data.ptr.pp_double[0][i] = 0.0; - } - for(i=0; i<=n*n-1; i++) - { - s->tmpp.ptr.p_double[i] = 0.0; - } - minbleiccreate(n*n, &s->tmpp, &s->bs, _state); -} - - -ae_bool _mcpdstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mcpdstate *p = (mcpdstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->states, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->data, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->ec, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->bndl, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->bndu, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->c, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ct, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->pw, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->priorp, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_minbleicstate_init(&p->bs, _state, make_automatic) ) - return ae_false; - if( !_minbleicreport_init(&p->br, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->effectivew, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->effectivebndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->effectivebndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->effectivec, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->effectivect, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->h, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->p, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mcpdstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mcpdstate *dst = (mcpdstate*)_dst; - mcpdstate *src = (mcpdstate*)_src; - dst->n = src->n; - if( !ae_vector_init_copy(&dst->states, &src->states, _state, make_automatic) ) - return ae_false; - dst->npairs = src->npairs; - if( !ae_matrix_init_copy(&dst->data, &src->data, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->ec, &src->ec, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->c, &src->c, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ct, &src->ct, _state, make_automatic) ) - return ae_false; - dst->ccnt = src->ccnt; - if( !ae_vector_init_copy(&dst->pw, &src->pw, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->priorp, &src->priorp, _state, make_automatic) ) - return ae_false; - dst->regterm = src->regterm; - if( !_minbleicstate_init_copy(&dst->bs, &src->bs, _state, make_automatic) ) - return ae_false; - dst->repinneriterationscount = src->repinneriterationscount; - dst->repouteriterationscount = src->repouteriterationscount; - dst->repnfev = src->repnfev; - dst->repterminationtype = src->repterminationtype; - if( !_minbleicreport_init_copy(&dst->br, &src->br, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpp, &src->tmpp, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->effectivew, &src->effectivew, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->effectivebndl, &src->effectivebndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->effectivebndu, &src->effectivebndu, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->effectivec, &src->effectivec, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->effectivect, &src->effectivect, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->h, &src->h, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->p, &src->p, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _mcpdstate_clear(void* _p) -{ - mcpdstate *p = (mcpdstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->states); - ae_matrix_clear(&p->data); - ae_matrix_clear(&p->ec); - ae_matrix_clear(&p->bndl); - ae_matrix_clear(&p->bndu); - ae_matrix_clear(&p->c); - ae_vector_clear(&p->ct); - ae_vector_clear(&p->pw); - ae_matrix_clear(&p->priorp); - _minbleicstate_clear(&p->bs); - _minbleicreport_clear(&p->br); - ae_vector_clear(&p->tmpp); - ae_vector_clear(&p->effectivew); - ae_vector_clear(&p->effectivebndl); - ae_vector_clear(&p->effectivebndu); - ae_matrix_clear(&p->effectivec); - ae_vector_clear(&p->effectivect); - ae_vector_clear(&p->h); - ae_matrix_clear(&p->p); -} - - -void _mcpdstate_destroy(void* _p) -{ - mcpdstate *p = (mcpdstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->states); - ae_matrix_destroy(&p->data); - ae_matrix_destroy(&p->ec); - ae_matrix_destroy(&p->bndl); - ae_matrix_destroy(&p->bndu); - ae_matrix_destroy(&p->c); - ae_vector_destroy(&p->ct); - ae_vector_destroy(&p->pw); - ae_matrix_destroy(&p->priorp); - _minbleicstate_destroy(&p->bs); - _minbleicreport_destroy(&p->br); - ae_vector_destroy(&p->tmpp); - ae_vector_destroy(&p->effectivew); - ae_vector_destroy(&p->effectivebndl); - ae_vector_destroy(&p->effectivebndu); - ae_matrix_destroy(&p->effectivec); - ae_vector_destroy(&p->effectivect); - ae_vector_destroy(&p->h); - ae_matrix_destroy(&p->p); -} - - -ae_bool _mcpdreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mcpdreport *p = (mcpdreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _mcpdreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mcpdreport *dst = (mcpdreport*)_dst; - mcpdreport *src = (mcpdreport*)_src; - dst->inneriterationscount = src->inneriterationscount; - dst->outeriterationscount = src->outeriterationscount; - dst->nfev = src->nfev; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _mcpdreport_clear(void* _p) -{ - mcpdreport *p = (mcpdreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _mcpdreport_destroy(void* _p) -{ - mcpdreport *p = (mcpdreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -Like MLPCreate0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate0(ae_int_t nin, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreate0(nin, nout, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreate1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreate1(nin, nhid, nout, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreate2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreate2(nin, nhid1, nhid2, nout, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateB0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb0(ae_int_t nin, - ae_int_t nout, - double b, - double d, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreateb0(nin, nout, b, d, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateB1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double b, - double d, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreateb1(nin, nhid, nout, b, d, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateB2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double b, - double d, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreateb2(nin, nhid1, nhid2, nout, b, d, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateR0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater0(ae_int_t nin, - ae_int_t nout, - double a, - double b, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreater0(nin, nout, a, b, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateR1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double a, - double b, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreater1(nin, nhid, nout, a, b, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateR2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double a, - double b, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreater2(nin, nhid1, nhid2, nout, a, b, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateC0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec0(ae_int_t nin, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreatec0(nin, nout, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateC1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreatec1(nin, nhid, nout, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Like MLPCreateC2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_frame _frame_block; - multilayerperceptron net; - - ae_frame_make(_state, &_frame_block); - _mlpensemble_clear(ensemble); - _multilayerperceptron_init(&net, _state, ae_true); - - mlpcreatec2(nin, nhid1, nhid2, nout, &net, _state); - mlpecreatefromnetwork(&net, ensemblesize, ensemble, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Creates ensemble from network. Only network geometry is copied. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatefromnetwork(multilayerperceptron* network, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_int_t i; - ae_int_t ccount; - ae_int_t wcount; - - _mlpensemble_clear(ensemble); - - ae_assert(ensemblesize>0, "MLPECreate: incorrect ensemble size!", _state); - - /* - * Copy network - */ - mlpcopy(network, &ensemble->network, _state); - - /* - * network properties - */ - if( mlpissoftmax(network, _state) ) - { - ccount = mlpgetinputscount(&ensemble->network, _state); - } - else - { - ccount = mlpgetinputscount(&ensemble->network, _state)+mlpgetoutputscount(&ensemble->network, _state); - } - wcount = mlpgetweightscount(&ensemble->network, _state); - ensemble->ensemblesize = ensemblesize; - - /* - * weights, means, sigmas - */ - ae_vector_set_length(&ensemble->weights, ensemblesize*wcount, _state); - ae_vector_set_length(&ensemble->columnmeans, ensemblesize*ccount, _state); - ae_vector_set_length(&ensemble->columnsigmas, ensemblesize*ccount, _state); - for(i=0; i<=ensemblesize*wcount-1; i++) - { - ensemble->weights.ptr.p_double[i] = ae_randomreal(_state)-0.5; - } - for(i=0; i<=ensemblesize-1; i++) - { - ae_v_move(&ensemble->columnmeans.ptr.p_double[i*ccount], 1, &network->columnmeans.ptr.p_double[0], 1, ae_v_len(i*ccount,(i+1)*ccount-1)); - ae_v_move(&ensemble->columnsigmas.ptr.p_double[i*ccount], 1, &network->columnsigmas.ptr.p_double[0], 1, ae_v_len(i*ccount,(i+1)*ccount-1)); - } - - /* - * temporaries, internal buffers - */ - ae_vector_set_length(&ensemble->y, mlpgetoutputscount(&ensemble->network, _state), _state); -} - - -/************************************************************************* -Copying of MLPEnsemble strucure - -INPUT PARAMETERS: - Ensemble1 - original - -OUTPUT PARAMETERS: - Ensemble2 - copy - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecopy(mlpensemble* ensemble1, - mlpensemble* ensemble2, - ae_state *_state) -{ - ae_int_t ccount; - ae_int_t wcount; - - _mlpensemble_clear(ensemble2); - - - /* - * Unload info - */ - if( mlpissoftmax(&ensemble1->network, _state) ) - { - ccount = mlpgetinputscount(&ensemble1->network, _state); - } - else - { - ccount = mlpgetinputscount(&ensemble1->network, _state)+mlpgetoutputscount(&ensemble1->network, _state); - } - wcount = mlpgetweightscount(&ensemble1->network, _state); - - /* - * Allocate space - */ - ae_vector_set_length(&ensemble2->weights, ensemble1->ensemblesize*wcount, _state); - ae_vector_set_length(&ensemble2->columnmeans, ensemble1->ensemblesize*ccount, _state); - ae_vector_set_length(&ensemble2->columnsigmas, ensemble1->ensemblesize*ccount, _state); - ae_vector_set_length(&ensemble2->y, mlpgetoutputscount(&ensemble1->network, _state), _state); - - /* - * Copy - */ - ensemble2->ensemblesize = ensemble1->ensemblesize; - ae_v_move(&ensemble2->weights.ptr.p_double[0], 1, &ensemble1->weights.ptr.p_double[0], 1, ae_v_len(0,ensemble1->ensemblesize*wcount-1)); - ae_v_move(&ensemble2->columnmeans.ptr.p_double[0], 1, &ensemble1->columnmeans.ptr.p_double[0], 1, ae_v_len(0,ensemble1->ensemblesize*ccount-1)); - ae_v_move(&ensemble2->columnsigmas.ptr.p_double[0], 1, &ensemble1->columnsigmas.ptr.p_double[0], 1, ae_v_len(0,ensemble1->ensemblesize*ccount-1)); - mlpcopy(&ensemble1->network, &ensemble2->network, _state); -} - - -/************************************************************************* -Randomization of MLP ensemble - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlperandomize(mlpensemble* ensemble, ae_state *_state) -{ - ae_int_t i; - ae_int_t wcount; - - - wcount = mlpgetweightscount(&ensemble->network, _state); - for(i=0; i<=ensemble->ensemblesize*wcount-1; i++) - { - ensemble->weights.ptr.p_double[i] = ae_randomreal(_state)-0.5; - } -} - - -/************************************************************************* -Return ensemble properties (number of inputs and outputs). - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeproperties(mlpensemble* ensemble, - ae_int_t* nin, - ae_int_t* nout, - ae_state *_state) -{ - - *nin = 0; - *nout = 0; - - *nin = mlpgetinputscount(&ensemble->network, _state); - *nout = mlpgetoutputscount(&ensemble->network, _state); -} - - -/************************************************************************* -Return normalization type (whether ensemble is SOFTMAX-normalized or not). - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool mlpeissoftmax(mlpensemble* ensemble, ae_state *_state) -{ - ae_bool result; - - - result = mlpissoftmax(&ensemble->network, _state); - return result; -} - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - Ensemble- neural networks ensemble - X - input vector, array[0..NIn-1]. - Y - (possibly) preallocated buffer; if size of Y is less than - NOut, it will be reallocated. If it is large enough, it - is NOT reallocated, so we can save some time on reallocation. - - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeprocess(mlpensemble* ensemble, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t es; - ae_int_t wc; - ae_int_t cc; - double v; - ae_int_t nout; - - - if( y->cntnetwork, _state) ) - { - ae_vector_set_length(y, mlpgetoutputscount(&ensemble->network, _state), _state); - } - es = ensemble->ensemblesize; - wc = mlpgetweightscount(&ensemble->network, _state); - if( mlpissoftmax(&ensemble->network, _state) ) - { - cc = mlpgetinputscount(&ensemble->network, _state); - } - else - { - cc = mlpgetinputscount(&ensemble->network, _state)+mlpgetoutputscount(&ensemble->network, _state); - } - v = (double)1/(double)es; - nout = mlpgetoutputscount(&ensemble->network, _state); - for(i=0; i<=nout-1; i++) - { - y->ptr.p_double[i] = 0; - } - for(i=0; i<=es-1; i++) - { - ae_v_move(&ensemble->network.weights.ptr.p_double[0], 1, &ensemble->weights.ptr.p_double[i*wc], 1, ae_v_len(0,wc-1)); - ae_v_move(&ensemble->network.columnmeans.ptr.p_double[0], 1, &ensemble->columnmeans.ptr.p_double[i*cc], 1, ae_v_len(0,cc-1)); - ae_v_move(&ensemble->network.columnsigmas.ptr.p_double[0], 1, &ensemble->columnsigmas.ptr.p_double[i*cc], 1, ae_v_len(0,cc-1)); - mlpprocess(&ensemble->network, x, &ensemble->y, _state); - ae_v_addd(&y->ptr.p_double[0], 1, &ensemble->y.ptr.p_double[0], 1, ae_v_len(0,nout-1), v); - } -} - - -/************************************************************************* -'interactive' variant of MLPEProcess for languages like Python which -support constructs like "Y = MLPEProcess(LM,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeprocessi(mlpensemble* ensemble, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - - ae_vector_clear(y); - - mlpeprocess(ensemble, x, y, _state); -} - - -/************************************************************************* -Calculation of all types of errors - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeallerrorsx(mlpensemble* ensemble, - /* Real */ ae_matrix* densexy, - sparsematrix* sparsexy, - ae_int_t datasetsize, - ae_int_t datasettype, - /* Integer */ ae_vector* idx, - ae_int_t subset0, - ae_int_t subset1, - ae_int_t subsettype, - ae_shared_pool* buf, - modelerrors* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t nin; - ae_int_t nout; - ae_bool iscls; - ae_int_t srcidx; - mlpbuffers *pbuf; - ae_smart_ptr _pbuf; - modelerrors rep0; - modelerrors rep1; - - ae_frame_make(_state, &_frame_block); - ae_smart_ptr_init(&_pbuf, (void**)&pbuf, _state, ae_true); - _modelerrors_init(&rep0, _state, ae_true); - _modelerrors_init(&rep1, _state, ae_true); - - - /* - * Get network information - */ - nin = mlpgetinputscount(&ensemble->network, _state); - nout = mlpgetoutputscount(&ensemble->network, _state); - iscls = mlpissoftmax(&ensemble->network, _state); - - /* - * Retrieve buffer, prepare, process data, recycle buffer - */ - ae_shared_pool_retrieve(buf, &_pbuf, _state); - if( iscls ) - { - dserrallocate(nout, &pbuf->tmp0, _state); - } - else - { - dserrallocate(-nout, &pbuf->tmp0, _state); - } - rvectorsetlengthatleast(&pbuf->x, nin, _state); - rvectorsetlengthatleast(&pbuf->y, nout, _state); - rvectorsetlengthatleast(&pbuf->desiredy, nout, _state); - for(i=subset0; i<=subset1-1; i++) - { - srcidx = -1; - if( subsettype==0 ) - { - srcidx = i; - } - if( subsettype==1 ) - { - srcidx = idx->ptr.p_int[i]; - } - ae_assert(srcidx>=0, "MLPEAllErrorsX: internal error", _state); - if( datasettype==0 ) - { - ae_v_move(&pbuf->x.ptr.p_double[0], 1, &densexy->ptr.pp_double[srcidx][0], 1, ae_v_len(0,nin-1)); - } - if( datasettype==1 ) - { - sparsegetrow(sparsexy, srcidx, &pbuf->x, _state); - } - mlpeprocess(ensemble, &pbuf->x, &pbuf->y, _state); - if( mlpissoftmax(&ensemble->network, _state) ) - { - if( datasettype==0 ) - { - pbuf->desiredy.ptr.p_double[0] = densexy->ptr.pp_double[srcidx][nin]; - } - if( datasettype==1 ) - { - pbuf->desiredy.ptr.p_double[0] = sparseget(sparsexy, srcidx, nin, _state); - } - } - else - { - if( datasettype==0 ) - { - ae_v_move(&pbuf->desiredy.ptr.p_double[0], 1, &densexy->ptr.pp_double[srcidx][nin], 1, ae_v_len(0,nout-1)); - } - if( datasettype==1 ) - { - for(j=0; j<=nout-1; j++) - { - pbuf->desiredy.ptr.p_double[j] = sparseget(sparsexy, srcidx, nin+j, _state); - } - } - } - dserraccumulate(&pbuf->tmp0, &pbuf->y, &pbuf->desiredy, _state); - } - dserrfinish(&pbuf->tmp0, _state); - rep->relclserror = pbuf->tmp0.ptr.p_double[0]; - rep->avgce = pbuf->tmp0.ptr.p_double[1]/ae_log(2, _state); - rep->rmserror = pbuf->tmp0.ptr.p_double[2]; - rep->avgerror = pbuf->tmp0.ptr.p_double[3]; - rep->avgrelerror = pbuf->tmp0.ptr.p_double[4]; - ae_shared_pool_recycle(buf, &_pbuf, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Calculation of all types of errors on dataset given by sparse matrix - - -- ALGLIB -- - Copyright 10.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpeallerrorssparse(mlpensemble* ensemble, - sparsematrix* xy, - ae_int_t npoints, - double* relcls, - double* avgce, - double* rms, - double* avg, - double* avgrel, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector buf; - ae_vector workx; - ae_vector y; - ae_vector dy; - ae_int_t nin; - ae_int_t nout; - - ae_frame_make(_state, &_frame_block); - *relcls = 0; - *avgce = 0; - *rms = 0; - *avg = 0; - *avgrel = 0; - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&workx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dy, 0, DT_REAL, _state, ae_true); - - nin = mlpgetinputscount(&ensemble->network, _state); - nout = mlpgetoutputscount(&ensemble->network, _state); - if( mlpissoftmax(&ensemble->network, _state) ) - { - ae_vector_set_length(&dy, 1, _state); - dserrallocate(nout, &buf, _state); - } - else - { - ae_vector_set_length(&dy, nout, _state); - dserrallocate(-nout, &buf, _state); - } - for(i=0; i<=npoints-1; i++) - { - sparsegetrow(xy, i, &workx, _state); - mlpeprocess(ensemble, &workx, &y, _state); - if( mlpissoftmax(&ensemble->network, _state) ) - { - dy.ptr.p_double[0] = workx.ptr.p_double[nin]; - } - else - { - ae_v_move(&dy.ptr.p_double[0], 1, &workx.ptr.p_double[nin], 1, ae_v_len(0,nout-1)); - } - dserraccumulate(&buf, &y, &dy, _state); - } - dserrfinish(&buf, _state); - *relcls = buf.ptr.p_double[0]; - *avgce = buf.ptr.p_double[1]; - *rms = buf.ptr.p_double[2]; - *avg = buf.ptr.p_double[3]; - *avgrel = buf.ptr.p_double[4]; - ae_frame_leave(_state); -} - - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - Works both for classifier betwork and for regression networks which -are used as classifiers. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlperelclserror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - modelerrors rep; - double result; - - ae_frame_make(_state, &_frame_block); - _modelerrors_init(&rep, _state, ae_true); - - mlpeallerrorsx(ensemble, xy, &ensemble->network.dummysxy, npoints, 0, &ensemble->network.dummyidx, 0, npoints, 0, &ensemble->network.buf, &rep, _state); - result = rep.relclserror; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*LN(2)). - Zero if ensemble solves regression task. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgce(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - modelerrors rep; - double result; - - ae_frame_make(_state, &_frame_block); - _modelerrors_init(&rep, _state, ae_true); - - mlpeallerrorsx(ensemble, xy, &ensemble->network.dummysxy, npoints, 0, &ensemble->network.dummyidx, 0, npoints, 0, &ensemble->network.buf, &rep, _state); - result = rep.avgce; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - Its meaning for regression task is obvious. As for classification task -RMS error means error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpermserror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - modelerrors rep; - double result; - - ae_frame_make(_state, &_frame_block); - _modelerrors_init(&rep, _state, ae_true); - - mlpeallerrorsx(ensemble, xy, &ensemble->network.dummysxy, npoints, 0, &ensemble->network.dummyidx, 0, npoints, 0, &ensemble->network.buf, &rep, _state); - result = rep.rmserror; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for classification task -it means average error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgerror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - modelerrors rep; - double result; - - ae_frame_make(_state, &_frame_block); - _modelerrors_init(&rep, _state, ae_true); - - mlpeallerrorsx(ensemble, xy, &ensemble->network.dummysxy, npoints, 0, &ensemble->network.dummyidx, 0, npoints, 0, &ensemble->network.buf, &rep, _state); - result = rep.avgerror; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for classification task -it means average relative error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgrelerror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_frame _frame_block; - modelerrors rep; - double result; - - ae_frame_make(_state, &_frame_block); - _modelerrors_init(&rep, _state, ae_true); - - mlpeallerrorsx(ensemble, xy, &ensemble->network.dummysxy, npoints, 0, &ensemble->network.dummyidx, 0, npoints, 0, &ensemble->network.buf, &rep, _state); - result = rep.avgrelerror; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Serializer: allocation - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpealloc(ae_serializer* s, mlpensemble* ensemble, ae_state *_state) -{ - - - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - allocrealarray(s, &ensemble->weights, -1, _state); - allocrealarray(s, &ensemble->columnmeans, -1, _state); - allocrealarray(s, &ensemble->columnsigmas, -1, _state); - mlpalloc(s, &ensemble->network, _state); -} - - -/************************************************************************* -Serializer: serialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpeserialize(ae_serializer* s, - mlpensemble* ensemble, - ae_state *_state) -{ - - - ae_serializer_serialize_int(s, getmlpeserializationcode(_state), _state); - ae_serializer_serialize_int(s, mlpe_mlpefirstversion, _state); - ae_serializer_serialize_int(s, ensemble->ensemblesize, _state); - serializerealarray(s, &ensemble->weights, -1, _state); - serializerealarray(s, &ensemble->columnmeans, -1, _state); - serializerealarray(s, &ensemble->columnsigmas, -1, _state); - mlpserialize(s, &ensemble->network, _state); -} - - -/************************************************************************* -Serializer: unserialization - - -- ALGLIB -- - Copyright 14.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpeunserialize(ae_serializer* s, - mlpensemble* ensemble, - ae_state *_state) -{ - ae_int_t i0; - ae_int_t i1; - - _mlpensemble_clear(ensemble); - - - /* - * check correctness of header - */ - ae_serializer_unserialize_int(s, &i0, _state); - ae_assert(i0==getmlpeserializationcode(_state), "MLPEUnserialize: stream header corrupted", _state); - ae_serializer_unserialize_int(s, &i1, _state); - ae_assert(i1==mlpe_mlpefirstversion, "MLPEUnserialize: stream header corrupted", _state); - - /* - * Create network - */ - ae_serializer_unserialize_int(s, &ensemble->ensemblesize, _state); - unserializerealarray(s, &ensemble->weights, _state); - unserializerealarray(s, &ensemble->columnmeans, _state); - unserializerealarray(s, &ensemble->columnsigmas, _state); - mlpunserialize(s, &ensemble->network, _state); - - /* - * Allocate termoraries - */ - ae_vector_set_length(&ensemble->y, mlpgetoutputscount(&ensemble->network, _state), _state); -} - - -ae_bool _mlpensemble_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlpensemble *p = (mlpensemble*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->weights, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->columnmeans, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->columnsigmas, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_multilayerperceptron_init(&p->network, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mlpensemble_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlpensemble *dst = (mlpensemble*)_dst; - mlpensemble *src = (mlpensemble*)_src; - dst->ensemblesize = src->ensemblesize; - if( !ae_vector_init_copy(&dst->weights, &src->weights, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->columnmeans, &src->columnmeans, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->columnsigmas, &src->columnsigmas, _state, make_automatic) ) - return ae_false; - if( !_multilayerperceptron_init_copy(&dst->network, &src->network, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _mlpensemble_clear(void* _p) -{ - mlpensemble *p = (mlpensemble*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->weights); - ae_vector_clear(&p->columnmeans); - ae_vector_clear(&p->columnsigmas); - _multilayerperceptron_clear(&p->network); - ae_vector_clear(&p->y); -} - - -void _mlpensemble_destroy(void* _p) -{ - mlpensemble *p = (mlpensemble*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->weights); - ae_vector_destroy(&p->columnmeans); - ae_vector_destroy(&p->columnsigmas); - _multilayerperceptron_destroy(&p->network); - ae_vector_destroy(&p->y); -} - - - - -/************************************************************************* -Neural network training using modified Levenberg-Marquardt with exact -Hessian calculation and regularization. Subroutine trains neural network -with restarts from random positions. Algorithm is well suited for small -and medium scale problems (hundreds of weights). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts from random position, >0. - If you don't know what Restarts to choose, use 2. - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -9, if internal matrix inverse subroutine failed - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlptrainlm(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - double lmsteptol; - ae_int_t i; - ae_int_t k; - double v; - double e; - double enew; - double xnorm2; - double stepnorm; - ae_vector g; - ae_vector d; - ae_matrix h; - ae_matrix hmod; - ae_matrix z; - ae_bool spd; - double nu; - double lambdav; - double lambdaup; - double lambdadown; - minlbfgsreport internalrep; - minlbfgsstate state; - ae_vector x; - ae_vector y; - ae_vector wbase; - ae_vector wdir; - ae_vector wt; - ae_vector wx; - ae_int_t pass; - ae_vector wbest; - double ebest; - ae_int_t invinfo; - matinvreport invrep; - ae_int_t solverinfo; - densesolverreport solverrep; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _mlpreport_clear(rep); - ae_vector_init(&g, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&h, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&hmod, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z, 0, 0, DT_REAL, _state, ae_true); - _minlbfgsreport_init(&internalrep, _state, ae_true); - _minlbfgsstate_init(&state, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wbase, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wdir, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wbest, 0, DT_REAL, _state, ae_true); - _matinvreport_init(&invrep, _state, ae_true); - _densesolverreport_init(&solverrep, _state, ae_true); - - mlpproperties(network, &nin, &nout, &wcount, _state); - lambdaup = 10; - lambdadown = 0.3; - lmsteptol = 0.001; - - /* - * Test for inputs - */ - if( npoints<=0||restarts<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( mlpissoftmax(network, _state) ) - { - for(i=0; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nin], _state)<0||ae_round(xy->ptr.pp_double[i][nin], _state)>=nout ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - } - decay = ae_maxreal(decay, mlptrain_mindecay, _state); - *info = 2; - - /* - * Initialize data - */ - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - - /* - * General case. - * Prepare task and network. Allocate space. - */ - mlpinitpreprocessor(network, xy, npoints, _state); - ae_vector_set_length(&g, wcount-1+1, _state); - ae_matrix_set_length(&h, wcount-1+1, wcount-1+1, _state); - ae_matrix_set_length(&hmod, wcount-1+1, wcount-1+1, _state); - ae_vector_set_length(&wbase, wcount-1+1, _state); - ae_vector_set_length(&wdir, wcount-1+1, _state); - ae_vector_set_length(&wbest, wcount-1+1, _state); - ae_vector_set_length(&wt, wcount-1+1, _state); - ae_vector_set_length(&wx, wcount-1+1, _state); - ebest = ae_maxrealnumber; - - /* - * Multiple passes - */ - for(pass=1; pass<=restarts; pass++) - { - - /* - * Initialize weights - */ - mlprandomize(network, _state); - - /* - * First stage of the hybrid algorithm: LBFGS - */ - ae_v_move(&wbase.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - minlbfgscreate(wcount, ae_minint(wcount, 5, _state), &wbase, &state, _state); - minlbfgssetcond(&state, 0, 0, 0, ae_maxint(25, wcount, _state), _state); - while(minlbfgsiteration(&state, _state)) - { - - /* - * gradient - */ - ae_v_move(&network->weights.ptr.p_double[0], 1, &state.x.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - mlpgradbatch(network, xy, npoints, &state.f, &state.g, _state); - - /* - * weight decay - */ - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - state.f = state.f+0.5*decay*v; - ae_v_addd(&state.g.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - - /* - * next iteration - */ - rep->ngrad = rep->ngrad+1; - } - minlbfgsresults(&state, &wbase, &internalrep, _state); - ae_v_move(&network->weights.ptr.p_double[0], 1, &wbase.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - - /* - * Second stage of the hybrid algorithm: LM - * - * Initialize H with identity matrix, - * G with gradient, - * E with regularized error. - */ - mlphessianbatch(network, xy, npoints, &e, &g, &h, _state); - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = e+0.5*decay*v; - ae_v_addd(&g.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - for(k=0; k<=wcount-1; k++) - { - h.ptr.pp_double[k][k] = h.ptr.pp_double[k][k]+decay; - } - rep->nhess = rep->nhess+1; - lambdav = 0.001; - nu = 2; - for(;;) - { - - /* - * 1. HMod = H+lambda*I - * 2. Try to solve (H+Lambda*I)*dx = -g. - * Increase lambda if left part is not positive definite. - */ - for(i=0; i<=wcount-1; i++) - { - ae_v_move(&hmod.ptr.pp_double[i][0], 1, &h.ptr.pp_double[i][0], 1, ae_v_len(0,wcount-1)); - hmod.ptr.pp_double[i][i] = hmod.ptr.pp_double[i][i]+lambdav; - } - spd = spdmatrixcholesky(&hmod, wcount, ae_true, _state); - rep->ncholesky = rep->ncholesky+1; - if( !spd ) - { - lambdav = lambdav*lambdaup*nu; - nu = nu*2; - continue; - } - spdmatrixcholeskysolve(&hmod, wcount, ae_true, &g, &solverinfo, &solverrep, &wdir, _state); - if( solverinfo<0 ) - { - lambdav = lambdav*lambdaup*nu; - nu = nu*2; - continue; - } - ae_v_muld(&wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1), -1); - - /* - * Lambda found. - * 1. Save old w in WBase - * 1. Test some stopping criterions - * 2. If error(w+wdir)>error(w), increase lambda - */ - ae_v_add(&network->weights.ptr.p_double[0], 1, &wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - xnorm2 = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - stepnorm = ae_v_dotproduct(&wdir.ptr.p_double[0], 1, &wdir.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - stepnorm = ae_sqrt(stepnorm, _state); - enew = mlperror(network, xy, npoints, _state)+0.5*decay*xnorm2; - if( ae_fp_less(stepnorm,lmsteptol*(1+ae_sqrt(xnorm2, _state))) ) - { - break; - } - if( ae_fp_greater(enew,e) ) - { - lambdav = lambdav*lambdaup*nu; - nu = nu*2; - continue; - } - - /* - * Optimize using inv(cholesky(H)) as preconditioner - */ - rmatrixtrinverse(&hmod, wcount, ae_true, ae_false, &invinfo, &invrep, _state); - if( invinfo<=0 ) - { - - /* - * if matrix can't be inverted then exit with errors - * TODO: make WCount steps in direction suggested by HMod - */ - *info = -9; - ae_frame_leave(_state); - return; - } - ae_v_move(&wbase.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - for(i=0; i<=wcount-1; i++) - { - wt.ptr.p_double[i] = 0; - } - minlbfgscreatex(wcount, wcount, &wt, 1, 0.0, &state, _state); - minlbfgssetcond(&state, 0, 0, 0, 5, _state); - while(minlbfgsiteration(&state, _state)) - { - - /* - * gradient - */ - for(i=0; i<=wcount-1; i++) - { - v = ae_v_dotproduct(&state.x.ptr.p_double[i], 1, &hmod.ptr.pp_double[i][i], 1, ae_v_len(i,wcount-1)); - network->weights.ptr.p_double[i] = wbase.ptr.p_double[i]+v; - } - mlpgradbatch(network, xy, npoints, &state.f, &g, _state); - for(i=0; i<=wcount-1; i++) - { - state.g.ptr.p_double[i] = 0; - } - for(i=0; i<=wcount-1; i++) - { - v = g.ptr.p_double[i]; - ae_v_addd(&state.g.ptr.p_double[i], 1, &hmod.ptr.pp_double[i][i], 1, ae_v_len(i,wcount-1), v); - } - - /* - * weight decay - * grad(x'*x) = A'*(x0+A*t) - */ - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - state.f = state.f+0.5*decay*v; - for(i=0; i<=wcount-1; i++) - { - v = decay*network->weights.ptr.p_double[i]; - ae_v_addd(&state.g.ptr.p_double[i], 1, &hmod.ptr.pp_double[i][i], 1, ae_v_len(i,wcount-1), v); - } - - /* - * next iteration - */ - rep->ngrad = rep->ngrad+1; - } - minlbfgsresults(&state, &wt, &internalrep, _state); - - /* - * Accept new position. - * Calculate Hessian - */ - for(i=0; i<=wcount-1; i++) - { - v = ae_v_dotproduct(&wt.ptr.p_double[i], 1, &hmod.ptr.pp_double[i][i], 1, ae_v_len(i,wcount-1)); - network->weights.ptr.p_double[i] = wbase.ptr.p_double[i]+v; - } - mlphessianbatch(network, xy, npoints, &e, &g, &h, _state); - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = e+0.5*decay*v; - ae_v_addd(&g.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - for(k=0; k<=wcount-1; k++) - { - h.ptr.pp_double[k][k] = h.ptr.pp_double[k][k]+decay; - } - rep->nhess = rep->nhess+1; - - /* - * Update lambda - */ - lambdav = lambdav*lambdadown; - nu = 2; - } - - /* - * update WBest - */ - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = 0.5*decay*v+mlperror(network, xy, npoints, _state); - if( ae_fp_less(e,ebest) ) - { - ebest = e; - ae_v_move(&wbest.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - } - } - - /* - * copy WBest to output - */ - ae_v_move(&network->weights.ptr.p_double[0], 1, &wbest.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Neural network training using L-BFGS algorithm with regularization. -Subroutine trains neural network with restarts from random positions. -Algorithm is well suited for problems of any dimensionality (memory -requirements and step complexity are linear by weights number). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts from random position, >0. - If you don't know what Restarts to choose, use 2. - WStep - stopping criterion. Algorithm stops if step size is - less than WStep. Recommended value - 0.01. Zero step - size means stopping after MaxIts iterations. - MaxIts - stopping criterion. Algorithm stops after MaxIts - iterations (NOT gradient calculations). Zero MaxIts - means stopping when step is sufficiently small. - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -8, if both WStep=0 and MaxIts=0 - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlptrainlbfgs(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_int_t* info, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t pass; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_vector w; - ae_vector wbest; - double e; - double v; - double ebest; - minlbfgsreport internalrep; - minlbfgsstate state; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _mlpreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wbest, 0, DT_REAL, _state, ae_true); - _minlbfgsreport_init(&internalrep, _state, ae_true); - _minlbfgsstate_init(&state, _state, ae_true); - - - /* - * Test inputs, parse flags, read network geometry - */ - if( ae_fp_eq(wstep,0)&&maxits==0 ) - { - *info = -8; - ae_frame_leave(_state); - return; - } - if( ((npoints<=0||restarts<1)||ae_fp_less(wstep,0))||maxits<0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - mlpproperties(network, &nin, &nout, &wcount, _state); - if( mlpissoftmax(network, _state) ) - { - for(i=0; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nin], _state)<0||ae_round(xy->ptr.pp_double[i][nin], _state)>=nout ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - } - decay = ae_maxreal(decay, mlptrain_mindecay, _state); - *info = 2; - - /* - * Prepare - */ - mlpinitpreprocessor(network, xy, npoints, _state); - ae_vector_set_length(&w, wcount-1+1, _state); - ae_vector_set_length(&wbest, wcount-1+1, _state); - ebest = ae_maxrealnumber; - - /* - * Multiple starts - */ - rep->ncholesky = 0; - rep->nhess = 0; - rep->ngrad = 0; - for(pass=1; pass<=restarts; pass++) - { - - /* - * Process - */ - mlprandomize(network, _state); - ae_v_move(&w.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - minlbfgscreate(wcount, ae_minint(wcount, 10, _state), &w, &state, _state); - minlbfgssetcond(&state, 0.0, 0.0, wstep, maxits, _state); - while(minlbfgsiteration(&state, _state)) - { - ae_v_move(&network->weights.ptr.p_double[0], 1, &state.x.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - mlpgradnbatch(network, xy, npoints, &state.f, &state.g, _state); - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - state.f = state.f+0.5*decay*v; - ae_v_addd(&state.g.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - rep->ngrad = rep->ngrad+1; - } - minlbfgsresults(&state, &w, &internalrep, _state); - ae_v_move(&network->weights.ptr.p_double[0], 1, &w.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - - /* - * Compare with best - */ - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = mlperrorn(network, xy, npoints, _state)+0.5*decay*v; - if( ae_fp_less(e,ebest) ) - { - ae_v_move(&wbest.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - ebest = e; - } - } - - /* - * The best network - */ - ae_v_move(&network->weights.ptr.p_double[0], 1, &wbest.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Neural network training using early stopping (base algorithm - L-BFGS with -regularization). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - TrnXY - training set - TrnSize - training set size, TrnSize>0 - ValXY - validation set - ValSize - validation set size, ValSize>0 - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts, either: - * strictly positive number - algorithm make specified - number of restarts from random position. - * -1, in which case algorithm makes exactly one run - from the initial state of the network (no randomization). - If you don't know what Restarts to choose, choose one - one the following: - * -1 (deterministic start) - * +1 (one random restart) - * +5 (moderate amount of random restarts) - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1, ...). - * 2, task has been solved, stopping criterion met - - sufficiently small step size. Not expected (we - use EARLY stopping) but possible and not an - error. - * 6, task has been solved, stopping criterion met - - increasing of validation set error. - Rep - training report - -NOTE: - -Algorithm stops if validation set error increases for a long enough or -step size is small enought (there are task where validation set may -decrease for eternity). In any case solution returned corresponds to the -minimum of validation set error. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlptraines(multilayerperceptron* network, - /* Real */ ae_matrix* trnxy, - ae_int_t trnsize, - /* Real */ ae_matrix* valxy, - ae_int_t valsize, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t pass; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_vector w; - ae_vector wbest; - double e; - double v; - double ebest; - ae_vector wfinal; - double efinal; - ae_int_t itcnt; - ae_int_t itbest; - minlbfgsreport internalrep; - minlbfgsstate state; - double wstep; - ae_bool needrandomization; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _mlpreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wbest, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wfinal, 0, DT_REAL, _state, ae_true); - _minlbfgsreport_init(&internalrep, _state, ae_true); - _minlbfgsstate_init(&state, _state, ae_true); - - wstep = 0.001; - - /* - * Test inputs, parse flags, read network geometry - */ - if( ((trnsize<=0||valsize<=0)||(restarts<1&&restarts!=-1))||ae_fp_less(decay,0) ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( restarts==-1 ) - { - needrandomization = ae_false; - restarts = 1; - } - else - { - needrandomization = ae_true; - } - mlpproperties(network, &nin, &nout, &wcount, _state); - if( mlpissoftmax(network, _state) ) - { - for(i=0; i<=trnsize-1; i++) - { - if( ae_round(trnxy->ptr.pp_double[i][nin], _state)<0||ae_round(trnxy->ptr.pp_double[i][nin], _state)>=nout ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - for(i=0; i<=valsize-1; i++) - { - if( ae_round(valxy->ptr.pp_double[i][nin], _state)<0||ae_round(valxy->ptr.pp_double[i][nin], _state)>=nout ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - } - *info = 2; - - /* - * Prepare - */ - mlpinitpreprocessor(network, trnxy, trnsize, _state); - ae_vector_set_length(&w, wcount-1+1, _state); - ae_vector_set_length(&wbest, wcount-1+1, _state); - ae_vector_set_length(&wfinal, wcount-1+1, _state); - efinal = ae_maxrealnumber; - for(i=0; i<=wcount-1; i++) - { - wfinal.ptr.p_double[i] = 0; - } - - /* - * Multiple starts - */ - rep->ncholesky = 0; - rep->nhess = 0; - rep->ngrad = 0; - for(pass=1; pass<=restarts; pass++) - { - - /* - * Process - */ - if( needrandomization ) - { - mlprandomize(network, _state); - } - ebest = mlperror(network, valxy, valsize, _state); - ae_v_move(&wbest.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - itbest = 0; - itcnt = 0; - ae_v_move(&w.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - minlbfgscreate(wcount, ae_minint(wcount, 10, _state), &w, &state, _state); - minlbfgssetcond(&state, 0.0, 0.0, wstep, 0, _state); - minlbfgssetxrep(&state, ae_true, _state); - while(minlbfgsiteration(&state, _state)) - { - - /* - * Calculate gradient - */ - if( state.needfg ) - { - ae_v_move(&network->weights.ptr.p_double[0], 1, &state.x.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - mlpgradnbatch(network, trnxy, trnsize, &state.f, &state.g, _state); - v = ae_v_dotproduct(&network->weights.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - state.f = state.f+0.5*decay*v; - ae_v_addd(&state.g.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - rep->ngrad = rep->ngrad+1; - } - - /* - * Validation set - */ - if( state.xupdated ) - { - ae_v_move(&network->weights.ptr.p_double[0], 1, &state.x.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - e = mlperror(network, valxy, valsize, _state); - if( ae_fp_less(e,ebest) ) - { - ebest = e; - ae_v_move(&wbest.ptr.p_double[0], 1, &network->weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - itbest = itcnt; - } - if( itcnt>30&&ae_fp_greater(itcnt,1.5*itbest) ) - { - *info = 6; - break; - } - itcnt = itcnt+1; - } - } - minlbfgsresults(&state, &w, &internalrep, _state); - - /* - * Compare with final answer - */ - if( ae_fp_less(ebest,efinal) ) - { - ae_v_move(&wfinal.ptr.p_double[0], 1, &wbest.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - efinal = ebest; - } - } - - /* - * The best network - */ - ae_v_move(&network->weights.ptr.p_double[0], 1, &wfinal.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Cross-validation estimate of generalization error. - -Base algorithm - L-BFGS. - -INPUT PARAMETERS: - Network - neural network with initialized geometry. Network is - not changed during cross-validation - it is used only - as a representative of its architecture. - XY - training set. - SSize - training set size - Decay - weight decay, same as in MLPTrainLBFGS - Restarts - number of restarts, >0. - restarts are counted for each partition separately, so - total number of restarts will be Restarts*FoldsCount. - WStep - stopping criterion, same as in MLPTrainLBFGS - MaxIts - stopping criterion, same as in MLPTrainLBFGS - FoldsCount - number of folds in k-fold cross-validation, - 2<=FoldsCount<=SSize. - recommended value: 10. - -OUTPUT PARAMETERS: - Info - return code, same as in MLPTrainLBFGS - Rep - report, same as in MLPTrainLM/MLPTrainLBFGS - CVRep - generalization error estimates - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcvlbfgs(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_int_t foldscount, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* cvrep, - ae_state *_state) -{ - - *info = 0; - _mlpreport_clear(rep); - _mlpcvreport_clear(cvrep); - - mlptrain_mlpkfoldcvgeneral(network, xy, npoints, decay, restarts, foldscount, ae_false, wstep, maxits, info, rep, cvrep, _state); -} - - -/************************************************************************* -Cross-validation estimate of generalization error. - -Base algorithm - Levenberg-Marquardt. - -INPUT PARAMETERS: - Network - neural network with initialized geometry. Network is - not changed during cross-validation - it is used only - as a representative of its architecture. - XY - training set. - SSize - training set size - Decay - weight decay, same as in MLPTrainLBFGS - Restarts - number of restarts, >0. - restarts are counted for each partition separately, so - total number of restarts will be Restarts*FoldsCount. - FoldsCount - number of folds in k-fold cross-validation, - 2<=FoldsCount<=SSize. - recommended value: 10. - -OUTPUT PARAMETERS: - Info - return code, same as in MLPTrainLBFGS - Rep - report, same as in MLPTrainLM/MLPTrainLBFGS - CVRep - generalization error estimates - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcvlm(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t foldscount, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* cvrep, - ae_state *_state) -{ - - *info = 0; - _mlpreport_clear(rep); - _mlpcvreport_clear(cvrep); - - mlptrain_mlpkfoldcvgeneral(network, xy, npoints, decay, restarts, foldscount, ae_true, 0.0, 0, info, rep, cvrep, _state); -} - - -/************************************************************************* -This function estimates generalization error using cross-validation on the -current dataset with current training settings. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * FoldsCount cross-validation rounds (always) - ! * NRestarts training sessions performed within each of - ! cross-validation rounds (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. Network is not changed during cross- - validation and is not trained - it is used only as - representative of its architecture. I.e., we estimate - generalization properties of ARCHITECTURE, not some - specific network. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that for each cross-validation - round specified number of random restarts is - performed, with best network being chosen after - training. - * NRestarts=0 is same as NRestarts=1 - FoldsCount - number of folds in k-fold cross-validation: - * 2<=FoldsCount<=size of dataset - * recommended value: 10. - * values larger than dataset size will be silently - truncated down to dataset size - -OUTPUT PARAMETERS: - Rep - structure which contains cross-validation estimates: - * Rep.RelCLSError - fraction of misclassified cases. - * Rep.AvgCE - acerage cross-entropy - * Rep.RMSError - root-mean-square error - * Rep.AvgError - average error - * Rep.AvgRelError - average relative error - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - or subset with only one point was given, zeros are returned as - estimates. - -NOTE: this method performs FoldsCount cross-validation rounds, each one - with NRestarts random starts. Thus, FoldsCount*NRestarts networks - are trained in total. - -NOTE: Rep.RelCLSError/Rep.AvgCE are zero on regression problems. - -NOTE: on classification problems Rep.RMSError/Rep.AvgError/Rep.AvgRelError - contain errors in prediction of posterior probabilities. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcv(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - ae_int_t foldscount, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_shared_pool pooldatacv; - mlpparallelizationcv datacv; - mlpparallelizationcv *sdatacv; - ae_smart_ptr _sdatacv; - ae_matrix cvy; - ae_vector folds; - ae_vector buf; - ae_vector dy; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t rowsize; - ae_int_t ntype; - ae_int_t ttype; - ae_int_t i; - ae_int_t j; - ae_int_t k; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - _mlpreport_clear(rep); - ae_shared_pool_init(&pooldatacv, _state, ae_true); - _mlpparallelizationcv_init(&datacv, _state, ae_true); - ae_smart_ptr_init(&_sdatacv, (void**)&sdatacv, _state, ae_true); - ae_matrix_init(&cvy, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&folds, 0, DT_INT, _state, ae_true); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dy, 0, DT_REAL, _state, ae_true); - _hqrndstate_init(&rs, _state, ae_true); - - if( !mlpissoftmax(network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - ae_assert(ntype==ttype, "MLPKFoldCV: type of input network is not similar to network type in trainer object", _state); - ae_assert(s->npoints>=0, "MLPKFoldCV: possible trainer S is not initialized(S.NPoints<0)", _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - ae_assert(s->nin==nin, "MLPKFoldCV: number of inputs in trainer is not equal to number of inputs in network", _state); - ae_assert(s->nout==nout, "MLPKFoldCV: number of outputs in trainer is not equal to number of outputs in network", _state); - ae_assert(nrestarts>=0, "MLPKFoldCV: NRestarts<0", _state); - ae_assert(foldscount>=2, "MLPKFoldCV: FoldsCount<2", _state); - if( foldscount>s->npoints ) - { - foldscount = s->npoints; - } - rep->relclserror = 0; - rep->avgce = 0; - rep->rmserror = 0; - rep->avgerror = 0; - rep->avgrelerror = 0; - hqrndrandomize(&rs, _state); - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - if( s->npoints==0||s->npoints==1 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Read network geometry, test parameters - */ - if( s->rcpar ) - { - rowsize = nin+nout; - ae_vector_set_length(&dy, nout, _state); - dserrallocate(-nout, &buf, _state); - } - else - { - rowsize = nin+1; - ae_vector_set_length(&dy, 1, _state); - dserrallocate(nout, &buf, _state); - } - - /* - * Folds - */ - ae_vector_set_length(&folds, s->npoints, _state); - for(i=0; i<=s->npoints-1; i++) - { - folds.ptr.p_int[i] = i*foldscount/s->npoints; - } - for(i=0; i<=s->npoints-2; i++) - { - j = i+hqrnduniformi(&rs, s->npoints-i, _state); - if( j!=i ) - { - k = folds.ptr.p_int[i]; - folds.ptr.p_int[i] = folds.ptr.p_int[j]; - folds.ptr.p_int[j] = k; - } - } - ae_matrix_set_length(&cvy, s->npoints, nout, _state); - - /* - * Initialize SEED-value for shared pool - */ - datacv.ngrad = 0; - mlpcopy(network, &datacv.network, _state); - ae_vector_set_length(&datacv.subset, s->npoints, _state); - ae_vector_set_length(&datacv.xyrow, rowsize, _state); - ae_vector_set_length(&datacv.y, nout, _state); - - /* - * Create shared pool - */ - ae_shared_pool_set_seed(&pooldatacv, &datacv, sizeof(datacv), _mlpparallelizationcv_init, _mlpparallelizationcv_init_copy, _mlpparallelizationcv_destroy, _state); - - /* - * Parallelization - */ - mlptrain_mthreadcv(s, rowsize, nrestarts, &folds, 0, foldscount, &cvy, &pooldatacv, _state); - - /* - * Calculate value for NGrad - */ - ae_shared_pool_first_recycled(&pooldatacv, &_sdatacv, _state); - while(sdatacv!=NULL) - { - rep->ngrad = rep->ngrad+sdatacv->ngrad; - ae_shared_pool_next_recycled(&pooldatacv, &_sdatacv, _state); - } - - /* - * Connect of results and calculate cross-validation error - */ - for(i=0; i<=s->npoints-1; i++) - { - if( s->datatype==0 ) - { - ae_v_move(&datacv.xyrow.ptr.p_double[0], 1, &s->densexy.ptr.pp_double[i][0], 1, ae_v_len(0,rowsize-1)); - } - if( s->datatype==1 ) - { - sparsegetrow(&s->sparsexy, i, &datacv.xyrow, _state); - } - ae_v_move(&datacv.y.ptr.p_double[0], 1, &cvy.ptr.pp_double[i][0], 1, ae_v_len(0,nout-1)); - if( s->rcpar ) - { - ae_v_move(&dy.ptr.p_double[0], 1, &datacv.xyrow.ptr.p_double[nin], 1, ae_v_len(0,nout-1)); - } - else - { - dy.ptr.p_double[0] = datacv.xyrow.ptr.p_double[nin]; - } - dserraccumulate(&buf, &datacv.y, &dy, _state); - } - dserrfinish(&buf, _state); - rep->relclserror = buf.ptr.p_double[0]; - rep->avgce = buf.ptr.p_double[1]; - rep->rmserror = buf.ptr.p_double[2]; - rep->avgerror = buf.ptr.p_double[3]; - rep->avgrelerror = buf.ptr.p_double[4]; - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlpkfoldcv(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - ae_int_t foldscount, - mlpreport* rep, ae_state *_state) -{ - mlpkfoldcv(s,network,nrestarts,foldscount,rep, _state); -} - - -/************************************************************************* -Creation of the network trainer object for regression networks - -INPUT PARAMETERS: - NIn - number of inputs, NIn>=1 - NOut - number of outputs, NOut>=1 - -OUTPUT PARAMETERS: - S - neural network trainer object. - This structure can be used to train any regression - network with NIn inputs and NOut outputs. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatetrainer(ae_int_t nin, - ae_int_t nout, - mlptrainer* s, - ae_state *_state) -{ - - _mlptrainer_clear(s); - - ae_assert(nin>=1, "MLPCreateTrainer: NIn<1.", _state); - ae_assert(nout>=1, "MLPCreateTrainer: NOut<1.", _state); - s->nin = nin; - s->nout = nout; - s->rcpar = ae_true; - s->lbfgsfactor = mlptrain_defaultlbfgsfactor; - s->decay = 1.0E-6; - mlpsetcond(s, 0, 0, _state); - s->datatype = 0; - s->npoints = 0; - mlpsetalgobatch(s, _state); -} - - -/************************************************************************* -Creation of the network trainer object for classification networks - -INPUT PARAMETERS: - NIn - number of inputs, NIn>=1 - NClasses - number of classes, NClasses>=2 - -OUTPUT PARAMETERS: - S - neural network trainer object. - This structure can be used to train any classification - network with NIn inputs and NOut outputs. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatetrainercls(ae_int_t nin, - ae_int_t nclasses, - mlptrainer* s, - ae_state *_state) -{ - - _mlptrainer_clear(s); - - ae_assert(nin>=1, "MLPCreateTrainerCls: NIn<1.", _state); - ae_assert(nclasses>=2, "MLPCreateTrainerCls: NClasses<2.", _state); - s->nin = nin; - s->nout = nclasses; - s->rcpar = ae_false; - s->lbfgsfactor = mlptrain_defaultlbfgsfactor; - s->decay = 1.0E-6; - mlpsetcond(s, 0, 0, _state); - s->datatype = 0; - s->npoints = 0; - mlpsetalgobatch(s, _state); -} - - -/************************************************************************* -This function sets "current dataset" of the trainer object to one passed -by user. - -INPUT PARAMETERS: - S - trainer object - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. - NPoints - points count, >=0. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -datasetformat is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetdataset(mlptrainer* s, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - ae_int_t ndim; - ae_int_t i; - ae_int_t j; - - - ae_assert(s->nin>=1, "MLPSetDataset: possible parameter S is not initialized or spoiled(S.NIn<=0).", _state); - ae_assert(npoints>=0, "MLPSetDataset: NPoint<0", _state); - ae_assert(npoints<=xy->rows, "MLPSetDataset: invalid size of matrix XY(NPoint more then rows of matrix XY)", _state); - s->datatype = 0; - s->npoints = npoints; - if( npoints==0 ) - { - return; - } - if( s->rcpar ) - { - ae_assert(s->nout>=1, "MLPSetDataset: possible parameter S is not initialized or is spoiled(NOut<1 for regression).", _state); - ndim = s->nin+s->nout; - ae_assert(ndim<=xy->cols, "MLPSetDataset: invalid size of matrix XY(too few columns in matrix XY).", _state); - ae_assert(apservisfinitematrix(xy, npoints, ndim, _state), "MLPSetDataset: parameter XY contains Infinite or NaN.", _state); - } - else - { - ae_assert(s->nout>=2, "MLPSetDataset: possible parameter S is not initialized or is spoiled(NClasses<2 for classifier).", _state); - ndim = s->nin+1; - ae_assert(ndim<=xy->cols, "MLPSetDataset: invalid size of matrix XY(too few columns in matrix XY).", _state); - ae_assert(apservisfinitematrix(xy, npoints, ndim, _state), "MLPSetDataset: parameter XY contains Infinite or NaN.", _state); - for(i=0; i<=npoints-1; i++) - { - ae_assert(ae_round(xy->ptr.pp_double[i][s->nin], _state)>=0&&ae_round(xy->ptr.pp_double[i][s->nin], _state)nout, "MLPSetDataset: invalid parameter XY(in classifier used nonexistent class number: either XY[.,NIn]<0 or XY[.,NIn]>=NClasses).", _state); - } - } - rmatrixsetlengthatleast(&s->densexy, npoints, ndim, _state); - for(i=0; i<=npoints-1; i++) - { - for(j=0; j<=ndim-1; j++) - { - s->densexy.ptr.pp_double[i][j] = xy->ptr.pp_double[i][j]; - } - } -} - - -/************************************************************************* -This function sets "current dataset" of the trainer object to one passed -by user (sparse matrix is used to store dataset). - -INPUT PARAMETERS: - S - trainer object - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Any sparse storage format can be used: - Hash-table, CRS... - NPoints - points count, >=0 - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -datasetformat is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetsparsedataset(mlptrainer* s, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state) -{ - double v; - ae_int_t t0; - ae_int_t t1; - ae_int_t i; - ae_int_t j; - - - - /* - * Check correctness of the data - */ - ae_assert(s->nin>0, "MLPSetSparseDataset: possible parameter S is not initialized or spoiled(S.NIn<=0).", _state); - ae_assert(npoints>=0, "MLPSetSparseDataset: NPoint<0", _state); - ae_assert(npoints<=sparsegetnrows(xy, _state), "MLPSetSparseDataset: invalid size of sparse matrix XY(NPoint more then rows of matrix XY)", _state); - if( npoints>0 ) - { - t0 = 0; - t1 = 0; - if( s->rcpar ) - { - ae_assert(s->nout>=1, "MLPSetSparseDataset: possible parameter S is not initialized or is spoiled(NOut<1 for regression).", _state); - ae_assert(s->nin+s->nout<=sparsegetncols(xy, _state), "MLPSetSparseDataset: invalid size of sparse matrix XY(too few columns in sparse matrix XY).", _state); - while(sparseenumerate(xy, &t0, &t1, &i, &j, &v, _state)) - { - if( inin+s->nout ) - { - ae_assert(ae_isfinite(v, _state), "MLPSetSparseDataset: sparse matrix XY contains Infinite or NaN.", _state); - } - } - } - else - { - ae_assert(s->nout>=2, "MLPSetSparseDataset: possible parameter S is not initialized or is spoiled(NClasses<2 for classifier).", _state); - ae_assert(s->nin+1<=sparsegetncols(xy, _state), "MLPSetSparseDataset: invalid size of sparse matrix XY(too few columns in sparse matrix XY).", _state); - while(sparseenumerate(xy, &t0, &t1, &i, &j, &v, _state)) - { - if( inin ) - { - if( j!=s->nin ) - { - ae_assert(ae_isfinite(v, _state), "MLPSetSparseDataset: sparse matrix XY contains Infinite or NaN.", _state); - } - else - { - ae_assert((ae_isfinite(v, _state)&&ae_round(v, _state)>=0)&&ae_round(v, _state)nout, "MLPSetSparseDataset: invalid sparse matrix XY(in classifier used nonexistent class number: either XY[.,NIn]<0 or XY[.,NIn]>=NClasses).", _state); - } - } - } - } - } - - /* - * Set dataset - */ - s->datatype = 1; - s->npoints = npoints; - sparsecopytocrs(xy, &s->sparsexy, _state); -} - - -/************************************************************************* -This function sets weight decay coefficient which is used for training. - -INPUT PARAMETERS: - S - trainer object - Decay - weight decay coefficient, >=0. Weight decay term - 'Decay*||Weights||^2' is added to error function. If - you don't know what Decay to choose, use 1.0E-3. - Weight decay can be set to zero, in this case network - is trained without weight decay. - -NOTE: by default network uses some small nonzero value for weight decay. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetdecay(mlptrainer* s, double decay, ae_state *_state) -{ - - - ae_assert(ae_isfinite(decay, _state), "MLPSetDecay: parameter Decay contains Infinite or NaN.", _state); - ae_assert(ae_fp_greater_eq(decay,0), "MLPSetDecay: Decay<0.", _state); - s->decay = decay; -} - - -/************************************************************************* -This function sets stopping criteria for the optimizer. - -INPUT PARAMETERS: - S - trainer object - WStep - stopping criterion. Algorithm stops if step size is - less than WStep. Recommended value - 0.01. Zero step - size means stopping after MaxIts iterations. - WStep>=0. - MaxIts - stopping criterion. Algorithm stops after MaxIts - epochs (full passes over entire dataset). Zero MaxIts - means stopping when step is sufficiently small. - MaxIts>=0. - -NOTE: by default, WStep=0.005 and MaxIts=0 are used. These values are also - used when MLPSetCond() is called with WStep=0 and MaxIts=0. - -NOTE: these stopping criteria are used for all kinds of neural training - - from "conventional" networks to early stopping ensembles. When used - for "conventional" networks, they are used as the only stopping - criteria. When combined with early stopping, they used as ADDITIONAL - stopping criteria which can terminate early stopping algorithm. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetcond(mlptrainer* s, - double wstep, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(wstep, _state), "MLPSetCond: parameter WStep contains Infinite or NaN.", _state); - ae_assert(ae_fp_greater_eq(wstep,0), "MLPSetCond: WStep<0.", _state); - ae_assert(maxits>=0, "MLPSetCond: MaxIts<0.", _state); - if( ae_fp_neq(wstep,0)||maxits!=0 ) - { - s->wstep = wstep; - s->maxits = maxits; - } - else - { - s->wstep = 0.005; - s->maxits = 0; - } -} - - -/************************************************************************* -This function sets training algorithm: batch training using L-BFGS will be -used. - -This algorithm: -* the most robust for small-scale problems, but may be too slow for large - scale ones. -* perfoms full pass through the dataset before performing step -* uses conditions specified by MLPSetCond() for stopping -* is default one used by trainer object - -INPUT PARAMETERS: - S - trainer object - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetalgobatch(mlptrainer* s, ae_state *_state) -{ - - - s->algokind = 0; -} - - -/************************************************************************* -This function trains neural network passed to this function, using current -dataset (one which was passed to MLPSetDataset() or MLPSetSparseDataset()) -and current training settings. Training from NRestarts random starting -positions is performed, best network is chosen. - -Training is performed using current training algorithm. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * NRestarts training sessions performed within each of - ! cross-validation rounds (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that specified number of random - restarts are performed, best network is chosen after - training - * NRestarts=0 means that current state of the network - is used for training. - -OUTPUT PARAMETERS: - Network - trained network - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - network is filled by zero values. Same behavior for functions - MLPStartTraining and MLPContinueTraining. - -NOTE: this method uses sum-of-squares error function for training. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlptrainnetwork(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntype; - ae_int_t ttype; - ae_shared_pool trnpool; - - ae_frame_make(_state, &_frame_block); - _mlpreport_clear(rep); - ae_shared_pool_init(&trnpool, _state, ae_true); - - ae_assert(s->npoints>=0, "MLPTrainNetwork: parameter S is not initialized or is spoiled(S.NPoints<0)", _state); - if( !mlpissoftmax(network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - ae_assert(ntype==ttype, "MLPTrainNetwork: type of input network is not similar to network type in trainer object", _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - ae_assert(s->nin==nin, "MLPTrainNetwork: number of inputs in trainer is not equal to number of inputs in network", _state); - ae_assert(s->nout==nout, "MLPTrainNetwork: number of outputs in trainer is not equal to number of outputs in network", _state); - ae_assert(nrestarts>=0, "MLPTrainNetwork: NRestarts<0.", _state); - - /* - * Train - */ - mlptrain_mlptrainnetworkx(s, nrestarts, -1, &s->subset, -1, &s->subset, 0, network, rep, ae_true, &trnpool, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlptrainnetwork(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - mlpreport* rep, ae_state *_state) -{ - mlptrainnetwork(s,network,nrestarts,rep, _state); -} - - -/************************************************************************* -IMPORTANT: this is an "expert" version of the MLPTrain() function. We do - not recommend you to use it unless you are pretty sure that you - need ability to monitor training progress. - -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTraining() call, -and then user subsequently calls MLPContinueTraining() to perform one more -iteration of the training. - -After call to this function trainer object remembers network and is ready -to train it. However, no training is performed until first call to -MLPContinueTraining() function. Subsequent calls to MLPContinueTraining() -will advance training progress one iteration further. - -EXAMPLE: - > - > ...initialize network and trainer object.... - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > ...visualize training progress... - > - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. - RandomStart - randomize network before training or not: - * True means that network is randomized and its - initial state (one which was passed to the trainer - object) is lost. - * False means that training is started from the - current state of the network - -OUTPUT PARAMETERS: - Network - neural network which is ready to training (weights are - initialized, preprocessor is initialized using current - training set) - -NOTE: this method uses sum-of-squares error function for training. - -NOTE: it is expected that trainer object settings are NOT changed during - step-by-step training, i.e. no one changes stopping criteria or - training set during training. It is possible and there is no defense - against such actions, but algorithm behavior in such cases is - undefined and can be unpredictable. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpstarttraining(mlptrainer* s, - multilayerperceptron* network, - ae_bool randomstart, - ae_state *_state) -{ - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntype; - ae_int_t ttype; - - - ae_assert(s->npoints>=0, "MLPStartTraining: parameter S is not initialized or is spoiled(S.NPoints<0)", _state); - if( !mlpissoftmax(network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - ae_assert(ntype==ttype, "MLPStartTraining: type of input network is not similar to network type in trainer object", _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - ae_assert(s->nin==nin, "MLPStartTraining: number of inputs in trainer is not equal to number of inputs in the network.", _state); - ae_assert(s->nout==nout, "MLPStartTraining: number of outputs in trainer is not equal to number of outputs in the network.", _state); - - /* - * Initialize temporaries - */ - mlptrain_initmlptrnsession(network, randomstart, s, &s->session, _state); - - /* - * Train network - */ - mlptrain_mlpstarttrainingx(s, randomstart, -1, &s->subset, -1, &s->session, _state); - - /* - * Update network - */ - mlpcopytunableparameters(&s->session.network, network, _state); -} - - -/************************************************************************* -IMPORTANT: this is an "expert" version of the MLPTrain() function. We do - not recommend you to use it unless you are pretty sure that you - need ability to monitor training progress. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTraining() call, -and then user subsequently calls MLPContinueTraining() to perform one more -iteration of the training. - -This function performs one more iteration of the training and returns -either True (training continues) or False (training stopped). In case True -was returned, Network weights are updated according to the current state -of the optimization progress. In case False was returned, no additional -updates is performed (previous update of the network weights moved us to -the final point, and no additional updates is needed). - -EXAMPLE: - > - > [initialize network and trainer object] - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > [visualize training progress] - > - -INPUT PARAMETERS: - S - trainer object - Network - neural network structure, which is used to store - current state of the training process. - -OUTPUT PARAMETERS: - Network - weights of the neural network are rewritten by the - current approximation. - -NOTE: this method uses sum-of-squares error function for training. - -NOTE: it is expected that trainer object settings are NOT changed during - step-by-step training, i.e. no one changes stopping criteria or - training set during training. It is possible and there is no defense - against such actions, but algorithm behavior in such cases is - undefined and can be unpredictable. - -NOTE: It is expected that Network is the same one which was passed to - MLPStartTraining() function. However, THIS function checks only - following: - * that number of network inputs is consistent with trainer object - settings - * that number of network outputs/classes is consistent with trainer - object settings - * that number of network weights is the same as number of weights in - the network passed to MLPStartTraining() function - Exception is thrown when these conditions are violated. - - It is also expected that you do not change state of the network on - your own - the only party who has right to change network during its - training is a trainer object. Any attempt to interfere with trainer - may lead to unpredictable results. - - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool mlpcontinuetraining(mlptrainer* s, - multilayerperceptron* network, - ae_state *_state) -{ - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntype; - ae_int_t ttype; - ae_bool result; - - - ae_assert(s->npoints>=0, "MLPContinueTraining: parameter S is not initialized or is spoiled(S.NPoints<0)", _state); - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - if( !mlpissoftmax(network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - ae_assert(ntype==ttype, "MLPContinueTraining: type of input network is not similar to network type in trainer object.", _state); - mlpproperties(network, &nin, &nout, &wcount, _state); - ae_assert(s->nin==nin, "MLPContinueTraining: number of inputs in trainer is not equal to number of inputs in the network.", _state); - ae_assert(s->nout==nout, "MLPContinueTraining: number of outputs in trainer is not equal to number of outputs in the network.", _state); - result = mlptrain_mlpcontinuetrainingx(s, &s->subset, -1, &s->ngradbatch, &s->session, _state); - if( result ) - { - ae_v_move(&network->weights.ptr.p_double[0], 1, &s->session.network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - } - return result; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -ae_bool _pexec_mlpcontinuetraining(mlptrainer* s, - multilayerperceptron* network, ae_state *_state) -{ - return mlpcontinuetraining(s,network, _state); -} - - -/************************************************************************* -Training neural networks ensemble using bootstrap aggregating (bagging). -Modified Levenberg-Marquardt algorithm is used as base training method. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpebagginglm(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* ooberrors, - ae_state *_state) -{ - - *info = 0; - _mlpreport_clear(rep); - _mlpcvreport_clear(ooberrors); - - mlptrain_mlpebagginginternal(ensemble, xy, npoints, decay, restarts, 0.0, 0, ae_true, info, rep, ooberrors, _state); -} - - -/************************************************************************* -Training neural networks ensemble using bootstrap aggregating (bagging). -L-BFGS algorithm is used as base training method. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - WStep - stopping criterion, same as in MLPTrainLBFGS - MaxIts - stopping criterion, same as in MLPTrainLBFGS - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -8, if both WStep=0 and MaxIts=0 - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpebagginglbfgs(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* ooberrors, - ae_state *_state) -{ - - *info = 0; - _mlpreport_clear(rep); - _mlpcvreport_clear(ooberrors); - - mlptrain_mlpebagginginternal(ensemble, xy, npoints, decay, restarts, wstep, maxits, ae_false, info, rep, ooberrors, _state); -} - - -/************************************************************************* -Training neural networks ensemble using early stopping. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 6, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpetraines(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t k; - ae_int_t ccount; - ae_int_t pcount; - ae_matrix trnxy; - ae_matrix valxy; - ae_int_t trnsize; - ae_int_t valsize; - ae_int_t tmpinfo; - mlpreport tmprep; - modelerrors moderr; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _mlpreport_clear(rep); - ae_matrix_init(&trnxy, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&valxy, 0, 0, DT_REAL, _state, ae_true); - _mlpreport_init(&tmprep, _state, ae_true); - _modelerrors_init(&moderr, _state, ae_true); - - nin = mlpgetinputscount(&ensemble->network, _state); - nout = mlpgetoutputscount(&ensemble->network, _state); - wcount = mlpgetweightscount(&ensemble->network, _state); - if( (npoints<2||restarts<1)||ae_fp_less(decay,0) ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( mlpissoftmax(&ensemble->network, _state) ) - { - for(i=0; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nin], _state)<0||ae_round(xy->ptr.pp_double[i][nin], _state)>=nout ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - } - *info = 6; - - /* - * allocate - */ - if( mlpissoftmax(&ensemble->network, _state) ) - { - ccount = nin+1; - pcount = nin; - } - else - { - ccount = nin+nout; - pcount = nin+nout; - } - ae_matrix_set_length(&trnxy, npoints, ccount, _state); - ae_matrix_set_length(&valxy, npoints, ccount, _state); - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - - /* - * train networks - */ - for(k=0; k<=ensemble->ensemblesize-1; k++) - { - - /* - * Split set - */ - do - { - trnsize = 0; - valsize = 0; - for(i=0; i<=npoints-1; i++) - { - if( ae_fp_less(ae_randomreal(_state),0.66) ) - { - - /* - * Assign sample to training set - */ - ae_v_move(&trnxy.ptr.pp_double[trnsize][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,ccount-1)); - trnsize = trnsize+1; - } - else - { - - /* - * Assign sample to validation set - */ - ae_v_move(&valxy.ptr.pp_double[valsize][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,ccount-1)); - valsize = valsize+1; - } - } - } - while(!(trnsize!=0&&valsize!=0)); - - /* - * Train - */ - mlptraines(&ensemble->network, &trnxy, trnsize, &valxy, valsize, decay, restarts, &tmpinfo, &tmprep, _state); - if( tmpinfo<0 ) - { - *info = tmpinfo; - ae_frame_leave(_state); - return; - } - - /* - * save results - */ - ae_v_move(&ensemble->weights.ptr.p_double[k*wcount], 1, &ensemble->network.weights.ptr.p_double[0], 1, ae_v_len(k*wcount,(k+1)*wcount-1)); - ae_v_move(&ensemble->columnmeans.ptr.p_double[k*pcount], 1, &ensemble->network.columnmeans.ptr.p_double[0], 1, ae_v_len(k*pcount,(k+1)*pcount-1)); - ae_v_move(&ensemble->columnsigmas.ptr.p_double[k*pcount], 1, &ensemble->network.columnsigmas.ptr.p_double[0], 1, ae_v_len(k*pcount,(k+1)*pcount-1)); - rep->ngrad = rep->ngrad+tmprep.ngrad; - rep->nhess = rep->nhess+tmprep.nhess; - rep->ncholesky = rep->ncholesky+tmprep.ncholesky; - } - mlpeallerrorsx(ensemble, xy, &ensemble->network.dummysxy, npoints, 0, &ensemble->network.dummyidx, 0, npoints, 0, &ensemble->network.buf, &moderr, _state); - rep->relclserror = moderr.relclserror; - rep->avgce = moderr.avgce; - rep->rmserror = moderr.rmserror; - rep->avgerror = moderr.avgerror; - rep->avgrelerror = moderr.avgrelerror; - ae_frame_leave(_state); -} - - -/************************************************************************* -This function trains neural network ensemble passed to this function using -current dataset and early stopping training algorithm. Each early stopping -round performs NRestarts random restarts (thus, EnsembleSize*NRestarts -training rounds is performed in total). - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * EnsembleSize training sessions performed for each of ensemble - ! members (always parallelized) - ! * NRestarts training sessions performed within each of training - ! sessions (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object; - Ensemble - neural network ensemble. It must have same number of - inputs and outputs/classes as was specified during - creation of the trainer object. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that specified number of random - restarts are performed during each ES round; - * NRestarts=0 is silently replaced by 1. - -OUTPUT PARAMETERS: - Ensemble - trained ensemble; - Rep - it contains all type of errors. - -NOTE: this training method uses BOTH early stopping and weight decay! So, - you should select weight decay before starting training just as you - select it before training "conventional" networks. - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - or single-point dataset was passed, ensemble is filled by zero - values. - -NOTE: this method uses sum-of-squares error function for training. - - -- ALGLIB -- - Copyright 22.08.2012 by Bochkanov Sergey -*************************************************************************/ -void mlptrainensemblees(mlptrainer* s, - mlpensemble* ensemble, - ae_int_t nrestarts, - mlpreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t ntype; - ae_int_t ttype; - ae_shared_pool esessions; - sinteger sgrad; - modelerrors tmprep; - - ae_frame_make(_state, &_frame_block); - _mlpreport_clear(rep); - ae_shared_pool_init(&esessions, _state, ae_true); - _sinteger_init(&sgrad, _state, ae_true); - _modelerrors_init(&tmprep, _state, ae_true); - - ae_assert(s->npoints>=0, "MLPTrainEnsembleES: parameter S is not initialized or is spoiled(S.NPoints<0)", _state); - if( !mlpeissoftmax(ensemble, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - ae_assert(ntype==ttype, "MLPTrainEnsembleES: internal error - type of input network is not similar to network type in trainer object", _state); - nin = mlpgetinputscount(&ensemble->network, _state); - ae_assert(s->nin==nin, "MLPTrainEnsembleES: number of inputs in trainer is not equal to number of inputs in ensemble network", _state); - nout = mlpgetoutputscount(&ensemble->network, _state); - ae_assert(s->nout==nout, "MLPTrainEnsembleES: number of outputs in trainer is not equal to number of outputs in ensemble network", _state); - ae_assert(nrestarts>=0, "MLPTrainEnsembleES: NRestarts<0.", _state); - - /* - * Initialize parameter Rep - */ - rep->relclserror = 0; - rep->avgce = 0; - rep->rmserror = 0; - rep->avgerror = 0; - rep->avgrelerror = 0; - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - - /* - * Allocate - */ - ivectorsetlengthatleast(&s->subset, s->npoints, _state); - ivectorsetlengthatleast(&s->valsubset, s->npoints, _state); - - /* - * Start training - * - * NOTE: ESessions is not initialized because MLPTrainEnsembleX - * needs uninitialized pool. - */ - sgrad.val = 0; - mlptrain_mlptrainensemblex(s, ensemble, 0, ensemble->ensemblesize, nrestarts, 0, &sgrad, ae_true, &esessions, _state); - rep->ngrad = sgrad.val; - - /* - * Calculate errors. - */ - if( s->datatype==0 ) - { - mlpeallerrorsx(ensemble, &s->densexy, &s->sparsexy, s->npoints, 0, &ensemble->network.dummyidx, 0, s->npoints, 0, &ensemble->network.buf, &tmprep, _state); - } - if( s->datatype==1 ) - { - mlpeallerrorsx(ensemble, &s->densexy, &s->sparsexy, s->npoints, 1, &ensemble->network.dummyidx, 0, s->npoints, 0, &ensemble->network.buf, &tmprep, _state); - } - rep->relclserror = tmprep.relclserror; - rep->avgce = tmprep.avgce; - rep->rmserror = tmprep.rmserror; - rep->avgerror = tmprep.avgerror; - rep->avgrelerror = tmprep.avgrelerror; - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_mlptrainensemblees(mlptrainer* s, - mlpensemble* ensemble, - ae_int_t nrestarts, - mlpreport* rep, ae_state *_state) -{ - mlptrainensemblees(s,ensemble,nrestarts,rep, _state); -} - - -/************************************************************************* -Internal cross-validation subroutine -*************************************************************************/ -static void mlptrain_mlpkfoldcvgeneral(multilayerperceptron* n, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t foldscount, - ae_bool lmalgorithm, - double wstep, - ae_int_t maxits, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* cvrep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t fold; - ae_int_t j; - ae_int_t k; - multilayerperceptron network; - ae_int_t nin; - ae_int_t nout; - ae_int_t rowlen; - ae_int_t wcount; - ae_int_t nclasses; - ae_int_t tssize; - ae_int_t cvssize; - ae_matrix cvset; - ae_matrix testset; - ae_vector folds; - ae_int_t relcnt; - mlpreport internalrep; - ae_vector x; - ae_vector y; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _mlpreport_clear(rep); - _mlpcvreport_clear(cvrep); - _multilayerperceptron_init(&network, _state, ae_true); - ae_matrix_init(&cvset, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&testset, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&folds, 0, DT_INT, _state, ae_true); - _mlpreport_init(&internalrep, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - - /* - * Read network geometry, test parameters - */ - mlpproperties(n, &nin, &nout, &wcount, _state); - if( mlpissoftmax(n, _state) ) - { - nclasses = nout; - rowlen = nin+1; - } - else - { - nclasses = -nout; - rowlen = nin+nout; - } - if( (npoints<=0||foldscount<2)||foldscount>npoints ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - mlpcopy(n, &network, _state); - - /* - * K-fold out cross-validation. - * First, estimate generalization error - */ - ae_matrix_set_length(&testset, npoints-1+1, rowlen-1+1, _state); - ae_matrix_set_length(&cvset, npoints-1+1, rowlen-1+1, _state); - ae_vector_set_length(&x, nin-1+1, _state); - ae_vector_set_length(&y, nout-1+1, _state); - mlptrain_mlpkfoldsplit(xy, npoints, nclasses, foldscount, ae_false, &folds, _state); - cvrep->relclserror = 0; - cvrep->avgce = 0; - cvrep->rmserror = 0; - cvrep->avgerror = 0; - cvrep->avgrelerror = 0; - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - relcnt = 0; - for(fold=0; fold<=foldscount-1; fold++) - { - - /* - * Separate set - */ - tssize = 0; - cvssize = 0; - for(i=0; i<=npoints-1; i++) - { - if( folds.ptr.p_int[i]==fold ) - { - ae_v_move(&testset.ptr.pp_double[tssize][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,rowlen-1)); - tssize = tssize+1; - } - else - { - ae_v_move(&cvset.ptr.pp_double[cvssize][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,rowlen-1)); - cvssize = cvssize+1; - } - } - - /* - * Train on CV training set - */ - if( lmalgorithm ) - { - mlptrainlm(&network, &cvset, cvssize, decay, restarts, info, &internalrep, _state); - } - else - { - mlptrainlbfgs(&network, &cvset, cvssize, decay, restarts, wstep, maxits, info, &internalrep, _state); - } - if( *info<0 ) - { - cvrep->relclserror = 0; - cvrep->avgce = 0; - cvrep->rmserror = 0; - cvrep->avgerror = 0; - cvrep->avgrelerror = 0; - ae_frame_leave(_state); - return; - } - rep->ngrad = rep->ngrad+internalrep.ngrad; - rep->nhess = rep->nhess+internalrep.nhess; - rep->ncholesky = rep->ncholesky+internalrep.ncholesky; - - /* - * Estimate error using CV test set - */ - if( mlpissoftmax(&network, _state) ) - { - - /* - * classification-only code - */ - cvrep->relclserror = cvrep->relclserror+mlpclserror(&network, &testset, tssize, _state); - cvrep->avgce = cvrep->avgce+mlperrorn(&network, &testset, tssize, _state); - } - for(i=0; i<=tssize-1; i++) - { - ae_v_move(&x.ptr.p_double[0], 1, &testset.ptr.pp_double[i][0], 1, ae_v_len(0,nin-1)); - mlpprocess(&network, &x, &y, _state); - if( mlpissoftmax(&network, _state) ) - { - - /* - * Classification-specific code - */ - k = ae_round(testset.ptr.pp_double[i][nin], _state); - for(j=0; j<=nout-1; j++) - { - if( j==k ) - { - cvrep->rmserror = cvrep->rmserror+ae_sqr(y.ptr.p_double[j]-1, _state); - cvrep->avgerror = cvrep->avgerror+ae_fabs(y.ptr.p_double[j]-1, _state); - cvrep->avgrelerror = cvrep->avgrelerror+ae_fabs(y.ptr.p_double[j]-1, _state); - relcnt = relcnt+1; - } - else - { - cvrep->rmserror = cvrep->rmserror+ae_sqr(y.ptr.p_double[j], _state); - cvrep->avgerror = cvrep->avgerror+ae_fabs(y.ptr.p_double[j], _state); - } - } - } - else - { - - /* - * Regression-specific code - */ - for(j=0; j<=nout-1; j++) - { - cvrep->rmserror = cvrep->rmserror+ae_sqr(y.ptr.p_double[j]-testset.ptr.pp_double[i][nin+j], _state); - cvrep->avgerror = cvrep->avgerror+ae_fabs(y.ptr.p_double[j]-testset.ptr.pp_double[i][nin+j], _state); - if( ae_fp_neq(testset.ptr.pp_double[i][nin+j],0) ) - { - cvrep->avgrelerror = cvrep->avgrelerror+ae_fabs((y.ptr.p_double[j]-testset.ptr.pp_double[i][nin+j])/testset.ptr.pp_double[i][nin+j], _state); - relcnt = relcnt+1; - } - } - } - } - } - if( mlpissoftmax(&network, _state) ) - { - cvrep->relclserror = cvrep->relclserror/npoints; - cvrep->avgce = cvrep->avgce/(ae_log(2, _state)*npoints); - } - cvrep->rmserror = ae_sqrt(cvrep->rmserror/(npoints*nout), _state); - cvrep->avgerror = cvrep->avgerror/(npoints*nout); - if( relcnt>0 ) - { - cvrep->avgrelerror = cvrep->avgrelerror/relcnt; - } - *info = 1; - ae_frame_leave(_state); -} - - -/************************************************************************* -Subroutine prepares K-fold split of the training set. - -NOTES: - "NClasses>0" means that we have classification task. - "NClasses<0" means regression task with -NClasses real outputs. -*************************************************************************/ -static void mlptrain_mlpkfoldsplit(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nclasses, - ae_int_t foldscount, - ae_bool stratifiedsplits, - /* Integer */ ae_vector* folds, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(folds); - _hqrndstate_init(&rs, _state, ae_true); - - - /* - * test parameters - */ - ae_assert(npoints>0, "MLPKFoldSplit: wrong NPoints!", _state); - ae_assert(nclasses>1||nclasses<0, "MLPKFoldSplit: wrong NClasses!", _state); - ae_assert(foldscount>=2&&foldscount<=npoints, "MLPKFoldSplit: wrong FoldsCount!", _state); - ae_assert(!stratifiedsplits, "MLPKFoldSplit: stratified splits are not supported!", _state); - - /* - * Folds - */ - hqrndrandomize(&rs, _state); - ae_vector_set_length(folds, npoints-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - folds->ptr.p_int[i] = i*foldscount/npoints; - } - for(i=0; i<=npoints-2; i++) - { - j = i+hqrnduniformi(&rs, npoints-i, _state); - if( j!=i ) - { - k = folds->ptr.p_int[i]; - folds->ptr.p_int[i] = folds->ptr.p_int[j]; - folds->ptr.p_int[j] = k; - } - } - ae_frame_leave(_state); -} - - -static void mlptrain_mthreadcv(mlptrainer* s, - ae_int_t rowsize, - ae_int_t nrestarts, - /* Integer */ ae_vector* folds, - ae_int_t fold, - ae_int_t dfold, - /* Real */ ae_matrix* cvy, - ae_shared_pool* pooldatacv, - ae_state *_state) -{ - ae_frame _frame_block; - mlpparallelizationcv *datacv; - ae_smart_ptr _datacv; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_smart_ptr_init(&_datacv, (void**)&datacv, _state, ae_true); - - if( fold==dfold-1 ) - { - - /* - * Separate set - */ - ae_shared_pool_retrieve(pooldatacv, &_datacv, _state); - datacv->subsetsize = 0; - for(i=0; i<=s->npoints-1; i++) - { - if( folds->ptr.p_int[i]!=fold ) - { - datacv->subset.ptr.p_int[datacv->subsetsize] = i; - datacv->subsetsize = datacv->subsetsize+1; - } - } - - /* - * Train on CV training set - */ - mlptrain_mlptrainnetworkx(s, nrestarts, -1, &datacv->subset, datacv->subsetsize, &datacv->subset, 0, &datacv->network, &datacv->rep, ae_true, &datacv->trnpool, _state); - datacv->ngrad = datacv->ngrad+datacv->rep.ngrad; - - /* - * Estimate error using CV test set - */ - for(i=0; i<=s->npoints-1; i++) - { - if( folds->ptr.p_int[i]==fold ) - { - if( s->datatype==0 ) - { - ae_v_move(&datacv->xyrow.ptr.p_double[0], 1, &s->densexy.ptr.pp_double[i][0], 1, ae_v_len(0,rowsize-1)); - } - if( s->datatype==1 ) - { - sparsegetrow(&s->sparsexy, i, &datacv->xyrow, _state); - } - mlpprocess(&datacv->network, &datacv->xyrow, &datacv->y, _state); - ae_v_move(&cvy->ptr.pp_double[i][0], 1, &datacv->y.ptr.p_double[0], 1, ae_v_len(0,s->nout-1)); - } - } - ae_shared_pool_recycle(pooldatacv, &_datacv, _state); - } - else - { - ae_assert(foldDFold-1).", _state); - mlptrain_mthreadcv(s, rowsize, nrestarts, folds, fold, (fold+dfold)/2, cvy, pooldatacv, _state); - mlptrain_mthreadcv(s, rowsize, nrestarts, folds, (fold+dfold)/2, dfold, cvy, pooldatacv, _state); - } - ae_frame_leave(_state); -} - - -static void mlptrain_mlptrainnetworkx(mlptrainer* s, - ae_int_t nrestarts, - ae_int_t algokind, - /* Integer */ ae_vector* trnsubset, - ae_int_t trnsubsetsize, - /* Integer */ ae_vector* valsubset, - ae_int_t valsubsetsize, - multilayerperceptron* network, - mlpreport* rep, - ae_bool isrootcall, - ae_shared_pool* sessions, - ae_state *_state) -{ - ae_frame _frame_block; - modelerrors modrep; - double eval; - double ebest; - ae_int_t ngradbatch; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t pcount; - ae_int_t itbest; - ae_int_t itcnt; - ae_int_t ntype; - ae_int_t ttype; - ae_bool rndstart; - ae_int_t i; - ae_int_t nr0; - ae_int_t nr1; - mlpreport rep0; - mlpreport rep1; - ae_bool randomizenetwork; - double bestrmserror; - smlptrnsession *psession; - ae_smart_ptr _psession; - - ae_frame_make(_state, &_frame_block); - _modelerrors_init(&modrep, _state, ae_true); - _mlpreport_init(&rep0, _state, ae_true); - _mlpreport_init(&rep1, _state, ae_true); - ae_smart_ptr_init(&_psession, (void**)&psession, _state, ae_true); - - mlpproperties(network, &nin, &nout, &wcount, _state); - - /* - * Process root call - */ - if( isrootcall ) - { - - /* - * Check correctness of parameters - */ - ae_assert(algokind==0||algokind==-1, "MLPTrainNetworkX: unexpected AlgoKind", _state); - ae_assert(s->npoints>=0, "MLPTrainNetworkX: internal error - parameter S is not initialized or is spoiled(S.NPoints<0)", _state); - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - if( !mlpissoftmax(network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - ae_assert(ntype==ttype, "MLPTrainNetworkX: internal error - type of the training network is not similar to network type in trainer object", _state); - ae_assert(s->nin==nin, "MLPTrainNetworkX: internal error - number of inputs in trainer is not equal to number of inputs in the training network.", _state); - ae_assert(s->nout==nout, "MLPTrainNetworkX: internal error - number of outputs in trainer is not equal to number of outputs in the training network.", _state); - ae_assert(nrestarts>=0, "MLPTrainNetworkX: internal error - NRestarts<0.", _state); - ae_assert(trnsubset->cnt>=trnsubsetsize, "MLPTrainNetworkX: internal error - parameter TrnSubsetSize more than input subset size(Length(TrnSubset)ptr.p_int[i]>=0&&trnsubset->ptr.p_int[i]<=s->npoints-1, "MLPTrainNetworkX: internal error - parameter TrnSubset contains incorrect index(TrnSubset[I]<0 or TrnSubset[I]>S.NPoints-1)", _state); - } - ae_assert(valsubset->cnt>=valsubsetsize, "MLPTrainNetworkX: internal error - parameter ValSubsetSize more than input subset size(Length(ValSubset)ptr.p_int[i]>=0&&valsubset->ptr.p_int[i]<=s->npoints-1, "MLPTrainNetworkX: internal error - parameter ValSubset contains incorrect index(ValSubset[I]<0 or ValSubset[I]>S.NPoints-1)", _state); - } - - /* - * Train - */ - randomizenetwork = nrestarts>0; - mlptrain_initmlptrnsessions(network, randomizenetwork, s, sessions, _state); - mlptrain_mlptrainnetworkx(s, nrestarts, algokind, trnsubset, trnsubsetsize, valsubset, valsubsetsize, network, rep, ae_false, sessions, _state); - - /* - * Choose best network - */ - bestrmserror = ae_maxrealnumber; - ae_shared_pool_first_recycled(sessions, &_psession, _state); - while(psession!=NULL) - { - if( ae_fp_less(psession->bestrmserror,bestrmserror) ) - { - mlpimporttunableparameters(network, &psession->bestparameters, _state); - bestrmserror = psession->bestrmserror; - } - ae_shared_pool_next_recycled(sessions, &_psession, _state); - } - - /* - * Calculate errors - */ - if( s->datatype==0 ) - { - mlpallerrorssubset(network, &s->densexy, s->npoints, trnsubset, trnsubsetsize, &modrep, _state); - } - if( s->datatype==1 ) - { - mlpallerrorssparsesubset(network, &s->sparsexy, s->npoints, trnsubset, trnsubsetsize, &modrep, _state); - } - rep->relclserror = modrep.relclserror; - rep->avgce = modrep.avgce; - rep->rmserror = modrep.rmserror; - rep->avgerror = modrep.avgerror; - rep->avgrelerror = modrep.avgrelerror; - - /* - * Done - */ - ae_frame_leave(_state); - return; - } - - /* - * Split problem, if we have more than 1 restart - */ - if( nrestarts>=2 ) - { - - /* - * Divide problem with NRestarts into two: NR0 and NR1. - */ - nr0 = nrestarts/2; - nr1 = nrestarts-nr0; - mlptrain_mlptrainnetworkx(s, nr0, algokind, trnsubset, trnsubsetsize, valsubset, valsubsetsize, network, &rep0, ae_false, sessions, _state); - mlptrain_mlptrainnetworkx(s, nr1, algokind, trnsubset, trnsubsetsize, valsubset, valsubsetsize, network, &rep1, ae_false, sessions, _state); - - /* - * Aggregate results - */ - rep->ngrad = rep0.ngrad+rep1.ngrad; - rep->nhess = rep0.nhess+rep1.nhess; - rep->ncholesky = rep0.ncholesky+rep1.ncholesky; - - /* - * Done :) - */ - ae_frame_leave(_state); - return; - } - - /* - * Execution with NRestarts=1 or NRestarts=0: - * * NRestarts=1 means that network is restarted from random position - * * NRestarts=0 means that network is not randomized - */ - ae_assert(nrestarts==0||nrestarts==1, "MLPTrainNetworkX: internal error", _state); - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - ae_shared_pool_retrieve(sessions, &_psession, _state); - if( ((s->datatype==0||s->datatype==1)&&s->npoints>0)&&trnsubsetsize!=0 ) - { - - /* - * Train network using combination of early stopping and step-size - * and step-count based criteria. Network state with best value of - * validation set error is stored in WBuf0. When validation set is - * zero, most recent state of network is stored. - */ - rndstart = nrestarts!=0; - ngradbatch = 0; - eval = 0; - ebest = 0; - itbest = 0; - itcnt = 0; - mlptrain_mlpstarttrainingx(s, rndstart, algokind, trnsubset, trnsubsetsize, psession, _state); - if( s->datatype==0 ) - { - ebest = mlperrorsubset(&psession->network, &s->densexy, s->npoints, valsubset, valsubsetsize, _state); - } - if( s->datatype==1 ) - { - ebest = mlperrorsparsesubset(&psession->network, &s->sparsexy, s->npoints, valsubset, valsubsetsize, _state); - } - ae_v_move(&psession->wbuf0.ptr.p_double[0], 1, &psession->network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - while(mlptrain_mlpcontinuetrainingx(s, trnsubset, trnsubsetsize, &ngradbatch, psession, _state)) - { - if( s->datatype==0 ) - { - eval = mlperrorsubset(&psession->network, &s->densexy, s->npoints, valsubset, valsubsetsize, _state); - } - if( s->datatype==1 ) - { - eval = mlperrorsparsesubset(&psession->network, &s->sparsexy, s->npoints, valsubset, valsubsetsize, _state); - } - if( ae_fp_less_eq(eval,ebest)||valsubsetsize==0 ) - { - ae_v_move(&psession->wbuf0.ptr.p_double[0], 1, &psession->network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - ebest = eval; - itbest = itcnt; - } - if( itcnt>30&&ae_fp_greater(itcnt,1.5*itbest) ) - { - break; - } - itcnt = itcnt+1; - } - ae_v_move(&psession->network.weights.ptr.p_double[0], 1, &psession->wbuf0.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - rep->ngrad = ngradbatch; - } - else - { - for(i=0; i<=wcount-1; i++) - { - psession->network.weights.ptr.p_double[i] = 0; - } - } - - /* - * Evaluate network performance and update PSession.BestParameters/BestRMSError - * (if needed). - */ - if( s->datatype==0 ) - { - mlpallerrorssubset(&psession->network, &s->densexy, s->npoints, trnsubset, trnsubsetsize, &modrep, _state); - } - if( s->datatype==1 ) - { - mlpallerrorssparsesubset(&psession->network, &s->sparsexy, s->npoints, trnsubset, trnsubsetsize, &modrep, _state); - } - if( ae_fp_less(modrep.rmserror,psession->bestrmserror) ) - { - mlpexporttunableparameters(&psession->network, &psession->bestparameters, &pcount, _state); - psession->bestrmserror = modrep.rmserror; - } - - /* - * Move session back to pool - */ - ae_shared_pool_recycle(sessions, &_psession, _state); - ae_frame_leave(_state); -} - - -static void mlptrain_mlptrainensemblex(mlptrainer* s, - mlpensemble* ensemble, - ae_int_t idx0, - ae_int_t idx1, - ae_int_t nrestarts, - ae_int_t trainingmethod, - sinteger* ngrad, - ae_bool isrootcall, - ae_shared_pool* esessions, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t pcount; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t trnsubsetsize; - ae_int_t valsubsetsize; - ae_int_t k0; - sinteger ngrad0; - sinteger ngrad1; - mlpetrnsession *psession; - ae_smart_ptr _psession; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - _sinteger_init(&ngrad0, _state, ae_true); - _sinteger_init(&ngrad1, _state, ae_true); - ae_smart_ptr_init(&_psession, (void**)&psession, _state, ae_true); - _hqrndstate_init(&rs, _state, ae_true); - - nin = mlpgetinputscount(&ensemble->network, _state); - nout = mlpgetoutputscount(&ensemble->network, _state); - wcount = mlpgetweightscount(&ensemble->network, _state); - if( mlpissoftmax(&ensemble->network, _state) ) - { - pcount = nin; - } - else - { - pcount = nin+nout; - } - if( nrestarts<=0 ) - { - nrestarts = 1; - } - - /* - * Handle degenerate case - */ - if( s->npoints<2 ) - { - for(i=idx0; i<=idx1-1; i++) - { - for(j=0; j<=wcount-1; j++) - { - ensemble->weights.ptr.p_double[i*wcount+j] = 0.0; - } - for(j=0; j<=pcount-1; j++) - { - ensemble->columnmeans.ptr.p_double[i*pcount+j] = 0.0; - ensemble->columnsigmas.ptr.p_double[i*pcount+j] = 1.0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Process root call - */ - if( isrootcall ) - { - - /* - * Prepare: - * * prepare MLPETrnSessions - * * fill ensemble by zeros (helps to detect errors) - */ - mlptrain_initmlpetrnsessions(&ensemble->network, s, esessions, _state); - for(i=idx0; i<=idx1-1; i++) - { - for(j=0; j<=wcount-1; j++) - { - ensemble->weights.ptr.p_double[i*wcount+j] = 0.0; - } - for(j=0; j<=pcount-1; j++) - { - ensemble->columnmeans.ptr.p_double[i*pcount+j] = 0.0; - ensemble->columnsigmas.ptr.p_double[i*pcount+j] = 0.0; - } - } - - /* - * Train in non-root mode and exit - */ - mlptrain_mlptrainensemblex(s, ensemble, idx0, idx1, nrestarts, trainingmethod, ngrad, ae_false, esessions, _state); - ae_frame_leave(_state); - return; - } - - /* - * Split problem - */ - if( idx1-idx0>=2 ) - { - k0 = (idx1-idx0)/2; - ngrad0.val = 0; - ngrad1.val = 0; - mlptrain_mlptrainensemblex(s, ensemble, idx0, idx0+k0, nrestarts, trainingmethod, &ngrad0, ae_false, esessions, _state); - mlptrain_mlptrainensemblex(s, ensemble, idx0+k0, idx1, nrestarts, trainingmethod, &ngrad1, ae_false, esessions, _state); - ngrad->val = ngrad0.val+ngrad1.val; - ae_frame_leave(_state); - return; - } - - /* - * Retrieve and prepare session - */ - ae_shared_pool_retrieve(esessions, &_psession, _state); - - /* - * Train - */ - hqrndrandomize(&rs, _state); - for(k=idx0; k<=idx1-1; k++) - { - - /* - * Split set - */ - trnsubsetsize = 0; - valsubsetsize = 0; - if( trainingmethod==0 ) - { - do - { - trnsubsetsize = 0; - valsubsetsize = 0; - for(i=0; i<=s->npoints-1; i++) - { - if( ae_fp_less(ae_randomreal(_state),0.66) ) - { - - /* - * Assign sample to training set - */ - psession->trnsubset.ptr.p_int[trnsubsetsize] = i; - trnsubsetsize = trnsubsetsize+1; - } - else - { - - /* - * Assign sample to validation set - */ - psession->valsubset.ptr.p_int[valsubsetsize] = i; - valsubsetsize = valsubsetsize+1; - } - } - } - while(!(trnsubsetsize!=0&&valsubsetsize!=0)); - } - if( trainingmethod==1 ) - { - valsubsetsize = 0; - trnsubsetsize = s->npoints; - for(i=0; i<=s->npoints-1; i++) - { - psession->trnsubset.ptr.p_int[i] = hqrnduniformi(&rs, s->npoints, _state); - } - } - - /* - * Train - */ - mlptrain_mlptrainnetworkx(s, nrestarts, -1, &psession->trnsubset, trnsubsetsize, &psession->valsubset, valsubsetsize, &psession->network, &psession->mlprep, ae_true, &psession->mlpsessions, _state); - ngrad->val = ngrad->val+psession->mlprep.ngrad; - - /* - * Save results - */ - ae_v_move(&ensemble->weights.ptr.p_double[k*wcount], 1, &psession->network.weights.ptr.p_double[0], 1, ae_v_len(k*wcount,(k+1)*wcount-1)); - ae_v_move(&ensemble->columnmeans.ptr.p_double[k*pcount], 1, &psession->network.columnmeans.ptr.p_double[0], 1, ae_v_len(k*pcount,(k+1)*pcount-1)); - ae_v_move(&ensemble->columnsigmas.ptr.p_double[k*pcount], 1, &psession->network.columnsigmas.ptr.p_double[0], 1, ae_v_len(k*pcount,(k+1)*pcount-1)); - } - - /* - * Recycle session - */ - ae_shared_pool_recycle(esessions, &_psession, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTrainingX call, -and then user subsequently calls MLPContinueTrainingX to perform one more -iteration of the training. - -After call to this function trainer object remembers network and is ready -to train it. However, no training is performed until first call to -MLPContinueTraining() function. Subsequent calls to MLPContinueTraining() -will advance traing progress one iteration further. - - - -- ALGLIB -- - Copyright 13.08.2012 by Bochkanov Sergey -*************************************************************************/ -static void mlptrain_mlpstarttrainingx(mlptrainer* s, - ae_bool randomstart, - ae_int_t algokind, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - smlptrnsession* session, - ae_state *_state) -{ - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t ntype; - ae_int_t ttype; - ae_int_t i; - - - - /* - * Check parameters - */ - ae_assert(s->npoints>=0, "MLPStartTrainingX: internal error - parameter S is not initialized or is spoiled(S.NPoints<0)", _state); - ae_assert(algokind==0||algokind==-1, "MLPStartTrainingX: unexpected AlgoKind", _state); - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - if( !mlpissoftmax(&session->network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - ae_assert(ntype==ttype, "MLPStartTrainingX: internal error - type of the resulting network is not similar to network type in trainer object", _state); - mlpproperties(&session->network, &nin, &nout, &wcount, _state); - ae_assert(s->nin==nin, "MLPStartTrainingX: number of inputs in trainer is not equal to number of inputs in the network.", _state); - ae_assert(s->nout==nout, "MLPStartTrainingX: number of outputs in trainer is not equal to number of outputs in the network.", _state); - ae_assert(subset->cnt>=subsetsize, "MLPStartTrainingX: internal error - parameter SubsetSize more than input subset size(Length(Subset)ptr.p_int[i]>=0&&subset->ptr.p_int[i]<=s->npoints-1, "MLPStartTrainingX: internal error - parameter Subset contains incorrect index(Subset[I]<0 or Subset[I]>S.NPoints-1)", _state); - } - - /* - * Prepare session - */ - minlbfgssetcond(&session->optimizer, 0.0, 0.0, s->wstep, s->maxits, _state); - if( s->npoints>0&&subsetsize!=0 ) - { - if( randomstart ) - { - mlprandomize(&session->network, _state); - } - minlbfgsrestartfrom(&session->optimizer, &session->network.weights, _state); - } - else - { - for(i=0; i<=wcount-1; i++) - { - session->network.weights.ptr.p_double[i] = 0; - } - } - if( algokind==-1 ) - { - session->algoused = s->algokind; - if( s->algokind==1 ) - { - session->minibatchsize = s->minibatchsize; - } - } - else - { - session->algoused = 0; - } - hqrndrandomize(&session->generator, _state); - ae_vector_set_length(&session->rstate.ia, 15+1, _state); - ae_vector_set_length(&session->rstate.ra, 1+1, _state); - session->rstate.stage = -1; -} - - -/************************************************************************* -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTrainingX call, -and then user subsequently calls MLPContinueTrainingX to perform one more -iteration of the training. - -This function performs one more iteration of the training and returns -either True (training continues) or False (training stopped). In case True -was returned, Network weights are updated according to the current state -of the optimization progress. In case False was returned, no additional -updates is performed (previous update of the network weights moved us to -the final point, and no additional updates is needed). - -EXAMPLE: - > - > [initialize network and trainer object] - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > [visualize training progress] - > - - - -- ALGLIB -- - Copyright 13.08.2012 by Bochkanov Sergey -*************************************************************************/ -static ae_bool mlptrain_mlpcontinuetrainingx(mlptrainer* s, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - ae_int_t* ngradbatch, - smlptrnsession* session, - ae_state *_state) -{ - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t twcount; - ae_int_t ntype; - ae_int_t ttype; - double decay; - double v; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t trnsetsize; - ae_int_t epoch; - ae_int_t minibatchcount; - ae_int_t minibatchidx; - ae_int_t cursize; - ae_int_t idx0; - ae_int_t idx1; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( session->rstate.stage>=0 ) - { - nin = session->rstate.ia.ptr.p_int[0]; - nout = session->rstate.ia.ptr.p_int[1]; - wcount = session->rstate.ia.ptr.p_int[2]; - twcount = session->rstate.ia.ptr.p_int[3]; - ntype = session->rstate.ia.ptr.p_int[4]; - ttype = session->rstate.ia.ptr.p_int[5]; - i = session->rstate.ia.ptr.p_int[6]; - j = session->rstate.ia.ptr.p_int[7]; - k = session->rstate.ia.ptr.p_int[8]; - trnsetsize = session->rstate.ia.ptr.p_int[9]; - epoch = session->rstate.ia.ptr.p_int[10]; - minibatchcount = session->rstate.ia.ptr.p_int[11]; - minibatchidx = session->rstate.ia.ptr.p_int[12]; - cursize = session->rstate.ia.ptr.p_int[13]; - idx0 = session->rstate.ia.ptr.p_int[14]; - idx1 = session->rstate.ia.ptr.p_int[15]; - decay = session->rstate.ra.ptr.p_double[0]; - v = session->rstate.ra.ptr.p_double[1]; - } - else - { - nin = -983; - nout = -989; - wcount = -834; - twcount = 900; - ntype = -287; - ttype = 364; - i = 214; - j = -338; - k = -686; - trnsetsize = 912; - epoch = 585; - minibatchcount = 497; - minibatchidx = -271; - cursize = -581; - idx0 = 745; - idx1 = -533; - decay = -77; - v = 678; - } - if( session->rstate.stage==0 ) - { - goto lbl_0; - } - - /* - * Routine body - */ - - /* - * Check correctness of inputs - */ - ae_assert(s->npoints>=0, "MLPContinueTrainingX: internal error - parameter S is not initialized or is spoiled(S.NPoints<0).", _state); - if( s->rcpar ) - { - ttype = 0; - } - else - { - ttype = 1; - } - if( !mlpissoftmax(&session->network, _state) ) - { - ntype = 0; - } - else - { - ntype = 1; - } - ae_assert(ntype==ttype, "MLPContinueTrainingX: internal error - type of the resulting network is not similar to network type in trainer object.", _state); - mlpproperties(&session->network, &nin, &nout, &wcount, _state); - ae_assert(s->nin==nin, "MLPContinueTrainingX: internal error - number of inputs in trainer is not equal to number of inputs in the network.", _state); - ae_assert(s->nout==nout, "MLPContinueTrainingX: internal error - number of outputs in trainer is not equal to number of outputs in the network.", _state); - ae_assert(subset->cnt>=subsetsize, "MLPContinueTrainingX: internal error - parameter SubsetSize more than input subset size(Length(Subset)ptr.p_int[i]>=0&&subset->ptr.p_int[i]<=s->npoints-1, "MLPContinueTrainingX: internal error - parameter Subset contains incorrect index(Subset[I]<0 or Subset[I]>S.NPoints-1).", _state); - } - - /* - * Quick exit on empty training set - */ - if( s->npoints==0||subsetsize==0 ) - { - result = ae_false; - return result; - } - - /* - * Minibatch training - */ - if( session->algoused==1 ) - { - ae_assert(ae_false, "MINIBATCH TRAINING IS NOT IMPLEMENTED YET", _state); - } - - /* - * Last option: full batch training - */ - decay = s->decay; -lbl_1: - if( !minlbfgsiteration(&session->optimizer, _state) ) - { - goto lbl_2; - } - if( !session->optimizer.xupdated ) - { - goto lbl_3; - } - ae_v_move(&session->network.weights.ptr.p_double[0], 1, &session->optimizer.x.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - session->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: -lbl_3: - ae_v_move(&session->network.weights.ptr.p_double[0], 1, &session->optimizer.x.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - if( s->datatype==0 ) - { - mlpgradbatchsubset(&session->network, &s->densexy, s->npoints, subset, subsetsize, &session->optimizer.f, &session->optimizer.g, _state); - } - if( s->datatype==1 ) - { - mlpgradbatchsparsesubset(&session->network, &s->sparsexy, s->npoints, subset, subsetsize, &session->optimizer.f, &session->optimizer.g, _state); - } - - /* - * Increment number of operations performed on batch gradient - */ - *ngradbatch = *ngradbatch+1; - v = ae_v_dotproduct(&session->network.weights.ptr.p_double[0], 1, &session->network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1)); - session->optimizer.f = session->optimizer.f+0.5*decay*v; - ae_v_addd(&session->optimizer.g.ptr.p_double[0], 1, &session->network.weights.ptr.p_double[0], 1, ae_v_len(0,wcount-1), decay); - goto lbl_1; -lbl_2: - minlbfgsresultsbuf(&session->optimizer, &session->network.weights, &session->optimizerrep, _state); - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - session->rstate.ia.ptr.p_int[0] = nin; - session->rstate.ia.ptr.p_int[1] = nout; - session->rstate.ia.ptr.p_int[2] = wcount; - session->rstate.ia.ptr.p_int[3] = twcount; - session->rstate.ia.ptr.p_int[4] = ntype; - session->rstate.ia.ptr.p_int[5] = ttype; - session->rstate.ia.ptr.p_int[6] = i; - session->rstate.ia.ptr.p_int[7] = j; - session->rstate.ia.ptr.p_int[8] = k; - session->rstate.ia.ptr.p_int[9] = trnsetsize; - session->rstate.ia.ptr.p_int[10] = epoch; - session->rstate.ia.ptr.p_int[11] = minibatchcount; - session->rstate.ia.ptr.p_int[12] = minibatchidx; - session->rstate.ia.ptr.p_int[13] = cursize; - session->rstate.ia.ptr.p_int[14] = idx0; - session->rstate.ia.ptr.p_int[15] = idx1; - session->rstate.ra.ptr.p_double[0] = decay; - session->rstate.ra.ptr.p_double[1] = v; - return result; -} - - -/************************************************************************* -Internal bagging subroutine. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -static void mlptrain_mlpebagginginternal(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_bool lmalgorithm, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* ooberrors, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix xys; - ae_vector s; - ae_matrix oobbuf; - ae_vector oobcntbuf; - ae_vector x; - ae_vector y; - ae_vector dy; - ae_vector dsbuf; - ae_int_t ccnt; - ae_int_t pcnt; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double v; - mlpreport tmprep; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _mlpreport_clear(rep); - _mlpcvreport_clear(ooberrors); - ae_matrix_init(&xys, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&s, 0, DT_BOOL, _state, ae_true); - ae_matrix_init(&oobbuf, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&oobcntbuf, 0, DT_INT, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dy, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dsbuf, 0, DT_REAL, _state, ae_true); - _mlpreport_init(&tmprep, _state, ae_true); - _hqrndstate_init(&rs, _state, ae_true); - - nin = mlpgetinputscount(&ensemble->network, _state); - nout = mlpgetoutputscount(&ensemble->network, _state); - wcount = mlpgetweightscount(&ensemble->network, _state); - - /* - * Test for inputs - */ - if( (!lmalgorithm&&ae_fp_eq(wstep,0))&&maxits==0 ) - { - *info = -8; - ae_frame_leave(_state); - return; - } - if( ((npoints<=0||restarts<1)||ae_fp_less(wstep,0))||maxits<0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( mlpissoftmax(&ensemble->network, _state) ) - { - for(i=0; i<=npoints-1; i++) - { - if( ae_round(xy->ptr.pp_double[i][nin], _state)<0||ae_round(xy->ptr.pp_double[i][nin], _state)>=nout ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - } - - /* - * allocate temporaries - */ - *info = 2; - rep->ngrad = 0; - rep->nhess = 0; - rep->ncholesky = 0; - ooberrors->relclserror = 0; - ooberrors->avgce = 0; - ooberrors->rmserror = 0; - ooberrors->avgerror = 0; - ooberrors->avgrelerror = 0; - if( mlpissoftmax(&ensemble->network, _state) ) - { - ccnt = nin+1; - pcnt = nin; - } - else - { - ccnt = nin+nout; - pcnt = nin+nout; - } - ae_matrix_set_length(&xys, npoints, ccnt, _state); - ae_vector_set_length(&s, npoints, _state); - ae_matrix_set_length(&oobbuf, npoints, nout, _state); - ae_vector_set_length(&oobcntbuf, npoints, _state); - ae_vector_set_length(&x, nin, _state); - ae_vector_set_length(&y, nout, _state); - if( mlpissoftmax(&ensemble->network, _state) ) - { - ae_vector_set_length(&dy, 1, _state); - } - else - { - ae_vector_set_length(&dy, nout, _state); - } - for(i=0; i<=npoints-1; i++) - { - for(j=0; j<=nout-1; j++) - { - oobbuf.ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=npoints-1; i++) - { - oobcntbuf.ptr.p_int[i] = 0; - } - - /* - * main bagging cycle - */ - hqrndrandomize(&rs, _state); - for(k=0; k<=ensemble->ensemblesize-1; k++) - { - - /* - * prepare dataset - */ - for(i=0; i<=npoints-1; i++) - { - s.ptr.p_bool[i] = ae_false; - } - for(i=0; i<=npoints-1; i++) - { - j = hqrnduniformi(&rs, npoints, _state); - s.ptr.p_bool[j] = ae_true; - ae_v_move(&xys.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[j][0], 1, ae_v_len(0,ccnt-1)); - } - - /* - * train - */ - if( lmalgorithm ) - { - mlptrainlm(&ensemble->network, &xys, npoints, decay, restarts, info, &tmprep, _state); - } - else - { - mlptrainlbfgs(&ensemble->network, &xys, npoints, decay, restarts, wstep, maxits, info, &tmprep, _state); - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * save results - */ - rep->ngrad = rep->ngrad+tmprep.ngrad; - rep->nhess = rep->nhess+tmprep.nhess; - rep->ncholesky = rep->ncholesky+tmprep.ncholesky; - ae_v_move(&ensemble->weights.ptr.p_double[k*wcount], 1, &ensemble->network.weights.ptr.p_double[0], 1, ae_v_len(k*wcount,(k+1)*wcount-1)); - ae_v_move(&ensemble->columnmeans.ptr.p_double[k*pcnt], 1, &ensemble->network.columnmeans.ptr.p_double[0], 1, ae_v_len(k*pcnt,(k+1)*pcnt-1)); - ae_v_move(&ensemble->columnsigmas.ptr.p_double[k*pcnt], 1, &ensemble->network.columnsigmas.ptr.p_double[0], 1, ae_v_len(k*pcnt,(k+1)*pcnt-1)); - - /* - * OOB estimates - */ - for(i=0; i<=npoints-1; i++) - { - if( !s.ptr.p_bool[i] ) - { - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nin-1)); - mlpprocess(&ensemble->network, &x, &y, _state); - ae_v_add(&oobbuf.ptr.pp_double[i][0], 1, &y.ptr.p_double[0], 1, ae_v_len(0,nout-1)); - oobcntbuf.ptr.p_int[i] = oobcntbuf.ptr.p_int[i]+1; - } - } - } - - /* - * OOB estimates - */ - if( mlpissoftmax(&ensemble->network, _state) ) - { - dserrallocate(nout, &dsbuf, _state); - } - else - { - dserrallocate(-nout, &dsbuf, _state); - } - for(i=0; i<=npoints-1; i++) - { - if( oobcntbuf.ptr.p_int[i]!=0 ) - { - v = (double)1/(double)oobcntbuf.ptr.p_int[i]; - ae_v_moved(&y.ptr.p_double[0], 1, &oobbuf.ptr.pp_double[i][0], 1, ae_v_len(0,nout-1), v); - if( mlpissoftmax(&ensemble->network, _state) ) - { - dy.ptr.p_double[0] = xy->ptr.pp_double[i][nin]; - } - else - { - ae_v_moved(&dy.ptr.p_double[0], 1, &xy->ptr.pp_double[i][nin], 1, ae_v_len(0,nout-1), v); - } - dserraccumulate(&dsbuf, &y, &dy, _state); - } - } - dserrfinish(&dsbuf, _state); - ooberrors->relclserror = dsbuf.ptr.p_double[0]; - ooberrors->avgce = dsbuf.ptr.p_double[1]; - ooberrors->rmserror = dsbuf.ptr.p_double[2]; - ooberrors->avgerror = dsbuf.ptr.p_double[3]; - ooberrors->avgrelerror = dsbuf.ptr.p_double[4]; - ae_frame_leave(_state); -} - - -/************************************************************************* -This function initializes temporaries needed for training session. - - - -- ALGLIB -- - Copyright 01.07.2013 by Bochkanov Sergey -*************************************************************************/ -static void mlptrain_initmlptrnsession(multilayerperceptron* networktrained, - ae_bool randomizenetwork, - mlptrainer* trainer, - smlptrnsession* session, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t nin; - ae_int_t nout; - ae_int_t wcount; - ae_int_t pcount; - ae_vector dummysubset; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&dummysubset, 0, DT_INT, _state, ae_true); - - - /* - * Prepare network: - * * copy input network to Session.Network - * * re-initialize preprocessor and weights if RandomizeNetwork=True - */ - mlpcopy(networktrained, &session->network, _state); - if( randomizenetwork ) - { - ae_assert(trainer->datatype==0||trainer->datatype==1, "InitTemporaries: unexpected Trainer.DataType", _state); - if( trainer->datatype==0 ) - { - mlpinitpreprocessorsubset(&session->network, &trainer->densexy, trainer->npoints, &dummysubset, -1, _state); - } - if( trainer->datatype==1 ) - { - mlpinitpreprocessorsparsesubset(&session->network, &trainer->sparsexy, trainer->npoints, &dummysubset, -1, _state); - } - mlprandomize(&session->network, _state); - session->randomizenetwork = ae_true; - } - else - { - session->randomizenetwork = ae_false; - } - - /* - * Determine network geometry and initialize optimizer - */ - mlpproperties(&session->network, &nin, &nout, &wcount, _state); - minlbfgscreate(wcount, ae_minint(wcount, trainer->lbfgsfactor, _state), &session->network.weights, &session->optimizer, _state); - minlbfgssetxrep(&session->optimizer, ae_true, _state); - - /* - * Create buffers - */ - ae_vector_set_length(&session->wbuf0, wcount, _state); - ae_vector_set_length(&session->wbuf1, wcount, _state); - - /* - * Initialize session result - */ - mlpexporttunableparameters(&session->network, &session->bestparameters, &pcount, _state); - session->bestrmserror = ae_maxrealnumber; - ae_frame_leave(_state); -} - - -/************************************************************************* -This function initializes temporaries needed for training session. - -*************************************************************************/ -static void mlptrain_initmlptrnsessions(multilayerperceptron* networktrained, - ae_bool randomizenetwork, - mlptrainer* trainer, - ae_shared_pool* sessions, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector dummysubset; - smlptrnsession t; - smlptrnsession *p; - ae_smart_ptr _p; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&dummysubset, 0, DT_INT, _state, ae_true); - _smlptrnsession_init(&t, _state, ae_true); - ae_smart_ptr_init(&_p, (void**)&p, _state, ae_true); - - if( ae_shared_pool_is_initialized(sessions) ) - { - - /* - * Pool was already initialized. - * Clear sessions stored in the pool. - */ - ae_shared_pool_first_recycled(sessions, &_p, _state); - while(p!=NULL) - { - ae_assert(mlpsamearchitecture(&p->network, networktrained, _state), "InitMLPTrnSessions: internal consistency error", _state); - p->bestrmserror = ae_maxrealnumber; - ae_shared_pool_next_recycled(sessions, &_p, _state); - } - } - else - { - - /* - * Prepare session and seed pool - */ - mlptrain_initmlptrnsession(networktrained, randomizenetwork, trainer, &t, _state); - ae_shared_pool_set_seed(sessions, &t, sizeof(t), _smlptrnsession_init, _smlptrnsession_init_copy, _smlptrnsession_destroy, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function initializes temporaries needed for ensemble training. - -*************************************************************************/ -static void mlptrain_initmlpetrnsession(multilayerperceptron* individualnetwork, - mlptrainer* trainer, - mlpetrnsession* session, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector dummysubset; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&dummysubset, 0, DT_INT, _state, ae_true); - - - /* - * Prepare network: - * * copy input network to Session.Network - * * re-initialize preprocessor and weights if RandomizeNetwork=True - */ - mlpcopy(individualnetwork, &session->network, _state); - mlptrain_initmlptrnsessions(individualnetwork, ae_true, trainer, &session->mlpsessions, _state); - ivectorsetlengthatleast(&session->trnsubset, trainer->npoints, _state); - ivectorsetlengthatleast(&session->valsubset, trainer->npoints, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function initializes temporaries needed for training session. - -*************************************************************************/ -static void mlptrain_initmlpetrnsessions(multilayerperceptron* individualnetwork, - mlptrainer* trainer, - ae_shared_pool* sessions, - ae_state *_state) -{ - ae_frame _frame_block; - mlpetrnsession t; - - ae_frame_make(_state, &_frame_block); - _mlpetrnsession_init(&t, _state, ae_true); - - if( !ae_shared_pool_is_initialized(sessions) ) - { - mlptrain_initmlpetrnsession(individualnetwork, trainer, &t, _state); - ae_shared_pool_set_seed(sessions, &t, sizeof(t), _mlpetrnsession_init, _mlpetrnsession_init_copy, _mlpetrnsession_destroy, _state); - } - ae_frame_leave(_state); -} - - -ae_bool _mlpreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlpreport *p = (mlpreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _mlpreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlpreport *dst = (mlpreport*)_dst; - mlpreport *src = (mlpreport*)_src; - dst->relclserror = src->relclserror; - dst->avgce = src->avgce; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->ngrad = src->ngrad; - dst->nhess = src->nhess; - dst->ncholesky = src->ncholesky; - return ae_true; -} - - -void _mlpreport_clear(void* _p) -{ - mlpreport *p = (mlpreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _mlpreport_destroy(void* _p) -{ - mlpreport *p = (mlpreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _mlpcvreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlpcvreport *p = (mlpcvreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _mlpcvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlpcvreport *dst = (mlpcvreport*)_dst; - mlpcvreport *src = (mlpcvreport*)_src; - dst->relclserror = src->relclserror; - dst->avgce = src->avgce; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - return ae_true; -} - - -void _mlpcvreport_clear(void* _p) -{ - mlpcvreport *p = (mlpcvreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _mlpcvreport_destroy(void* _p) -{ - mlpcvreport *p = (mlpcvreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _smlptrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - smlptrnsession *p = (smlptrnsession*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->bestparameters, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_multilayerperceptron_init(&p->network, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsstate_init(&p->optimizer, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsreport_init(&p->optimizerrep, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->wbuf0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->wbuf1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->allminibatches, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->currentminibatch, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !_hqrndstate_init(&p->generator, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _smlptrnsession_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - smlptrnsession *dst = (smlptrnsession*)_dst; - smlptrnsession *src = (smlptrnsession*)_src; - if( !ae_vector_init_copy(&dst->bestparameters, &src->bestparameters, _state, make_automatic) ) - return ae_false; - dst->bestrmserror = src->bestrmserror; - dst->randomizenetwork = src->randomizenetwork; - if( !_multilayerperceptron_init_copy(&dst->network, &src->network, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsstate_init_copy(&dst->optimizer, &src->optimizer, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsreport_init_copy(&dst->optimizerrep, &src->optimizerrep, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->wbuf0, &src->wbuf0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->wbuf1, &src->wbuf1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->allminibatches, &src->allminibatches, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->currentminibatch, &src->currentminibatch, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - dst->algoused = src->algoused; - dst->minibatchsize = src->minibatchsize; - if( !_hqrndstate_init_copy(&dst->generator, &src->generator, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _smlptrnsession_clear(void* _p) -{ - smlptrnsession *p = (smlptrnsession*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->bestparameters); - _multilayerperceptron_clear(&p->network); - _minlbfgsstate_clear(&p->optimizer); - _minlbfgsreport_clear(&p->optimizerrep); - ae_vector_clear(&p->wbuf0); - ae_vector_clear(&p->wbuf1); - ae_vector_clear(&p->allminibatches); - ae_vector_clear(&p->currentminibatch); - _rcommstate_clear(&p->rstate); - _hqrndstate_clear(&p->generator); -} - - -void _smlptrnsession_destroy(void* _p) -{ - smlptrnsession *p = (smlptrnsession*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->bestparameters); - _multilayerperceptron_destroy(&p->network); - _minlbfgsstate_destroy(&p->optimizer); - _minlbfgsreport_destroy(&p->optimizerrep); - ae_vector_destroy(&p->wbuf0); - ae_vector_destroy(&p->wbuf1); - ae_vector_destroy(&p->allminibatches); - ae_vector_destroy(&p->currentminibatch); - _rcommstate_destroy(&p->rstate); - _hqrndstate_destroy(&p->generator); -} - - -ae_bool _mlpetrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlpetrnsession *p = (mlpetrnsession*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->trnsubset, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->valsubset, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init(&p->mlpsessions, _state, make_automatic) ) - return ae_false; - if( !_mlpreport_init(&p->mlprep, _state, make_automatic) ) - return ae_false; - if( !_multilayerperceptron_init(&p->network, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mlpetrnsession_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlpetrnsession *dst = (mlpetrnsession*)_dst; - mlpetrnsession *src = (mlpetrnsession*)_src; - if( !ae_vector_init_copy(&dst->trnsubset, &src->trnsubset, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->valsubset, &src->valsubset, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init_copy(&dst->mlpsessions, &src->mlpsessions, _state, make_automatic) ) - return ae_false; - if( !_mlpreport_init_copy(&dst->mlprep, &src->mlprep, _state, make_automatic) ) - return ae_false; - if( !_multilayerperceptron_init_copy(&dst->network, &src->network, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _mlpetrnsession_clear(void* _p) -{ - mlpetrnsession *p = (mlpetrnsession*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->trnsubset); - ae_vector_clear(&p->valsubset); - ae_shared_pool_clear(&p->mlpsessions); - _mlpreport_clear(&p->mlprep); - _multilayerperceptron_clear(&p->network); -} - - -void _mlpetrnsession_destroy(void* _p) -{ - mlpetrnsession *p = (mlpetrnsession*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->trnsubset); - ae_vector_destroy(&p->valsubset); - ae_shared_pool_destroy(&p->mlpsessions); - _mlpreport_destroy(&p->mlprep); - _multilayerperceptron_destroy(&p->network); -} - - -ae_bool _mlptrainer_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlptrainer *p = (mlptrainer*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->densexy, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_sparsematrix_init(&p->sparsexy, _state, make_automatic) ) - return ae_false; - if( !_smlptrnsession_init(&p->session, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->subset, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->valsubset, 0, DT_INT, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mlptrainer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlptrainer *dst = (mlptrainer*)_dst; - mlptrainer *src = (mlptrainer*)_src; - dst->nin = src->nin; - dst->nout = src->nout; - dst->rcpar = src->rcpar; - dst->lbfgsfactor = src->lbfgsfactor; - dst->decay = src->decay; - dst->wstep = src->wstep; - dst->maxits = src->maxits; - dst->datatype = src->datatype; - dst->npoints = src->npoints; - if( !ae_matrix_init_copy(&dst->densexy, &src->densexy, _state, make_automatic) ) - return ae_false; - if( !_sparsematrix_init_copy(&dst->sparsexy, &src->sparsexy, _state, make_automatic) ) - return ae_false; - if( !_smlptrnsession_init_copy(&dst->session, &src->session, _state, make_automatic) ) - return ae_false; - dst->ngradbatch = src->ngradbatch; - if( !ae_vector_init_copy(&dst->subset, &src->subset, _state, make_automatic) ) - return ae_false; - dst->subsetsize = src->subsetsize; - if( !ae_vector_init_copy(&dst->valsubset, &src->valsubset, _state, make_automatic) ) - return ae_false; - dst->valsubsetsize = src->valsubsetsize; - dst->algokind = src->algokind; - dst->minibatchsize = src->minibatchsize; - return ae_true; -} - - -void _mlptrainer_clear(void* _p) -{ - mlptrainer *p = (mlptrainer*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->densexy); - _sparsematrix_clear(&p->sparsexy); - _smlptrnsession_clear(&p->session); - ae_vector_clear(&p->subset); - ae_vector_clear(&p->valsubset); -} - - -void _mlptrainer_destroy(void* _p) -{ - mlptrainer *p = (mlptrainer*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->densexy); - _sparsematrix_destroy(&p->sparsexy); - _smlptrnsession_destroy(&p->session); - ae_vector_destroy(&p->subset); - ae_vector_destroy(&p->valsubset); -} - - -ae_bool _mlpparallelizationcv_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mlpparallelizationcv *p = (mlpparallelizationcv*)_p; - ae_touch_ptr((void*)p); - if( !_multilayerperceptron_init(&p->network, _state, make_automatic) ) - return ae_false; - if( !_mlpreport_init(&p->rep, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->subset, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xyrow, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_shared_pool_init(&p->trnpool, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mlpparallelizationcv_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mlpparallelizationcv *dst = (mlpparallelizationcv*)_dst; - mlpparallelizationcv *src = (mlpparallelizationcv*)_src; - if( !_multilayerperceptron_init_copy(&dst->network, &src->network, _state, make_automatic) ) - return ae_false; - if( !_mlpreport_init_copy(&dst->rep, &src->rep, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->subset, &src->subset, _state, make_automatic) ) - return ae_false; - dst->subsetsize = src->subsetsize; - if( !ae_vector_init_copy(&dst->xyrow, &src->xyrow, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - dst->ngrad = src->ngrad; - if( !ae_shared_pool_init_copy(&dst->trnpool, &src->trnpool, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _mlpparallelizationcv_clear(void* _p) -{ - mlpparallelizationcv *p = (mlpparallelizationcv*)_p; - ae_touch_ptr((void*)p); - _multilayerperceptron_clear(&p->network); - _mlpreport_clear(&p->rep); - ae_vector_clear(&p->subset); - ae_vector_clear(&p->xyrow); - ae_vector_clear(&p->y); - ae_shared_pool_clear(&p->trnpool); -} - - -void _mlpparallelizationcv_destroy(void* _p) -{ - mlpparallelizationcv *p = (mlpparallelizationcv*)_p; - ae_touch_ptr((void*)p); - _multilayerperceptron_destroy(&p->network); - _mlpreport_destroy(&p->rep); - ae_vector_destroy(&p->subset); - ae_vector_destroy(&p->xyrow); - ae_vector_destroy(&p->y); - ae_shared_pool_destroy(&p->trnpool); -} - - - - -/************************************************************************* -Principal components analysis - -Subroutine builds orthogonal basis where first axis corresponds to -direction with maximum variance, second axis maximizes variance in subspace -orthogonal to first axis and so on. - -It should be noted that, unlike LDA, PCA does not use class labels. - -INPUT PARAMETERS: - X - dataset, array[0..NPoints-1,0..NVars-1]. - matrix contains ONLY INDEPENDENT VARIABLES. - NPoints - dataset size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - -ÂÛÕÎÄÍÛÅ ÏÀÐÀÌÅÒÐÛ: - Info - return code: - * -4, if SVD subroutine haven't converged - * -1, if wrong parameters has been passed (NPoints<0, - NVars<1) - * 1, if task is solved - S2 - array[0..NVars-1]. variance values corresponding - to basis vectors. - V - array[0..NVars-1,0..NVars-1] - matrix, whose columns store basis vectors. - - -- ALGLIB -- - Copyright 25.08.2008 by Bochkanov Sergey -*************************************************************************/ -void pcabuildbasis(/* Real */ ae_matrix* x, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - /* Real */ ae_vector* s2, - /* Real */ ae_matrix* v, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix a; - ae_matrix u; - ae_matrix vt; - ae_vector m; - ae_vector t; - ae_int_t i; - ae_int_t j; - double mean; - double variance; - double skewness; - double kurtosis; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(s2); - ae_matrix_clear(v); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&u, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vt, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&m, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - - /* - * Check input data - */ - if( npoints<0||nvars<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * Special case: NPoints=0 - */ - if( npoints==0 ) - { - ae_vector_set_length(s2, nvars-1+1, _state); - ae_matrix_set_length(v, nvars-1+1, nvars-1+1, _state); - for(i=0; i<=nvars-1; i++) - { - s2->ptr.p_double[i] = 0; - } - for(i=0; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - if( i==j ) - { - v->ptr.pp_double[i][j] = 1; - } - else - { - v->ptr.pp_double[i][j] = 0; - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * Calculate means - */ - ae_vector_set_length(&m, nvars-1+1, _state); - ae_vector_set_length(&t, npoints-1+1, _state); - for(j=0; j<=nvars-1; j++) - { - ae_v_move(&t.ptr.p_double[0], 1, &x->ptr.pp_double[0][j], x->stride, ae_v_len(0,npoints-1)); - samplemoments(&t, npoints, &mean, &variance, &skewness, &kurtosis, _state); - m.ptr.p_double[j] = mean; - } - - /* - * Center, apply SVD, prepare output - */ - ae_matrix_set_length(&a, ae_maxint(npoints, nvars, _state)-1+1, nvars-1+1, _state); - for(i=0; i<=npoints-1; i++) - { - ae_v_move(&a.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,nvars-1)); - ae_v_sub(&a.ptr.pp_double[i][0], 1, &m.ptr.p_double[0], 1, ae_v_len(0,nvars-1)); - } - for(i=npoints; i<=nvars-1; i++) - { - for(j=0; j<=nvars-1; j++) - { - a.ptr.pp_double[i][j] = 0; - } - } - if( !rmatrixsvd(&a, ae_maxint(npoints, nvars, _state), nvars, 0, 1, 2, s2, &u, &vt, _state) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - if( npoints!=1 ) - { - for(i=0; i<=nvars-1; i++) - { - s2->ptr.p_double[i] = ae_sqr(s2->ptr.p_double[i], _state)/(npoints-1); - } - } - ae_matrix_set_length(v, nvars-1+1, nvars-1+1, _state); - copyandtranspose(&vt, 0, nvars-1, 0, nvars-1, v, 0, nvars-1, 0, nvars-1, _state); - ae_frame_leave(_state); -} - - - -} - diff --git a/src/inc/alglib/dataanalysis.h b/src/inc/alglib/dataanalysis.h deleted file mode 100644 index 4aed876..0000000 --- a/src/inc/alglib/dataanalysis.h +++ /dev/null @@ -1,7394 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _dataanalysis_pkg_h -#define _dataanalysis_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "linalg.h" -#include "statistics.h" -#include "alglibmisc.h" -#include "specialfunctions.h" -#include "solvers.h" -#include "optimization.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - double relclserror; - double avgce; - double rmserror; - double avgerror; - double avgrelerror; -} cvreport; -typedef struct -{ - ae_int_t npoints; - ae_int_t nfeatures; - ae_int_t disttype; - ae_matrix xy; - ae_matrix d; - ae_int_t ahcalgo; - ae_int_t kmeansrestarts; - ae_int_t kmeansmaxits; -} clusterizerstate; -typedef struct -{ - ae_int_t npoints; - ae_vector p; - ae_matrix z; - ae_matrix pz; - ae_matrix pm; - ae_vector mergedist; -} ahcreport; -typedef struct -{ - ae_int_t npoints; - ae_int_t nfeatures; - ae_int_t terminationtype; - ae_int_t k; - ae_matrix c; - ae_vector cidx; -} kmeansreport; -typedef struct -{ - ae_int_t nvars; - ae_int_t nclasses; - ae_int_t ntrees; - ae_int_t bufsize; - ae_vector trees; -} decisionforest; -typedef struct -{ - double relclserror; - double avgce; - double rmserror; - double avgerror; - double avgrelerror; - double oobrelclserror; - double oobavgce; - double oobrmserror; - double oobavgerror; - double oobavgrelerror; -} dfreport; -typedef struct -{ - ae_vector treebuf; - ae_vector idxbuf; - ae_vector tmpbufr; - ae_vector tmpbufr2; - ae_vector tmpbufi; - ae_vector classibuf; - ae_vector sortrbuf; - ae_vector sortrbuf2; - ae_vector sortibuf; - ae_vector varpool; - ae_vector evsbin; - ae_vector evssplits; -} dfinternalbuffers; -typedef struct -{ - ae_vector w; -} linearmodel; -typedef struct -{ - ae_matrix c; - double rmserror; - double avgerror; - double avgrelerror; - double cvrmserror; - double cvavgerror; - double cvavgrelerror; - ae_int_t ncvdefects; - ae_vector cvdefects; -} lrreport; -typedef struct -{ - double relclserror; - double avgce; - double rmserror; - double avgerror; - double avgrelerror; -} modelerrors; -typedef struct -{ - double f; - ae_vector g; -} smlpgrad; -typedef struct -{ - ae_int_t hlnetworktype; - ae_int_t hlnormtype; - ae_vector hllayersizes; - ae_vector hlconnections; - ae_vector hlneurons; - ae_vector structinfo; - ae_vector weights; - ae_vector columnmeans; - ae_vector columnsigmas; - ae_vector neurons; - ae_vector dfdnet; - ae_vector derror; - ae_vector x; - ae_vector y; - ae_matrix xy; - ae_vector xyrow; - ae_vector nwbuf; - ae_vector integerbuf; - modelerrors err; - ae_vector rndbuf; - ae_shared_pool buf; - ae_shared_pool gradbuf; - ae_matrix dummydxy; - sparsematrix dummysxy; - ae_vector dummyidx; - ae_shared_pool dummypool; -} multilayerperceptron; -typedef struct -{ - ae_vector w; -} logitmodel; -typedef struct -{ - ae_bool brackt; - ae_bool stage1; - ae_int_t infoc; - double dg; - double dgm; - double dginit; - double dgtest; - double dgx; - double dgxm; - double dgy; - double dgym; - double finit; - double ftest1; - double fm; - double fx; - double fxm; - double fy; - double fym; - double stx; - double sty; - double stmin; - double stmax; - double width; - double width1; - double xtrapf; -} logitmcstate; -typedef struct -{ - ae_int_t ngrad; - ae_int_t nhess; -} mnlreport; -typedef struct -{ - ae_int_t n; - ae_vector states; - ae_int_t npairs; - ae_matrix data; - ae_matrix ec; - ae_matrix bndl; - ae_matrix bndu; - ae_matrix c; - ae_vector ct; - ae_int_t ccnt; - ae_vector pw; - ae_matrix priorp; - double regterm; - minbleicstate bs; - ae_int_t repinneriterationscount; - ae_int_t repouteriterationscount; - ae_int_t repnfev; - ae_int_t repterminationtype; - minbleicreport br; - ae_vector tmpp; - ae_vector effectivew; - ae_vector effectivebndl; - ae_vector effectivebndu; - ae_matrix effectivec; - ae_vector effectivect; - ae_vector h; - ae_matrix p; -} mcpdstate; -typedef struct -{ - ae_int_t inneriterationscount; - ae_int_t outeriterationscount; - ae_int_t nfev; - ae_int_t terminationtype; -} mcpdreport; -typedef struct -{ - ae_int_t ensemblesize; - ae_vector weights; - ae_vector columnmeans; - ae_vector columnsigmas; - multilayerperceptron network; - ae_vector y; -} mlpensemble; -typedef struct -{ - double relclserror; - double avgce; - double rmserror; - double avgerror; - double avgrelerror; - ae_int_t ngrad; - ae_int_t nhess; - ae_int_t ncholesky; -} mlpreport; -typedef struct -{ - double relclserror; - double avgce; - double rmserror; - double avgerror; - double avgrelerror; -} mlpcvreport; -typedef struct -{ - ae_vector bestparameters; - double bestrmserror; - ae_bool randomizenetwork; - multilayerperceptron network; - minlbfgsstate optimizer; - minlbfgsreport optimizerrep; - ae_vector wbuf0; - ae_vector wbuf1; - ae_vector allminibatches; - ae_vector currentminibatch; - rcommstate rstate; - ae_int_t algoused; - ae_int_t minibatchsize; - hqrndstate generator; -} smlptrnsession; -typedef struct -{ - ae_vector trnsubset; - ae_vector valsubset; - ae_shared_pool mlpsessions; - mlpreport mlprep; - multilayerperceptron network; -} mlpetrnsession; -typedef struct -{ - ae_int_t nin; - ae_int_t nout; - ae_bool rcpar; - ae_int_t lbfgsfactor; - double decay; - double wstep; - ae_int_t maxits; - ae_int_t datatype; - ae_int_t npoints; - ae_matrix densexy; - sparsematrix sparsexy; - smlptrnsession session; - ae_int_t ngradbatch; - ae_vector subset; - ae_int_t subsetsize; - ae_vector valsubset; - ae_int_t valsubsetsize; - ae_int_t algokind; - ae_int_t minibatchsize; -} mlptrainer; -typedef struct -{ - multilayerperceptron network; - mlpreport rep; - ae_vector subset; - ae_int_t subsetsize; - ae_vector xyrow; - ae_vector y; - ae_int_t ngrad; - ae_shared_pool trnpool; -} mlpparallelizationcv; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - - -/************************************************************************* -This structure is a clusterization engine. - -You should not try to access its fields directly. -Use ALGLIB functions in order to work with this object. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -class _clusterizerstate_owner -{ -public: - _clusterizerstate_owner(); - _clusterizerstate_owner(const _clusterizerstate_owner &rhs); - _clusterizerstate_owner& operator=(const _clusterizerstate_owner &rhs); - virtual ~_clusterizerstate_owner(); - alglib_impl::clusterizerstate* c_ptr(); - alglib_impl::clusterizerstate* c_ptr() const; -protected: - alglib_impl::clusterizerstate *p_struct; -}; -class clusterizerstate : public _clusterizerstate_owner -{ -public: - clusterizerstate(); - clusterizerstate(const clusterizerstate &rhs); - clusterizerstate& operator=(const clusterizerstate &rhs); - virtual ~clusterizerstate(); - -}; - - -/************************************************************************* -This structure is used to store results of the agglomerative hierarchical -clustering (AHC). - -Following information is returned: - -* NPoints contains number of points in the original dataset - -* Z contains information about merges performed (see below). Z contains - indexes from the original (unsorted) dataset and it can be used when you - need to know what points were merged. However, it is not convenient when - you want to build a dendrograd (see below). - -* if you want to build dendrogram, you can use Z, but it is not good - option, because Z contains indexes from unsorted dataset. Dendrogram - built from such dataset is likely to have intersections. So, you have to - reorder you points before building dendrogram. - Permutation which reorders point is returned in P. Another representation - of merges, which is more convenient for dendorgram construction, is - returned in PM. - -* more information on format of Z, P and PM can be found below and in the - examples from ALGLIB Reference Manual. - -FORMAL DESCRIPTION OF FIELDS: - NPoints number of points - Z array[NPoints-1,2], contains indexes of clusters - linked in pairs to form clustering tree. I-th row - corresponds to I-th merge: - * Z[I,0] - index of the first cluster to merge - * Z[I,1] - index of the second cluster to merge - * Z[I,0]=0 - NFeatures number of variables, >=1 - TerminationType completion code: - * -5 if distance type is anything different from - Euclidean metric - * -3 for degenerate dataset: a) less than K distinct - points, b) K=0 for non-empty dataset. - * +1 for successful completion - K number of clusters - C array[K,NFeatures], rows of the array store centers - CIdx array[NPoints], which contains cluster indexes - - -- ALGLIB -- - Copyright 27.11.2012 by Bochkanov Sergey -*************************************************************************/ -class _kmeansreport_owner -{ -public: - _kmeansreport_owner(); - _kmeansreport_owner(const _kmeansreport_owner &rhs); - _kmeansreport_owner& operator=(const _kmeansreport_owner &rhs); - virtual ~_kmeansreport_owner(); - alglib_impl::kmeansreport* c_ptr(); - alglib_impl::kmeansreport* c_ptr() const; -protected: - alglib_impl::kmeansreport *p_struct; -}; -class kmeansreport : public _kmeansreport_owner -{ -public: - kmeansreport(); - kmeansreport(const kmeansreport &rhs); - kmeansreport& operator=(const kmeansreport &rhs); - virtual ~kmeansreport(); - ae_int_t &npoints; - ae_int_t &nfeatures; - ae_int_t &terminationtype; - ae_int_t &k; - real_2d_array c; - integer_1d_array cidx; - -}; - - - -/************************************************************************* - -*************************************************************************/ -class _decisionforest_owner -{ -public: - _decisionforest_owner(); - _decisionforest_owner(const _decisionforest_owner &rhs); - _decisionforest_owner& operator=(const _decisionforest_owner &rhs); - virtual ~_decisionforest_owner(); - alglib_impl::decisionforest* c_ptr(); - alglib_impl::decisionforest* c_ptr() const; -protected: - alglib_impl::decisionforest *p_struct; -}; -class decisionforest : public _decisionforest_owner -{ -public: - decisionforest(); - decisionforest(const decisionforest &rhs); - decisionforest& operator=(const decisionforest &rhs); - virtual ~decisionforest(); - -}; - - -/************************************************************************* - -*************************************************************************/ -class _dfreport_owner -{ -public: - _dfreport_owner(); - _dfreport_owner(const _dfreport_owner &rhs); - _dfreport_owner& operator=(const _dfreport_owner &rhs); - virtual ~_dfreport_owner(); - alglib_impl::dfreport* c_ptr(); - alglib_impl::dfreport* c_ptr() const; -protected: - alglib_impl::dfreport *p_struct; -}; -class dfreport : public _dfreport_owner -{ -public: - dfreport(); - dfreport(const dfreport &rhs); - dfreport& operator=(const dfreport &rhs); - virtual ~dfreport(); - double &relclserror; - double &avgce; - double &rmserror; - double &avgerror; - double &avgrelerror; - double &oobrelclserror; - double &oobavgce; - double &oobrmserror; - double &oobavgerror; - double &oobavgrelerror; - -}; - -/************************************************************************* - -*************************************************************************/ -class _linearmodel_owner -{ -public: - _linearmodel_owner(); - _linearmodel_owner(const _linearmodel_owner &rhs); - _linearmodel_owner& operator=(const _linearmodel_owner &rhs); - virtual ~_linearmodel_owner(); - alglib_impl::linearmodel* c_ptr(); - alglib_impl::linearmodel* c_ptr() const; -protected: - alglib_impl::linearmodel *p_struct; -}; -class linearmodel : public _linearmodel_owner -{ -public: - linearmodel(); - linearmodel(const linearmodel &rhs); - linearmodel& operator=(const linearmodel &rhs); - virtual ~linearmodel(); - -}; - - -/************************************************************************* -LRReport structure contains additional information about linear model: -* C - covariation matrix, array[0..NVars,0..NVars]. - C[i,j] = Cov(A[i],A[j]) -* RMSError - root mean square error on a training set -* AvgError - average error on a training set -* AvgRelError - average relative error on a training set (excluding - observations with zero function value). -* CVRMSError - leave-one-out cross-validation estimate of - generalization error. Calculated using fast algorithm - with O(NVars*NPoints) complexity. -* CVAvgError - cross-validation estimate of average error -* CVAvgRelError - cross-validation estimate of average relative error - -All other fields of the structure are intended for internal use and should -not be used outside ALGLIB. -*************************************************************************/ -class _lrreport_owner -{ -public: - _lrreport_owner(); - _lrreport_owner(const _lrreport_owner &rhs); - _lrreport_owner& operator=(const _lrreport_owner &rhs); - virtual ~_lrreport_owner(); - alglib_impl::lrreport* c_ptr(); - alglib_impl::lrreport* c_ptr() const; -protected: - alglib_impl::lrreport *p_struct; -}; -class lrreport : public _lrreport_owner -{ -public: - lrreport(); - lrreport(const lrreport &rhs); - lrreport& operator=(const lrreport &rhs); - virtual ~lrreport(); - real_2d_array c; - double &rmserror; - double &avgerror; - double &avgrelerror; - double &cvrmserror; - double &cvavgerror; - double &cvavgrelerror; - ae_int_t &ncvdefects; - integer_1d_array cvdefects; - -}; - - - - - -/************************************************************************* -Model's errors: - * RelCLSError - fraction of misclassified cases. - * AvgCE - acerage cross-entropy - * RMSError - root-mean-square error - * AvgError - average error - * AvgRelError - average relative error - -NOTE 1: RelCLSError/AvgCE are zero on regression problems. - -NOTE 2: on classification problems RMSError/AvgError/AvgRelError contain - errors in prediction of posterior probabilities -*************************************************************************/ -class _modelerrors_owner -{ -public: - _modelerrors_owner(); - _modelerrors_owner(const _modelerrors_owner &rhs); - _modelerrors_owner& operator=(const _modelerrors_owner &rhs); - virtual ~_modelerrors_owner(); - alglib_impl::modelerrors* c_ptr(); - alglib_impl::modelerrors* c_ptr() const; -protected: - alglib_impl::modelerrors *p_struct; -}; -class modelerrors : public _modelerrors_owner -{ -public: - modelerrors(); - modelerrors(const modelerrors &rhs); - modelerrors& operator=(const modelerrors &rhs); - virtual ~modelerrors(); - double &relclserror; - double &avgce; - double &rmserror; - double &avgerror; - double &avgrelerror; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _multilayerperceptron_owner -{ -public: - _multilayerperceptron_owner(); - _multilayerperceptron_owner(const _multilayerperceptron_owner &rhs); - _multilayerperceptron_owner& operator=(const _multilayerperceptron_owner &rhs); - virtual ~_multilayerperceptron_owner(); - alglib_impl::multilayerperceptron* c_ptr(); - alglib_impl::multilayerperceptron* c_ptr() const; -protected: - alglib_impl::multilayerperceptron *p_struct; -}; -class multilayerperceptron : public _multilayerperceptron_owner -{ -public: - multilayerperceptron(); - multilayerperceptron(const multilayerperceptron &rhs); - multilayerperceptron& operator=(const multilayerperceptron &rhs); - virtual ~multilayerperceptron(); - -}; - -/************************************************************************* - -*************************************************************************/ -class _logitmodel_owner -{ -public: - _logitmodel_owner(); - _logitmodel_owner(const _logitmodel_owner &rhs); - _logitmodel_owner& operator=(const _logitmodel_owner &rhs); - virtual ~_logitmodel_owner(); - alglib_impl::logitmodel* c_ptr(); - alglib_impl::logitmodel* c_ptr() const; -protected: - alglib_impl::logitmodel *p_struct; -}; -class logitmodel : public _logitmodel_owner -{ -public: - logitmodel(); - logitmodel(const logitmodel &rhs); - logitmodel& operator=(const logitmodel &rhs); - virtual ~logitmodel(); - -}; - - -/************************************************************************* -MNLReport structure contains information about training process: -* NGrad - number of gradient calculations -* NHess - number of Hessian calculations -*************************************************************************/ -class _mnlreport_owner -{ -public: - _mnlreport_owner(); - _mnlreport_owner(const _mnlreport_owner &rhs); - _mnlreport_owner& operator=(const _mnlreport_owner &rhs); - virtual ~_mnlreport_owner(); - alglib_impl::mnlreport* c_ptr(); - alglib_impl::mnlreport* c_ptr() const; -protected: - alglib_impl::mnlreport *p_struct; -}; -class mnlreport : public _mnlreport_owner -{ -public: - mnlreport(); - mnlreport(const mnlreport &rhs); - mnlreport& operator=(const mnlreport &rhs); - virtual ~mnlreport(); - ae_int_t &ngrad; - ae_int_t &nhess; - -}; - -/************************************************************************* -This structure is a MCPD (Markov Chains for Population Data) solver. - -You should use ALGLIB functions in order to work with this object. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -class _mcpdstate_owner -{ -public: - _mcpdstate_owner(); - _mcpdstate_owner(const _mcpdstate_owner &rhs); - _mcpdstate_owner& operator=(const _mcpdstate_owner &rhs); - virtual ~_mcpdstate_owner(); - alglib_impl::mcpdstate* c_ptr(); - alglib_impl::mcpdstate* c_ptr() const; -protected: - alglib_impl::mcpdstate *p_struct; -}; -class mcpdstate : public _mcpdstate_owner -{ -public: - mcpdstate(); - mcpdstate(const mcpdstate &rhs); - mcpdstate& operator=(const mcpdstate &rhs); - virtual ~mcpdstate(); - -}; - - -/************************************************************************* -This structure is a MCPD training report: - InnerIterationsCount - number of inner iterations of the - underlying optimization algorithm - OuterIterationsCount - number of outer iterations of the - underlying optimization algorithm - NFEV - number of merit function evaluations - TerminationType - termination type - (same as for MinBLEIC optimizer, positive - values denote success, negative ones - - failure) - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -class _mcpdreport_owner -{ -public: - _mcpdreport_owner(); - _mcpdreport_owner(const _mcpdreport_owner &rhs); - _mcpdreport_owner& operator=(const _mcpdreport_owner &rhs); - virtual ~_mcpdreport_owner(); - alglib_impl::mcpdreport* c_ptr(); - alglib_impl::mcpdreport* c_ptr() const; -protected: - alglib_impl::mcpdreport *p_struct; -}; -class mcpdreport : public _mcpdreport_owner -{ -public: - mcpdreport(); - mcpdreport(const mcpdreport &rhs); - mcpdreport& operator=(const mcpdreport &rhs); - virtual ~mcpdreport(); - ae_int_t &inneriterationscount; - ae_int_t &outeriterationscount; - ae_int_t &nfev; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -Neural networks ensemble -*************************************************************************/ -class _mlpensemble_owner -{ -public: - _mlpensemble_owner(); - _mlpensemble_owner(const _mlpensemble_owner &rhs); - _mlpensemble_owner& operator=(const _mlpensemble_owner &rhs); - virtual ~_mlpensemble_owner(); - alglib_impl::mlpensemble* c_ptr(); - alglib_impl::mlpensemble* c_ptr() const; -protected: - alglib_impl::mlpensemble *p_struct; -}; -class mlpensemble : public _mlpensemble_owner -{ -public: - mlpensemble(); - mlpensemble(const mlpensemble &rhs); - mlpensemble& operator=(const mlpensemble &rhs); - virtual ~mlpensemble(); - -}; - -/************************************************************************* -Training report: - * RelCLSError - fraction of misclassified cases. - * AvgCE - acerage cross-entropy - * RMSError - root-mean-square error - * AvgError - average error - * AvgRelError - average relative error - * NGrad - number of gradient calculations - * NHess - number of Hessian calculations - * NCholesky - number of Cholesky decompositions - -NOTE 1: RelCLSError/AvgCE are zero on regression problems. - -NOTE 2: on classification problems RMSError/AvgError/AvgRelError contain - errors in prediction of posterior probabilities -*************************************************************************/ -class _mlpreport_owner -{ -public: - _mlpreport_owner(); - _mlpreport_owner(const _mlpreport_owner &rhs); - _mlpreport_owner& operator=(const _mlpreport_owner &rhs); - virtual ~_mlpreport_owner(); - alglib_impl::mlpreport* c_ptr(); - alglib_impl::mlpreport* c_ptr() const; -protected: - alglib_impl::mlpreport *p_struct; -}; -class mlpreport : public _mlpreport_owner -{ -public: - mlpreport(); - mlpreport(const mlpreport &rhs); - mlpreport& operator=(const mlpreport &rhs); - virtual ~mlpreport(); - double &relclserror; - double &avgce; - double &rmserror; - double &avgerror; - double &avgrelerror; - ae_int_t &ngrad; - ae_int_t &nhess; - ae_int_t &ncholesky; - -}; - - -/************************************************************************* -Cross-validation estimates of generalization error -*************************************************************************/ -class _mlpcvreport_owner -{ -public: - _mlpcvreport_owner(); - _mlpcvreport_owner(const _mlpcvreport_owner &rhs); - _mlpcvreport_owner& operator=(const _mlpcvreport_owner &rhs); - virtual ~_mlpcvreport_owner(); - alglib_impl::mlpcvreport* c_ptr(); - alglib_impl::mlpcvreport* c_ptr() const; -protected: - alglib_impl::mlpcvreport *p_struct; -}; -class mlpcvreport : public _mlpcvreport_owner -{ -public: - mlpcvreport(); - mlpcvreport(const mlpcvreport &rhs); - mlpcvreport& operator=(const mlpcvreport &rhs); - virtual ~mlpcvreport(); - double &relclserror; - double &avgce; - double &rmserror; - double &avgerror; - double &avgrelerror; - -}; - - -/************************************************************************* -Trainer object for neural network. - -You should not try to access fields of this object directly - use ALGLIB -functions to work with this object. -*************************************************************************/ -class _mlptrainer_owner -{ -public: - _mlptrainer_owner(); - _mlptrainer_owner(const _mlptrainer_owner &rhs); - _mlptrainer_owner& operator=(const _mlptrainer_owner &rhs); - virtual ~_mlptrainer_owner(); - alglib_impl::mlptrainer* c_ptr(); - alglib_impl::mlptrainer* c_ptr() const; -protected: - alglib_impl::mlptrainer *p_struct; -}; -class mlptrainer : public _mlptrainer_owner -{ -public: - mlptrainer(); - mlptrainer(const mlptrainer &rhs); - mlptrainer& operator=(const mlptrainer &rhs); - virtual ~mlptrainer(); - -}; - -/************************************************************************* -Optimal binary classification - -Algorithms finds optimal (=with minimal cross-entropy) binary partition. -Internal subroutine. - -INPUT PARAMETERS: - A - array[0..N-1], variable - C - array[0..N-1], class numbers (0 or 1). - N - array size - -OUTPUT PARAMETERS: - Info - completetion code: - * -3, all values of A[] are same (partition is impossible) - * -2, one of C[] is incorrect (<0, >1) - * -1, incorrect pararemets were passed (N<=0). - * 1, OK - Threshold- partiton boundary. Left part contains values which are - strictly less than Threshold. Right part contains values - which are greater than or equal to Threshold. - PAL, PBL- probabilities P(0|v=Threshold) and P(1|v>=Threshold) - CVE - cross-validation estimate of cross-entropy - - -- ALGLIB -- - Copyright 22.05.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplit2(const real_1d_array &a, const integer_1d_array &c, const ae_int_t n, ae_int_t &info, double &threshold, double &pal, double &pbl, double &par, double &pbr, double &cve); - - -/************************************************************************* -Optimal partition, internal subroutine. Fast version. - -Accepts: - A array[0..N-1] array of attributes array[0..N-1] - C array[0..N-1] array of class labels - TiesBuf array[0..N] temporaries (ties) - CntBuf array[0..2*NC-1] temporaries (counts) - Alpha centering factor (0<=alpha<=1, recommended value - 0.05) - BufR array[0..N-1] temporaries - BufI array[0..N-1] temporaries - -Output: - Info error code (">0"=OK, "<0"=bad) - RMS training set RMS error - CVRMS leave-one-out RMS error - -Note: - content of all arrays is changed by subroutine; - it doesn't allocate temporaries. - - -- ALGLIB -- - Copyright 11.12.2008 by Bochkanov Sergey -*************************************************************************/ -void dsoptimalsplit2fast(real_1d_array &a, integer_1d_array &c, integer_1d_array &tiesbuf, integer_1d_array &cntbuf, real_1d_array &bufr, integer_1d_array &bufi, const ae_int_t n, const ae_int_t nc, const double alpha, ae_int_t &info, double &threshold, double &rms, double &cvrms); - -/************************************************************************* -This function initializes clusterizer object. Newly initialized object is -empty, i.e. it does not contain dataset. You should use it as follows: -1. creation -2. dataset is added with ClusterizerSetPoints() -3. additional parameters are set -3. clusterization is performed with one of the clustering functions - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizercreate(clusterizerstate &s); - - -/************************************************************************* -This function adds dataset to the clusterizer structure. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -NOTE 1: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - -NOTE 2: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric - * k-means++ clustering algorithm may be used only with Euclidean - distance function - Thus, list of specific clustering algorithms you may use depends - on distance function you specify when you set your dataset. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetpoints(const clusterizerstate &s, const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype); -void clusterizersetpoints(const clusterizerstate &s, const real_2d_array &xy, const ae_int_t disttype); - - -/************************************************************************* -This function adds dataset given by distance matrix to the clusterizer -structure. It is important that dataset is not given explicitly - only -distance matrix is given. - -This function overrides all previous calls of ClusterizerSetPoints() or -ClusterizerSetDistances(). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - D - array[NPoints,NPoints], distance matrix given by its upper - or lower triangle (main diagonal is ignored because its - entries are expected to be zero). - NPoints - number of points - IsUpper - whether upper or lower triangle of D is given. - -NOTE 1: different clustering algorithms have different limitations: - * agglomerative hierarchical clustering algorithms may be used with - any kind of distance metric, including one which is given by - distance matrix - * k-means++ clustering algorithm may be used only with Euclidean - distance function and explicitly given points - it can not be - used with dataset given by distance matrix - Thus, if you call this function, you will be unable to use k-means - clustering algorithm to process your problem. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetdistances(const clusterizerstate &s, const real_2d_array &d, const ae_int_t npoints, const bool isupper); -void clusterizersetdistances(const clusterizerstate &s, const real_2d_array &d, const bool isupper); - - -/************************************************************************* -This function sets agglomerative hierarchical clustering algorithm - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - Algo - algorithm type: - * 0 complete linkage (default algorithm) - * 1 single linkage - * 2 unweighted average linkage - * 3 weighted average linkage - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetahcalgo(const clusterizerstate &s, const ae_int_t algo); - - -/************************************************************************* -This function sets k-means++ properties : number of restarts and maximum -number of iterations per one run. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - Restarts- restarts count, >=1. - k-means++ algorithm performs several restarts and chooses - best set of centers (one with minimum squared distance). - MaxIts - maximum number of k-means iterations performed during one - run. >=0, zero value means that algorithm performs unlimited - number of iterations. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizersetkmeanslimits(const clusterizerstate &s, const ae_int_t restarts, const ae_int_t maxits); - - -/************************************************************************* -This function performs agglomerative hierarchical clustering - -FOR USERS OF SMP EDITION: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Multicore version is pretty efficient on large - ! problems which need more than 1.000.000 operations to be solved, - ! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU - ! cycles), but gives no speed-up for small problems (less than 100.000 - ! operations). - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - -OUTPUT PARAMETERS: - Rep - clustering results; see description of AHCReport - structure for more information. - -NOTE 1: hierarchical clustering algorithms require large amounts of memory. - In particular, this implementation needs sizeof(double)*NPoints^2 - bytes, which are used to store distance matrix. In case we work - with user-supplied matrix, this amount is multiplied by 2 (we have - to store original matrix and to work with its copy). - - For example, problem with 10000 points would require 800M of RAM, - even when working in a 1-dimensional space. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizerrunahc(const clusterizerstate &s, ahcreport &rep); -void smp_clusterizerrunahc(const clusterizerstate &s, ahcreport &rep); - - -/************************************************************************* -This function performs clustering by k-means++ algorithm. - -You may change algorithm properties like number of restarts or iterations -limit by calling ClusterizerSetKMeansLimits() functions. - -INPUT PARAMETERS: - S - clusterizer state, initialized by ClusterizerCreate() - K - number of clusters, K>=0. - K can be zero only when algorithm is called for empty - dataset, in this case completion code is set to - success (+1). - If K=0 and dataset size is non-zero, we can not - meaningfully assign points to some center (there are no - centers because K=0) and return -3 as completion code - (failure). - -OUTPUT PARAMETERS: - Rep - clustering results; see description of KMeansReport - structure for more information. - -NOTE 1: k-means clustering can be performed only for datasets with - Euclidean distance function. Algorithm will return negative - completion code in Rep.TerminationType in case dataset was added - to clusterizer with DistType other than Euclidean (or dataset was - specified by distance matrix instead of explicitly given points). - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizerrunkmeans(const clusterizerstate &s, const ae_int_t k, kmeansreport &rep); - - -/************************************************************************* -This function returns distance matrix for dataset - -FOR USERS OF SMP EDITION: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Multicore version is pretty efficient on large - ! problems which need more than 1.000.000 operations to be solved, - ! gives moderate speed-up in mid-range (from 100.000 to 1.000.000 CPU - ! cycles), but gives no speed-up for small problems (less than 100.000 - ! operations). - -INPUT PARAMETERS: - XY - array[NPoints,NFeatures], dataset - NPoints - number of points, >=0 - NFeatures- number of features, >=1 - DistType- distance function: - * 0 Chebyshev distance (L-inf norm) - * 1 city block distance (L1 norm) - * 2 Euclidean distance (L2 norm) - * 10 Pearson correlation: - dist(a,b) = 1-corr(a,b) - * 11 Absolute Pearson correlation: - dist(a,b) = 1-|corr(a,b)| - * 12 Uncentered Pearson correlation (cosine of the angle): - dist(a,b) = a'*b/(|a|*|b|) - * 13 Absolute uncentered Pearson correlation - dist(a,b) = |a'*b|/(|a|*|b|) - * 20 Spearman rank correlation: - dist(a,b) = 1-rankcorr(a,b) - * 21 Absolute Spearman rank correlation - dist(a,b) = 1-|rankcorr(a,b)| - -OUTPUT PARAMETERS: - D - array[NPoints,NPoints], distance matrix - (full matrix is returned, with lower and upper triangles) - -NOTES: different distance functions have different performance penalty: - * Euclidean or Pearson correlation distances are the fastest ones - * Spearman correlation distance function is a bit slower - * city block and Chebyshev distances are order of magnitude slower - - The reason behing difference in performance is that correlation-based - distance functions are computed using optimized linear algebra kernels, - while Chebyshev and city block distance functions are computed using - simple nested loops with two branches at each iteration. - - -- ALGLIB -- - Copyright 10.07.2012 by Bochkanov Sergey -*************************************************************************/ -void clusterizergetdistances(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype, real_2d_array &d); -void smp_clusterizergetdistances(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures, const ae_int_t disttype, real_2d_array &d); - - -/************************************************************************* -This function takes as input clusterization report Rep, desired clusters -count K, and builds top K clusters from hierarchical clusterization tree. -It returns assignment of points to clusters (array of cluster indexes). - -INPUT PARAMETERS: - Rep - report from ClusterizerRunAHC() performed on XY - K - desired number of clusters, 1<=K<=NPoints. - K can be zero only when NPoints=0. - -OUTPUT PARAMETERS: - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I]=0 - -OUTPUT PARAMETERS: - K - number of clusters, 1<=K<=NPoints - CIdx - array[NPoints], I-th element contains cluster index (from - 0 to K-1) for I-th point of the dataset. - CZ - array[K]. This array allows to convert cluster indexes - returned by this function to indexes used by Rep.Z. J-th - cluster returned by this function corresponds to CZ[J]-th - cluster stored in Rep.Z/PZ/PM. - It is guaranteed that CZ[I]=1 - NVars - number of independent variables, NVars>=1 - NClasses - task type: - * NClasses=1 - regression task with one - dependent variable - * NClasses>1 - classification task with - NClasses classes. - NTrees - number of trees in a forest, NTrees>=1. - recommended values: 50-100. - R - percent of a training set used to build - individual trees. 01). - * 1, if task has been solved - DF - model built - Rep - training report, contains error on a training set - and out-of-bag estimates of generalization error. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfbuildrandomdecisionforest(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const ae_int_t ntrees, const double r, ae_int_t &info, decisionforest &df, dfreport &rep); - - -/************************************************************************* -This subroutine builds random decision forest. -This function gives ability to tune number of variables used when choosing -best split. - -INPUT PARAMETERS: - XY - training set - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - task type: - * NClasses=1 - regression task with one - dependent variable - * NClasses>1 - classification task with - NClasses classes. - NTrees - number of trees in a forest, NTrees>=1. - recommended values: 50-100. - NRndVars - number of variables used when choosing best split - R - percent of a training set used to build - individual trees. 01). - * 1, if task has been solved - DF - model built - Rep - training report, contains error on a training set - and out-of-bag estimates of generalization error. - - -- ALGLIB -- - Copyright 19.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfbuildrandomdecisionforestx1(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, const ae_int_t ntrees, const ae_int_t nrndvars, const double r, ae_int_t &info, decisionforest &df, dfreport &rep); - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - DF - decision forest model - X - input vector, array[0..NVars-1]. - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - -See also DFProcessI. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -void dfprocess(const decisionforest &df, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -'interactive' variant of DFProcess for languages like Python which support -constructs like "Y = DFProcessI(DF,X)" and interactive mode of interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 28.02.2010 by Bochkanov Sergey -*************************************************************************/ -void dfprocessi(const decisionforest &df, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - Zero if model solves regression task. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfrelclserror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*LN(2)). - Zero if model solves regression task. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgce(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - Its meaning for regression task is obvious. As for - classification task, RMS error means error when estimating posterior - probabilities. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfrmserror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for - classification task, it means average error when estimating posterior - probabilities. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgerror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - DF - decision forest model - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for - classification task, it means average relative error when estimating - posterior probability of belonging to the correct class. - - -- ALGLIB -- - Copyright 16.02.2009 by Bochkanov Sergey -*************************************************************************/ -double dfavgrelerror(const decisionforest &df, const real_2d_array &xy, const ae_int_t npoints); - -/************************************************************************* -Linear regression - -Subroutine builds model: - - Y = A(0)*X[0] + ... + A(N-1)*X[N-1] + A(N) - -and model found in ALGLIB format, covariation matrix, training set errors -(rms, average, average relative) and leave-one-out cross-validation -estimate of the generalization error. CV estimate calculated using fast -algorithm with O(NPoints*NVars) complexity. - -When covariation matrix is calculated standard deviations of function -values are assumed to be equal to RMS error on the training set. - -INPUT PARAMETERS: - XY - training set, array [0..NPoints-1,0..NVars]: - * NVars columns - independent variables - * last column - dependent variable - NPoints - training set size, NPoints>NVars+1 - NVars - number of independent variables - -OUTPUT PARAMETERS: - Info - return code: - * -255, in case of unknown internal error - * -4, if internal SVD subroutine haven't converged - * -1, if incorrect parameters was passed (NPoints0. - NPoints - training set size, NPoints>NVars+1 - NVars - number of independent variables - -OUTPUT PARAMETERS: - Info - return code: - * -255, in case of unknown internal error - * -4, if internal SVD subroutine haven't converged - * -1, if incorrect parameters was passed (NPoints=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filtersma(real_1d_array &x, const ae_int_t n, const ae_int_t k); -void filtersma(real_1d_array &x, const ae_int_t k); - - -/************************************************************************* -Filters: exponential moving averages. - -This filter replaces array by results of EMA(alpha) filter. EMA(alpha) is -defined as filter which replaces X[] by S[]: - S[0] = X[0] - S[t] = alpha*X[t] + (1-alpha)*S[t-1] - -INPUT PARAMETERS: - X - array[N], array to process. It can be larger than N, - in this case only first N points are processed. - N - points count, N>=0 - alpha - 0=0 - K - K>=1 (K can be larger than N , such cases will be - correctly handled). Window width. K=1 corresponds to - identity transformation (nothing changes). - -OUTPUT PARAMETERS: - X - array, whose first N elements were processed with SMA(K) - -NOTE 1: this function uses efficient in-place algorithm which does not - allocate temporary arrays. - -NOTE 2: this algorithm makes only one pass through array and uses running - sum to speed-up calculation of the averages. Additional measures - are taken to ensure that running sum on a long sequence of zero - elements will be correctly reset to zero even in the presence of - round-off error. - -NOTE 3: this is unsymmetric version of the algorithm, which does NOT - averages points after the current one. Only X[i], X[i-1], ... are - used when calculating new value of X[i]. We should also note that - this algorithm uses BOTH previous points and current one, i.e. - new value of X[i] depends on BOTH previous point and X[i] itself. - - -- ALGLIB -- - Copyright 25.10.2011 by Bochkanov Sergey -*************************************************************************/ -void filterlrma(real_1d_array &x, const ae_int_t n, const ae_int_t k); -void filterlrma(real_1d_array &x, const ae_int_t k); - -/************************************************************************* -Multiclass Fisher LDA - -Subroutine finds coefficients of linear combination which optimally separates -training set on classes. - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars]. - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - - -OUTPUT PARAMETERS: - Info - return code: - * -4, if internal EVD subroutine hasn't converged - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed (NPoints<0, - NVars<1, NClasses<2) - * 1, if task has been solved - * 2, if there was a multicollinearity in training set, - but task has been solved. - W - linear combination coefficients, array[0..NVars-1] - - -- ALGLIB -- - Copyright 31.05.2008 by Bochkanov Sergey -*************************************************************************/ -void fisherlda(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, ae_int_t &info, real_1d_array &w); - - -/************************************************************************* -N-dimensional multiclass Fisher LDA - -Subroutine finds coefficients of linear combinations which optimally separates -training set on classes. It returns N-dimensional basis whose vector are sorted -by quality of training set separation (in descending order). - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars]. - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - - -OUTPUT PARAMETERS: - Info - return code: - * -4, if internal EVD subroutine hasn't converged - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed (NPoints<0, - NVars<1, NClasses<2) - * 1, if task has been solved - * 2, if there was a multicollinearity in training set, - but task has been solved. - W - basis, array[0..NVars-1,0..NVars-1] - columns of matrix stores basis vectors, sorted by - quality of training set separation (in descending order) - - -- ALGLIB -- - Copyright 31.05.2008 by Bochkanov Sergey -*************************************************************************/ -void fisherldan(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nvars, const ae_int_t nclasses, ae_int_t &info, real_2d_array &w); - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void mlpserialize(multilayerperceptron &obj, std::string &s_out); - - -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void mlpunserialize(std::string &s_in, multilayerperceptron &obj); - - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers, with linear output layer. Network weights are filled with small -random values. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate0(const ae_int_t nin, const ae_int_t nout, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreate0, but with one hidden layer (NHid neurons) with -non-linear activation function. Output layer is linear. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreate0, but with two hidden layers (NHid1 and NHid2 neurons) -with non-linear activation function. Output layer is linear. - $ALL - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreate2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, multilayerperceptron &network); - - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers with non-linear output layer. Network weights are filled with small -random values. - -Activation function of the output layer takes values: - - (B, +INF), if D>=0 - -or - - (-INF, B), if D<0. - - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb0(const ae_int_t nin, const ae_int_t nout, const double b, const double d, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreateB0 but with non-linear hidden layer. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double b, const double d, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreateB0 but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreateb2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double b, const double d, multilayerperceptron &network); - - -/************************************************************************* -Creates neural network with NIn inputs, NOut outputs, without hidden -layers with non-linear output layer. Network weights are filled with small -random values. Activation function of the output layer takes values [A,B]. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater0(const ae_int_t nin, const ae_int_t nout, const double a, const double b, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreateR0, but with non-linear hidden layer. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double a, const double b, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreateR0, but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 30.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlpcreater2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double a, const double b, multilayerperceptron &network); - - -/************************************************************************* -Creates classifier network with NIn inputs and NOut possible classes. -Network contains no hidden layers and linear output layer with SOFTMAX- -normalization (so outputs sums up to 1.0 and converge to posterior -probabilities). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec0(const ae_int_t nin, const ae_int_t nout, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreateC0, but with one non-linear hidden layer. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, multilayerperceptron &network); - - -/************************************************************************* -Same as MLPCreateC0, but with two non-linear hidden layers. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatec2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, multilayerperceptron &network); - - -/************************************************************************* -Randomization of neural network weights - - -- ALGLIB -- - Copyright 06.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlprandomize(const multilayerperceptron &network); - - -/************************************************************************* -Randomization of neural network weights and standartisator - - -- ALGLIB -- - Copyright 10.03.2008 by Bochkanov Sergey -*************************************************************************/ -void mlprandomizefull(const multilayerperceptron &network); - - -/************************************************************************* -Returns information about initialized network: number of inputs, outputs, -weights. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpproperties(const multilayerperceptron &network, ae_int_t &nin, ae_int_t &nout, ae_int_t &wcount); - - -/************************************************************************* -Returns number of inputs. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetinputscount(const multilayerperceptron &network); - - -/************************************************************************* -Returns number of outputs. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetoutputscount(const multilayerperceptron &network); - - -/************************************************************************* -Returns number of weights. - - -- ALGLIB -- - Copyright 19.10.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetweightscount(const multilayerperceptron &network); - - -/************************************************************************* -Tells whether network is SOFTMAX-normalized (i.e. classifier) or not. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -bool mlpissoftmax(const multilayerperceptron &network); - - -/************************************************************************* -This function returns total number of layers (including input, hidden and -output layers). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetlayerscount(const multilayerperceptron &network); - - -/************************************************************************* -This function returns size of K-th layer. - -K=0 corresponds to input layer, K=CNT-1 corresponds to output layer. - -Size of the output layer is always equal to the number of outputs, although -when we have softmax-normalized network, last neuron doesn't have any -connections - it is just zero. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpgetlayersize(const multilayerperceptron &network, const ae_int_t k); - - -/************************************************************************* -This function returns offset/scaling coefficients for I-th input of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - -OUTPUT PARAMETERS: - Mean - mean term - Sigma - sigma term, guaranteed to be nonzero. - -I-th input is passed through linear transformation - IN[i] = (IN[i]-Mean)/Sigma -before feeding to the network - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetinputscaling(const multilayerperceptron &network, const ae_int_t i, double &mean, double &sigma); - - -/************************************************************************* -This function returns offset/scaling coefficients for I-th output of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - -OUTPUT PARAMETERS: - Mean - mean term - Sigma - sigma term, guaranteed to be nonzero. - -I-th output is passed through linear transformation - OUT[i] = OUT[i]*Sigma+Mean -before returning it to user. In case we have SOFTMAX-normalized network, -we return (Mean,Sigma)=(0.0,1.0). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetoutputscaling(const multilayerperceptron &network, const ae_int_t i, double &mean, double &sigma); - - -/************************************************************************* -This function returns information about Ith neuron of Kth layer - -INPUT PARAMETERS: - Network - network - K - layer index - I - neuron index (within layer) - -OUTPUT PARAMETERS: - FKind - activation function type (used by MLPActivationFunction()) - this value is zero for input or linear neurons - Threshold - also called offset, bias - zero for input neurons - -NOTE: this function throws exception if layer or neuron with given index -do not exists. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpgetneuroninfo(const multilayerperceptron &network, const ae_int_t k, const ae_int_t i, ae_int_t &fkind, double &threshold); - - -/************************************************************************* -This function returns information about connection from I0-th neuron of -K0-th layer to I1-th neuron of K1-th layer. - -INPUT PARAMETERS: - Network - network - K0 - layer index - I0 - neuron index (within layer) - K1 - layer index - I1 - neuron index (within layer) - -RESULT: - connection weight (zero for non-existent connections) - -This function: -1. throws exception if layer or neuron with given index do not exists. -2. returns zero if neurons exist, but there is no connection between them - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -double mlpgetweight(const multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1); - - -/************************************************************************* -This function sets offset/scaling coefficients for I-th input of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - Mean - mean term - Sigma - sigma term (if zero, will be replaced by 1.0) - -NTE: I-th input is passed through linear transformation - IN[i] = (IN[i]-Mean)/Sigma -before feeding to the network. This function sets Mean and Sigma. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetinputscaling(const multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma); - - -/************************************************************************* -This function sets offset/scaling coefficients for I-th output of the -network. - -INPUT PARAMETERS: - Network - network - I - input index - Mean - mean term - Sigma - sigma term (if zero, will be replaced by 1.0) - -OUTPUT PARAMETERS: - -NOTE: I-th output is passed through linear transformation - OUT[i] = OUT[i]*Sigma+Mean -before returning it to user. This function sets Sigma/Mean. In case we -have SOFTMAX-normalized network, you can not set (Sigma,Mean) to anything -other than(0.0,1.0) - this function will throw exception. - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetoutputscaling(const multilayerperceptron &network, const ae_int_t i, const double mean, const double sigma); - - -/************************************************************************* -This function modifies information about Ith neuron of Kth layer - -INPUT PARAMETERS: - Network - network - K - layer index - I - neuron index (within layer) - FKind - activation function type (used by MLPActivationFunction()) - this value must be zero for input neurons - (you can not set activation function for input neurons) - Threshold - also called offset, bias - this value must be zero for input neurons - (you can not set threshold for input neurons) - -NOTES: -1. this function throws exception if layer or neuron with given index do - not exists. -2. this function also throws exception when you try to set non-linear - activation function for input neurons (any kind of network) or for output - neurons of classifier network. -3. this function throws exception when you try to set non-zero threshold for - input neurons (any kind of network). - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetneuroninfo(const multilayerperceptron &network, const ae_int_t k, const ae_int_t i, const ae_int_t fkind, const double threshold); - - -/************************************************************************* -This function modifies information about connection from I0-th neuron of -K0-th layer to I1-th neuron of K1-th layer. - -INPUT PARAMETERS: - Network - network - K0 - layer index - I0 - neuron index (within layer) - K1 - layer index - I1 - neuron index (within layer) - W - connection weight (must be zero for non-existent - connections) - -This function: -1. throws exception if layer or neuron with given index do not exists. -2. throws exception if you try to set non-zero weight for non-existent - connection - - -- ALGLIB -- - Copyright 25.03.2011 by Bochkanov Sergey -*************************************************************************/ -void mlpsetweight(const multilayerperceptron &network, const ae_int_t k0, const ae_int_t i0, const ae_int_t k1, const ae_int_t i1, const double w); - - -/************************************************************************* -Neural network activation function - -INPUT PARAMETERS: - NET - neuron input - K - function index (zero for linear function) - -OUTPUT PARAMETERS: - F - function - DF - its derivative - D2F - its second derivative - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpactivationfunction(const double net, const ae_int_t k, double &f, double &df, double &d2f); - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - Network - neural network - X - input vector, array[0..NIn-1]. - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - -See also MLPProcessI - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpprocess(const multilayerperceptron &network, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -'interactive' variant of MLPProcess for languages like Python which -support constructs like "Y = MLPProcess(NN,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 21.09.2010 by Bochkanov Sergey -*************************************************************************/ -void mlpprocessi(const multilayerperceptron &network, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -Error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x, depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlperror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -double smp_mlperror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Error of the neural network on dataset given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x, depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0 - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); -double smp_mlperrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -Natural error function for neural network, internal subroutine. - -NOTE: this function is single-threaded. Unlike other error function, it -receives no speed-up from being executed in SMP mode. - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlperrorn(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize); - - -/************************************************************************* -Classification error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: - classification error (number of misclassified cases) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -ae_int_t mlpclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -ae_int_t smp_mlpclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Relative classification error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Percent of incorrectly classified cases. Works both for classifier -networks and general purpose networks used as classifiers. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 25.12.2008 by Bochkanov Sergey -*************************************************************************/ -double mlprelclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -double smp_mlprelclserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Relative classification error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. Sparse matrix must use CRS format - for storage. - NPoints - points count, >=0. - -RESULT: -Percent of incorrectly classified cases. Works both for classifier -networks and general purpose networks used as classifiers. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlprelclserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); -double smp_mlprelclserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -CrossEntropy/(NPoints*LN(2)). -Zero if network solves regression task. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 08.01.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpavgce(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -double smp_mlpavgce(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set given by -sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -CrossEntropy/(NPoints*LN(2)). -Zero if network solves regression task. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 9.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgcesparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); -double smp_mlpavgcesparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -RMS error on the test set given. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Root mean square error. Its meaning for regression task is obvious. As for -classification task, RMS error means error when estimating posterior -probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -double mlprmserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -double smp_mlprmserror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -RMS error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Root mean square error. Its meaning for regression task is obvious. As for -classification task, RMS error means error when estimating posterior -probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlprmserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); -double smp_mlprmserrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -Average absolute error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average error when estimating posterior probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 11.03.2008 by Bochkanov Sergey -*************************************************************************/ -double mlpavgerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -double smp_mlpavgerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average absolute error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average error when estimating posterior probabilities. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); -double smp_mlpavgerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -Average relative error on the test set. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - NPoints - points count. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average relative error when estimating posterior probability of -belonging to the correct class. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 11.03.2008 by Bochkanov Sergey -*************************************************************************/ -double mlpavgrelerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); -double smp_mlpavgrelerror(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average relative error on the test set given by sparse matrix. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - NPoints - points count, >=0. - -RESULT: -Its meaning for regression task is obvious. As for classification task, it -means average relative error when estimating posterior probability of -belonging to the correct class. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 09.08.2012 by Bochkanov Sergey -*************************************************************************/ -double mlpavgrelerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); -double smp_mlpavgrelerrorsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -Gradient calculation - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - X - input vector, length of array must be at least NIn - DesiredY- desired outputs, length of array must be at least NOut - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgrad(const multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad); - - -/************************************************************************* -Gradient calculation (natural error function is used) - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - X - input vector, length of array must be at least NIn - DesiredY- desired outputs, length of array must be at least NOut - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, sum-of-squares for regression networks, - cross-entropy for classification networks. - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradn(const multilayerperceptron &network, const real_1d_array &x, const real_1d_array &desiredy, double &e, real_1d_array &grad); - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in dense format; one sample = one row: - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad); -void smp_mlpgradbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad); - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs given by sparse -matrices - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in sparse format; one sample = one row: - * MATRIX MUST BE STORED IN CRS FORMAT - * first NIn columns contain inputs. - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad); -void smp_mlpgradbatchsparse(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t ssize, double &e, real_1d_array &grad); - - -/************************************************************************* -Batch gradient calculation for a subset of dataset - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in dense format; one sample = one row: - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array: - * positive value means that subset given by Idx[] is processed - * zero value results in zero gradient - * negative value means that full dataset is processed - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, - array[WCount] - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad); -void smp_mlpgradbatchsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad); - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs for a subset of -dataset given by set of indexes. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset in sparse format; one sample = one row: - * MATRIX MUST BE STORED IN CRS FORMAT - * first NIn columns contain inputs, - * for regression problem, next NOut columns store - desired outputs. - * for classification problem, next column (just one!) - stores class number. - SetSize - real size of XY, SetSize>=0; - Idx - subset of SubsetSize elements, array[SubsetSize]: - * Idx[I] stores row index in the original dataset which is - given by XY. Gradient is calculated with respect to rows - whose indexes are stored in Idx[]. - * Idx[] must store correct indexes; this function throws - an exception in case incorrect index (less than 0 or - larger than rows(XY)) is given - * Idx[] may store indexes in any order and even with - repetitions. - SubsetSize- number of elements in Idx[] array: - * positive value means that subset given by Idx[] is processed - * zero value results in zero gradient - * negative value means that full dataset is processed - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, SUM(sqr(y[i]-desiredy[i])/2,i) - Grad - gradient of E with respect to weights of network, - array[WCount] - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatchSparse - function. - - -- ALGLIB -- - Copyright 26.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpgradbatchsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad); -void smp_mlpgradbatchsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &idx, const ae_int_t subsetsize, double &e, real_1d_array &grad); - - -/************************************************************************* -Batch gradient calculation for a set of inputs/outputs -(natural error function is used) - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - set of inputs/outputs; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SSize - number of elements in XY - Grad - possibly preallocated array. If size of array is smaller - than WCount, it will be reallocated. It is recommended to - reuse previously allocated array to reduce allocation - overhead. - -OUTPUT PARAMETERS: - E - error function, sum-of-squares for regression networks, - cross-entropy for classification networks. - Grad - gradient of E with respect to weights of network, array[WCount] - - -- ALGLIB -- - Copyright 04.11.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpgradnbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad); - - -/************************************************************************* -Batch Hessian calculation (natural error function) using R-algorithm. -Internal subroutine. - - -- ALGLIB -- - Copyright 26.01.2008 by Bochkanov Sergey. - - Hessian calculation based on R-algorithm described in - "Fast Exact Multiplication by the Hessian", - B. A. Pearlmutter, - Neural Computation, 1994. -*************************************************************************/ -void mlphessiannbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h); - - -/************************************************************************* -Batch Hessian calculation using R-algorithm. -Internal subroutine. - - -- ALGLIB -- - Copyright 26.01.2008 by Bochkanov Sergey. - - Hessian calculation based on R-algorithm described in - "Fast Exact Multiplication by the Hessian", - B. A. Pearlmutter, - Neural Computation, 1994. -*************************************************************************/ -void mlphessianbatch(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t ssize, double &e, real_1d_array &grad, real_2d_array &h); - - -/************************************************************************* -Calculation of all types of errors. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset; one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -OUTPUT PARAMETERS: - Rep - it contains all type of errors. - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function. - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpallerrorssubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep); -void smp_mlpallerrorssubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep); - - -/************************************************************************* -Calculation of all types of errors on sparse dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - network initialized with one of the network creation funcs - XY - original dataset given by sparse matrix; - one sample = one row; - first NIn columns contain inputs, - next NOut columns - desired outputs. - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -OUTPUT PARAMETERS: - Rep - it contains all type of errors. - -NOTE: when SubsetSize<0 is used full dataset by call MLPGradBatch function. - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpallerrorssparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep); -void smp_mlpallerrorssparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize, modelerrors &rep); - - -/************************************************************************* -Error of the neural network on dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format; - SetSize - real size of XY, SetSize>=0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize); -double smp_mlperrorsubset(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize); - - -/************************************************************************* -Error of the neural network on sparse dataset. - - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support - ! - ! First improvement gives close-to-linear speedup on multicore systems. - ! Second improvement gives constant speedup (2-3x depending on your CPU) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - - -INPUT PARAMETERS: - Network - neural network; - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Sparse matrix must use CRS format for - storage. - SetSize - real size of XY, SetSize>=0; - it is used when SubsetSize<0; - Subset - subset of SubsetSize elements, array[SubsetSize]; - SubsetSize- number of elements in Subset[] array. - -RESULT: - sum-of-squares error, SUM(sqr(y[i]-desired_y[i])/2) - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -dataset format is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 04.09.2012 by Bochkanov Sergey -*************************************************************************/ -double mlperrorsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize); -double smp_mlperrorsparsesubset(const multilayerperceptron &network, const sparsematrix &xy, const ae_int_t setsize, const integer_1d_array &subset, const ae_int_t subsetsize); - -/************************************************************************* -This subroutine trains logit model. - -INPUT PARAMETERS: - XY - training set, array[0..NPoints-1,0..NVars] - First NVars columns store values of independent - variables, next column stores number of class (from 0 - to NClasses-1) which dataset element belongs to. Fractional - values are rounded to nearest integer. - NPoints - training set size, NPoints>=1 - NVars - number of independent variables, NVars>=1 - NClasses - number of classes, NClasses>=2 - -OUTPUT PARAMETERS: - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints=1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreate(const ae_int_t n, mcpdstate &s); - - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Entry-state" model, i.e. model where transition from X[i] to X[i+1] -is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -and one selected component of X[] is called "entry" state and is treated -in a special way: - system state always transits from "entry" state to some another state - system state can not transit from any state into "entry" state -Such conditions basically mean that row of P which corresponds to "entry" -state is zero. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant - at every moment of time there is some - (unpredictable) amount of "new" individuals, which can transit into one - of the states at the next turn, but still no one leaves population -* you want to model transitions of individuals from one state into another -* but you do NOT want to predict amount of "new" individuals because it - does not depends on individuals already present (hence system can not - transit INTO entry state - it can only transit FROM it). - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - EntryState- index of entry state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateentry(const ae_int_t n, const ae_int_t entrystate, mcpdstate &s); - - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Exit-state" model, i.e. model where transition from X[i] to X[i+1] -is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -and one selected component of X[] is called "exit" state and is treated -in a special way: - system state can transit from any state into "exit" state - system state can not transit from "exit" state into any other state - transition operator discards "exit" state (makes it zero at each turn) -Such conditions basically mean that column of P which corresponds to -"exit" state is zero. Multiplication by such P may decrease sum of vector -components. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant - individuals can move into "exit" state - and leave population at the next turn, but there are no new individuals -* amount of individuals which leave population can be predicted -* you want to model transitions of individuals from one state into another - (including transitions into the "exit" state) - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - ExitState- index of exit state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateexit(const ae_int_t n, const ae_int_t exitstate, mcpdstate &s); - - -/************************************************************************* -DESCRIPTION: - -This function is a specialized version of MCPDCreate() function, and we -recommend you to read comments for this function for general information -about MCPD solver. - -This function creates MCPD (Markov Chains for Population Data) solver -for "Entry-Exit-states" model, i.e. model where transition from X[i] to -X[i+1] is modelled as - X[i+1] = P*X[i] -where - X[i] and X[i+1] are N-dimensional state vectors - P is a N*N transition matrix -one selected component of X[] is called "entry" state and is treated in a -special way: - system state always transits from "entry" state to some another state - system state can not transit from any state into "entry" state -and another one component of X[] is called "exit" state and is treated in -a special way too: - system state can transit from any state into "exit" state - system state can not transit from "exit" state into any other state - transition operator discards "exit" state (makes it zero at each turn) -Such conditions basically mean that: - row of P which corresponds to "entry" state is zero - column of P which corresponds to "exit" state is zero -Multiplication by such P may decrease sum of vector components. - -Such models arise when: -* there is some population of individuals -* individuals can have different states -* individuals can transit from one state to another -* population size is NOT constant -* at every moment of time there is some (unpredictable) amount of "new" - individuals, which can transit into one of the states at the next turn -* some individuals can move (predictably) into "exit" state and leave - population at the next turn -* you want to model transitions of individuals from one state into another, - including transitions from the "entry" state and into the "exit" state. -* but you do NOT want to predict amount of "new" individuals because it - does not depends on individuals already present (hence system can not - transit INTO entry state - it can only transit FROM it). - -This model is discussed in more details in the ALGLIB User Guide (see -http://www.alglib.net/dataanalysis/ for more data). - -INPUT PARAMETERS: - N - problem dimension, N>=2 - EntryState- index of entry state, in 0..N-1 - ExitState- index of exit state, in 0..N-1 - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdcreateentryexit(const ae_int_t n, const ae_int_t entrystate, const ae_int_t exitstate, mcpdstate &s); - - -/************************************************************************* -This function is used to add a track - sequence of system states at the -different moments of its evolution. - -You may add one or several tracks to the MCPD solver. In case you have -several tracks, they won't overwrite each other. For example, if you pass -two tracks, A1-A2-A3 (system at t=A+1, t=A+2 and t=A+3) and B1-B2-B3, then -solver will try to model transitions from t=A+1 to t=A+2, t=A+2 to t=A+3, -t=B+1 to t=B+2, t=B+2 to t=B+3. But it WONT mix these two tracks - i.e. it -wont try to model transition from t=A+3 to t=B+1. - -INPUT PARAMETERS: - S - solver - XY - track, array[K,N]: - * I-th row is a state at t=I - * elements of XY must be non-negative (exception will be - thrown on negative elements) - K - number of points in a track - * if given, only leading K rows of XY are used - * if not given, automatically determined from size of XY - -NOTES: - -1. Track may contain either proportional or population data: - * with proportional data all rows of XY must sum to 1.0, i.e. we have - proportions instead of absolute population values - * with population data rows of XY contain population counts and generally - do not sum to 1.0 (although they still must be non-negative) - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddtrack(const mcpdstate &s, const real_2d_array &xy, const ae_int_t k); -void mcpdaddtrack(const mcpdstate &s, const real_2d_array &xy); - - -/************************************************************************* -This function is used to add equality constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to place equality constraints on arbitrary -subset of elements of P. Set of constraints is specified by EC, which may -contain either NAN's or finite numbers from [0,1]. NAN denotes absence of -constraint, finite number denotes equality constraint on specific element -of P. - -You can also use MCPDAddEC() function which allows to ADD equality -constraint for one element of P without changing constraints for other -elements. - -These functions (MCPDSetEC and MCPDAddEC) interact as follows: -* there is internal matrix of equality constraints which is stored in the - MCPD solver -* MCPDSetEC() replaces this matrix by another one (SET) -* MCPDAddEC() modifies one element of this matrix and leaves other ones - unchanged (ADD) -* thus MCPDAddEC() call preserves all modifications done by previous - calls, while MCPDSetEC() completely discards all changes done to the - equality constraints. - -INPUT PARAMETERS: - S - solver - EC - equality constraints, array[N,N]. Elements of EC can be - either NAN's or finite numbers from [0,1]. NAN denotes - absence of constraints, while finite value denotes - equality constraint on the corresponding element of P. - -NOTES: - -1. infinite values of EC will lead to exception being thrown. Values less -than 0.0 or greater than 1.0 will lead to error code being returned after -call to MCPDSolve(). - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetec(const mcpdstate &s, const real_2d_array &ec); - - -/************************************************************************* -This function is used to add equality constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to ADD equality constraint for one element of P -without changing constraints for other elements. - -You can also use MCPDSetEC() function which allows you to specify -arbitrary set of equality constraints in one call. - -These functions (MCPDSetEC and MCPDAddEC) interact as follows: -* there is internal matrix of equality constraints which is stored in the - MCPD solver -* MCPDSetEC() replaces this matrix by another one (SET) -* MCPDAddEC() modifies one element of this matrix and leaves other ones - unchanged (ADD) -* thus MCPDAddEC() call preserves all modifications done by previous - calls, while MCPDSetEC() completely discards all changes done to the - equality constraints. - -INPUT PARAMETERS: - S - solver - I - row index of element being constrained - J - column index of element being constrained - C - value (constraint for P[I,J]). Can be either NAN (no - constraint) or finite value from [0,1]. - -NOTES: - -1. infinite values of C will lead to exception being thrown. Values less -than 0.0 or greater than 1.0 will lead to error code being returned after -call to MCPDSolve(). - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdaddec(const mcpdstate &s, const ae_int_t i, const ae_int_t j, const double c); - - -/************************************************************************* -This function is used to add bound constraints on the elements of the -transition matrix P. - -MCPD solver has four types of constraints which can be placed on P: -* user-specified equality constraints (optional) -* user-specified bound constraints (optional) -* user-specified general linear constraints (optional) -* basic constraints (always present): - * non-negativity: P[i,j]>=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to place bound constraints on arbitrary -subset of elements of P. Set of constraints is specified by BndL/BndU -matrices, which may contain arbitrary combination of finite numbers or -infinities (like -INF=0 - * consistency: every column of P sums to 1.0 - -Final constraints which are passed to the underlying optimizer are -calculated as intersection of all present constraints. For example, you -may specify boundary constraint on P[0,0] and equality one: - 0.1<=P[0,0]<=0.9 - P[0,0]=0.5 -Such combination of constraints will be silently reduced to their -intersection, which is P[0,0]=0.5. - -This function can be used to ADD bound constraint for one element of P -without changing constraints for other elements. - -You can also use MCPDSetBC() function which allows to place bound -constraints on arbitrary subset of elements of P. Set of constraints is -specified by BndL/BndU matrices, which may contain arbitrary combination -of finite numbers or infinities (like -INF=" (CT[i]>0). - -Your constraint may involve only some subset of P (less than N*N elements). -For example it can be something like - P[0,0] + P[0,1] = 0.5 -In this case you still should pass matrix with N*N+1 columns, but all its -elements (except for C[0,0], C[0,1] and C[0,N*N-1]) will be zero. - -INPUT PARAMETERS: - S - solver - C - array[K,N*N+1] - coefficients of constraints - (see above for complete description) - CT - array[K] - constraint types - (see above for complete description) - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetlc(const mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k); -void mcpdsetlc(const mcpdstate &s, const real_2d_array &c, const integer_1d_array &ct); - - -/************************************************************************* -This function allows to tune amount of Tikhonov regularization being -applied to your problem. - -By default, regularizing term is equal to r*||P-prior_P||^2, where r is a -small non-zero value, P is transition matrix, prior_P is identity matrix, -||X||^2 is a sum of squared elements of X. - -This function allows you to change coefficient r. You can also change -prior values with MCPDSetPrior() function. - -INPUT PARAMETERS: - S - solver - V - regularization coefficient, finite non-negative value. It - is not recommended to specify zero value unless you are - pretty sure that you want it. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsettikhonovregularizer(const mcpdstate &s, const double v); - - -/************************************************************************* -This function allows to set prior values used for regularization of your -problem. - -By default, regularizing term is equal to r*||P-prior_P||^2, where r is a -small non-zero value, P is transition matrix, prior_P is identity matrix, -||X||^2 is a sum of squared elements of X. - -This function allows you to change prior values prior_P. You can also -change r with MCPDSetTikhonovRegularizer() function. - -INPUT PARAMETERS: - S - solver - PP - array[N,N], matrix of prior values: - 1. elements must be real numbers from [0,1] - 2. columns must sum to 1.0. - First property is checked (exception is thrown otherwise), - while second one is not checked/enforced. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetprior(const mcpdstate &s, const real_2d_array &pp); - - -/************************************************************************* -This function is used to change prediction weights - -MCPD solver scales prediction errors as follows - Error(P) = ||W*(y-P*x)||^2 -where - x is a system state at time t - y is a system state at time t+1 - P is a transition matrix - W is a diagonal scaling matrix - -By default, weights are chosen in order to minimize relative prediction -error instead of absolute one. For example, if one component of state is -about 0.5 in magnitude and another one is about 0.05, then algorithm will -make corresponding weights equal to 2.0 and 20.0. - -INPUT PARAMETERS: - S - solver - PW - array[N], weights: - * must be non-negative values (exception will be thrown otherwise) - * zero values will be replaced by automatically chosen values - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsetpredictionweights(const mcpdstate &s, const real_1d_array &pw); - - -/************************************************************************* -This function is used to start solution of the MCPD problem. - -After return from this function, you can use MCPDResults() to get solution -and completion code. - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdsolve(const mcpdstate &s); - - -/************************************************************************* -MCPD results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - P - array[N,N], transition matrix - Rep - optimization report. You should check Rep.TerminationType - in order to distinguish successful termination from - unsuccessful one. Speaking short, positive values denote - success, negative ones are failures. - More information about fields of this structure can be - found in the comments on MCPDReport datatype. - - - -- ALGLIB -- - Copyright 23.05.2010 by Bochkanov Sergey -*************************************************************************/ -void mcpdresults(const mcpdstate &s, real_2d_array &p, mcpdreport &rep); - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void mlpeserialize(mlpensemble &obj, std::string &s_out); - - -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void mlpeunserialize(std::string &s_in, mlpensemble &obj); - - -/************************************************************************* -Like MLPCreate0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate0(const ae_int_t nin, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreate1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreate2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreate2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateB0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb0(const ae_int_t nin, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateB1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateB2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreateb2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double b, const double d, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateR0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater0(const ae_int_t nin, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateR1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateR2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreater2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const double a, const double b, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateC0, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec0(const ae_int_t nin, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateC1, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec1(const ae_int_t nin, const ae_int_t nhid, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Like MLPCreateC2, but for ensembles. - - -- ALGLIB -- - Copyright 18.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatec2(const ae_int_t nin, const ae_int_t nhid1, const ae_int_t nhid2, const ae_int_t nout, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Creates ensemble from network. Only network geometry is copied. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpecreatefromnetwork(const multilayerperceptron &network, const ae_int_t ensemblesize, mlpensemble &ensemble); - - -/************************************************************************* -Randomization of MLP ensemble - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlperandomize(const mlpensemble &ensemble); - - -/************************************************************************* -Return ensemble properties (number of inputs and outputs). - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeproperties(const mlpensemble &ensemble, ae_int_t &nin, ae_int_t &nout); - - -/************************************************************************* -Return normalization type (whether ensemble is SOFTMAX-normalized or not). - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -bool mlpeissoftmax(const mlpensemble &ensemble); - - -/************************************************************************* -Procesing - -INPUT PARAMETERS: - Ensemble- neural networks ensemble - X - input vector, array[0..NIn-1]. - Y - (possibly) preallocated buffer; if size of Y is less than - NOut, it will be reallocated. If it is large enough, it - is NOT reallocated, so we can save some time on reallocation. - - -OUTPUT PARAMETERS: - Y - result. Regression estimate when solving regression task, - vector of posterior probabilities for classification task. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeprocess(const mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -'interactive' variant of MLPEProcess for languages like Python which -support constructs like "Y = MLPEProcess(LM,X)" and interactive mode of the -interpreter - -This function allocates new array on each call, so it is significantly -slower than its 'non-interactive' counterpart, but it is more convenient -when you call it from command line. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpeprocessi(const mlpensemble &ensemble, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -Relative classification error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - percent of incorrectly classified cases. - Works both for classifier betwork and for regression networks which -are used as classifiers. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlperelclserror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average cross-entropy (in bits per element) on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - CrossEntropy/(NPoints*LN(2)). - Zero if ensemble solves regression task. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgce(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -RMS error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - root mean square error. - Its meaning for regression task is obvious. As for classification task -RMS error means error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpermserror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for classification task -it means average error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgerror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -Average relative error on the test set - -INPUT PARAMETERS: - Ensemble- ensemble - XY - test set - NPoints - test set size - -RESULT: - Its meaning for regression task is obvious. As for classification task -it means average relative error when estimating posterior probabilities. - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -double mlpeavgrelerror(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints); - -/************************************************************************* -Neural network training using modified Levenberg-Marquardt with exact -Hessian calculation and regularization. Subroutine trains neural network -with restarts from random positions. Algorithm is well suited for small -and medium scale problems (hundreds of weights). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts from random position, >0. - If you don't know what Restarts to choose, use 2. - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -9, if internal matrix inverse subroutine failed - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlptrainlm(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep); - - -/************************************************************************* -Neural network training using L-BFGS algorithm with regularization. -Subroutine trains neural network with restarts from random positions. -Algorithm is well suited for problems of any dimensionality (memory -requirements and step complexity are linear by weights number). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts from random position, >0. - If you don't know what Restarts to choose, use 2. - WStep - stopping criterion. Algorithm stops if step size is - less than WStep. Recommended value - 0.01. Zero step - size means stopping after MaxIts iterations. - MaxIts - stopping criterion. Algorithm stops after MaxIts - iterations (NOT gradient calculations). Zero MaxIts - means stopping when step is sufficiently small. - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -8, if both WStep=0 and MaxIts=0 - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlptrainlbfgs(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, ae_int_t &info, mlpreport &rep); - - -/************************************************************************* -Neural network training using early stopping (base algorithm - L-BFGS with -regularization). - -INPUT PARAMETERS: - Network - neural network with initialized geometry - TrnXY - training set - TrnSize - training set size, TrnSize>0 - ValXY - validation set - ValSize - validation set size, ValSize>0 - Decay - weight decay constant, >=0.001 - Decay term 'Decay*||Weights||^2' is added to error - function. - If you don't know what Decay to choose, use 0.001. - Restarts - number of restarts, either: - * strictly positive number - algorithm make specified - number of restarts from random position. - * -1, in which case algorithm makes exactly one run - from the initial state of the network (no randomization). - If you don't know what Restarts to choose, choose one - one the following: - * -1 (deterministic start) - * +1 (one random restart) - * +5 (moderate amount of random restarts) - -OUTPUT PARAMETERS: - Network - trained neural network. - Info - return code: - * -2, if there is a point with class number - outside of [0..NOut-1]. - * -1, if wrong parameters specified - (NPoints<0, Restarts<1, ...). - * 2, task has been solved, stopping criterion met - - sufficiently small step size. Not expected (we - use EARLY stopping) but possible and not an - error. - * 6, task has been solved, stopping criterion met - - increasing of validation set error. - Rep - training report - -NOTE: - -Algorithm stops if validation set error increases for a long enough or -step size is small enought (there are task where validation set may -decrease for eternity). In any case solution returned corresponds to the -minimum of validation set error. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlptraines(const multilayerperceptron &network, const real_2d_array &trnxy, const ae_int_t trnsize, const real_2d_array &valxy, const ae_int_t valsize, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep); - - -/************************************************************************* -Cross-validation estimate of generalization error. - -Base algorithm - L-BFGS. - -INPUT PARAMETERS: - Network - neural network with initialized geometry. Network is - not changed during cross-validation - it is used only - as a representative of its architecture. - XY - training set. - SSize - training set size - Decay - weight decay, same as in MLPTrainLBFGS - Restarts - number of restarts, >0. - restarts are counted for each partition separately, so - total number of restarts will be Restarts*FoldsCount. - WStep - stopping criterion, same as in MLPTrainLBFGS - MaxIts - stopping criterion, same as in MLPTrainLBFGS - FoldsCount - number of folds in k-fold cross-validation, - 2<=FoldsCount<=SSize. - recommended value: 10. - -OUTPUT PARAMETERS: - Info - return code, same as in MLPTrainLBFGS - Rep - report, same as in MLPTrainLM/MLPTrainLBFGS - CVRep - generalization error estimates - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcvlbfgs(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, const ae_int_t foldscount, ae_int_t &info, mlpreport &rep, mlpcvreport &cvrep); - - -/************************************************************************* -Cross-validation estimate of generalization error. - -Base algorithm - Levenberg-Marquardt. - -INPUT PARAMETERS: - Network - neural network with initialized geometry. Network is - not changed during cross-validation - it is used only - as a representative of its architecture. - XY - training set. - SSize - training set size - Decay - weight decay, same as in MLPTrainLBFGS - Restarts - number of restarts, >0. - restarts are counted for each partition separately, so - total number of restarts will be Restarts*FoldsCount. - FoldsCount - number of folds in k-fold cross-validation, - 2<=FoldsCount<=SSize. - recommended value: 10. - -OUTPUT PARAMETERS: - Info - return code, same as in MLPTrainLBFGS - Rep - report, same as in MLPTrainLM/MLPTrainLBFGS - CVRep - generalization error estimates - - -- ALGLIB -- - Copyright 09.12.2007 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcvlm(const multilayerperceptron &network, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const ae_int_t foldscount, ae_int_t &info, mlpreport &rep, mlpcvreport &cvrep); - - -/************************************************************************* -This function estimates generalization error using cross-validation on the -current dataset with current training settings. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * FoldsCount cross-validation rounds (always) - ! * NRestarts training sessions performed within each of - ! cross-validation rounds (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. Network is not changed during cross- - validation and is not trained - it is used only as - representative of its architecture. I.e., we estimate - generalization properties of ARCHITECTURE, not some - specific network. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that for each cross-validation - round specified number of random restarts is - performed, with best network being chosen after - training. - * NRestarts=0 is same as NRestarts=1 - FoldsCount - number of folds in k-fold cross-validation: - * 2<=FoldsCount<=size of dataset - * recommended value: 10. - * values larger than dataset size will be silently - truncated down to dataset size - -OUTPUT PARAMETERS: - Rep - structure which contains cross-validation estimates: - * Rep.RelCLSError - fraction of misclassified cases. - * Rep.AvgCE - acerage cross-entropy - * Rep.RMSError - root-mean-square error - * Rep.AvgError - average error - * Rep.AvgRelError - average relative error - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - or subset with only one point was given, zeros are returned as - estimates. - -NOTE: this method performs FoldsCount cross-validation rounds, each one - with NRestarts random starts. Thus, FoldsCount*NRestarts networks - are trained in total. - -NOTE: Rep.RelCLSError/Rep.AvgCE are zero on regression problems. - -NOTE: on classification problems Rep.RMSError/Rep.AvgError/Rep.AvgRelError - contain errors in prediction of posterior probabilities. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpkfoldcv(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep); -void smp_mlpkfoldcv(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, const ae_int_t foldscount, mlpreport &rep); - - -/************************************************************************* -Creation of the network trainer object for regression networks - -INPUT PARAMETERS: - NIn - number of inputs, NIn>=1 - NOut - number of outputs, NOut>=1 - -OUTPUT PARAMETERS: - S - neural network trainer object. - This structure can be used to train any regression - network with NIn inputs and NOut outputs. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatetrainer(const ae_int_t nin, const ae_int_t nout, mlptrainer &s); - - -/************************************************************************* -Creation of the network trainer object for classification networks - -INPUT PARAMETERS: - NIn - number of inputs, NIn>=1 - NClasses - number of classes, NClasses>=2 - -OUTPUT PARAMETERS: - S - neural network trainer object. - This structure can be used to train any classification - network with NIn inputs and NOut outputs. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpcreatetrainercls(const ae_int_t nin, const ae_int_t nclasses, mlptrainer &s); - - -/************************************************************************* -This function sets "current dataset" of the trainer object to one passed -by user. - -INPUT PARAMETERS: - S - trainer object - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. - NPoints - points count, >=0. - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -datasetformat is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetdataset(const mlptrainer &s, const real_2d_array &xy, const ae_int_t npoints); - - -/************************************************************************* -This function sets "current dataset" of the trainer object to one passed -by user (sparse matrix is used to store dataset). - -INPUT PARAMETERS: - S - trainer object - XY - training set, see below for information on the - training set format. This function checks correctness - of the dataset (no NANs/INFs, class numbers are - correct) and throws exception when incorrect dataset - is passed. Any sparse storage format can be used: - Hash-table, CRS... - NPoints - points count, >=0 - -DATASET FORMAT: - -This function uses two different dataset formats - one for regression -networks, another one for classification networks. - -For regression networks with NIn inputs and NOut outputs following dataset -format is used: -* dataset is given by NPoints*(NIn+NOut) matrix -* each row corresponds to one example -* first NIn columns are inputs, next NOut columns are outputs - -For classification networks with NIn inputs and NClasses clases following -datasetformat is used: -* dataset is given by NPoints*(NIn+1) matrix -* each row corresponds to one example -* first NIn columns are inputs, last column stores class number (from 0 to - NClasses-1). - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetsparsedataset(const mlptrainer &s, const sparsematrix &xy, const ae_int_t npoints); - - -/************************************************************************* -This function sets weight decay coefficient which is used for training. - -INPUT PARAMETERS: - S - trainer object - Decay - weight decay coefficient, >=0. Weight decay term - 'Decay*||Weights||^2' is added to error function. If - you don't know what Decay to choose, use 1.0E-3. - Weight decay can be set to zero, in this case network - is trained without weight decay. - -NOTE: by default network uses some small nonzero value for weight decay. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetdecay(const mlptrainer &s, const double decay); - - -/************************************************************************* -This function sets stopping criteria for the optimizer. - -INPUT PARAMETERS: - S - trainer object - WStep - stopping criterion. Algorithm stops if step size is - less than WStep. Recommended value - 0.01. Zero step - size means stopping after MaxIts iterations. - WStep>=0. - MaxIts - stopping criterion. Algorithm stops after MaxIts - epochs (full passes over entire dataset). Zero MaxIts - means stopping when step is sufficiently small. - MaxIts>=0. - -NOTE: by default, WStep=0.005 and MaxIts=0 are used. These values are also - used when MLPSetCond() is called with WStep=0 and MaxIts=0. - -NOTE: these stopping criteria are used for all kinds of neural training - - from "conventional" networks to early stopping ensembles. When used - for "conventional" networks, they are used as the only stopping - criteria. When combined with early stopping, they used as ADDITIONAL - stopping criteria which can terminate early stopping algorithm. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetcond(const mlptrainer &s, const double wstep, const ae_int_t maxits); - - -/************************************************************************* -This function sets training algorithm: batch training using L-BFGS will be -used. - -This algorithm: -* the most robust for small-scale problems, but may be too slow for large - scale ones. -* perfoms full pass through the dataset before performing step -* uses conditions specified by MLPSetCond() for stopping -* is default one used by trainer object - -INPUT PARAMETERS: - S - trainer object - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpsetalgobatch(const mlptrainer &s); - - -/************************************************************************* -This function trains neural network passed to this function, using current -dataset (one which was passed to MLPSetDataset() or MLPSetSparseDataset()) -and current training settings. Training from NRestarts random starting -positions is performed, best network is chosen. - -Training is performed using current training algorithm. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * NRestarts training sessions performed within each of - ! cross-validation rounds (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that specified number of random - restarts are performed, best network is chosen after - training - * NRestarts=0 means that current state of the network - is used for training. - -OUTPUT PARAMETERS: - Network - trained network - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - network is filled by zero values. Same behavior for functions - MLPStartTraining and MLPContinueTraining. - -NOTE: this method uses sum-of-squares error function for training. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlptrainnetwork(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep); -void smp_mlptrainnetwork(const mlptrainer &s, const multilayerperceptron &network, const ae_int_t nrestarts, mlpreport &rep); - - -/************************************************************************* -IMPORTANT: this is an "expert" version of the MLPTrain() function. We do - not recommend you to use it unless you are pretty sure that you - need ability to monitor training progress. - -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTraining() call, -and then user subsequently calls MLPContinueTraining() to perform one more -iteration of the training. - -After call to this function trainer object remembers network and is ready -to train it. However, no training is performed until first call to -MLPContinueTraining() function. Subsequent calls to MLPContinueTraining() -will advance training progress one iteration further. - -EXAMPLE: - > - > ...initialize network and trainer object.... - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > ...visualize training progress... - > - -INPUT PARAMETERS: - S - trainer object - Network - neural network. It must have same number of inputs and - output/classes as was specified during creation of the - trainer object. - RandomStart - randomize network before training or not: - * True means that network is randomized and its - initial state (one which was passed to the trainer - object) is lost. - * False means that training is started from the - current state of the network - -OUTPUT PARAMETERS: - Network - neural network which is ready to training (weights are - initialized, preprocessor is initialized using current - training set) - -NOTE: this method uses sum-of-squares error function for training. - -NOTE: it is expected that trainer object settings are NOT changed during - step-by-step training, i.e. no one changes stopping criteria or - training set during training. It is possible and there is no defense - against such actions, but algorithm behavior in such cases is - undefined and can be unpredictable. - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -void mlpstarttraining(const mlptrainer &s, const multilayerperceptron &network, const bool randomstart); - - -/************************************************************************* -IMPORTANT: this is an "expert" version of the MLPTrain() function. We do - not recommend you to use it unless you are pretty sure that you - need ability to monitor training progress. - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -This function performs step-by-step training of the neural network. Here -"step-by-step" means that training starts with MLPStartTraining() call, -and then user subsequently calls MLPContinueTraining() to perform one more -iteration of the training. - -This function performs one more iteration of the training and returns -either True (training continues) or False (training stopped). In case True -was returned, Network weights are updated according to the current state -of the optimization progress. In case False was returned, no additional -updates is performed (previous update of the network weights moved us to -the final point, and no additional updates is needed). - -EXAMPLE: - > - > [initialize network and trainer object] - > - > MLPStartTraining(Trainer, Network, True) - > while MLPContinueTraining(Trainer, Network) do - > [visualize training progress] - > - -INPUT PARAMETERS: - S - trainer object - Network - neural network structure, which is used to store - current state of the training process. - -OUTPUT PARAMETERS: - Network - weights of the neural network are rewritten by the - current approximation. - -NOTE: this method uses sum-of-squares error function for training. - -NOTE: it is expected that trainer object settings are NOT changed during - step-by-step training, i.e. no one changes stopping criteria or - training set during training. It is possible and there is no defense - against such actions, but algorithm behavior in such cases is - undefined and can be unpredictable. - -NOTE: It is expected that Network is the same one which was passed to - MLPStartTraining() function. However, THIS function checks only - following: - * that number of network inputs is consistent with trainer object - settings - * that number of network outputs/classes is consistent with trainer - object settings - * that number of network weights is the same as number of weights in - the network passed to MLPStartTraining() function - Exception is thrown when these conditions are violated. - - It is also expected that you do not change state of the network on - your own - the only party who has right to change network during its - training is a trainer object. Any attempt to interfere with trainer - may lead to unpredictable results. - - - -- ALGLIB -- - Copyright 23.07.2012 by Bochkanov Sergey -*************************************************************************/ -bool mlpcontinuetraining(const mlptrainer &s, const multilayerperceptron &network); -bool smp_mlpcontinuetraining(const mlptrainer &s, const multilayerperceptron &network); - - -/************************************************************************* -Training neural networks ensemble using bootstrap aggregating (bagging). -Modified Levenberg-Marquardt algorithm is used as base training method. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpebagginglm(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep, mlpcvreport &ooberrors); - - -/************************************************************************* -Training neural networks ensemble using bootstrap aggregating (bagging). -L-BFGS algorithm is used as base training method. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - WStep - stopping criterion, same as in MLPTrainLBFGS - MaxIts - stopping criterion, same as in MLPTrainLBFGS - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -8, if both WStep=0 and MaxIts=0 - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 2, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 17.02.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpebagginglbfgs(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, const double wstep, const ae_int_t maxits, ae_int_t &info, mlpreport &rep, mlpcvreport &ooberrors); - - -/************************************************************************* -Training neural networks ensemble using early stopping. - -INPUT PARAMETERS: - Ensemble - model with initialized geometry - XY - training set - NPoints - training set size - Decay - weight decay coefficient, >=0.001 - Restarts - restarts, >0. - -OUTPUT PARAMETERS: - Ensemble - trained model - Info - return code: - * -2, if there is a point with class number - outside of [0..NClasses-1]. - * -1, if incorrect parameters was passed - (NPoints<0, Restarts<1). - * 6, if task has been solved. - Rep - training report. - OOBErrors - out-of-bag generalization error estimate - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void mlpetraines(const mlpensemble &ensemble, const real_2d_array &xy, const ae_int_t npoints, const double decay, const ae_int_t restarts, ae_int_t &info, mlpreport &rep); - - -/************************************************************************* -This function trains neural network ensemble passed to this function using -current dataset and early stopping training algorithm. Each early stopping -round performs NRestarts random restarts (thus, EnsembleSize*NRestarts -training rounds is performed in total). - -FOR USERS OF COMMERCIAL EDITION: - - ! Commercial version of ALGLIB includes two important improvements of - ! this function: - ! * multicore support (C++ and C# computational cores) - ! * SSE support (C++ computational core) - ! - ! Second improvement gives constant speedup (2-3X). First improvement - ! gives close-to-linear speedup on multicore systems. Following - ! operations can be executed in parallel: - ! * EnsembleSize training sessions performed for each of ensemble - ! members (always parallelized) - ! * NRestarts training sessions performed within each of training - ! sessions (if NRestarts>1) - ! * gradient calculation over large dataset (if dataset is large enough) - ! - ! In order to use multicore features you have to: - ! * use commercial version of ALGLIB - ! * call this function with "smp_" prefix, which indicates that - ! multicore code will be used (for multicore support) - ! - ! In order to use SSE features you have to: - ! * use commercial version of ALGLIB on Intel processors - ! * use C++ computational core - ! - ! This note is given for users of commercial edition; if you use GPL - ! edition, you still will be able to call smp-version of this function, - ! but all computations will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - -INPUT PARAMETERS: - S - trainer object; - Ensemble - neural network ensemble. It must have same number of - inputs and outputs/classes as was specified during - creation of the trainer object. - NRestarts - number of restarts, >=0: - * NRestarts>0 means that specified number of random - restarts are performed during each ES round; - * NRestarts=0 is silently replaced by 1. - -OUTPUT PARAMETERS: - Ensemble - trained ensemble; - Rep - it contains all type of errors. - -NOTE: this training method uses BOTH early stopping and weight decay! So, - you should select weight decay before starting training just as you - select it before training "conventional" networks. - -NOTE: when no dataset was specified with MLPSetDataset/SetSparseDataset(), - or single-point dataset was passed, ensemble is filled by zero - values. - -NOTE: this method uses sum-of-squares error function for training. - - -- ALGLIB -- - Copyright 22.08.2012 by Bochkanov Sergey -*************************************************************************/ -void mlptrainensemblees(const mlptrainer &s, const mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep); -void smp_mlptrainensemblees(const mlptrainer &s, const mlpensemble &ensemble, const ae_int_t nrestarts, mlpreport &rep); - -/************************************************************************* -Principal components analysis - -Subroutine builds orthogonal basis where first axis corresponds to -direction with maximum variance, second axis maximizes variance in subspace -orthogonal to first axis and so on. - -It should be noted that, unlike LDA, PCA does not use class labels. - -INPUT PARAMETERS: - X - dataset, array[0..NPoints-1,0..NVars-1]. - matrix contains ONLY INDEPENDENT VARIABLES. - NPoints - dataset size, NPoints>=0 - NVars - number of independent variables, NVars>=1 - -ÂÛÕÎÄÍÛÅ ÏÀÐÀÌÅÒÐÛ: - Info - return code: - * -4, if SVD subroutine haven't converged - * -1, if wrong parameters has been passed (NPoints<0, - NVars<1) - * 1, if task is solved - S2 - array[0..NVars-1]. variance values corresponding - to basis vectors. - V - array[0..NVars-1,0..NVars-1] - matrix, whose columns store basis vectors. - - -- ALGLIB -- - Copyright 25.08.2008 by Bochkanov Sergey -*************************************************************************/ -void pcabuildbasis(const real_2d_array &x, const ae_int_t npoints, const ae_int_t nvars, ae_int_t &info, real_1d_array &s2, real_2d_array &v); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void dserrallocate(ae_int_t nclasses, - /* Real */ ae_vector* buf, - ae_state *_state); -void dserraccumulate(/* Real */ ae_vector* buf, - /* Real */ ae_vector* y, - /* Real */ ae_vector* desiredy, - ae_state *_state); -void dserrfinish(/* Real */ ae_vector* buf, ae_state *_state); -void dsnormalize(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - /* Real */ ae_vector* means, - /* Real */ ae_vector* sigmas, - ae_state *_state); -void dsnormalizec(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - /* Real */ ae_vector* means, - /* Real */ ae_vector* sigmas, - ae_state *_state); -double dsgetmeanmindistance(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_state *_state); -void dstie(/* Real */ ae_vector* a, - ae_int_t n, - /* Integer */ ae_vector* ties, - ae_int_t* tiecount, - /* Integer */ ae_vector* p1, - /* Integer */ ae_vector* p2, - ae_state *_state); -void dstiefasti(/* Real */ ae_vector* a, - /* Integer */ ae_vector* b, - ae_int_t n, - /* Integer */ ae_vector* ties, - ae_int_t* tiecount, - /* Real */ ae_vector* bufr, - /* Integer */ ae_vector* bufi, - ae_state *_state); -void dsoptimalsplit2(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - ae_int_t n, - ae_int_t* info, - double* threshold, - double* pal, - double* pbl, - double* par, - double* pbr, - double* cve, - ae_state *_state); -void dsoptimalsplit2fast(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - /* Integer */ ae_vector* tiesbuf, - /* Integer */ ae_vector* cntbuf, - /* Real */ ae_vector* bufr, - /* Integer */ ae_vector* bufi, - ae_int_t n, - ae_int_t nc, - double alpha, - ae_int_t* info, - double* threshold, - double* rms, - double* cvrms, - ae_state *_state); -void dssplitk(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - ae_int_t n, - ae_int_t nc, - ae_int_t kmax, - ae_int_t* info, - /* Real */ ae_vector* thresholds, - ae_int_t* ni, - double* cve, - ae_state *_state); -void dsoptimalsplitk(/* Real */ ae_vector* a, - /* Integer */ ae_vector* c, - ae_int_t n, - ae_int_t nc, - ae_int_t kmax, - ae_int_t* info, - /* Real */ ae_vector* thresholds, - ae_int_t* ni, - double* cve, - ae_state *_state); -ae_bool _cvreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _cvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _cvreport_clear(void* _p); -void _cvreport_destroy(void* _p); -void clusterizercreate(clusterizerstate* s, ae_state *_state); -void clusterizersetpoints(clusterizerstate* s, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_int_t disttype, - ae_state *_state); -void clusterizersetdistances(clusterizerstate* s, - /* Real */ ae_matrix* d, - ae_int_t npoints, - ae_bool isupper, - ae_state *_state); -void clusterizersetahcalgo(clusterizerstate* s, - ae_int_t algo, - ae_state *_state); -void clusterizersetkmeanslimits(clusterizerstate* s, - ae_int_t restarts, - ae_int_t maxits, - ae_state *_state); -void clusterizerrunahc(clusterizerstate* s, - ahcreport* rep, - ae_state *_state); -void _pexec_clusterizerrunahc(clusterizerstate* s, - ahcreport* rep, ae_state *_state); -void clusterizerrunkmeans(clusterizerstate* s, - ae_int_t k, - kmeansreport* rep, - ae_state *_state); -void clusterizergetdistances(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_int_t disttype, - /* Real */ ae_matrix* d, - ae_state *_state); -void _pexec_clusterizergetdistances(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_int_t disttype, - /* Real */ ae_matrix* d, ae_state *_state); -void clusterizergetkclusters(ahcreport* rep, - ae_int_t k, - /* Integer */ ae_vector* cidx, - /* Integer */ ae_vector* cz, - ae_state *_state); -void clusterizerseparatedbydist(ahcreport* rep, - double r, - ae_int_t* k, - /* Integer */ ae_vector* cidx, - /* Integer */ ae_vector* cz, - ae_state *_state); -void clusterizerseparatedbycorr(ahcreport* rep, - double r, - ae_int_t* k, - /* Integer */ ae_vector* cidx, - /* Integer */ ae_vector* cz, - ae_state *_state); -void kmeansgenerateinternal(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t k, - ae_int_t maxits, - ae_int_t restarts, - ae_int_t* info, - /* Real */ ae_matrix* ccol, - ae_bool needccol, - /* Real */ ae_matrix* crow, - ae_bool needcrow, - /* Integer */ ae_vector* xyc, - ae_state *_state); -ae_bool _clusterizerstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _clusterizerstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _clusterizerstate_clear(void* _p); -void _clusterizerstate_destroy(void* _p); -ae_bool _ahcreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _ahcreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _ahcreport_clear(void* _p); -void _ahcreport_destroy(void* _p); -ae_bool _kmeansreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _kmeansreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _kmeansreport_clear(void* _p); -void _kmeansreport_destroy(void* _p); -void kmeansgenerate(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t k, - ae_int_t restarts, - ae_int_t* info, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* xyc, - ae_state *_state); -void dfbuildrandomdecisionforest(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t ntrees, - double r, - ae_int_t* info, - decisionforest* df, - dfreport* rep, - ae_state *_state); -void dfbuildrandomdecisionforestx1(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t ntrees, - ae_int_t nrndvars, - double r, - ae_int_t* info, - decisionforest* df, - dfreport* rep, - ae_state *_state); -void dfbuildinternal(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t ntrees, - ae_int_t samplesize, - ae_int_t nfeatures, - ae_int_t flags, - ae_int_t* info, - decisionforest* df, - dfreport* rep, - ae_state *_state); -void dfprocess(decisionforest* df, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void dfprocessi(decisionforest* df, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -double dfrelclserror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double dfavgce(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double dfrmserror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double dfavgerror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double dfavgrelerror(decisionforest* df, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -void dfcopy(decisionforest* df1, decisionforest* df2, ae_state *_state); -void dfalloc(ae_serializer* s, decisionforest* forest, ae_state *_state); -void dfserialize(ae_serializer* s, - decisionforest* forest, - ae_state *_state); -void dfunserialize(ae_serializer* s, - decisionforest* forest, - ae_state *_state); -ae_bool _decisionforest_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _decisionforest_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _decisionforest_clear(void* _p); -void _decisionforest_destroy(void* _p); -ae_bool _dfreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _dfreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _dfreport_clear(void* _p); -void _dfreport_destroy(void* _p); -ae_bool _dfinternalbuffers_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _dfinternalbuffers_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _dfinternalbuffers_clear(void* _p); -void _dfinternalbuffers_destroy(void* _p); -void lrbuild(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state); -void lrbuilds(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state); -void lrbuildzs(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state); -void lrbuildz(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - linearmodel* lm, - lrreport* ar, - ae_state *_state); -void lrunpack(linearmodel* lm, - /* Real */ ae_vector* v, - ae_int_t* nvars, - ae_state *_state); -void lrpack(/* Real */ ae_vector* v, - ae_int_t nvars, - linearmodel* lm, - ae_state *_state); -double lrprocess(linearmodel* lm, - /* Real */ ae_vector* x, - ae_state *_state); -double lrrmserror(linearmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double lravgerror(linearmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double lravgrelerror(linearmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -void lrcopy(linearmodel* lm1, linearmodel* lm2, ae_state *_state); -void lrlines(/* Real */ ae_matrix* xy, - /* Real */ ae_vector* s, - ae_int_t n, - ae_int_t* info, - double* a, - double* b, - double* vara, - double* varb, - double* covab, - double* corrab, - double* p, - ae_state *_state); -void lrline(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t* info, - double* a, - double* b, - ae_state *_state); -ae_bool _linearmodel_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _linearmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _linearmodel_clear(void* _p); -void _linearmodel_destroy(void* _p); -ae_bool _lrreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _lrreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _lrreport_clear(void* _p); -void _lrreport_destroy(void* _p); -void filtersma(/* Real */ ae_vector* x, - ae_int_t n, - ae_int_t k, - ae_state *_state); -void filterema(/* Real */ ae_vector* x, - ae_int_t n, - double alpha, - ae_state *_state); -void filterlrma(/* Real */ ae_vector* x, - ae_int_t n, - ae_int_t k, - ae_state *_state); -void fisherlda(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t* info, - /* Real */ ae_vector* w, - ae_state *_state); -void fisherldan(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t* info, - /* Real */ ae_matrix* w, - ae_state *_state); -ae_int_t mlpgradsplitcost(ae_state *_state); -ae_int_t mlpgradsplitsize(ae_state *_state); -void mlpcreate0(ae_int_t nin, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state); -void mlpcreate1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state); -void mlpcreate2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state); -void mlpcreateb0(ae_int_t nin, - ae_int_t nout, - double b, - double d, - multilayerperceptron* network, - ae_state *_state); -void mlpcreateb1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double b, - double d, - multilayerperceptron* network, - ae_state *_state); -void mlpcreateb2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double b, - double d, - multilayerperceptron* network, - ae_state *_state); -void mlpcreater0(ae_int_t nin, - ae_int_t nout, - double a, - double b, - multilayerperceptron* network, - ae_state *_state); -void mlpcreater1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double a, - double b, - multilayerperceptron* network, - ae_state *_state); -void mlpcreater2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double a, - double b, - multilayerperceptron* network, - ae_state *_state); -void mlpcreatec0(ae_int_t nin, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state); -void mlpcreatec1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state); -void mlpcreatec2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - multilayerperceptron* network, - ae_state *_state); -void mlpcopy(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state); -void mlpcopyshared(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state); -ae_bool mlpsamearchitecture(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state); -void mlpcopytunableparameters(multilayerperceptron* network1, - multilayerperceptron* network2, - ae_state *_state); -void mlpexporttunableparameters(multilayerperceptron* network, - /* Real */ ae_vector* p, - ae_int_t* pcount, - ae_state *_state); -void mlpimporttunableparameters(multilayerperceptron* network, - /* Real */ ae_vector* p, - ae_state *_state); -void mlpserializeold(multilayerperceptron* network, - /* Real */ ae_vector* ra, - ae_int_t* rlen, - ae_state *_state); -void mlpunserializeold(/* Real */ ae_vector* ra, - multilayerperceptron* network, - ae_state *_state); -void mlprandomize(multilayerperceptron* network, ae_state *_state); -void mlprandomizefull(multilayerperceptron* network, ae_state *_state); -void mlpinitpreprocessor(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_state *_state); -void mlpinitpreprocessorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t ssize, - ae_state *_state); -void mlpinitpreprocessorsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - ae_state *_state); -void mlpinitpreprocessorsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - ae_state *_state); -void mlpproperties(multilayerperceptron* network, - ae_int_t* nin, - ae_int_t* nout, - ae_int_t* wcount, - ae_state *_state); -ae_int_t mlpntotal(multilayerperceptron* network, ae_state *_state); -ae_int_t mlpgetinputscount(multilayerperceptron* network, - ae_state *_state); -ae_int_t mlpgetoutputscount(multilayerperceptron* network, - ae_state *_state); -ae_int_t mlpgetweightscount(multilayerperceptron* network, - ae_state *_state); -ae_bool mlpissoftmax(multilayerperceptron* network, ae_state *_state); -ae_int_t mlpgetlayerscount(multilayerperceptron* network, - ae_state *_state); -ae_int_t mlpgetlayersize(multilayerperceptron* network, - ae_int_t k, - ae_state *_state); -void mlpgetinputscaling(multilayerperceptron* network, - ae_int_t i, - double* mean, - double* sigma, - ae_state *_state); -void mlpgetoutputscaling(multilayerperceptron* network, - ae_int_t i, - double* mean, - double* sigma, - ae_state *_state); -void mlpgetneuroninfo(multilayerperceptron* network, - ae_int_t k, - ae_int_t i, - ae_int_t* fkind, - double* threshold, - ae_state *_state); -double mlpgetweight(multilayerperceptron* network, - ae_int_t k0, - ae_int_t i0, - ae_int_t k1, - ae_int_t i1, - ae_state *_state); -void mlpsetinputscaling(multilayerperceptron* network, - ae_int_t i, - double mean, - double sigma, - ae_state *_state); -void mlpsetoutputscaling(multilayerperceptron* network, - ae_int_t i, - double mean, - double sigma, - ae_state *_state); -void mlpsetneuroninfo(multilayerperceptron* network, - ae_int_t k, - ae_int_t i, - ae_int_t fkind, - double threshold, - ae_state *_state); -void mlpsetweight(multilayerperceptron* network, - ae_int_t k0, - ae_int_t i0, - ae_int_t k1, - ae_int_t i1, - double w, - ae_state *_state); -void mlpactivationfunction(double net, - ae_int_t k, - double* f, - double* df, - double* d2f, - ae_state *_state); -void mlpprocess(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void mlpprocessi(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -double mlperror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlperror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlperrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlperrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state); -double mlperrorn(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_state *_state); -ae_int_t mlpclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -ae_int_t _pexec_mlpclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlprelclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlprelclserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlprelclserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlprelclserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state); -double mlpavgce(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlpavgce(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlpavgcesparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlpavgcesparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state); -double mlprmserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlprmserror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlprmserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlprmserrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state); -double mlpavgerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlpavgerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlpavgerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlpavgerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state); -double mlpavgrelerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlpavgrelerror(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, ae_state *_state); -double mlpavgrelerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -double _pexec_mlpavgrelerrorsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t npoints, ae_state *_state); -void mlpgrad(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* desiredy, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void mlpgradn(multilayerperceptron* network, - /* Real */ ae_vector* x, - /* Real */ ae_vector* desiredy, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void mlpgradbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void _pexec_mlpgradbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state); -void mlpgradbatchsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void _pexec_mlpgradbatchsparse(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state); -void mlpgradbatchsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void _pexec_mlpgradbatchsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state); -void mlpgradbatchsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void _pexec_mlpgradbatchsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* idx, - ae_int_t subsetsize, - double* e, - /* Real */ ae_vector* grad, ae_state *_state); -void mlpgradbatchx(multilayerperceptron* network, - /* Real */ ae_matrix* densexy, - sparsematrix* sparsexy, - ae_int_t datasetsize, - ae_int_t datasettype, - /* Integer */ ae_vector* idx, - ae_int_t subset0, - ae_int_t subset1, - ae_int_t subsettype, - ae_shared_pool* buf, - ae_shared_pool* gradbuf, - ae_state *_state); -void mlpgradnbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - ae_state *_state); -void mlphessiannbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - /* Real */ ae_matrix* h, - ae_state *_state); -void mlphessianbatch(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - double* e, - /* Real */ ae_vector* grad, - /* Real */ ae_matrix* h, - ae_state *_state); -void mlpinternalprocessvector(/* Integer */ ae_vector* structinfo, - /* Real */ ae_vector* weights, - /* Real */ ae_vector* columnmeans, - /* Real */ ae_vector* columnsigmas, - /* Real */ ae_vector* neurons, - /* Real */ ae_vector* dfdnet, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void mlpalloc(ae_serializer* s, - multilayerperceptron* network, - ae_state *_state); -void mlpserialize(ae_serializer* s, - multilayerperceptron* network, - ae_state *_state); -void mlpunserialize(ae_serializer* s, - multilayerperceptron* network, - ae_state *_state); -void mlpallerrorssubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, - ae_state *_state); -void _pexec_mlpallerrorssubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, ae_state *_state); -void mlpallerrorssparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, - ae_state *_state); -void _pexec_mlpallerrorssparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - modelerrors* rep, ae_state *_state); -double mlperrorsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - ae_state *_state); -double _pexec_mlperrorsubset(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, ae_state *_state); -double mlperrorsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, - ae_state *_state); -double _pexec_mlperrorsparsesubset(multilayerperceptron* network, - sparsematrix* xy, - ae_int_t setsize, - /* Integer */ ae_vector* subset, - ae_int_t subsetsize, ae_state *_state); -void mlpallerrorsx(multilayerperceptron* network, - /* Real */ ae_matrix* densexy, - sparsematrix* sparsexy, - ae_int_t datasetsize, - ae_int_t datasettype, - /* Integer */ ae_vector* idx, - ae_int_t subset0, - ae_int_t subset1, - ae_int_t subsettype, - ae_shared_pool* buf, - modelerrors* rep, - ae_state *_state); -ae_bool _modelerrors_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _modelerrors_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _modelerrors_clear(void* _p); -void _modelerrors_destroy(void* _p); -ae_bool _smlpgrad_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _smlpgrad_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _smlpgrad_clear(void* _p); -void _smlpgrad_destroy(void* _p); -ae_bool _multilayerperceptron_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _multilayerperceptron_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _multilayerperceptron_clear(void* _p); -void _multilayerperceptron_destroy(void* _p); -void mnltrainh(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t nclasses, - ae_int_t* info, - logitmodel* lm, - mnlreport* rep, - ae_state *_state); -void mnlprocess(logitmodel* lm, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void mnlprocessi(logitmodel* lm, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void mnlunpack(logitmodel* lm, - /* Real */ ae_matrix* a, - ae_int_t* nvars, - ae_int_t* nclasses, - ae_state *_state); -void mnlpack(/* Real */ ae_matrix* a, - ae_int_t nvars, - ae_int_t nclasses, - logitmodel* lm, - ae_state *_state); -void mnlcopy(logitmodel* lm1, logitmodel* lm2, ae_state *_state); -double mnlavgce(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mnlrelclserror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mnlrmserror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mnlavgerror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mnlavgrelerror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t ssize, - ae_state *_state); -ae_int_t mnlclserror(logitmodel* lm, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -ae_bool _logitmodel_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _logitmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _logitmodel_clear(void* _p); -void _logitmodel_destroy(void* _p); -ae_bool _logitmcstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _logitmcstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _logitmcstate_clear(void* _p); -void _logitmcstate_destroy(void* _p); -ae_bool _mnlreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mnlreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mnlreport_clear(void* _p); -void _mnlreport_destroy(void* _p); -void mcpdcreate(ae_int_t n, mcpdstate* s, ae_state *_state); -void mcpdcreateentry(ae_int_t n, - ae_int_t entrystate, - mcpdstate* s, - ae_state *_state); -void mcpdcreateexit(ae_int_t n, - ae_int_t exitstate, - mcpdstate* s, - ae_state *_state); -void mcpdcreateentryexit(ae_int_t n, - ae_int_t entrystate, - ae_int_t exitstate, - mcpdstate* s, - ae_state *_state); -void mcpdaddtrack(mcpdstate* s, - /* Real */ ae_matrix* xy, - ae_int_t k, - ae_state *_state); -void mcpdsetec(mcpdstate* s, - /* Real */ ae_matrix* ec, - ae_state *_state); -void mcpdaddec(mcpdstate* s, - ae_int_t i, - ae_int_t j, - double c, - ae_state *_state); -void mcpdsetbc(mcpdstate* s, - /* Real */ ae_matrix* bndl, - /* Real */ ae_matrix* bndu, - ae_state *_state); -void mcpdaddbc(mcpdstate* s, - ae_int_t i, - ae_int_t j, - double bndl, - double bndu, - ae_state *_state); -void mcpdsetlc(mcpdstate* s, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state); -void mcpdsettikhonovregularizer(mcpdstate* s, double v, ae_state *_state); -void mcpdsetprior(mcpdstate* s, - /* Real */ ae_matrix* pp, - ae_state *_state); -void mcpdsetpredictionweights(mcpdstate* s, - /* Real */ ae_vector* pw, - ae_state *_state); -void mcpdsolve(mcpdstate* s, ae_state *_state); -void mcpdresults(mcpdstate* s, - /* Real */ ae_matrix* p, - mcpdreport* rep, - ae_state *_state); -ae_bool _mcpdstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mcpdstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mcpdstate_clear(void* _p); -void _mcpdstate_destroy(void* _p); -ae_bool _mcpdreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mcpdreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mcpdreport_clear(void* _p); -void _mcpdreport_destroy(void* _p); -void mlpecreate0(ae_int_t nin, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreate1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreate2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreateb0(ae_int_t nin, - ae_int_t nout, - double b, - double d, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreateb1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double b, - double d, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreateb2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double b, - double d, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreater0(ae_int_t nin, - ae_int_t nout, - double a, - double b, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreater1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - double a, - double b, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreater2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - double a, - double b, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreatec0(ae_int_t nin, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreatec1(ae_int_t nin, - ae_int_t nhid, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreatec2(ae_int_t nin, - ae_int_t nhid1, - ae_int_t nhid2, - ae_int_t nout, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecreatefromnetwork(multilayerperceptron* network, - ae_int_t ensemblesize, - mlpensemble* ensemble, - ae_state *_state); -void mlpecopy(mlpensemble* ensemble1, - mlpensemble* ensemble2, - ae_state *_state); -void mlperandomize(mlpensemble* ensemble, ae_state *_state); -void mlpeproperties(mlpensemble* ensemble, - ae_int_t* nin, - ae_int_t* nout, - ae_state *_state); -ae_bool mlpeissoftmax(mlpensemble* ensemble, ae_state *_state); -void mlpeprocess(mlpensemble* ensemble, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void mlpeprocessi(mlpensemble* ensemble, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void mlpeallerrorsx(mlpensemble* ensemble, - /* Real */ ae_matrix* densexy, - sparsematrix* sparsexy, - ae_int_t datasetsize, - ae_int_t datasettype, - /* Integer */ ae_vector* idx, - ae_int_t subset0, - ae_int_t subset1, - ae_int_t subsettype, - ae_shared_pool* buf, - modelerrors* rep, - ae_state *_state); -void mlpeallerrorssparse(mlpensemble* ensemble, - sparsematrix* xy, - ae_int_t npoints, - double* relcls, - double* avgce, - double* rms, - double* avg, - double* avgrel, - ae_state *_state); -double mlperelclserror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mlpeavgce(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mlpermserror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mlpeavgerror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -double mlpeavgrelerror(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -void mlpealloc(ae_serializer* s, mlpensemble* ensemble, ae_state *_state); -void mlpeserialize(ae_serializer* s, - mlpensemble* ensemble, - ae_state *_state); -void mlpeunserialize(ae_serializer* s, - mlpensemble* ensemble, - ae_state *_state); -ae_bool _mlpensemble_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlpensemble_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlpensemble_clear(void* _p); -void _mlpensemble_destroy(void* _p); -void mlptrainlm(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - ae_state *_state); -void mlptrainlbfgs(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_int_t* info, - mlpreport* rep, - ae_state *_state); -void mlptraines(multilayerperceptron* network, - /* Real */ ae_matrix* trnxy, - ae_int_t trnsize, - /* Real */ ae_matrix* valxy, - ae_int_t valsize, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - ae_state *_state); -void mlpkfoldcvlbfgs(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_int_t foldscount, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* cvrep, - ae_state *_state); -void mlpkfoldcvlm(multilayerperceptron* network, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t foldscount, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* cvrep, - ae_state *_state); -void mlpkfoldcv(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - ae_int_t foldscount, - mlpreport* rep, - ae_state *_state); -void _pexec_mlpkfoldcv(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - ae_int_t foldscount, - mlpreport* rep, ae_state *_state); -void mlpcreatetrainer(ae_int_t nin, - ae_int_t nout, - mlptrainer* s, - ae_state *_state); -void mlpcreatetrainercls(ae_int_t nin, - ae_int_t nclasses, - mlptrainer* s, - ae_state *_state); -void mlpsetdataset(mlptrainer* s, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_state *_state); -void mlpsetsparsedataset(mlptrainer* s, - sparsematrix* xy, - ae_int_t npoints, - ae_state *_state); -void mlpsetdecay(mlptrainer* s, double decay, ae_state *_state); -void mlpsetcond(mlptrainer* s, - double wstep, - ae_int_t maxits, - ae_state *_state); -void mlpsetalgobatch(mlptrainer* s, ae_state *_state); -void mlptrainnetwork(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - mlpreport* rep, - ae_state *_state); -void _pexec_mlptrainnetwork(mlptrainer* s, - multilayerperceptron* network, - ae_int_t nrestarts, - mlpreport* rep, ae_state *_state); -void mlpstarttraining(mlptrainer* s, - multilayerperceptron* network, - ae_bool randomstart, - ae_state *_state); -ae_bool mlpcontinuetraining(mlptrainer* s, - multilayerperceptron* network, - ae_state *_state); -ae_bool _pexec_mlpcontinuetraining(mlptrainer* s, - multilayerperceptron* network, ae_state *_state); -void mlpebagginglm(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* ooberrors, - ae_state *_state); -void mlpebagginglbfgs(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - double wstep, - ae_int_t maxits, - ae_int_t* info, - mlpreport* rep, - mlpcvreport* ooberrors, - ae_state *_state); -void mlpetraines(mlpensemble* ensemble, - /* Real */ ae_matrix* xy, - ae_int_t npoints, - double decay, - ae_int_t restarts, - ae_int_t* info, - mlpreport* rep, - ae_state *_state); -void mlptrainensemblees(mlptrainer* s, - mlpensemble* ensemble, - ae_int_t nrestarts, - mlpreport* rep, - ae_state *_state); -void _pexec_mlptrainensemblees(mlptrainer* s, - mlpensemble* ensemble, - ae_int_t nrestarts, - mlpreport* rep, ae_state *_state); -ae_bool _mlpreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlpreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlpreport_clear(void* _p); -void _mlpreport_destroy(void* _p); -ae_bool _mlpcvreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlpcvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlpcvreport_clear(void* _p); -void _mlpcvreport_destroy(void* _p); -ae_bool _smlptrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _smlptrnsession_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _smlptrnsession_clear(void* _p); -void _smlptrnsession_destroy(void* _p); -ae_bool _mlpetrnsession_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlpetrnsession_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlpetrnsession_clear(void* _p); -void _mlpetrnsession_destroy(void* _p); -ae_bool _mlptrainer_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlptrainer_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlptrainer_clear(void* _p); -void _mlptrainer_destroy(void* _p); -ae_bool _mlpparallelizationcv_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mlpparallelizationcv_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mlpparallelizationcv_clear(void* _p); -void _mlpparallelizationcv_destroy(void* _p); -void pcabuildbasis(/* Real */ ae_matrix* x, - ae_int_t npoints, - ae_int_t nvars, - ae_int_t* info, - /* Real */ ae_vector* s2, - /* Real */ ae_matrix* v, - ae_state *_state); - -} -#endif - diff --git a/src/inc/alglib/diffequations.cpp b/src/inc/alglib/diffequations.cpp deleted file mode 100644 index 268ecd0..0000000 --- a/src/inc/alglib/diffequations.cpp +++ /dev/null @@ -1,1187 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "diffequations.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* - -*************************************************************************/ -_odesolverstate_owner::_odesolverstate_owner() -{ - p_struct = (alglib_impl::odesolverstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::odesolverstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_odesolverstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_odesolverstate_owner::_odesolverstate_owner(const _odesolverstate_owner &rhs) -{ - p_struct = (alglib_impl::odesolverstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::odesolverstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_odesolverstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_odesolverstate_owner& _odesolverstate_owner::operator=(const _odesolverstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_odesolverstate_clear(p_struct); - if( !alglib_impl::_odesolverstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_odesolverstate_owner::~_odesolverstate_owner() -{ - alglib_impl::_odesolverstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::odesolverstate* _odesolverstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::odesolverstate* _odesolverstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -odesolverstate::odesolverstate() : _odesolverstate_owner() ,needdy(p_struct->needdy),y(&p_struct->y),dy(&p_struct->dy),x(p_struct->x) -{ -} - -odesolverstate::odesolverstate(const odesolverstate &rhs):_odesolverstate_owner(rhs) ,needdy(p_struct->needdy),y(&p_struct->y),dy(&p_struct->dy),x(p_struct->x) -{ -} - -odesolverstate& odesolverstate::operator=(const odesolverstate &rhs) -{ - if( this==&rhs ) - return *this; - _odesolverstate_owner::operator=(rhs); - return *this; -} - -odesolverstate::~odesolverstate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_odesolverreport_owner::_odesolverreport_owner() -{ - p_struct = (alglib_impl::odesolverreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::odesolverreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_odesolverreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_odesolverreport_owner::_odesolverreport_owner(const _odesolverreport_owner &rhs) -{ - p_struct = (alglib_impl::odesolverreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::odesolverreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_odesolverreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_odesolverreport_owner& _odesolverreport_owner::operator=(const _odesolverreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_odesolverreport_clear(p_struct); - if( !alglib_impl::_odesolverreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_odesolverreport_owner::~_odesolverreport_owner() -{ - alglib_impl::_odesolverreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::odesolverreport* _odesolverreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::odesolverreport* _odesolverreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -odesolverreport::odesolverreport() : _odesolverreport_owner() ,nfev(p_struct->nfev),terminationtype(p_struct->terminationtype) -{ -} - -odesolverreport::odesolverreport(const odesolverreport &rhs):_odesolverreport_owner(rhs) ,nfev(p_struct->nfev),terminationtype(p_struct->terminationtype) -{ -} - -odesolverreport& odesolverreport::operator=(const odesolverreport &rhs) -{ - if( this==&rhs ) - return *this; - _odesolverreport_owner::operator=(rhs); - return *this; -} - -odesolverreport::~odesolverreport() -{ -} - -/************************************************************************* -Cash-Karp adaptive ODE solver. - -This subroutine solves ODE Y'=f(Y,x) with initial conditions Y(xs)=Ys -(here Y may be single variable or vector of N variables). - -INPUT PARAMETERS: - Y - initial conditions, array[0..N-1]. - contains values of Y[] at X[0] - N - system size - X - points at which Y should be tabulated, array[0..M-1] - integrations starts at X[0], ends at X[M-1], intermediate - values at X[i] are returned too. - SHOULD BE ORDERED BY ASCENDING OR BY DESCENDING!!!! - M - number of intermediate points + first point + last point: - * M>2 means that you need both Y(X[M-1]) and M-2 values at - intermediate points - * M=2 means that you want just to integrate from X[0] to - X[1] and don't interested in intermediate values. - * M=1 means that you don't want to integrate :) - it is degenerate case, but it will be handled correctly. - * M<1 means error - Eps - tolerance (absolute/relative error on each step will be - less than Eps). When passing: - * Eps>0, it means desired ABSOLUTE error - * Eps<0, it means desired RELATIVE error. Relative errors - are calculated with respect to maximum values of Y seen - so far. Be careful to use this criterion when starting - from Y[] that are close to zero. - H - initial step lenth, it will be adjusted automatically - after the first step. If H=0, step will be selected - automatically (usualy it will be equal to 0.001 of - min(x[i]-x[j])). - -OUTPUT PARAMETERS - State - structure which stores algorithm state between subsequent - calls of OdeSolverIteration. Used for reverse communication. - This structure should be passed to the OdeSolverIteration - subroutine. - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKIteration, AutoGKResults. - - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverrkck(const real_1d_array &y, const ae_int_t n, const real_1d_array &x, const ae_int_t m, const double eps, const double h, odesolverstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::odesolverrkck(const_cast(y.c_ptr()), n, const_cast(x.c_ptr()), m, eps, h, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cash-Karp adaptive ODE solver. - -This subroutine solves ODE Y'=f(Y,x) with initial conditions Y(xs)=Ys -(here Y may be single variable or vector of N variables). - -INPUT PARAMETERS: - Y - initial conditions, array[0..N-1]. - contains values of Y[] at X[0] - N - system size - X - points at which Y should be tabulated, array[0..M-1] - integrations starts at X[0], ends at X[M-1], intermediate - values at X[i] are returned too. - SHOULD BE ORDERED BY ASCENDING OR BY DESCENDING!!!! - M - number of intermediate points + first point + last point: - * M>2 means that you need both Y(X[M-1]) and M-2 values at - intermediate points - * M=2 means that you want just to integrate from X[0] to - X[1] and don't interested in intermediate values. - * M=1 means that you don't want to integrate :) - it is degenerate case, but it will be handled correctly. - * M<1 means error - Eps - tolerance (absolute/relative error on each step will be - less than Eps). When passing: - * Eps>0, it means desired ABSOLUTE error - * Eps<0, it means desired RELATIVE error. Relative errors - are calculated with respect to maximum values of Y seen - so far. Be careful to use this criterion when starting - from Y[] that are close to zero. - H - initial step lenth, it will be adjusted automatically - after the first step. If H=0, step will be selected - automatically (usualy it will be equal to 0.001 of - min(x[i]-x[j])). - -OUTPUT PARAMETERS - State - structure which stores algorithm state between subsequent - calls of OdeSolverIteration. Used for reverse communication. - This structure should be passed to the OdeSolverIteration - subroutine. - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKIteration, AutoGKResults. - - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverrkck(const real_1d_array &y, const real_1d_array &x, const double eps, const double h, odesolverstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = y.length(); - m = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::odesolverrkck(const_cast(y.c_ptr()), n, const_cast(x.c_ptr()), m, eps, h, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool odesolveriteration(const odesolverstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::odesolveriteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void odesolversolve(odesolverstate &state, - void (*diff)(const real_1d_array &y, double x, real_1d_array &dy, void *ptr), - void *ptr){ - alglib_impl::ae_state _alglib_env_state; - if( diff==NULL ) - throw ap_error("ALGLIB: error in 'odesolversolve()' (diff is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::odesolveriteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needdy ) - { - diff(state.y, state.x, state.dy, ptr); - continue; - } - throw ap_error("ALGLIB: unexpected error in 'odesolversolve'"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -ODE solver results - -Called after OdeSolverIteration returned False. - -INPUT PARAMETERS: - State - algorithm state (used by OdeSolverIteration). - -OUTPUT PARAMETERS: - M - number of tabulated values, M>=1 - XTbl - array[0..M-1], values of X - YTbl - array[0..M-1,0..N-1], values of Y in X[i] - Rep - solver report: - * Rep.TerminationType completetion code: - * -2 X is not ordered by ascending/descending or - there are non-distinct X[], i.e. X[i]=X[i+1] - * -1 incorrect parameters were specified - * 1 task has been solved - * Rep.NFEV contains number of function calculations - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverresults(const odesolverstate &state, ae_int_t &m, real_1d_array &xtbl, real_2d_array &ytbl, odesolverreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::odesolverresults(const_cast(state.c_ptr()), &m, const_cast(xtbl.c_ptr()), const_cast(ytbl.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static double odesolver_odesolvermaxgrow = 3.0; -static double odesolver_odesolvermaxshrink = 10.0; -static void odesolver_odesolverinit(ae_int_t solvertype, - /* Real */ ae_vector* y, - ae_int_t n, - /* Real */ ae_vector* x, - ae_int_t m, - double eps, - double h, - odesolverstate* state, - ae_state *_state); - - - - - -/************************************************************************* -Cash-Karp adaptive ODE solver. - -This subroutine solves ODE Y'=f(Y,x) with initial conditions Y(xs)=Ys -(here Y may be single variable or vector of N variables). - -INPUT PARAMETERS: - Y - initial conditions, array[0..N-1]. - contains values of Y[] at X[0] - N - system size - X - points at which Y should be tabulated, array[0..M-1] - integrations starts at X[0], ends at X[M-1], intermediate - values at X[i] are returned too. - SHOULD BE ORDERED BY ASCENDING OR BY DESCENDING!!!! - M - number of intermediate points + first point + last point: - * M>2 means that you need both Y(X[M-1]) and M-2 values at - intermediate points - * M=2 means that you want just to integrate from X[0] to - X[1] and don't interested in intermediate values. - * M=1 means that you don't want to integrate :) - it is degenerate case, but it will be handled correctly. - * M<1 means error - Eps - tolerance (absolute/relative error on each step will be - less than Eps). When passing: - * Eps>0, it means desired ABSOLUTE error - * Eps<0, it means desired RELATIVE error. Relative errors - are calculated with respect to maximum values of Y seen - so far. Be careful to use this criterion when starting - from Y[] that are close to zero. - H - initial step lenth, it will be adjusted automatically - after the first step. If H=0, step will be selected - automatically (usualy it will be equal to 0.001 of - min(x[i]-x[j])). - -OUTPUT PARAMETERS - State - structure which stores algorithm state between subsequent - calls of OdeSolverIteration. Used for reverse communication. - This structure should be passed to the OdeSolverIteration - subroutine. - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKIteration, AutoGKResults. - - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverrkck(/* Real */ ae_vector* y, - ae_int_t n, - /* Real */ ae_vector* x, - ae_int_t m, - double eps, - double h, - odesolverstate* state, - ae_state *_state) -{ - - _odesolverstate_clear(state); - - ae_assert(n>=1, "ODESolverRKCK: N<1!", _state); - ae_assert(m>=1, "ODESolverRKCK: M<1!", _state); - ae_assert(y->cnt>=n, "ODESolverRKCK: Length(Y)cnt>=m, "ODESolverRKCK: Length(X)rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - i = state->rstate.ia.ptr.p_int[2]; - j = state->rstate.ia.ptr.p_int[3]; - k = state->rstate.ia.ptr.p_int[4]; - klimit = state->rstate.ia.ptr.p_int[5]; - gridpoint = state->rstate.ba.ptr.p_bool[0]; - xc = state->rstate.ra.ptr.p_double[0]; - v = state->rstate.ra.ptr.p_double[1]; - h = state->rstate.ra.ptr.p_double[2]; - h2 = state->rstate.ra.ptr.p_double[3]; - err = state->rstate.ra.ptr.p_double[4]; - maxgrowpow = state->rstate.ra.ptr.p_double[5]; - } - else - { - n = -983; - m = -989; - i = -834; - j = 900; - k = -287; - klimit = 364; - gridpoint = ae_false; - xc = -338; - v = -686; - h = 912; - h2 = 585; - err = 497; - maxgrowpow = -271; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - - /* - * Routine body - */ - - /* - * prepare - */ - if( state->repterminationtype!=0 ) - { - result = ae_false; - return result; - } - n = state->n; - m = state->m; - h = state->h; - maxgrowpow = ae_pow(odesolver_odesolvermaxgrow, 5, _state); - state->repnfev = 0; - - /* - * some preliminary checks for internal errors - * after this we assume that H>0 and M>1 - */ - ae_assert(ae_fp_greater(state->h,0), "ODESolver: internal error", _state); - ae_assert(m>1, "ODESolverIteration: internal error", _state); - - /* - * choose solver - */ - if( state->solvertype!=0 ) - { - goto lbl_1; - } - - /* - * Cask-Karp solver - * Prepare coefficients table. - * Check it for errors - */ - ae_vector_set_length(&state->rka, 6, _state); - state->rka.ptr.p_double[0] = 0; - state->rka.ptr.p_double[1] = (double)1/(double)5; - state->rka.ptr.p_double[2] = (double)3/(double)10; - state->rka.ptr.p_double[3] = (double)3/(double)5; - state->rka.ptr.p_double[4] = 1; - state->rka.ptr.p_double[5] = (double)7/(double)8; - ae_matrix_set_length(&state->rkb, 6, 5, _state); - state->rkb.ptr.pp_double[1][0] = (double)1/(double)5; - state->rkb.ptr.pp_double[2][0] = (double)3/(double)40; - state->rkb.ptr.pp_double[2][1] = (double)9/(double)40; - state->rkb.ptr.pp_double[3][0] = (double)3/(double)10; - state->rkb.ptr.pp_double[3][1] = -(double)9/(double)10; - state->rkb.ptr.pp_double[3][2] = (double)6/(double)5; - state->rkb.ptr.pp_double[4][0] = -(double)11/(double)54; - state->rkb.ptr.pp_double[4][1] = (double)5/(double)2; - state->rkb.ptr.pp_double[4][2] = -(double)70/(double)27; - state->rkb.ptr.pp_double[4][3] = (double)35/(double)27; - state->rkb.ptr.pp_double[5][0] = (double)1631/(double)55296; - state->rkb.ptr.pp_double[5][1] = (double)175/(double)512; - state->rkb.ptr.pp_double[5][2] = (double)575/(double)13824; - state->rkb.ptr.pp_double[5][3] = (double)44275/(double)110592; - state->rkb.ptr.pp_double[5][4] = (double)253/(double)4096; - ae_vector_set_length(&state->rkc, 6, _state); - state->rkc.ptr.p_double[0] = (double)37/(double)378; - state->rkc.ptr.p_double[1] = 0; - state->rkc.ptr.p_double[2] = (double)250/(double)621; - state->rkc.ptr.p_double[3] = (double)125/(double)594; - state->rkc.ptr.p_double[4] = 0; - state->rkc.ptr.p_double[5] = (double)512/(double)1771; - ae_vector_set_length(&state->rkcs, 6, _state); - state->rkcs.ptr.p_double[0] = (double)2825/(double)27648; - state->rkcs.ptr.p_double[1] = 0; - state->rkcs.ptr.p_double[2] = (double)18575/(double)48384; - state->rkcs.ptr.p_double[3] = (double)13525/(double)55296; - state->rkcs.ptr.p_double[4] = (double)277/(double)14336; - state->rkcs.ptr.p_double[5] = (double)1/(double)4; - ae_matrix_set_length(&state->rkk, 6, n, _state); - - /* - * Main cycle consists of two iterations: - * * outer where we travel from X[i-1] to X[i] - * * inner where we travel inside [X[i-1],X[i]] - */ - ae_matrix_set_length(&state->ytbl, m, n, _state); - ae_vector_set_length(&state->escale, n, _state); - ae_vector_set_length(&state->yn, n, _state); - ae_vector_set_length(&state->yns, n, _state); - xc = state->xg.ptr.p_double[0]; - ae_v_move(&state->ytbl.ptr.pp_double[0][0], 1, &state->yc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(j=0; j<=n-1; j++) - { - state->escale.ptr.p_double[j] = 0; - } - i = 1; -lbl_3: - if( i>m-1 ) - { - goto lbl_5; - } - - /* - * begin inner iteration - */ -lbl_6: - if( ae_false ) - { - goto lbl_7; - } - - /* - * truncate step if needed (beyond right boundary). - * determine should we store X or not - */ - if( ae_fp_greater_eq(xc+h,state->xg.ptr.p_double[i]) ) - { - h = state->xg.ptr.p_double[i]-xc; - gridpoint = ae_true; - } - else - { - gridpoint = ae_false; - } - - /* - * Update error scale maximums - * - * These maximums are initialized by zeros, - * then updated every iterations. - */ - for(j=0; j<=n-1; j++) - { - state->escale.ptr.p_double[j] = ae_maxreal(state->escale.ptr.p_double[j], ae_fabs(state->yc.ptr.p_double[j], _state), _state); - } - - /* - * make one step: - * 1. calculate all info needed to do step - * 2. update errors scale maximums using values/derivatives - * obtained during (1) - * - * Take into account that we use scaling of X to reduce task - * to the form where x[0] < x[1] < ... < x[n-1]. So X is - * replaced by x=xscale*t, and dy/dx=f(y,x) is replaced - * by dy/dt=xscale*f(y,xscale*t). - */ - ae_v_move(&state->yn.ptr.p_double[0], 1, &state->yc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->yns.ptr.p_double[0], 1, &state->yc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - k = 0; -lbl_8: - if( k>5 ) - { - goto lbl_10; - } - - /* - * prepare data for the next update of YN/YNS - */ - state->x = state->xscale*(xc+state->rka.ptr.p_double[k]*h); - ae_v_move(&state->y.ptr.p_double[0], 1, &state->yc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(j=0; j<=k-1; j++) - { - v = state->rkb.ptr.pp_double[k][j]; - ae_v_addd(&state->y.ptr.p_double[0], 1, &state->rkk.ptr.pp_double[j][0], 1, ae_v_len(0,n-1), v); - } - state->needdy = ae_true; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->needdy = ae_false; - state->repnfev = state->repnfev+1; - v = h*state->xscale; - ae_v_moved(&state->rkk.ptr.pp_double[k][0], 1, &state->dy.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - - /* - * update YN/YNS - */ - v = state->rkc.ptr.p_double[k]; - ae_v_addd(&state->yn.ptr.p_double[0], 1, &state->rkk.ptr.pp_double[k][0], 1, ae_v_len(0,n-1), v); - v = state->rkcs.ptr.p_double[k]; - ae_v_addd(&state->yns.ptr.p_double[0], 1, &state->rkk.ptr.pp_double[k][0], 1, ae_v_len(0,n-1), v); - k = k+1; - goto lbl_8; -lbl_10: - - /* - * estimate error - */ - err = 0; - for(j=0; j<=n-1; j++) - { - if( !state->fraceps ) - { - - /* - * absolute error is estimated - */ - err = ae_maxreal(err, ae_fabs(state->yn.ptr.p_double[j]-state->yns.ptr.p_double[j], _state), _state); - } - else - { - - /* - * Relative error is estimated - */ - v = state->escale.ptr.p_double[j]; - if( ae_fp_eq(v,0) ) - { - v = 1; - } - err = ae_maxreal(err, ae_fabs(state->yn.ptr.p_double[j]-state->yns.ptr.p_double[j], _state)/v, _state); - } - } - - /* - * calculate new step, restart if necessary - */ - if( ae_fp_less_eq(maxgrowpow*err,state->eps) ) - { - h2 = odesolver_odesolvermaxgrow*h; - } - else - { - h2 = h*ae_pow(state->eps/err, 0.2, _state); - } - if( ae_fp_less(h2,h/odesolver_odesolvermaxshrink) ) - { - h2 = h/odesolver_odesolvermaxshrink; - } - if( ae_fp_greater(err,state->eps) ) - { - h = h2; - goto lbl_6; - } - - /* - * advance position - */ - xc = xc+h; - ae_v_move(&state->yc.ptr.p_double[0], 1, &state->yn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * update H - */ - h = h2; - - /* - * break on grid point - */ - if( gridpoint ) - { - goto lbl_7; - } - goto lbl_6; -lbl_7: - - /* - * save result - */ - ae_v_move(&state->ytbl.ptr.pp_double[i][0], 1, &state->yc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - i = i+1; - goto lbl_3; -lbl_5: - state->repterminationtype = 1; - result = ae_false; - return result; -lbl_1: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = i; - state->rstate.ia.ptr.p_int[3] = j; - state->rstate.ia.ptr.p_int[4] = k; - state->rstate.ia.ptr.p_int[5] = klimit; - state->rstate.ba.ptr.p_bool[0] = gridpoint; - state->rstate.ra.ptr.p_double[0] = xc; - state->rstate.ra.ptr.p_double[1] = v; - state->rstate.ra.ptr.p_double[2] = h; - state->rstate.ra.ptr.p_double[3] = h2; - state->rstate.ra.ptr.p_double[4] = err; - state->rstate.ra.ptr.p_double[5] = maxgrowpow; - return result; -} - - -/************************************************************************* -ODE solver results - -Called after OdeSolverIteration returned False. - -INPUT PARAMETERS: - State - algorithm state (used by OdeSolverIteration). - -OUTPUT PARAMETERS: - M - number of tabulated values, M>=1 - XTbl - array[0..M-1], values of X - YTbl - array[0..M-1,0..N-1], values of Y in X[i] - Rep - solver report: - * Rep.TerminationType completetion code: - * -2 X is not ordered by ascending/descending or - there are non-distinct X[], i.e. X[i]=X[i+1] - * -1 incorrect parameters were specified - * 1 task has been solved - * Rep.NFEV contains number of function calculations - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverresults(odesolverstate* state, - ae_int_t* m, - /* Real */ ae_vector* xtbl, - /* Real */ ae_matrix* ytbl, - odesolverreport* rep, - ae_state *_state) -{ - double v; - ae_int_t i; - - *m = 0; - ae_vector_clear(xtbl); - ae_matrix_clear(ytbl); - _odesolverreport_clear(rep); - - rep->terminationtype = state->repterminationtype; - if( rep->terminationtype>0 ) - { - *m = state->m; - rep->nfev = state->repnfev; - ae_vector_set_length(xtbl, state->m, _state); - v = state->xscale; - ae_v_moved(&xtbl->ptr.p_double[0], 1, &state->xg.ptr.p_double[0], 1, ae_v_len(0,state->m-1), v); - ae_matrix_set_length(ytbl, state->m, state->n, _state); - for(i=0; i<=state->m-1; i++) - { - ae_v_move(&ytbl->ptr.pp_double[i][0], 1, &state->ytbl.ptr.pp_double[i][0], 1, ae_v_len(0,state->n-1)); - } - } - else - { - rep->nfev = 0; - } -} - - -/************************************************************************* -Internal initialization subroutine -*************************************************************************/ -static void odesolver_odesolverinit(ae_int_t solvertype, - /* Real */ ae_vector* y, - ae_int_t n, - /* Real */ ae_vector* x, - ae_int_t m, - double eps, - double h, - odesolverstate* state, - ae_state *_state) -{ - ae_int_t i; - double v; - - _odesolverstate_clear(state); - - - /* - * Prepare RComm - */ - ae_vector_set_length(&state->rstate.ia, 5+1, _state); - ae_vector_set_length(&state->rstate.ba, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 5+1, _state); - state->rstate.stage = -1; - state->needdy = ae_false; - - /* - * check parameters. - */ - if( (n<=0||m<1)||ae_fp_eq(eps,0) ) - { - state->repterminationtype = -1; - return; - } - if( ae_fp_less(h,0) ) - { - h = -h; - } - - /* - * quick exit if necessary. - * after this block we assume that M>1 - */ - if( m==1 ) - { - state->repnfev = 0; - state->repterminationtype = 1; - ae_matrix_set_length(&state->ytbl, 1, n, _state); - ae_v_move(&state->ytbl.ptr.pp_double[0][0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_vector_set_length(&state->xg, m, _state); - ae_v_move(&state->xg.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,m-1)); - return; - } - - /* - * check again: correct order of X[] - */ - if( ae_fp_eq(x->ptr.p_double[1],x->ptr.p_double[0]) ) - { - state->repterminationtype = -2; - return; - } - for(i=1; i<=m-1; i++) - { - if( (ae_fp_greater(x->ptr.p_double[1],x->ptr.p_double[0])&&ae_fp_less_eq(x->ptr.p_double[i],x->ptr.p_double[i-1]))||(ae_fp_less(x->ptr.p_double[1],x->ptr.p_double[0])&&ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i-1])) ) - { - state->repterminationtype = -2; - return; - } - } - - /* - * auto-select H if necessary - */ - if( ae_fp_eq(h,0) ) - { - v = ae_fabs(x->ptr.p_double[1]-x->ptr.p_double[0], _state); - for(i=2; i<=m-1; i++) - { - v = ae_minreal(v, ae_fabs(x->ptr.p_double[i]-x->ptr.p_double[i-1], _state), _state); - } - h = 0.001*v; - } - - /* - * store parameters - */ - state->n = n; - state->m = m; - state->h = h; - state->eps = ae_fabs(eps, _state); - state->fraceps = ae_fp_less(eps,0); - ae_vector_set_length(&state->xg, m, _state); - ae_v_move(&state->xg.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,m-1)); - if( ae_fp_greater(x->ptr.p_double[1],x->ptr.p_double[0]) ) - { - state->xscale = 1; - } - else - { - state->xscale = -1; - ae_v_muld(&state->xg.ptr.p_double[0], 1, ae_v_len(0,m-1), -1); - } - ae_vector_set_length(&state->yc, n, _state); - ae_v_move(&state->yc.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->solvertype = solvertype; - state->repterminationtype = 0; - - /* - * Allocate arrays - */ - ae_vector_set_length(&state->y, n, _state); - ae_vector_set_length(&state->dy, n, _state); -} - - -ae_bool _odesolverstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - odesolverstate *p = (odesolverstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->yc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->escale, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dy, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->ytbl, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->yn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->yns, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rka, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rkc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rkcs, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->rkb, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->rkk, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _odesolverstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - odesolverstate *dst = (odesolverstate*)_dst; - odesolverstate *src = (odesolverstate*)_src; - dst->n = src->n; - dst->m = src->m; - dst->xscale = src->xscale; - dst->h = src->h; - dst->eps = src->eps; - dst->fraceps = src->fraceps; - if( !ae_vector_init_copy(&dst->yc, &src->yc, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->escale, &src->escale, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xg, &src->xg, _state, make_automatic) ) - return ae_false; - dst->solvertype = src->solvertype; - dst->needdy = src->needdy; - dst->x = src->x; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dy, &src->dy, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->ytbl, &src->ytbl, _state, make_automatic) ) - return ae_false; - dst->repterminationtype = src->repterminationtype; - dst->repnfev = src->repnfev; - if( !ae_vector_init_copy(&dst->yn, &src->yn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->yns, &src->yns, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rka, &src->rka, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rkc, &src->rkc, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rkcs, &src->rkcs, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->rkb, &src->rkb, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->rkk, &src->rkk, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _odesolverstate_clear(void* _p) -{ - odesolverstate *p = (odesolverstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->yc); - ae_vector_clear(&p->escale); - ae_vector_clear(&p->xg); - ae_vector_clear(&p->y); - ae_vector_clear(&p->dy); - ae_matrix_clear(&p->ytbl); - ae_vector_clear(&p->yn); - ae_vector_clear(&p->yns); - ae_vector_clear(&p->rka); - ae_vector_clear(&p->rkc); - ae_vector_clear(&p->rkcs); - ae_matrix_clear(&p->rkb); - ae_matrix_clear(&p->rkk); - _rcommstate_clear(&p->rstate); -} - - -void _odesolverstate_destroy(void* _p) -{ - odesolverstate *p = (odesolverstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->yc); - ae_vector_destroy(&p->escale); - ae_vector_destroy(&p->xg); - ae_vector_destroy(&p->y); - ae_vector_destroy(&p->dy); - ae_matrix_destroy(&p->ytbl); - ae_vector_destroy(&p->yn); - ae_vector_destroy(&p->yns); - ae_vector_destroy(&p->rka); - ae_vector_destroy(&p->rkc); - ae_vector_destroy(&p->rkcs); - ae_matrix_destroy(&p->rkb); - ae_matrix_destroy(&p->rkk); - _rcommstate_destroy(&p->rstate); -} - - -ae_bool _odesolverreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - odesolverreport *p = (odesolverreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _odesolverreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - odesolverreport *dst = (odesolverreport*)_dst; - odesolverreport *src = (odesolverreport*)_src; - dst->nfev = src->nfev; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _odesolverreport_clear(void* _p) -{ - odesolverreport *p = (odesolverreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _odesolverreport_destroy(void* _p) -{ - odesolverreport *p = (odesolverreport*)_p; - ae_touch_ptr((void*)p); -} - - - -} - diff --git a/src/inc/alglib/diffequations.h b/src/inc/alglib/diffequations.h deleted file mode 100644 index f288f9b..0000000 --- a/src/inc/alglib/diffequations.h +++ /dev/null @@ -1,267 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _diffequations_pkg_h -#define _diffequations_pkg_h -#include "ap.h" -#include "alglibinternal.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - ae_int_t n; - ae_int_t m; - double xscale; - double h; - double eps; - ae_bool fraceps; - ae_vector yc; - ae_vector escale; - ae_vector xg; - ae_int_t solvertype; - ae_bool needdy; - double x; - ae_vector y; - ae_vector dy; - ae_matrix ytbl; - ae_int_t repterminationtype; - ae_int_t repnfev; - ae_vector yn; - ae_vector yns; - ae_vector rka; - ae_vector rkc; - ae_vector rkcs; - ae_matrix rkb; - ae_matrix rkk; - rcommstate rstate; -} odesolverstate; -typedef struct -{ - ae_int_t nfev; - ae_int_t terminationtype; -} odesolverreport; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - -/************************************************************************* - -*************************************************************************/ -class _odesolverstate_owner -{ -public: - _odesolverstate_owner(); - _odesolverstate_owner(const _odesolverstate_owner &rhs); - _odesolverstate_owner& operator=(const _odesolverstate_owner &rhs); - virtual ~_odesolverstate_owner(); - alglib_impl::odesolverstate* c_ptr(); - alglib_impl::odesolverstate* c_ptr() const; -protected: - alglib_impl::odesolverstate *p_struct; -}; -class odesolverstate : public _odesolverstate_owner -{ -public: - odesolverstate(); - odesolverstate(const odesolverstate &rhs); - odesolverstate& operator=(const odesolverstate &rhs); - virtual ~odesolverstate(); - ae_bool &needdy; - real_1d_array y; - real_1d_array dy; - double &x; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _odesolverreport_owner -{ -public: - _odesolverreport_owner(); - _odesolverreport_owner(const _odesolverreport_owner &rhs); - _odesolverreport_owner& operator=(const _odesolverreport_owner &rhs); - virtual ~_odesolverreport_owner(); - alglib_impl::odesolverreport* c_ptr(); - alglib_impl::odesolverreport* c_ptr() const; -protected: - alglib_impl::odesolverreport *p_struct; -}; -class odesolverreport : public _odesolverreport_owner -{ -public: - odesolverreport(); - odesolverreport(const odesolverreport &rhs); - odesolverreport& operator=(const odesolverreport &rhs); - virtual ~odesolverreport(); - ae_int_t &nfev; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -Cash-Karp adaptive ODE solver. - -This subroutine solves ODE Y'=f(Y,x) with initial conditions Y(xs)=Ys -(here Y may be single variable or vector of N variables). - -INPUT PARAMETERS: - Y - initial conditions, array[0..N-1]. - contains values of Y[] at X[0] - N - system size - X - points at which Y should be tabulated, array[0..M-1] - integrations starts at X[0], ends at X[M-1], intermediate - values at X[i] are returned too. - SHOULD BE ORDERED BY ASCENDING OR BY DESCENDING!!!! - M - number of intermediate points + first point + last point: - * M>2 means that you need both Y(X[M-1]) and M-2 values at - intermediate points - * M=2 means that you want just to integrate from X[0] to - X[1] and don't interested in intermediate values. - * M=1 means that you don't want to integrate :) - it is degenerate case, but it will be handled correctly. - * M<1 means error - Eps - tolerance (absolute/relative error on each step will be - less than Eps). When passing: - * Eps>0, it means desired ABSOLUTE error - * Eps<0, it means desired RELATIVE error. Relative errors - are calculated with respect to maximum values of Y seen - so far. Be careful to use this criterion when starting - from Y[] that are close to zero. - H - initial step lenth, it will be adjusted automatically - after the first step. If H=0, step will be selected - automatically (usualy it will be equal to 0.001 of - min(x[i]-x[j])). - -OUTPUT PARAMETERS - State - structure which stores algorithm state between subsequent - calls of OdeSolverIteration. Used for reverse communication. - This structure should be passed to the OdeSolverIteration - subroutine. - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKIteration, AutoGKResults. - - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverrkck(const real_1d_array &y, const ae_int_t n, const real_1d_array &x, const ae_int_t m, const double eps, const double h, odesolverstate &state); -void odesolverrkck(const real_1d_array &y, const real_1d_array &x, const double eps, const double h, odesolverstate &state); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool odesolveriteration(const odesolverstate &state); - - -/************************************************************************* -This function is used to launcn iterations of ODE solver - -It accepts following parameters: - diff - callback which calculates dy/dx for given y and x - ptr - optional pointer which is passed to diff; can be NULL - - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey - -*************************************************************************/ -void odesolversolve(odesolverstate &state, - void (*diff)(const real_1d_array &y, double x, real_1d_array &dy, void *ptr), - void *ptr = NULL); - - -/************************************************************************* -ODE solver results - -Called after OdeSolverIteration returned False. - -INPUT PARAMETERS: - State - algorithm state (used by OdeSolverIteration). - -OUTPUT PARAMETERS: - M - number of tabulated values, M>=1 - XTbl - array[0..M-1], values of X - YTbl - array[0..M-1,0..N-1], values of Y in X[i] - Rep - solver report: - * Rep.TerminationType completetion code: - * -2 X is not ordered by ascending/descending or - there are non-distinct X[], i.e. X[i]=X[i+1] - * -1 incorrect parameters were specified - * 1 task has been solved - * Rep.NFEV contains number of function calculations - - -- ALGLIB -- - Copyright 01.09.2009 by Bochkanov Sergey -*************************************************************************/ -void odesolverresults(const odesolverstate &state, ae_int_t &m, real_1d_array &xtbl, real_2d_array &ytbl, odesolverreport &rep); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void odesolverrkck(/* Real */ ae_vector* y, - ae_int_t n, - /* Real */ ae_vector* x, - ae_int_t m, - double eps, - double h, - odesolverstate* state, - ae_state *_state); -ae_bool odesolveriteration(odesolverstate* state, ae_state *_state); -void odesolverresults(odesolverstate* state, - ae_int_t* m, - /* Real */ ae_vector* xtbl, - /* Real */ ae_matrix* ytbl, - odesolverreport* rep, - ae_state *_state); -ae_bool _odesolverstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _odesolverstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _odesolverstate_clear(void* _p); -void _odesolverstate_destroy(void* _p); -ae_bool _odesolverreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _odesolverreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _odesolverreport_clear(void* _p); -void _odesolverreport_destroy(void* _p); - -} -#endif - diff --git a/src/inc/alglib/fasttransforms.cpp b/src/inc/alglib/fasttransforms.cpp deleted file mode 100644 index 9b7864f..0000000 --- a/src/inc/alglib/fasttransforms.cpp +++ /dev/null @@ -1,3554 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "fasttransforms.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -1-dimensional complex FFT. - -Array size N may be arbitrary number (composite or prime). Composite N's -are handled with cache-oblivious variation of a Cooley-Tukey algorithm. -Small prime-factors are transformed using hard coded codelets (similar to -FFTW codelets, but without low-level optimization), large prime-factors -are handled with Bluestein's algorithm. - -Fastests transforms are for smooth N's (prime factors are 2, 3, 5 only), -most fast for powers of 2. When N have prime factors larger than these, -but orders of magnitude smaller than N, computations will be about 4 times -slower than for nearby highly composite N's. When N itself is prime, speed -will be 6 times lower. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1d(complex_1d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftc1d(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex FFT. - -Array size N may be arbitrary number (composite or prime). Composite N's -are handled with cache-oblivious variation of a Cooley-Tukey algorithm. -Small prime-factors are transformed using hard coded codelets (similar to -FFTW codelets, but without low-level optimization), large prime-factors -are handled with Bluestein's algorithm. - -Fastests transforms are for smooth N's (prime factors are 2, 3, 5 only), -most fast for powers of 2. When N have prime factors larger than these, -but orders of magnitude smaller than N, computations will be about 4 times -slower than for nearby highly composite N's. When N itself is prime, speed -will be 6 times lower. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1d(complex_1d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = a.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftc1d(const_cast(a.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex inverse FFT. - -Array size N may be arbitrary number (composite or prime). Algorithm has -O(N*logN) complexity for any N (composite or prime). - -See FFTC1D() description for more information about algorithm performance. - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]/N*exp(+2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1dinv(complex_1d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftc1dinv(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex inverse FFT. - -Array size N may be arbitrary number (composite or prime). Algorithm has -O(N*logN) complexity for any N (composite or prime). - -See FFTC1D() description for more information about algorithm performance. - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]/N*exp(+2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1dinv(complex_1d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = a.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftc1dinv(const_cast(a.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - F - DFT of a input array, array[0..N-1] - F[j] = SUM(A[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - -NOTE: - F[] satisfies symmetry property F[k] = conj(F[N-k]), so just one half -of array is usually needed. But for convinience subroutine returns full -complex array (with frequencies above N/2), so its result may be used by -other FFT-related subroutines. - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1d(const real_1d_array &a, const ae_int_t n, complex_1d_array &f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftr1d(const_cast(a.c_ptr()), n, const_cast(f.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - F - DFT of a input array, array[0..N-1] - F[j] = SUM(A[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - -NOTE: - F[] satisfies symmetry property F[k] = conj(F[N-k]), so just one half -of array is usually needed. But for convinience subroutine returns full -complex array (with frequencies above N/2), so its result may be used by -other FFT-related subroutines. - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1d(const real_1d_array &a, complex_1d_array &f) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = a.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftr1d(const_cast(a.c_ptr()), n, const_cast(f.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real inverse FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - F - array[0..floor(N/2)] - frequencies from forward real FFT - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - -NOTE: - F[] should satisfy symmetry property F[k] = conj(F[N-k]), so just one -half of frequencies array is needed - elements from 0 to floor(N/2). F[0] -is ALWAYS real. If N is even F[floor(N/2)] is real too. If N is odd, then -F[floor(N/2)] has no special properties. - -Relying on properties noted above, FFTR1DInv subroutine uses only elements -from 0th to floor(N/2)-th. It ignores imaginary part of F[0], and in case -N is even it ignores imaginary part of F[floor(N/2)] too. - -When you call this function using full arguments list - "FFTR1DInv(F,N,A)" -- you can pass either either frequencies array with N elements or reduced -array with roughly N/2 elements - subroutine will successfully transform -both. - -If you call this function using reduced arguments list - "FFTR1DInv(F,A)" -- you must pass FULL array with N elements (although higher N/2 are still -not used) because array size is used to automatically determine FFT length - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1dinv(const complex_1d_array &f, const ae_int_t n, real_1d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftr1dinv(const_cast(f.c_ptr()), n, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real inverse FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - F - array[0..floor(N/2)] - frequencies from forward real FFT - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - -NOTE: - F[] should satisfy symmetry property F[k] = conj(F[N-k]), so just one -half of frequencies array is needed - elements from 0 to floor(N/2). F[0] -is ALWAYS real. If N is even F[floor(N/2)] is real too. If N is odd, then -F[floor(N/2)] has no special properties. - -Relying on properties noted above, FFTR1DInv subroutine uses only elements -from 0th to floor(N/2)-th. It ignores imaginary part of F[0], and in case -N is even it ignores imaginary part of F[floor(N/2)] too. - -When you call this function using full arguments list - "FFTR1DInv(F,N,A)" -- you can pass either either frequencies array with N elements or reduced -array with roughly N/2 elements - subroutine will successfully transform -both. - -If you call this function using reduced arguments list - "FFTR1DInv(F,A)" -- you must pass FULL array with N elements (although higher N/2 are still -not used) because array size is used to automatically determine FFT length - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1dinv(const complex_1d_array &f, real_1d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = f.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fftr1dinv(const_cast(f.c_ptr()), n, const_cast(a.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex convolution. - -For given A/B returns conv(A,B) (non-circular). Subroutine can automatically -choose between three implementations: straightforward O(M*N) formula for -very small N (or M), overlap-add algorithm for cases where max(M,N) is -significantly larger than min(M,N), but O(M*N) algorithm is too slow, and -general FFT-based formula for cases where two previois algorithms are too -slow. - -Algorithm has max(M,N)*log(max(M,N)) complexity for any M/N. - -INPUT PARAMETERS - A - array[0..M-1] - complex function to be transformed - M - problem size - B - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-2]. - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1d(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convc1d(const_cast(a.c_ptr()), m, const_cast(b.c_ptr()), n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex non-circular deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length, N<=M - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dinv(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convc1dinv(const_cast(a.c_ptr()), m, const_cast(b.c_ptr()), n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional circular complex convolution. - -For given S/R returns conv(S,R) (circular). Algorithm has linearithmic -complexity for any M/N. - -IMPORTANT: normal convolution is commutative, i.e. it is symmetric - -conv(A,B)=conv(B,A). Cyclic convolution IS NOT. One function - S - is a -signal, periodic function, and another - R - is a response, non-periodic -function with limited length. - -INPUT PARAMETERS - S - array[0..M-1] - complex periodic signal - M - problem size - B - array[0..N-1] - complex non-periodic response - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dcircular(const complex_1d_array &s, const ae_int_t m, const complex_1d_array &r, const ae_int_t n, complex_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convc1dcircular(const_cast(s.c_ptr()), m, const_cast(r.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional circular complex deconvolution (inverse of ConvC1DCircular()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved periodic signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - non-periodic response - N - response length - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-1]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dcircularinv(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convc1dcircularinv(const_cast(a.c_ptr()), m, const_cast(b.c_ptr()), n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real convolution. - -Analogous to ConvC1D(), see ConvC1D() comments for more details. - -INPUT PARAMETERS - A - array[0..M-1] - real function to be transformed - M - problem size - B - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-2]. - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1d(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convr1d(const_cast(a.c_ptr()), m, const_cast(b.c_ptr()), n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length, N<=M - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dinv(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convr1dinv(const_cast(a.c_ptr()), m, const_cast(b.c_ptr()), n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional circular real convolution. - -Analogous to ConvC1DCircular(), see ConvC1DCircular() comments for more details. - -INPUT PARAMETERS - S - array[0..M-1] - real signal - M - problem size - B - array[0..N-1] - real response - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dcircular(const real_1d_array &s, const ae_int_t m, const real_1d_array &r, const ae_int_t n, real_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convr1dcircular(const_cast(s.c_ptr()), m, const_cast(r.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dcircularinv(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::convr1dcircularinv(const_cast(a.c_ptr()), m, const_cast(b.c_ptr()), n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional complex cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (non-circular). - -Correlation is calculated using reduction to convolution. Algorithm with -max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info -about performance). - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrC1D(Signal, Pattern) = Pattern x Signal (using traditional - definition of cross-correlation, denoting cross-correlation as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - complex function to be transformed, - signal containing pattern - N - problem size - Pattern - array[0..M-1] - complex function to be transformed, - pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - cross-correlation, array[0..N+M-2]: - * positive lags are stored in R[0..N-1], - R[i] = sum(conj(pattern[j])*signal[i+j] - * negative lags are stored in R[N..N+M-2], - R[N+M-1-i] = sum(conj(pattern[j])*signal[-i+j] - -NOTE: - It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero -on [-K..M-1], you can still use this subroutine, just shift result by K. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrc1d(const complex_1d_array &signal, const ae_int_t n, const complex_1d_array &pattern, const ae_int_t m, complex_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::corrc1d(const_cast(signal.c_ptr()), n, const_cast(pattern.c_ptr()), m, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional circular complex cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (circular). -Algorithm has linearithmic complexity for any M/N. - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrC1DCircular(Signal, Pattern) = Pattern x Signal (using - traditional definition of cross-correlation, denoting cross-correlation - as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - complex function to be transformed, - periodic signal containing pattern - N - problem size - Pattern - array[0..M-1] - complex function to be transformed, - non-periodic pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrc1dcircular(const complex_1d_array &signal, const ae_int_t m, const complex_1d_array &pattern, const ae_int_t n, complex_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::corrc1dcircular(const_cast(signal.c_ptr()), m, const_cast(pattern.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional real cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (non-circular). - -Correlation is calculated using reduction to convolution. Algorithm with -max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info -about performance). - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrR1D(Signal, Pattern) = Pattern x Signal (using traditional - definition of cross-correlation, denoting cross-correlation as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - real function to be transformed, - signal containing pattern - N - problem size - Pattern - array[0..M-1] - real function to be transformed, - pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - cross-correlation, array[0..N+M-2]: - * positive lags are stored in R[0..N-1], - R[i] = sum(pattern[j]*signal[i+j] - * negative lags are stored in R[N..N+M-2], - R[N+M-1-i] = sum(pattern[j]*signal[-i+j] - -NOTE: - It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero -on [-K..M-1], you can still use this subroutine, just shift result by K. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrr1d(const real_1d_array &signal, const ae_int_t n, const real_1d_array &pattern, const ae_int_t m, real_1d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::corrr1d(const_cast(signal.c_ptr()), n, const_cast(pattern.c_ptr()), m, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional circular real cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (circular). -Algorithm has linearithmic complexity for any M/N. - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrR1DCircular(Signal, Pattern) = Pattern x Signal (using - traditional definition of cross-correlation, denoting cross-correlation - as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - real function to be transformed, - periodic signal containing pattern - N - problem size - Pattern - array[0..M-1] - real function to be transformed, - non-periodic pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrr1dcircular(const real_1d_array &signal, const ae_int_t m, const real_1d_array &pattern, const ae_int_t n, real_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::corrr1dcircular(const_cast(signal.c_ptr()), m, const_cast(pattern.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional Fast Hartley Transform. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - FHT of a input array, array[0..N-1], - A_out[k] = sum(A_in[j]*(cos(2*pi*j*k/N)+sin(2*pi*j*k/N)), j=0..N-1) - - - -- ALGLIB -- - Copyright 04.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fhtr1d(real_1d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fhtr1d(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional inverse FHT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse FHT of a input array, array[0..N-1] - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fhtr1dinv(real_1d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fhtr1dinv(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - - - - - - - - - - - -/************************************************************************* -1-dimensional complex FFT. - -Array size N may be arbitrary number (composite or prime). Composite N's -are handled with cache-oblivious variation of a Cooley-Tukey algorithm. -Small prime-factors are transformed using hard coded codelets (similar to -FFTW codelets, but without low-level optimization), large prime-factors -are handled with Bluestein's algorithm. - -Fastests transforms are for smooth N's (prime factors are 2, 3, 5 only), -most fast for powers of 2. When N have prime factors larger than these, -but orders of magnitude smaller than N, computations will be about 4 times -slower than for nearby highly composite N's. When N itself is prime, speed -will be 6 times lower. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1d(/* Complex */ ae_vector* a, ae_int_t n, ae_state *_state) -{ - ae_frame _frame_block; - fasttransformplan plan; - ae_int_t i; - ae_vector buf; - - ae_frame_make(_state, &_frame_block); - _fasttransformplan_init(&plan, _state, ae_true); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "FFTC1D: incorrect N!", _state); - ae_assert(a->cnt>=n, "FFTC1D: Length(A)ptr.p_complex[i].x; - buf.ptr.p_double[2*i+1] = a->ptr.p_complex[i].y; - } - - /* - * Generate plan and execute it. - * - * Plan is a combination of a successive factorizations of N and - * precomputed data. It is much like a FFTW plan, but is not stored - * between subroutine calls and is much simpler. - */ - ftcomplexfftplan(n, 1, &plan, _state); - ftapplyplan(&plan, &buf, 0, 1, _state); - - /* - * result - */ - for(i=0; i<=n-1; i++) - { - a->ptr.p_complex[i].x = buf.ptr.p_double[2*i+0]; - a->ptr.p_complex[i].y = buf.ptr.p_double[2*i+1]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional complex inverse FFT. - -Array size N may be arbitrary number (composite or prime). Algorithm has -O(N*logN) complexity for any N (composite or prime). - -See FFTC1D() description for more information about algorithm performance. - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]/N*exp(+2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1dinv(/* Complex */ ae_vector* a, ae_int_t n, ae_state *_state) -{ - ae_int_t i; - - - ae_assert(n>0, "FFTC1DInv: incorrect N!", _state); - ae_assert(a->cnt>=n, "FFTC1DInv: Length(A)ptr.p_complex[i].y = -a->ptr.p_complex[i].y; - } - fftc1d(a, n, _state); - for(i=0; i<=n-1; i++) - { - a->ptr.p_complex[i].x = a->ptr.p_complex[i].x/n; - a->ptr.p_complex[i].y = -a->ptr.p_complex[i].y/n; - } -} - - -/************************************************************************* -1-dimensional real FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - F - DFT of a input array, array[0..N-1] - F[j] = SUM(A[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - -NOTE: - F[] satisfies symmetry property F[k] = conj(F[N-k]), so just one half -of array is usually needed. But for convinience subroutine returns full -complex array (with frequencies above N/2), so its result may be used by -other FFT-related subroutines. - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1d(/* Real */ ae_vector* a, - ae_int_t n, - /* Complex */ ae_vector* f, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t n2; - ae_int_t idx; - ae_complex hn; - ae_complex hmnc; - ae_complex v; - ae_vector buf; - fasttransformplan plan; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(f); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - _fasttransformplan_init(&plan, _state, ae_true); - - ae_assert(n>0, "FFTR1D: incorrect N!", _state); - ae_assert(a->cnt>=n, "FFTR1D: Length(A)ptr.p_complex[0] = ae_complex_from_d(a->ptr.p_double[0]); - ae_frame_leave(_state); - return; - } - if( n==2 ) - { - ae_vector_set_length(f, 2, _state); - f->ptr.p_complex[0].x = a->ptr.p_double[0]+a->ptr.p_double[1]; - f->ptr.p_complex[0].y = 0; - f->ptr.p_complex[1].x = a->ptr.p_double[0]-a->ptr.p_double[1]; - f->ptr.p_complex[1].y = 0; - ae_frame_leave(_state); - return; - } - - /* - * Choose between odd-size and even-size FFTs - */ - if( n%2==0 ) - { - - /* - * even-size real FFT, use reduction to the complex task - */ - n2 = n/2; - ae_vector_set_length(&buf, n, _state); - ae_v_move(&buf.ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ftcomplexfftplan(n2, 1, &plan, _state); - ftapplyplan(&plan, &buf, 0, 1, _state); - ae_vector_set_length(f, n, _state); - for(i=0; i<=n2; i++) - { - idx = 2*(i%n2); - hn.x = buf.ptr.p_double[idx+0]; - hn.y = buf.ptr.p_double[idx+1]; - idx = 2*((n2-i)%n2); - hmnc.x = buf.ptr.p_double[idx+0]; - hmnc.y = -buf.ptr.p_double[idx+1]; - v.x = -ae_sin(-2*ae_pi*i/n, _state); - v.y = ae_cos(-2*ae_pi*i/n, _state); - f->ptr.p_complex[i] = ae_c_sub(ae_c_add(hn,hmnc),ae_c_mul(v,ae_c_sub(hn,hmnc))); - f->ptr.p_complex[i].x = 0.5*f->ptr.p_complex[i].x; - f->ptr.p_complex[i].y = 0.5*f->ptr.p_complex[i].y; - } - for(i=n2+1; i<=n-1; i++) - { - f->ptr.p_complex[i] = ae_c_conj(f->ptr.p_complex[n-i], _state); - } - } - else - { - - /* - * use complex FFT - */ - ae_vector_set_length(f, n, _state); - for(i=0; i<=n-1; i++) - { - f->ptr.p_complex[i] = ae_complex_from_d(a->ptr.p_double[i]); - } - fftc1d(f, n, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional real inverse FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - F - array[0..floor(N/2)] - frequencies from forward real FFT - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - -NOTE: - F[] should satisfy symmetry property F[k] = conj(F[N-k]), so just one -half of frequencies array is needed - elements from 0 to floor(N/2). F[0] -is ALWAYS real. If N is even F[floor(N/2)] is real too. If N is odd, then -F[floor(N/2)] has no special properties. - -Relying on properties noted above, FFTR1DInv subroutine uses only elements -from 0th to floor(N/2)-th. It ignores imaginary part of F[0], and in case -N is even it ignores imaginary part of F[floor(N/2)] too. - -When you call this function using full arguments list - "FFTR1DInv(F,N,A)" -- you can pass either either frequencies array with N elements or reduced -array with roughly N/2 elements - subroutine will successfully transform -both. - -If you call this function using reduced arguments list - "FFTR1DInv(F,A)" -- you must pass FULL array with N elements (although higher N/2 are still -not used) because array size is used to automatically determine FFT length - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1dinv(/* Complex */ ae_vector* f, - ae_int_t n, - /* Real */ ae_vector* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector h; - ae_vector fh; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(a); - ae_vector_init(&h, 0, DT_REAL, _state, ae_true); - ae_vector_init(&fh, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0, "FFTR1DInv: incorrect N!", _state); - ae_assert(f->cnt>=ae_ifloor((double)n/(double)2, _state)+1, "FFTR1DInv: Length(F)ptr.p_complex[0].x, _state), "FFTR1DInv: F contains infinite or NAN values!", _state); - for(i=1; i<=ae_ifloor((double)n/(double)2, _state)-1; i++) - { - ae_assert(ae_isfinite(f->ptr.p_complex[i].x, _state)&&ae_isfinite(f->ptr.p_complex[i].y, _state), "FFTR1DInv: F contains infinite or NAN values!", _state); - } - ae_assert(ae_isfinite(f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].x, _state), "FFTR1DInv: F contains infinite or NAN values!", _state); - if( n%2!=0 ) - { - ae_assert(ae_isfinite(f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].y, _state), "FFTR1DInv: F contains infinite or NAN values!", _state); - } - - /* - * Special case: N=1, FFT is just identity transform. - * After this block we assume that N is strictly greater than 1. - */ - if( n==1 ) - { - ae_vector_set_length(a, 1, _state); - a->ptr.p_double[0] = f->ptr.p_complex[0].x; - ae_frame_leave(_state); - return; - } - - /* - * inverse real FFT is reduced to the inverse real FHT, - * which is reduced to the forward real FHT, - * which is reduced to the forward real FFT. - * - * Don't worry, it is really compact and efficient reduction :) - */ - ae_vector_set_length(&h, n, _state); - ae_vector_set_length(a, n, _state); - h.ptr.p_double[0] = f->ptr.p_complex[0].x; - for(i=1; i<=ae_ifloor((double)n/(double)2, _state)-1; i++) - { - h.ptr.p_double[i] = f->ptr.p_complex[i].x-f->ptr.p_complex[i].y; - h.ptr.p_double[n-i] = f->ptr.p_complex[i].x+f->ptr.p_complex[i].y; - } - if( n%2==0 ) - { - h.ptr.p_double[ae_ifloor((double)n/(double)2, _state)] = f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].x; - } - else - { - h.ptr.p_double[ae_ifloor((double)n/(double)2, _state)] = f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].x-f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].y; - h.ptr.p_double[ae_ifloor((double)n/(double)2, _state)+1] = f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].x+f->ptr.p_complex[ae_ifloor((double)n/(double)2, _state)].y; - } - fftr1d(&h, n, &fh, _state); - for(i=0; i<=n-1; i++) - { - a->ptr.p_double[i] = (fh.ptr.p_complex[i].x-fh.ptr.p_complex[i].y)/n; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. Never call it directly! - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1dinternaleven(/* Real */ ae_vector* a, - ae_int_t n, - /* Real */ ae_vector* buf, - fasttransformplan* plan, - ae_state *_state) -{ - double x; - double y; - ae_int_t i; - ae_int_t n2; - ae_int_t idx; - ae_complex hn; - ae_complex hmnc; - ae_complex v; - - - ae_assert(n>0&&n%2==0, "FFTR1DEvenInplace: incorrect N!", _state); - - /* - * Special cases: - * * N=2 - * - * After this block we assume that N is strictly greater than 2 - */ - if( n==2 ) - { - x = a->ptr.p_double[0]+a->ptr.p_double[1]; - y = a->ptr.p_double[0]-a->ptr.p_double[1]; - a->ptr.p_double[0] = x; - a->ptr.p_double[1] = y; - return; - } - - /* - * even-size real FFT, use reduction to the complex task - */ - n2 = n/2; - ae_v_move(&buf->ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ftapplyplan(plan, buf, 0, 1, _state); - a->ptr.p_double[0] = buf->ptr.p_double[0]+buf->ptr.p_double[1]; - for(i=1; i<=n2-1; i++) - { - idx = 2*(i%n2); - hn.x = buf->ptr.p_double[idx+0]; - hn.y = buf->ptr.p_double[idx+1]; - idx = 2*(n2-i); - hmnc.x = buf->ptr.p_double[idx+0]; - hmnc.y = -buf->ptr.p_double[idx+1]; - v.x = -ae_sin(-2*ae_pi*i/n, _state); - v.y = ae_cos(-2*ae_pi*i/n, _state); - v = ae_c_sub(ae_c_add(hn,hmnc),ae_c_mul(v,ae_c_sub(hn,hmnc))); - a->ptr.p_double[2*i+0] = 0.5*v.x; - a->ptr.p_double[2*i+1] = 0.5*v.y; - } - a->ptr.p_double[1] = buf->ptr.p_double[0]-buf->ptr.p_double[1]; -} - - -/************************************************************************* -Internal subroutine. Never call it directly! - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1dinvinternaleven(/* Real */ ae_vector* a, - ae_int_t n, - /* Real */ ae_vector* buf, - fasttransformplan* plan, - ae_state *_state) -{ - double x; - double y; - double t; - ae_int_t i; - ae_int_t n2; - - - ae_assert(n>0&&n%2==0, "FFTR1DInvInternalEven: incorrect N!", _state); - - /* - * Special cases: - * * N=2 - * - * After this block we assume that N is strictly greater than 2 - */ - if( n==2 ) - { - x = 0.5*(a->ptr.p_double[0]+a->ptr.p_double[1]); - y = 0.5*(a->ptr.p_double[0]-a->ptr.p_double[1]); - a->ptr.p_double[0] = x; - a->ptr.p_double[1] = y; - return; - } - - /* - * inverse real FFT is reduced to the inverse real FHT, - * which is reduced to the forward real FHT, - * which is reduced to the forward real FFT. - * - * Don't worry, it is really compact and efficient reduction :) - */ - n2 = n/2; - buf->ptr.p_double[0] = a->ptr.p_double[0]; - for(i=1; i<=n2-1; i++) - { - x = a->ptr.p_double[2*i+0]; - y = a->ptr.p_double[2*i+1]; - buf->ptr.p_double[i] = x-y; - buf->ptr.p_double[n-i] = x+y; - } - buf->ptr.p_double[n2] = a->ptr.p_double[1]; - fftr1dinternaleven(buf, n, a, plan, _state); - a->ptr.p_double[0] = buf->ptr.p_double[0]/n; - t = (double)1/(double)n; - for(i=1; i<=n2-1; i++) - { - x = buf->ptr.p_double[2*i+0]; - y = buf->ptr.p_double[2*i+1]; - a->ptr.p_double[i] = t*(x-y); - a->ptr.p_double[n-i] = t*(x+y); - } - a->ptr.p_double[n2] = buf->ptr.p_double[1]/n; -} - - - - -/************************************************************************* -1-dimensional complex convolution. - -For given A/B returns conv(A,B) (non-circular). Subroutine can automatically -choose between three implementations: straightforward O(M*N) formula for -very small N (or M), overlap-add algorithm for cases where max(M,N) is -significantly larger than min(M,N), but O(M*N) algorithm is too slow, and -general FFT-based formula for cases where two previois algorithms are too -slow. - -Algorithm has max(M,N)*log(max(M,N)) complexity for any M/N. - -INPUT PARAMETERS - A - array[0..M-1] - complex function to be transformed - M - problem size - B - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-2]. - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1d(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Complex */ ae_vector* r, - ae_state *_state) -{ - - ae_vector_clear(r); - - ae_assert(n>0&&m>0, "ConvC1D: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer that B. - */ - if( m0&&m>0)&&n<=m, "ConvC1DInv: incorrect N or M!", _state); - p = ftbasefindsmooth(m, _state); - ftcomplexfftplan(p, 1, &plan, _state); - ae_vector_set_length(&buf, 2*p, _state); - for(i=0; i<=m-1; i++) - { - buf.ptr.p_double[2*i+0] = a->ptr.p_complex[i].x; - buf.ptr.p_double[2*i+1] = a->ptr.p_complex[i].y; - } - for(i=m; i<=p-1; i++) - { - buf.ptr.p_double[2*i+0] = 0; - buf.ptr.p_double[2*i+1] = 0; - } - ae_vector_set_length(&buf2, 2*p, _state); - for(i=0; i<=n-1; i++) - { - buf2.ptr.p_double[2*i+0] = b->ptr.p_complex[i].x; - buf2.ptr.p_double[2*i+1] = b->ptr.p_complex[i].y; - } - for(i=n; i<=p-1; i++) - { - buf2.ptr.p_double[2*i+0] = 0; - buf2.ptr.p_double[2*i+1] = 0; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - ftapplyplan(&plan, &buf2, 0, 1, _state); - for(i=0; i<=p-1; i++) - { - c1.x = buf.ptr.p_double[2*i+0]; - c1.y = buf.ptr.p_double[2*i+1]; - c2.x = buf2.ptr.p_double[2*i+0]; - c2.y = buf2.ptr.p_double[2*i+1]; - c3 = ae_c_div(c1,c2); - buf.ptr.p_double[2*i+0] = c3.x; - buf.ptr.p_double[2*i+1] = -c3.y; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - t = (double)1/(double)p; - ae_vector_set_length(r, m-n+1, _state); - for(i=0; i<=m-n; i++) - { - r->ptr.p_complex[i].x = t*buf.ptr.p_double[2*i+0]; - r->ptr.p_complex[i].y = -t*buf.ptr.p_double[2*i+1]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional circular complex convolution. - -For given S/R returns conv(S,R) (circular). Algorithm has linearithmic -complexity for any M/N. - -IMPORTANT: normal convolution is commutative, i.e. it is symmetric - -conv(A,B)=conv(B,A). Cyclic convolution IS NOT. One function - S - is a -signal, periodic function, and another - R - is a response, non-periodic -function with limited length. - -INPUT PARAMETERS - S - array[0..M-1] - complex periodic signal - M - problem size - B - array[0..N-1] - complex non-periodic response - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dcircular(/* Complex */ ae_vector* s, - ae_int_t m, - /* Complex */ ae_vector* r, - ae_int_t n, - /* Complex */ ae_vector* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector buf; - ae_int_t i1; - ae_int_t i2; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(c); - ae_vector_init(&buf, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DCircular: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer (at least - not shorter) that B. - */ - if( mptr.p_complex[i1], 1, "N", ae_v_len(0,j2)); - i1 = i1+m; - } - convc1dcircular(s, m, &buf, m, c, _state); - ae_frame_leave(_state); - return; - } - convc1dx(s, m, r, n, ae_true, -1, 0, c, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional circular complex deconvolution (inverse of ConvC1DCircular()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved periodic signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - non-periodic response - N - response length - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-1]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dcircularinv(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Complex */ ae_vector* r, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t i1; - ae_int_t i2; - ae_int_t j2; - ae_vector buf; - ae_vector buf2; - ae_vector cbuf; - fasttransformplan plan; - ae_complex c1; - ae_complex c2; - ae_complex c3; - double t; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&cbuf, 0, DT_COMPLEX, _state, ae_true); - _fasttransformplan_init(&plan, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DCircularInv: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer (at least - not shorter) that B. - */ - if( mptr.p_complex[i1], 1, "N", ae_v_len(0,j2)); - i1 = i1+m; - } - convc1dcircularinv(a, m, &cbuf, m, r, _state); - ae_frame_leave(_state); - return; - } - - /* - * Task is normalized - */ - ftcomplexfftplan(m, 1, &plan, _state); - ae_vector_set_length(&buf, 2*m, _state); - for(i=0; i<=m-1; i++) - { - buf.ptr.p_double[2*i+0] = a->ptr.p_complex[i].x; - buf.ptr.p_double[2*i+1] = a->ptr.p_complex[i].y; - } - ae_vector_set_length(&buf2, 2*m, _state); - for(i=0; i<=n-1; i++) - { - buf2.ptr.p_double[2*i+0] = b->ptr.p_complex[i].x; - buf2.ptr.p_double[2*i+1] = b->ptr.p_complex[i].y; - } - for(i=n; i<=m-1; i++) - { - buf2.ptr.p_double[2*i+0] = 0; - buf2.ptr.p_double[2*i+1] = 0; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - ftapplyplan(&plan, &buf2, 0, 1, _state); - for(i=0; i<=m-1; i++) - { - c1.x = buf.ptr.p_double[2*i+0]; - c1.y = buf.ptr.p_double[2*i+1]; - c2.x = buf2.ptr.p_double[2*i+0]; - c2.y = buf2.ptr.p_double[2*i+1]; - c3 = ae_c_div(c1,c2); - buf.ptr.p_double[2*i+0] = c3.x; - buf.ptr.p_double[2*i+1] = -c3.y; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - t = (double)1/(double)m; - ae_vector_set_length(r, m, _state); - for(i=0; i<=m-1; i++) - { - r->ptr.p_complex[i].x = t*buf.ptr.p_double[2*i+0]; - r->ptr.p_complex[i].y = -t*buf.ptr.p_double[2*i+1]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional real convolution. - -Analogous to ConvC1D(), see ConvC1D() comments for more details. - -INPUT PARAMETERS - A - array[0..M-1] - real function to be transformed - M - problem size - B - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-2]. - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1d(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* r, - ae_state *_state) -{ - - ae_vector_clear(r); - - ae_assert(n>0&&m>0, "ConvR1D: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer that B. - */ - if( m0&&m>0)&&n<=m, "ConvR1DInv: incorrect N or M!", _state); - p = ftbasefindsmootheven(m, _state); - ae_vector_set_length(&buf, p, _state); - ae_v_move(&buf.ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,m-1)); - for(i=m; i<=p-1; i++) - { - buf.ptr.p_double[i] = 0; - } - ae_vector_set_length(&buf2, p, _state); - ae_v_move(&buf2.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=n; i<=p-1; i++) - { - buf2.ptr.p_double[i] = 0; - } - ae_vector_set_length(&buf3, p, _state); - ftcomplexfftplan(p/2, 1, &plan, _state); - fftr1dinternaleven(&buf, p, &buf3, &plan, _state); - fftr1dinternaleven(&buf2, p, &buf3, &plan, _state); - buf.ptr.p_double[0] = buf.ptr.p_double[0]/buf2.ptr.p_double[0]; - buf.ptr.p_double[1] = buf.ptr.p_double[1]/buf2.ptr.p_double[1]; - for(i=1; i<=p/2-1; i++) - { - c1.x = buf.ptr.p_double[2*i+0]; - c1.y = buf.ptr.p_double[2*i+1]; - c2.x = buf2.ptr.p_double[2*i+0]; - c2.y = buf2.ptr.p_double[2*i+1]; - c3 = ae_c_div(c1,c2); - buf.ptr.p_double[2*i+0] = c3.x; - buf.ptr.p_double[2*i+1] = c3.y; - } - fftr1dinvinternaleven(&buf, p, &buf3, &plan, _state); - ae_vector_set_length(r, m-n+1, _state); - ae_v_move(&r->ptr.p_double[0], 1, &buf.ptr.p_double[0], 1, ae_v_len(0,m-n)); - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional circular real convolution. - -Analogous to ConvC1DCircular(), see ConvC1DCircular() comments for more details. - -INPUT PARAMETERS - S - array[0..M-1] - real signal - M - problem size - B - array[0..N-1] - real response - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dcircular(/* Real */ ae_vector* s, - ae_int_t m, - /* Real */ ae_vector* r, - ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector buf; - ae_int_t i1; - ae_int_t i2; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(c); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DCircular: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer (at least - not shorter) that B. - */ - if( mptr.p_double[i1], 1, ae_v_len(0,j2)); - i1 = i1+m; - } - convr1dcircular(s, m, &buf, m, c, _state); - ae_frame_leave(_state); - return; - } - - /* - * reduce to usual convolution - */ - convr1dx(s, m, r, n, ae_true, -1, 0, c, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional complex deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dcircularinv(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* r, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t i1; - ae_int_t i2; - ae_int_t j2; - ae_vector buf; - ae_vector buf2; - ae_vector buf3; - ae_vector cbuf; - ae_vector cbuf2; - fasttransformplan plan; - ae_complex c1; - ae_complex c2; - ae_complex c3; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&cbuf, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cbuf2, 0, DT_COMPLEX, _state, ae_true); - _fasttransformplan_init(&plan, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvR1DCircularInv: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer (at least - not shorter) that B. - */ - if( mptr.p_double[i1], 1, ae_v_len(0,j2)); - i1 = i1+m; - } - convr1dcircularinv(a, m, &buf, m, r, _state); - ae_frame_leave(_state); - return; - } - - /* - * Task is normalized - */ - if( m%2==0 ) - { - - /* - * size is even, use fast even-size FFT - */ - ae_vector_set_length(&buf, m, _state); - ae_v_move(&buf.ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_vector_set_length(&buf2, m, _state); - ae_v_move(&buf2.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=n; i<=m-1; i++) - { - buf2.ptr.p_double[i] = 0; - } - ae_vector_set_length(&buf3, m, _state); - ftcomplexfftplan(m/2, 1, &plan, _state); - fftr1dinternaleven(&buf, m, &buf3, &plan, _state); - fftr1dinternaleven(&buf2, m, &buf3, &plan, _state); - buf.ptr.p_double[0] = buf.ptr.p_double[0]/buf2.ptr.p_double[0]; - buf.ptr.p_double[1] = buf.ptr.p_double[1]/buf2.ptr.p_double[1]; - for(i=1; i<=m/2-1; i++) - { - c1.x = buf.ptr.p_double[2*i+0]; - c1.y = buf.ptr.p_double[2*i+1]; - c2.x = buf2.ptr.p_double[2*i+0]; - c2.y = buf2.ptr.p_double[2*i+1]; - c3 = ae_c_div(c1,c2); - buf.ptr.p_double[2*i+0] = c3.x; - buf.ptr.p_double[2*i+1] = c3.y; - } - fftr1dinvinternaleven(&buf, m, &buf3, &plan, _state); - ae_vector_set_length(r, m, _state); - ae_v_move(&r->ptr.p_double[0], 1, &buf.ptr.p_double[0], 1, ae_v_len(0,m-1)); - } - else - { - - /* - * odd-size, use general real FFT - */ - fftr1d(a, m, &cbuf, _state); - ae_vector_set_length(&buf2, m, _state); - ae_v_move(&buf2.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=n; i<=m-1; i++) - { - buf2.ptr.p_double[i] = 0; - } - fftr1d(&buf2, m, &cbuf2, _state); - for(i=0; i<=ae_ifloor((double)m/(double)2, _state); i++) - { - cbuf.ptr.p_complex[i] = ae_c_div(cbuf.ptr.p_complex[i],cbuf2.ptr.p_complex[i]); - } - fftr1dinv(&cbuf, m, r, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional complex convolution. - -Extended subroutine which allows to choose convolution algorithm. -Intended for internal use, ALGLIB users should call ConvC1D()/ConvC1DCircular(). - -INPUT PARAMETERS - A - array[0..M-1] - complex function to be transformed - M - problem size - B - array[0..N-1] - complex function to be transformed - N - problem size, N<=M - Alg - algorithm type: - *-2 auto-select Q for overlap-add - *-1 auto-select algorithm and parameters - * 0 straightforward formula for small N's - * 1 general FFT-based code - * 2 overlap-add with length Q - Q - length for overlap-add - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-1]. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dx(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - ae_bool circular, - ae_int_t alg, - ae_int_t q, - /* Complex */ ae_vector* r, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t p; - ae_int_t ptotal; - ae_int_t i1; - ae_int_t i2; - ae_int_t j1; - ae_int_t j2; - ae_vector bbuf; - ae_complex v; - double ax; - double ay; - double bx; - double by; - double t; - double tx; - double ty; - double flopcand; - double flopbest; - ae_int_t algbest; - fasttransformplan plan; - ae_vector buf; - ae_vector buf2; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - ae_vector_init(&bbuf, 0, DT_COMPLEX, _state, ae_true); - _fasttransformplan_init(&plan, _state, ae_true); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf2, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DX: incorrect N or M!", _state); - ae_assert(n<=m, "ConvC1DX: Nptr.p_complex[0]; - ae_v_cmovec(&r->ptr.p_complex[0], 1, &a->ptr.p_complex[0], 1, "N", ae_v_len(0,m-1), v); - ae_frame_leave(_state); - return; - } - - /* - * use straightforward formula - */ - if( circular ) - { - - /* - * circular convolution - */ - ae_vector_set_length(r, m, _state); - v = b->ptr.p_complex[0]; - ae_v_cmovec(&r->ptr.p_complex[0], 1, &a->ptr.p_complex[0], 1, "N", ae_v_len(0,m-1), v); - for(i=1; i<=n-1; i++) - { - v = b->ptr.p_complex[i]; - i1 = 0; - i2 = i-1; - j1 = m-i; - j2 = m-1; - ae_v_caddc(&r->ptr.p_complex[i1], 1, &a->ptr.p_complex[j1], 1, "N", ae_v_len(i1,i2), v); - i1 = i; - i2 = m-1; - j1 = 0; - j2 = m-i-1; - ae_v_caddc(&r->ptr.p_complex[i1], 1, &a->ptr.p_complex[j1], 1, "N", ae_v_len(i1,i2), v); - } - } - else - { - - /* - * non-circular convolution - */ - ae_vector_set_length(r, m+n-1, _state); - for(i=0; i<=m+n-2; i++) - { - r->ptr.p_complex[i] = ae_complex_from_d(0); - } - for(i=0; i<=n-1; i++) - { - v = b->ptr.p_complex[i]; - ae_v_caddc(&r->ptr.p_complex[i], 1, &a->ptr.p_complex[0], 1, "N", ae_v_len(i,i+m-1), v); - } - } - ae_frame_leave(_state); - return; - } - - /* - * general FFT-based code for - * circular and non-circular convolutions. - * - * First, if convolution is circular, we test whether M is smooth or not. - * If it is smooth, we just use M-length FFT to calculate convolution. - * If it is not, we calculate non-circular convolution and wrap it arount. - * - * IF convolution is non-circular, we use zero-padding + FFT. - */ - if( alg==1 ) - { - if( circular&&ftbaseissmooth(m, _state) ) - { - - /* - * special code for circular convolution with smooth M - */ - ftcomplexfftplan(m, 1, &plan, _state); - ae_vector_set_length(&buf, 2*m, _state); - for(i=0; i<=m-1; i++) - { - buf.ptr.p_double[2*i+0] = a->ptr.p_complex[i].x; - buf.ptr.p_double[2*i+1] = a->ptr.p_complex[i].y; - } - ae_vector_set_length(&buf2, 2*m, _state); - for(i=0; i<=n-1; i++) - { - buf2.ptr.p_double[2*i+0] = b->ptr.p_complex[i].x; - buf2.ptr.p_double[2*i+1] = b->ptr.p_complex[i].y; - } - for(i=n; i<=m-1; i++) - { - buf2.ptr.p_double[2*i+0] = 0; - buf2.ptr.p_double[2*i+1] = 0; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - ftapplyplan(&plan, &buf2, 0, 1, _state); - for(i=0; i<=m-1; i++) - { - ax = buf.ptr.p_double[2*i+0]; - ay = buf.ptr.p_double[2*i+1]; - bx = buf2.ptr.p_double[2*i+0]; - by = buf2.ptr.p_double[2*i+1]; - tx = ax*bx-ay*by; - ty = ax*by+ay*bx; - buf.ptr.p_double[2*i+0] = tx; - buf.ptr.p_double[2*i+1] = -ty; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - t = (double)1/(double)m; - ae_vector_set_length(r, m, _state); - for(i=0; i<=m-1; i++) - { - r->ptr.p_complex[i].x = t*buf.ptr.p_double[2*i+0]; - r->ptr.p_complex[i].y = -t*buf.ptr.p_double[2*i+1]; - } - } - else - { - - /* - * M is non-smooth, general code (circular/non-circular): - * * first part is the same for circular and non-circular - * convolutions. zero padding, FFTs, inverse FFTs - * * second part differs: - * * for non-circular convolution we just copy array - * * for circular convolution we add array tail to its head - */ - p = ftbasefindsmooth(m+n-1, _state); - ftcomplexfftplan(p, 1, &plan, _state); - ae_vector_set_length(&buf, 2*p, _state); - for(i=0; i<=m-1; i++) - { - buf.ptr.p_double[2*i+0] = a->ptr.p_complex[i].x; - buf.ptr.p_double[2*i+1] = a->ptr.p_complex[i].y; - } - for(i=m; i<=p-1; i++) - { - buf.ptr.p_double[2*i+0] = 0; - buf.ptr.p_double[2*i+1] = 0; - } - ae_vector_set_length(&buf2, 2*p, _state); - for(i=0; i<=n-1; i++) - { - buf2.ptr.p_double[2*i+0] = b->ptr.p_complex[i].x; - buf2.ptr.p_double[2*i+1] = b->ptr.p_complex[i].y; - } - for(i=n; i<=p-1; i++) - { - buf2.ptr.p_double[2*i+0] = 0; - buf2.ptr.p_double[2*i+1] = 0; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - ftapplyplan(&plan, &buf2, 0, 1, _state); - for(i=0; i<=p-1; i++) - { - ax = buf.ptr.p_double[2*i+0]; - ay = buf.ptr.p_double[2*i+1]; - bx = buf2.ptr.p_double[2*i+0]; - by = buf2.ptr.p_double[2*i+1]; - tx = ax*bx-ay*by; - ty = ax*by+ay*bx; - buf.ptr.p_double[2*i+0] = tx; - buf.ptr.p_double[2*i+1] = -ty; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - t = (double)1/(double)p; - if( circular ) - { - - /* - * circular, add tail to head - */ - ae_vector_set_length(r, m, _state); - for(i=0; i<=m-1; i++) - { - r->ptr.p_complex[i].x = t*buf.ptr.p_double[2*i+0]; - r->ptr.p_complex[i].y = -t*buf.ptr.p_double[2*i+1]; - } - for(i=m; i<=m+n-2; i++) - { - r->ptr.p_complex[i-m].x = r->ptr.p_complex[i-m].x+t*buf.ptr.p_double[2*i+0]; - r->ptr.p_complex[i-m].y = r->ptr.p_complex[i-m].y-t*buf.ptr.p_double[2*i+1]; - } - } - else - { - - /* - * non-circular, just copy - */ - ae_vector_set_length(r, m+n-1, _state); - for(i=0; i<=m+n-2; i++) - { - r->ptr.p_complex[i].x = t*buf.ptr.p_double[2*i+0]; - r->ptr.p_complex[i].y = -t*buf.ptr.p_double[2*i+1]; - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * overlap-add method for - * circular and non-circular convolutions. - * - * First part of code (separate FFTs of input blocks) is the same - * for all types of convolution. Second part (overlapping outputs) - * differs for different types of convolution. We just copy output - * when convolution is non-circular. We wrap it around, if it is - * circular. - */ - if( alg==2 ) - { - ae_vector_set_length(&buf, 2*(q+n-1), _state); - - /* - * prepare R - */ - if( circular ) - { - ae_vector_set_length(r, m, _state); - for(i=0; i<=m-1; i++) - { - r->ptr.p_complex[i] = ae_complex_from_d(0); - } - } - else - { - ae_vector_set_length(r, m+n-1, _state); - for(i=0; i<=m+n-2; i++) - { - r->ptr.p_complex[i] = ae_complex_from_d(0); - } - } - - /* - * pre-calculated FFT(B) - */ - ae_vector_set_length(&bbuf, q+n-1, _state); - ae_v_cmove(&bbuf.ptr.p_complex[0], 1, &b->ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - for(j=n; j<=q+n-2; j++) - { - bbuf.ptr.p_complex[j] = ae_complex_from_d(0); - } - fftc1d(&bbuf, q+n-1, _state); - - /* - * prepare FFT plan for chunks of A - */ - ftcomplexfftplan(q+n-1, 1, &plan, _state); - - /* - * main overlap-add cycle - */ - i = 0; - while(i<=m-1) - { - p = ae_minint(q, m-i, _state); - for(j=0; j<=p-1; j++) - { - buf.ptr.p_double[2*j+0] = a->ptr.p_complex[i+j].x; - buf.ptr.p_double[2*j+1] = a->ptr.p_complex[i+j].y; - } - for(j=p; j<=q+n-2; j++) - { - buf.ptr.p_double[2*j+0] = 0; - buf.ptr.p_double[2*j+1] = 0; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - for(j=0; j<=q+n-2; j++) - { - ax = buf.ptr.p_double[2*j+0]; - ay = buf.ptr.p_double[2*j+1]; - bx = bbuf.ptr.p_complex[j].x; - by = bbuf.ptr.p_complex[j].y; - tx = ax*bx-ay*by; - ty = ax*by+ay*bx; - buf.ptr.p_double[2*j+0] = tx; - buf.ptr.p_double[2*j+1] = -ty; - } - ftapplyplan(&plan, &buf, 0, 1, _state); - t = (double)1/(double)(q+n-1); - if( circular ) - { - j1 = ae_minint(i+p+n-2, m-1, _state)-i; - j2 = j1+1; - } - else - { - j1 = p+n-2; - j2 = j1+1; - } - for(j=0; j<=j1; j++) - { - r->ptr.p_complex[i+j].x = r->ptr.p_complex[i+j].x+buf.ptr.p_double[2*j+0]*t; - r->ptr.p_complex[i+j].y = r->ptr.p_complex[i+j].y-buf.ptr.p_double[2*j+1]*t; - } - for(j=j2; j<=p+n-2; j++) - { - r->ptr.p_complex[j-j2].x = r->ptr.p_complex[j-j2].x+buf.ptr.p_double[2*j+0]*t; - r->ptr.p_complex[j-j2].y = r->ptr.p_complex[j-j2].y-buf.ptr.p_double[2*j+1]*t; - } - i = i+p; - } - ae_frame_leave(_state); - return; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional real convolution. - -Extended subroutine which allows to choose convolution algorithm. -Intended for internal use, ALGLIB users should call ConvR1D(). - -INPUT PARAMETERS - A - array[0..M-1] - complex function to be transformed - M - problem size - B - array[0..N-1] - complex function to be transformed - N - problem size, N<=M - Alg - algorithm type: - *-2 auto-select Q for overlap-add - *-1 auto-select algorithm and parameters - * 0 straightforward formula for small N's - * 1 general FFT-based code - * 2 overlap-add with length Q - Q - length for overlap-add - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-1]. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dx(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - ae_bool circular, - ae_int_t alg, - ae_int_t q, - /* Real */ ae_vector* r, - ae_state *_state) -{ - ae_frame _frame_block; - double v; - ae_int_t i; - ae_int_t j; - ae_int_t p; - ae_int_t ptotal; - ae_int_t i1; - ae_int_t i2; - ae_int_t j1; - ae_int_t j2; - double ax; - double ay; - double bx; - double by; - double tx; - double ty; - double flopcand; - double flopbest; - ae_int_t algbest; - fasttransformplan plan; - ae_vector buf; - ae_vector buf2; - ae_vector buf3; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - _fasttransformplan_init(&plan, _state, ae_true); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf3, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DX: incorrect N or M!", _state); - ae_assert(n<=m, "ConvC1DX: Nptr.p_double[0]; - ae_v_moved(&r->ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,m-1), v); - ae_frame_leave(_state); - return; - } - - /* - * use straightforward formula - */ - if( circular ) - { - - /* - * circular convolution - */ - ae_vector_set_length(r, m, _state); - v = b->ptr.p_double[0]; - ae_v_moved(&r->ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,m-1), v); - for(i=1; i<=n-1; i++) - { - v = b->ptr.p_double[i]; - i1 = 0; - i2 = i-1; - j1 = m-i; - j2 = m-1; - ae_v_addd(&r->ptr.p_double[i1], 1, &a->ptr.p_double[j1], 1, ae_v_len(i1,i2), v); - i1 = i; - i2 = m-1; - j1 = 0; - j2 = m-i-1; - ae_v_addd(&r->ptr.p_double[i1], 1, &a->ptr.p_double[j1], 1, ae_v_len(i1,i2), v); - } - } - else - { - - /* - * non-circular convolution - */ - ae_vector_set_length(r, m+n-1, _state); - for(i=0; i<=m+n-2; i++) - { - r->ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = b->ptr.p_double[i]; - ae_v_addd(&r->ptr.p_double[i], 1, &a->ptr.p_double[0], 1, ae_v_len(i,i+m-1), v); - } - } - ae_frame_leave(_state); - return; - } - - /* - * general FFT-based code for - * circular and non-circular convolutions. - * - * First, if convolution is circular, we test whether M is smooth or not. - * If it is smooth, we just use M-length FFT to calculate convolution. - * If it is not, we calculate non-circular convolution and wrap it arount. - * - * If convolution is non-circular, we use zero-padding + FFT. - * - * We assume that M+N-1>2 - we should call small case code otherwise - */ - if( alg==1 ) - { - ae_assert(m+n-1>2, "ConvR1DX: internal error!", _state); - if( (circular&&ftbaseissmooth(m, _state))&&m%2==0 ) - { - - /* - * special code for circular convolution with smooth even M - */ - ae_vector_set_length(&buf, m, _state); - ae_v_move(&buf.ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_vector_set_length(&buf2, m, _state); - ae_v_move(&buf2.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=n; i<=m-1; i++) - { - buf2.ptr.p_double[i] = 0; - } - ae_vector_set_length(&buf3, m, _state); - ftcomplexfftplan(m/2, 1, &plan, _state); - fftr1dinternaleven(&buf, m, &buf3, &plan, _state); - fftr1dinternaleven(&buf2, m, &buf3, &plan, _state); - buf.ptr.p_double[0] = buf.ptr.p_double[0]*buf2.ptr.p_double[0]; - buf.ptr.p_double[1] = buf.ptr.p_double[1]*buf2.ptr.p_double[1]; - for(i=1; i<=m/2-1; i++) - { - ax = buf.ptr.p_double[2*i+0]; - ay = buf.ptr.p_double[2*i+1]; - bx = buf2.ptr.p_double[2*i+0]; - by = buf2.ptr.p_double[2*i+1]; - tx = ax*bx-ay*by; - ty = ax*by+ay*bx; - buf.ptr.p_double[2*i+0] = tx; - buf.ptr.p_double[2*i+1] = ty; - } - fftr1dinvinternaleven(&buf, m, &buf3, &plan, _state); - ae_vector_set_length(r, m, _state); - ae_v_move(&r->ptr.p_double[0], 1, &buf.ptr.p_double[0], 1, ae_v_len(0,m-1)); - } - else - { - - /* - * M is non-smooth or non-even, general code (circular/non-circular): - * * first part is the same for circular and non-circular - * convolutions. zero padding, FFTs, inverse FFTs - * * second part differs: - * * for non-circular convolution we just copy array - * * for circular convolution we add array tail to its head - */ - p = ftbasefindsmootheven(m+n-1, _state); - ae_vector_set_length(&buf, p, _state); - ae_v_move(&buf.ptr.p_double[0], 1, &a->ptr.p_double[0], 1, ae_v_len(0,m-1)); - for(i=m; i<=p-1; i++) - { - buf.ptr.p_double[i] = 0; - } - ae_vector_set_length(&buf2, p, _state); - ae_v_move(&buf2.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=n; i<=p-1; i++) - { - buf2.ptr.p_double[i] = 0; - } - ae_vector_set_length(&buf3, p, _state); - ftcomplexfftplan(p/2, 1, &plan, _state); - fftr1dinternaleven(&buf, p, &buf3, &plan, _state); - fftr1dinternaleven(&buf2, p, &buf3, &plan, _state); - buf.ptr.p_double[0] = buf.ptr.p_double[0]*buf2.ptr.p_double[0]; - buf.ptr.p_double[1] = buf.ptr.p_double[1]*buf2.ptr.p_double[1]; - for(i=1; i<=p/2-1; i++) - { - ax = buf.ptr.p_double[2*i+0]; - ay = buf.ptr.p_double[2*i+1]; - bx = buf2.ptr.p_double[2*i+0]; - by = buf2.ptr.p_double[2*i+1]; - tx = ax*bx-ay*by; - ty = ax*by+ay*bx; - buf.ptr.p_double[2*i+0] = tx; - buf.ptr.p_double[2*i+1] = ty; - } - fftr1dinvinternaleven(&buf, p, &buf3, &plan, _state); - if( circular ) - { - - /* - * circular, add tail to head - */ - ae_vector_set_length(r, m, _state); - ae_v_move(&r->ptr.p_double[0], 1, &buf.ptr.p_double[0], 1, ae_v_len(0,m-1)); - if( n>=2 ) - { - ae_v_add(&r->ptr.p_double[0], 1, &buf.ptr.p_double[m], 1, ae_v_len(0,n-2)); - } - } - else - { - - /* - * non-circular, just copy - */ - ae_vector_set_length(r, m+n-1, _state); - ae_v_move(&r->ptr.p_double[0], 1, &buf.ptr.p_double[0], 1, ae_v_len(0,m+n-2)); - } - } - ae_frame_leave(_state); - return; - } - - /* - * overlap-add method - */ - if( alg==2 ) - { - ae_assert((q+n-1)%2==0, "ConvR1DX: internal error!", _state); - ae_vector_set_length(&buf, q+n-1, _state); - ae_vector_set_length(&buf2, q+n-1, _state); - ae_vector_set_length(&buf3, q+n-1, _state); - ftcomplexfftplan((q+n-1)/2, 1, &plan, _state); - - /* - * prepare R - */ - if( circular ) - { - ae_vector_set_length(r, m, _state); - for(i=0; i<=m-1; i++) - { - r->ptr.p_double[i] = 0; - } - } - else - { - ae_vector_set_length(r, m+n-1, _state); - for(i=0; i<=m+n-2; i++) - { - r->ptr.p_double[i] = 0; - } - } - - /* - * pre-calculated FFT(B) - */ - ae_v_move(&buf2.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(j=n; j<=q+n-2; j++) - { - buf2.ptr.p_double[j] = 0; - } - fftr1dinternaleven(&buf2, q+n-1, &buf3, &plan, _state); - - /* - * main overlap-add cycle - */ - i = 0; - while(i<=m-1) - { - p = ae_minint(q, m-i, _state); - ae_v_move(&buf.ptr.p_double[0], 1, &a->ptr.p_double[i], 1, ae_v_len(0,p-1)); - for(j=p; j<=q+n-2; j++) - { - buf.ptr.p_double[j] = 0; - } - fftr1dinternaleven(&buf, q+n-1, &buf3, &plan, _state); - buf.ptr.p_double[0] = buf.ptr.p_double[0]*buf2.ptr.p_double[0]; - buf.ptr.p_double[1] = buf.ptr.p_double[1]*buf2.ptr.p_double[1]; - for(j=1; j<=(q+n-1)/2-1; j++) - { - ax = buf.ptr.p_double[2*j+0]; - ay = buf.ptr.p_double[2*j+1]; - bx = buf2.ptr.p_double[2*j+0]; - by = buf2.ptr.p_double[2*j+1]; - tx = ax*bx-ay*by; - ty = ax*by+ay*bx; - buf.ptr.p_double[2*j+0] = tx; - buf.ptr.p_double[2*j+1] = ty; - } - fftr1dinvinternaleven(&buf, q+n-1, &buf3, &plan, _state); - if( circular ) - { - j1 = ae_minint(i+p+n-2, m-1, _state)-i; - j2 = j1+1; - } - else - { - j1 = p+n-2; - j2 = j1+1; - } - ae_v_add(&r->ptr.p_double[i], 1, &buf.ptr.p_double[0], 1, ae_v_len(i,i+j1)); - if( p+n-2>=j2 ) - { - ae_v_add(&r->ptr.p_double[0], 1, &buf.ptr.p_double[j2], 1, ae_v_len(0,p+n-2-j2)); - } - i = i+p; - } - ae_frame_leave(_state); - return; - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -1-dimensional complex cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (non-circular). - -Correlation is calculated using reduction to convolution. Algorithm with -max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info -about performance). - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrC1D(Signal, Pattern) = Pattern x Signal (using traditional - definition of cross-correlation, denoting cross-correlation as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - complex function to be transformed, - signal containing pattern - N - problem size - Pattern - array[0..M-1] - complex function to be transformed, - pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - cross-correlation, array[0..N+M-2]: - * positive lags are stored in R[0..N-1], - R[i] = sum(conj(pattern[j])*signal[i+j] - * negative lags are stored in R[N..N+M-2], - R[N+M-1-i] = sum(conj(pattern[j])*signal[-i+j] - -NOTE: - It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero -on [-K..M-1], you can still use this subroutine, just shift result by K. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrc1d(/* Complex */ ae_vector* signal, - ae_int_t n, - /* Complex */ ae_vector* pattern, - ae_int_t m, - /* Complex */ ae_vector* r, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector p; - ae_vector b; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - ae_vector_init(&p, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&b, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0&&m>0, "CorrC1D: incorrect N or M!", _state); - ae_vector_set_length(&p, m, _state); - for(i=0; i<=m-1; i++) - { - p.ptr.p_complex[m-1-i] = ae_c_conj(pattern->ptr.p_complex[i], _state); - } - convc1d(&p, m, signal, n, &b, _state); - ae_vector_set_length(r, m+n-1, _state); - ae_v_cmove(&r->ptr.p_complex[0], 1, &b.ptr.p_complex[m-1], 1, "N", ae_v_len(0,n-1)); - if( m+n-2>=n ) - { - ae_v_cmove(&r->ptr.p_complex[n], 1, &b.ptr.p_complex[0], 1, "N", ae_v_len(n,m+n-2)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional circular complex cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (circular). -Algorithm has linearithmic complexity for any M/N. - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrC1DCircular(Signal, Pattern) = Pattern x Signal (using - traditional definition of cross-correlation, denoting cross-correlation - as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - complex function to be transformed, - periodic signal containing pattern - N - problem size - Pattern - array[0..M-1] - complex function to be transformed, - non-periodic pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrc1dcircular(/* Complex */ ae_vector* signal, - ae_int_t m, - /* Complex */ ae_vector* pattern, - ae_int_t n, - /* Complex */ ae_vector* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector p; - ae_vector b; - ae_int_t i1; - ae_int_t i2; - ae_int_t i; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(c); - ae_vector_init(&p, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&b, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DCircular: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer (at least - not shorter) that B. - */ - if( mptr.p_complex[i1], 1, "N", ae_v_len(0,j2)); - i1 = i1+m; - } - corrc1dcircular(signal, m, &b, m, c, _state); - ae_frame_leave(_state); - return; - } - - /* - * Task is normalized - */ - ae_vector_set_length(&p, n, _state); - for(i=0; i<=n-1; i++) - { - p.ptr.p_complex[n-1-i] = ae_c_conj(pattern->ptr.p_complex[i], _state); - } - convc1dcircular(signal, m, &p, n, &b, _state); - ae_vector_set_length(c, m, _state); - ae_v_cmove(&c->ptr.p_complex[0], 1, &b.ptr.p_complex[n-1], 1, "N", ae_v_len(0,m-n)); - if( m-n+1<=m-1 ) - { - ae_v_cmove(&c->ptr.p_complex[m-n+1], 1, &b.ptr.p_complex[0], 1, "N", ae_v_len(m-n+1,m-1)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional real cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (non-circular). - -Correlation is calculated using reduction to convolution. Algorithm with -max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info -about performance). - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrR1D(Signal, Pattern) = Pattern x Signal (using traditional - definition of cross-correlation, denoting cross-correlation as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - real function to be transformed, - signal containing pattern - N - problem size - Pattern - array[0..M-1] - real function to be transformed, - pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - cross-correlation, array[0..N+M-2]: - * positive lags are stored in R[0..N-1], - R[i] = sum(pattern[j]*signal[i+j] - * negative lags are stored in R[N..N+M-2], - R[N+M-1-i] = sum(pattern[j]*signal[-i+j] - -NOTE: - It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero -on [-K..M-1], you can still use this subroutine, just shift result by K. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrr1d(/* Real */ ae_vector* signal, - ae_int_t n, - /* Real */ ae_vector* pattern, - ae_int_t m, - /* Real */ ae_vector* r, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector p; - ae_vector b; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - ae_vector_init(&p, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0&&m>0, "CorrR1D: incorrect N or M!", _state); - ae_vector_set_length(&p, m, _state); - for(i=0; i<=m-1; i++) - { - p.ptr.p_double[m-1-i] = pattern->ptr.p_double[i]; - } - convr1d(&p, m, signal, n, &b, _state); - ae_vector_set_length(r, m+n-1, _state); - ae_v_move(&r->ptr.p_double[0], 1, &b.ptr.p_double[m-1], 1, ae_v_len(0,n-1)); - if( m+n-2>=n ) - { - ae_v_move(&r->ptr.p_double[n], 1, &b.ptr.p_double[0], 1, ae_v_len(n,m+n-2)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional circular real cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (circular). -Algorithm has linearithmic complexity for any M/N. - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrR1DCircular(Signal, Pattern) = Pattern x Signal (using - traditional definition of cross-correlation, denoting cross-correlation - as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - real function to be transformed, - periodic signal containing pattern - N - problem size - Pattern - array[0..M-1] - real function to be transformed, - non-periodic pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrr1dcircular(/* Real */ ae_vector* signal, - ae_int_t m, - /* Real */ ae_vector* pattern, - ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector p; - ae_vector b; - ae_int_t i1; - ae_int_t i2; - ae_int_t i; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(c); - ae_vector_init(&p, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0&&m>0, "ConvC1DCircular: incorrect N or M!", _state); - - /* - * normalize task: make M>=N, - * so A will be longer (at least - not shorter) that B. - */ - if( mptr.p_double[i1], 1, ae_v_len(0,j2)); - i1 = i1+m; - } - corrr1dcircular(signal, m, &b, m, c, _state); - ae_frame_leave(_state); - return; - } - - /* - * Task is normalized - */ - ae_vector_set_length(&p, n, _state); - for(i=0; i<=n-1; i++) - { - p.ptr.p_double[n-1-i] = pattern->ptr.p_double[i]; - } - convr1dcircular(signal, m, &p, n, &b, _state); - ae_vector_set_length(c, m, _state); - ae_v_move(&c->ptr.p_double[0], 1, &b.ptr.p_double[n-1], 1, ae_v_len(0,m-n)); - if( m-n+1<=m-1 ) - { - ae_v_move(&c->ptr.p_double[m-n+1], 1, &b.ptr.p_double[0], 1, ae_v_len(m-n+1,m-1)); - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -1-dimensional Fast Hartley Transform. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - FHT of a input array, array[0..N-1], - A_out[k] = sum(A_in[j]*(cos(2*pi*j*k/N)+sin(2*pi*j*k/N)), j=0..N-1) - - - -- ALGLIB -- - Copyright 04.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fhtr1d(/* Real */ ae_vector* a, ae_int_t n, ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector fa; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&fa, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0, "FHTR1D: incorrect N!", _state); - - /* - * Special case: N=1, FHT is just identity transform. - * After this block we assume that N is strictly greater than 1. - */ - if( n==1 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Reduce FHt to real FFT - */ - fftr1d(a, n, &fa, _state); - for(i=0; i<=n-1; i++) - { - a->ptr.p_double[i] = fa.ptr.p_complex[i].x-fa.ptr.p_complex[i].y; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -1-dimensional inverse FHT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse FHT of a input array, array[0..N-1] - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fhtr1dinv(/* Real */ ae_vector* a, ae_int_t n, ae_state *_state) -{ - ae_int_t i; - - - ae_assert(n>0, "FHTR1DInv: incorrect N!", _state); - - /* - * Special case: N=1, iFHT is just identity transform. - * After this block we assume that N is strictly greater than 1. - */ - if( n==1 ) - { - return; - } - - /* - * Inverse FHT can be expressed in terms of the FHT as - * - * invfht(x) = fht(x)/N - */ - fhtr1d(a, n, _state); - for(i=0; i<=n-1; i++) - { - a->ptr.p_double[i] = a->ptr.p_double[i]/n; - } -} - - - -} - diff --git a/src/inc/alglib/fasttransforms.h b/src/inc/alglib/fasttransforms.h deleted file mode 100644 index 079da71..0000000 --- a/src/inc/alglib/fasttransforms.h +++ /dev/null @@ -1,691 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _fasttransforms_pkg_h -#define _fasttransforms_pkg_h -#include "ap.h" -#include "alglibinternal.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -1-dimensional complex FFT. - -Array size N may be arbitrary number (composite or prime). Composite N's -are handled with cache-oblivious variation of a Cooley-Tukey algorithm. -Small prime-factors are transformed using hard coded codelets (similar to -FFTW codelets, but without low-level optimization), large prime-factors -are handled with Bluestein's algorithm. - -Fastests transforms are for smooth N's (prime factors are 2, 3, 5 only), -most fast for powers of 2. When N have prime factors larger than these, -but orders of magnitude smaller than N, computations will be about 4 times -slower than for nearby highly composite N's. When N itself is prime, speed -will be 6 times lower. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1d(complex_1d_array &a, const ae_int_t n); -void fftc1d(complex_1d_array &a); - - -/************************************************************************* -1-dimensional complex inverse FFT. - -Array size N may be arbitrary number (composite or prime). Algorithm has -O(N*logN) complexity for any N (composite or prime). - -See FFTC1D() description for more information about algorithm performance. - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - A_out[j] = SUM(A_in[k]/N*exp(+2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fftc1dinv(complex_1d_array &a, const ae_int_t n); -void fftc1dinv(complex_1d_array &a); - - -/************************************************************************* -1-dimensional real FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - F - DFT of a input array, array[0..N-1] - F[j] = SUM(A[k]*exp(-2*pi*sqrt(-1)*j*k/N), k = 0..N-1) - -NOTE: - F[] satisfies symmetry property F[k] = conj(F[N-k]), so just one half -of array is usually needed. But for convinience subroutine returns full -complex array (with frequencies above N/2), so its result may be used by -other FFT-related subroutines. - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1d(const real_1d_array &a, const ae_int_t n, complex_1d_array &f); -void fftr1d(const real_1d_array &a, complex_1d_array &f); - - -/************************************************************************* -1-dimensional real inverse FFT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - F - array[0..floor(N/2)] - frequencies from forward real FFT - N - problem size - -OUTPUT PARAMETERS - A - inverse DFT of a input array, array[0..N-1] - -NOTE: - F[] should satisfy symmetry property F[k] = conj(F[N-k]), so just one -half of frequencies array is needed - elements from 0 to floor(N/2). F[0] -is ALWAYS real. If N is even F[floor(N/2)] is real too. If N is odd, then -F[floor(N/2)] has no special properties. - -Relying on properties noted above, FFTR1DInv subroutine uses only elements -from 0th to floor(N/2)-th. It ignores imaginary part of F[0], and in case -N is even it ignores imaginary part of F[floor(N/2)] too. - -When you call this function using full arguments list - "FFTR1DInv(F,N,A)" -- you can pass either either frequencies array with N elements or reduced -array with roughly N/2 elements - subroutine will successfully transform -both. - -If you call this function using reduced arguments list - "FFTR1DInv(F,A)" -- you must pass FULL array with N elements (although higher N/2 are still -not used) because array size is used to automatically determine FFT length - - - -- ALGLIB -- - Copyright 01.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fftr1dinv(const complex_1d_array &f, const ae_int_t n, real_1d_array &a); -void fftr1dinv(const complex_1d_array &f, real_1d_array &a); - -/************************************************************************* -1-dimensional complex convolution. - -For given A/B returns conv(A,B) (non-circular). Subroutine can automatically -choose between three implementations: straightforward O(M*N) formula for -very small N (or M), overlap-add algorithm for cases where max(M,N) is -significantly larger than min(M,N), but O(M*N) algorithm is too slow, and -general FFT-based formula for cases where two previois algorithms are too -slow. - -Algorithm has max(M,N)*log(max(M,N)) complexity for any M/N. - -INPUT PARAMETERS - A - array[0..M-1] - complex function to be transformed - M - problem size - B - array[0..N-1] - complex function to be transformed - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-2]. - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1d(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r); - - -/************************************************************************* -1-dimensional complex non-circular deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length, N<=M - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dinv(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r); - - -/************************************************************************* -1-dimensional circular complex convolution. - -For given S/R returns conv(S,R) (circular). Algorithm has linearithmic -complexity for any M/N. - -IMPORTANT: normal convolution is commutative, i.e. it is symmetric - -conv(A,B)=conv(B,A). Cyclic convolution IS NOT. One function - S - is a -signal, periodic function, and another - R - is a response, non-periodic -function with limited length. - -INPUT PARAMETERS - S - array[0..M-1] - complex periodic signal - M - problem size - B - array[0..N-1] - complex non-periodic response - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dcircular(const complex_1d_array &s, const ae_int_t m, const complex_1d_array &r, const ae_int_t n, complex_1d_array &c); - - -/************************************************************************* -1-dimensional circular complex deconvolution (inverse of ConvC1DCircular()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved periodic signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - non-periodic response - N - response length - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-1]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convc1dcircularinv(const complex_1d_array &a, const ae_int_t m, const complex_1d_array &b, const ae_int_t n, complex_1d_array &r); - - -/************************************************************************* -1-dimensional real convolution. - -Analogous to ConvC1D(), see ConvC1D() comments for more details. - -INPUT PARAMETERS - A - array[0..M-1] - real function to be transformed - M - problem size - B - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..N+M-2]. - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1d(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r); - - -/************************************************************************* -1-dimensional real deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length, N<=M - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that A is zero at T<0, B is zero too. If one or both -functions have non-zero values at negative T's, you can still use this -subroutine - just shift its result correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dinv(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r); - - -/************************************************************************* -1-dimensional circular real convolution. - -Analogous to ConvC1DCircular(), see ConvC1DCircular() comments for more details. - -INPUT PARAMETERS - S - array[0..M-1] - real signal - M - problem size - B - array[0..N-1] - real response - N - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dcircular(const real_1d_array &s, const ae_int_t m, const real_1d_array &r, const ae_int_t n, real_1d_array &c); - - -/************************************************************************* -1-dimensional complex deconvolution (inverse of ConvC1D()). - -Algorithm has M*log(M)) complexity for any M (composite or prime). - -INPUT PARAMETERS - A - array[0..M-1] - convolved signal, A = conv(R, B) - M - convolved signal length - B - array[0..N-1] - response - N - response length - -OUTPUT PARAMETERS - R - deconvolved signal. array[0..M-N]. - -NOTE: - deconvolution is unstable process and may result in division by zero -(if your response function is degenerate, i.e. has zero Fourier coefficient). - -NOTE: - It is assumed that B is zero at T<0. If it has non-zero values at -negative T's, you can still use this subroutine - just shift its result -correspondingly. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void convr1dcircularinv(const real_1d_array &a, const ae_int_t m, const real_1d_array &b, const ae_int_t n, real_1d_array &r); - -/************************************************************************* -1-dimensional complex cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (non-circular). - -Correlation is calculated using reduction to convolution. Algorithm with -max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info -about performance). - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrC1D(Signal, Pattern) = Pattern x Signal (using traditional - definition of cross-correlation, denoting cross-correlation as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - complex function to be transformed, - signal containing pattern - N - problem size - Pattern - array[0..M-1] - complex function to be transformed, - pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - cross-correlation, array[0..N+M-2]: - * positive lags are stored in R[0..N-1], - R[i] = sum(conj(pattern[j])*signal[i+j] - * negative lags are stored in R[N..N+M-2], - R[N+M-1-i] = sum(conj(pattern[j])*signal[-i+j] - -NOTE: - It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero -on [-K..M-1], you can still use this subroutine, just shift result by K. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrc1d(const complex_1d_array &signal, const ae_int_t n, const complex_1d_array &pattern, const ae_int_t m, complex_1d_array &r); - - -/************************************************************************* -1-dimensional circular complex cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (circular). -Algorithm has linearithmic complexity for any M/N. - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrC1DCircular(Signal, Pattern) = Pattern x Signal (using - traditional definition of cross-correlation, denoting cross-correlation - as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - complex function to be transformed, - periodic signal containing pattern - N - problem size - Pattern - array[0..M-1] - complex function to be transformed, - non-periodic pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrc1dcircular(const complex_1d_array &signal, const ae_int_t m, const complex_1d_array &pattern, const ae_int_t n, complex_1d_array &c); - - -/************************************************************************* -1-dimensional real cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (non-circular). - -Correlation is calculated using reduction to convolution. Algorithm with -max(N,N)*log(max(N,N)) complexity is used (see ConvC1D() for more info -about performance). - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrR1D(Signal, Pattern) = Pattern x Signal (using traditional - definition of cross-correlation, denoting cross-correlation as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - real function to be transformed, - signal containing pattern - N - problem size - Pattern - array[0..M-1] - real function to be transformed, - pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - cross-correlation, array[0..N+M-2]: - * positive lags are stored in R[0..N-1], - R[i] = sum(pattern[j]*signal[i+j] - * negative lags are stored in R[N..N+M-2], - R[N+M-1-i] = sum(pattern[j]*signal[-i+j] - -NOTE: - It is assumed that pattern domain is [0..M-1]. If Pattern is non-zero -on [-K..M-1], you can still use this subroutine, just shift result by K. - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrr1d(const real_1d_array &signal, const ae_int_t n, const real_1d_array &pattern, const ae_int_t m, real_1d_array &r); - - -/************************************************************************* -1-dimensional circular real cross-correlation. - -For given Pattern/Signal returns corr(Pattern,Signal) (circular). -Algorithm has linearithmic complexity for any M/N. - -IMPORTANT: - for historical reasons subroutine accepts its parameters in reversed - order: CorrR1DCircular(Signal, Pattern) = Pattern x Signal (using - traditional definition of cross-correlation, denoting cross-correlation - as "x"). - -INPUT PARAMETERS - Signal - array[0..N-1] - real function to be transformed, - periodic signal containing pattern - N - problem size - Pattern - array[0..M-1] - real function to be transformed, - non-periodic pattern to search withing signal - M - problem size - -OUTPUT PARAMETERS - R - convolution: A*B. array[0..M-1]. - - - -- ALGLIB -- - Copyright 21.07.2009 by Bochkanov Sergey -*************************************************************************/ -void corrr1dcircular(const real_1d_array &signal, const ae_int_t m, const real_1d_array &pattern, const ae_int_t n, real_1d_array &c); - -/************************************************************************* -1-dimensional Fast Hartley Transform. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - real function to be transformed - N - problem size - -OUTPUT PARAMETERS - A - FHT of a input array, array[0..N-1], - A_out[k] = sum(A_in[j]*(cos(2*pi*j*k/N)+sin(2*pi*j*k/N)), j=0..N-1) - - - -- ALGLIB -- - Copyright 04.06.2009 by Bochkanov Sergey -*************************************************************************/ -void fhtr1d(real_1d_array &a, const ae_int_t n); - - -/************************************************************************* -1-dimensional inverse FHT. - -Algorithm has O(N*logN) complexity for any N (composite or prime). - -INPUT PARAMETERS - A - array[0..N-1] - complex array to be transformed - N - problem size - -OUTPUT PARAMETERS - A - inverse FHT of a input array, array[0..N-1] - - - -- ALGLIB -- - Copyright 29.05.2009 by Bochkanov Sergey -*************************************************************************/ -void fhtr1dinv(real_1d_array &a, const ae_int_t n); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void fftc1d(/* Complex */ ae_vector* a, ae_int_t n, ae_state *_state); -void fftc1dinv(/* Complex */ ae_vector* a, ae_int_t n, ae_state *_state); -void fftr1d(/* Real */ ae_vector* a, - ae_int_t n, - /* Complex */ ae_vector* f, - ae_state *_state); -void fftr1dinv(/* Complex */ ae_vector* f, - ae_int_t n, - /* Real */ ae_vector* a, - ae_state *_state); -void fftr1dinternaleven(/* Real */ ae_vector* a, - ae_int_t n, - /* Real */ ae_vector* buf, - fasttransformplan* plan, - ae_state *_state); -void fftr1dinvinternaleven(/* Real */ ae_vector* a, - ae_int_t n, - /* Real */ ae_vector* buf, - fasttransformplan* plan, - ae_state *_state); -void convc1d(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Complex */ ae_vector* r, - ae_state *_state); -void convc1dinv(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Complex */ ae_vector* r, - ae_state *_state); -void convc1dcircular(/* Complex */ ae_vector* s, - ae_int_t m, - /* Complex */ ae_vector* r, - ae_int_t n, - /* Complex */ ae_vector* c, - ae_state *_state); -void convc1dcircularinv(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - /* Complex */ ae_vector* r, - ae_state *_state); -void convr1d(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* r, - ae_state *_state); -void convr1dinv(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* r, - ae_state *_state); -void convr1dcircular(/* Real */ ae_vector* s, - ae_int_t m, - /* Real */ ae_vector* r, - ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state); -void convr1dcircularinv(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - /* Real */ ae_vector* r, - ae_state *_state); -void convc1dx(/* Complex */ ae_vector* a, - ae_int_t m, - /* Complex */ ae_vector* b, - ae_int_t n, - ae_bool circular, - ae_int_t alg, - ae_int_t q, - /* Complex */ ae_vector* r, - ae_state *_state); -void convr1dx(/* Real */ ae_vector* a, - ae_int_t m, - /* Real */ ae_vector* b, - ae_int_t n, - ae_bool circular, - ae_int_t alg, - ae_int_t q, - /* Real */ ae_vector* r, - ae_state *_state); -void corrc1d(/* Complex */ ae_vector* signal, - ae_int_t n, - /* Complex */ ae_vector* pattern, - ae_int_t m, - /* Complex */ ae_vector* r, - ae_state *_state); -void corrc1dcircular(/* Complex */ ae_vector* signal, - ae_int_t m, - /* Complex */ ae_vector* pattern, - ae_int_t n, - /* Complex */ ae_vector* c, - ae_state *_state); -void corrr1d(/* Real */ ae_vector* signal, - ae_int_t n, - /* Real */ ae_vector* pattern, - ae_int_t m, - /* Real */ ae_vector* r, - ae_state *_state); -void corrr1dcircular(/* Real */ ae_vector* signal, - ae_int_t m, - /* Real */ ae_vector* pattern, - ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state); -void fhtr1d(/* Real */ ae_vector* a, ae_int_t n, ae_state *_state); -void fhtr1dinv(/* Real */ ae_vector* a, ae_int_t n, ae_state *_state); - -} -#endif - diff --git a/src/inc/alglib/integration.cpp b/src/inc/alglib/integration.cpp deleted file mode 100644 index f35b744..0000000 --- a/src/inc/alglib/integration.cpp +++ /dev/null @@ -1,3961 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "integration.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Computation of nodes and weights for a Gauss quadrature formula - -The algorithm generates the N-point Gauss quadrature formula with weight -function given by coefficients alpha and beta of a recurrence relation -which generates a system of orthogonal polynomials: - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-1], alpha coefficients - Beta – array[0..N-1], beta coefficients - Zero-indexed element is not used and may be arbitrary. - Beta[I]>0. - Mu0 – zeroth moment of the weight function. - N – number of nodes of the quadrature formula, N>=1 - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgeneraterec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgeneraterec(const_cast(alpha.c_ptr()), const_cast(beta.c_ptr()), mu0, n, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Computation of nodes and weights for a Gauss-Lobatto quadrature formula - -The algorithm generates the N-point Gauss-Lobatto quadrature formula with -weight function given by coefficients alpha and beta of a recurrence which -generates a system of orthogonal polynomials. - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-2], alpha coefficients - Beta – array[0..N-2], beta coefficients. - Zero-indexed element is not used, may be arbitrary. - Beta[I]>0 - Mu0 – zeroth moment of the weighting function. - A – left boundary of the integration interval. - B – right boundary of the integration interval. - N – number of nodes of the quadrature formula, N>=3 - (including the left and right boundary nodes). - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslobattorec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const double a, const double b, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgenerategausslobattorec(const_cast(alpha.c_ptr()), const_cast(beta.c_ptr()), mu0, a, b, n, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Computation of nodes and weights for a Gauss-Radau quadrature formula - -The algorithm generates the N-point Gauss-Radau quadrature formula with -weight function given by the coefficients alpha and beta of a recurrence -which generates a system of orthogonal polynomials. - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-2], alpha coefficients. - Beta – array[0..N-1], beta coefficients - Zero-indexed element is not used. - Beta[I]>0 - Mu0 – zeroth moment of the weighting function. - A – left boundary of the integration interval. - N – number of nodes of the quadrature formula, N>=2 - (including the left boundary node). - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategaussradaurec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const double a, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgenerategaussradaurec(const_cast(alpha.c_ptr()), const_cast(beta.c_ptr()), mu0, a, n, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns nodes/weights for Gauss-Legendre quadrature on [-1,1] with N -nodes. - -INPUT PARAMETERS: - N - number of nodes, >=1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslegendre(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgenerategausslegendre(n, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns nodes/weights for Gauss-Jacobi quadrature on [-1,1] with weight -function W(x)=Power(1-x,Alpha)*Power(1+x,Beta). - -INPUT PARAMETERS: - N - number of nodes, >=1 - Alpha - power-law coefficient, Alpha>-1 - Beta - power-law coefficient, Beta>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. Alpha or Beta are too close - to -1 to obtain weights/nodes with high enough - accuracy, or, may be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha/Beta was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategaussjacobi(const ae_int_t n, const double alpha, const double beta, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgenerategaussjacobi(n, alpha, beta, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns nodes/weights for Gauss-Laguerre quadrature on [0,+inf) with -weight function W(x)=Power(x,Alpha)*Exp(-x) - -INPUT PARAMETERS: - N - number of nodes, >=1 - Alpha - power-law coefficient, Alpha>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. Alpha is too close to -1 to - obtain weights/nodes with high enough accuracy - or, may be, N is too large. Try to use - multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslaguerre(const ae_int_t n, const double alpha, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgenerategausslaguerre(n, alpha, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns nodes/weights for Gauss-Hermite quadrature on (-inf,+inf) with -weight function W(x)=Exp(-x*x) - -INPUT PARAMETERS: - N - number of nodes, >=1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. May be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausshermite(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gqgenerategausshermite(n, &info, const_cast(x.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Computation of nodes and weights of a Gauss-Kronrod quadrature formula - -The algorithm generates the N-point Gauss-Kronrod quadrature formula with -weight function given by coefficients alpha and beta of a recurrence -relation which generates a system of orthogonal polynomials: - - P-1(x) = 0 - P0(x) = 1 - Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zero moment Mu0 - - Mu0 = integral(W(x)dx,a,b) - - -INPUT PARAMETERS: - Alpha – alpha coefficients, array[0..floor(3*K/2)]. - Beta – beta coefficients, array[0..ceil(3*K/2)]. - Beta[0] is not used and may be arbitrary. - Beta[I]>0. - Mu0 – zeroth moment of the weight function. - N – number of nodes of the Gauss-Kronrod quadrature formula, - N >= 3, - N = 2*K+1. - -OUTPUT PARAMETERS: - Info - error code: - * -5 no real and positive Gauss-Kronrod formula can - be created for such a weight function with a - given number of nodes. - * -4 N is too large, task may be ill conditioned - - x[i]=x[i+1] found. - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - -- ALGLIB -- - Copyright 08.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgeneraterec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gkqgeneraterec(const_cast(alpha.c_ptr()), const_cast(beta.c_ptr()), mu0, n, &info, const_cast(x.c_ptr()), const_cast(wkronrod.c_ptr()), const_cast(wgauss.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes/weights for Gauss-Legendre -quadrature with N points. - -GKQLegendreCalc (calculation) or GKQLegendreTbl (precomputed table) is -used depending on machine precision and number of nodes. - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgenerategausslegendre(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gkqgenerategausslegendre(n, &info, const_cast(x.c_ptr()), const_cast(wkronrod.c_ptr()), const_cast(wgauss.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes/weights for Gauss-Jacobi -quadrature on [-1,1] with weight function - - W(x)=Power(1-x,Alpha)*Power(1+x,Beta). - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - Alpha - power-law coefficient, Alpha>-1 - Beta - power-law coefficient, Beta>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -5 no real and positive Gauss-Kronrod formula can - be created for such a weight function with a - given number of nodes. - * -4 an error was detected when calculating - weights/nodes. Alpha or Beta are too close - to -1 to obtain weights/nodes with high enough - accuracy, or, may be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - * +2 OK, but quadrature rule have exterior nodes, - x[0]<-1 or x[n-1]>+1 - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgenerategaussjacobi(const ae_int_t n, const double alpha, const double beta, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gkqgenerategaussjacobi(n, alpha, beta, &info, const_cast(x.c_ptr()), const_cast(wkronrod.c_ptr()), const_cast(wgauss.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes for quadrature with N points. - -Reduction to tridiagonal eigenproblem is used. - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqlegendrecalc(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gkqlegendrecalc(n, &info, const_cast(x.c_ptr()), const_cast(wkronrod.c_ptr()), const_cast(wgauss.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes for quadrature with N points using -pre-calculated table. Nodes/weights were computed with accuracy up to -1.0E-32 (if MPFR version of ALGLIB is used). In standard double precision -accuracy reduces to something about 2.0E-16 (depending on your compiler's -handling of long floating point constants). - -INPUT PARAMETERS: - N - number of Kronrod nodes. - N can be 15, 21, 31, 41, 51, 61. - -OUTPUT PARAMETERS: - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqlegendretbl(const ae_int_t n, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss, double &eps) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::gkqlegendretbl(n, const_cast(x.c_ptr()), const_cast(wkronrod.c_ptr()), const_cast(wgauss.c_ptr()), &eps, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Integration report: -* TerminationType = completetion code: - * -5 non-convergence of Gauss-Kronrod nodes - calculation subroutine. - * -1 incorrect parameters were specified - * 1 OK -* Rep.NFEV countains number of function calculations -* Rep.NIntervals contains number of intervals [a,b] - was partitioned into. -*************************************************************************/ -_autogkreport_owner::_autogkreport_owner() -{ - p_struct = (alglib_impl::autogkreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::autogkreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_autogkreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_autogkreport_owner::_autogkreport_owner(const _autogkreport_owner &rhs) -{ - p_struct = (alglib_impl::autogkreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::autogkreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_autogkreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_autogkreport_owner& _autogkreport_owner::operator=(const _autogkreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_autogkreport_clear(p_struct); - if( !alglib_impl::_autogkreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_autogkreport_owner::~_autogkreport_owner() -{ - alglib_impl::_autogkreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::autogkreport* _autogkreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::autogkreport* _autogkreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -autogkreport::autogkreport() : _autogkreport_owner() ,terminationtype(p_struct->terminationtype),nfev(p_struct->nfev),nintervals(p_struct->nintervals) -{ -} - -autogkreport::autogkreport(const autogkreport &rhs):_autogkreport_owner(rhs) ,terminationtype(p_struct->terminationtype),nfev(p_struct->nfev),nintervals(p_struct->nintervals) -{ -} - -autogkreport& autogkreport::operator=(const autogkreport &rhs) -{ - if( this==&rhs ) - return *this; - _autogkreport_owner::operator=(rhs); - return *this; -} - -autogkreport::~autogkreport() -{ -} - - -/************************************************************************* -This structure stores state of the integration algorithm. - -Although this class has public fields, they are not intended for external -use. You should use ALGLIB functions to work with this class: -* autogksmooth()/AutoGKSmoothW()/... to create objects -* autogkintegrate() to begin integration -* autogkresults() to get results -*************************************************************************/ -_autogkstate_owner::_autogkstate_owner() -{ - p_struct = (alglib_impl::autogkstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::autogkstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_autogkstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_autogkstate_owner::_autogkstate_owner(const _autogkstate_owner &rhs) -{ - p_struct = (alglib_impl::autogkstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::autogkstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_autogkstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_autogkstate_owner& _autogkstate_owner::operator=(const _autogkstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_autogkstate_clear(p_struct); - if( !alglib_impl::_autogkstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_autogkstate_owner::~_autogkstate_owner() -{ - alglib_impl::_autogkstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::autogkstate* _autogkstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::autogkstate* _autogkstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -autogkstate::autogkstate() : _autogkstate_owner() ,needf(p_struct->needf),x(p_struct->x),xminusa(p_struct->xminusa),bminusx(p_struct->bminusx),f(p_struct->f) -{ -} - -autogkstate::autogkstate(const autogkstate &rhs):_autogkstate_owner(rhs) ,needf(p_struct->needf),x(p_struct->x),xminusa(p_struct->xminusa),bminusx(p_struct->bminusx),f(p_struct->f) -{ -} - -autogkstate& autogkstate::operator=(const autogkstate &rhs) -{ - if( this==&rhs ) - return *this; - _autogkstate_owner::operator=(rhs); - return *this; -} - -autogkstate::~autogkstate() -{ -} - -/************************************************************************* -Integration of a smooth function F(x) on a finite interval [a,b]. - -Fast-convergent algorithm based on a Gauss-Kronrod formula is used. Result -is calculated with accuracy close to the machine precision. - -Algorithm works well only with smooth integrands. It may be used with -continuous non-smooth integrands, but with less performance. - -It should never be used with integrands which have integrable singularities -at lower or upper limits - algorithm may crash. Use AutoGKSingular in such -cases. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksmooth(const double a, const double b, autogkstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::autogksmooth(a, b, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Integration of a smooth function F(x) on a finite interval [a,b]. - -This subroutine is same as AutoGKSmooth(), but it guarantees that interval -[a,b] is partitioned into subintervals which have width at most XWidth. - -Subroutine can be used when integrating nearly-constant function with -narrow "bumps" (about XWidth wide). If "bumps" are too narrow, AutoGKSmooth -subroutine can overlook them. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmooth, AutoGKSingular, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksmoothw(const double a, const double b, const double xwidth, autogkstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::autogksmoothw(a, b, xwidth, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Integration on a finite interval [A,B]. -Integrand have integrable singularities at A/B. - -F(X) must diverge as "(x-A)^alpha" at A, as "(B-x)^beta" at B, with known -alpha/beta (alpha>-1, beta>-1). If alpha/beta are not known, estimates -from below can be used (but these estimates should be greater than -1 too). - -One of alpha/beta variables (or even both alpha/beta) may be equal to 0, -which means than function F(x) is non-singular at A/B. Anyway (singular at -bounds or not), function F(x) is supposed to be continuous on (A,B). - -Fast-convergent algorithm based on a Gauss-Kronrod formula is used. Result -is calculated with accuracy close to the machine precision. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - Alpha - power-law coefficient of the F(x) at A, - Alpha>-1 - Beta - power-law coefficient of the F(x) at B, - Beta>-1 - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmooth, AutoGKSmoothW, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksingular(const double a, const double b, const double alpha, const double beta, autogkstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::autogksingular(a, b, alpha, beta, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool autogkiteration(const autogkstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::autogkiteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void autogkintegrate(autogkstate &state, - void (*func)(double x, double xminusa, double bminusx, double &y, void *ptr), - void *ptr){ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'autogkintegrate()' (func is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::autogkiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.xminusa, state.bminusx, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: unexpected error in 'autogkintegrate()'"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -Adaptive integration results - -Called after AutoGKIteration returned False. - -Input parameters: - State - algorithm state (used by AutoGKIteration). - -Output parameters: - V - integral(f(x)dx,a,b) - Rep - optimization report (see AutoGKReport description) - - -- ALGLIB -- - Copyright 14.11.2007 by Bochkanov Sergey -*************************************************************************/ -void autogkresults(const autogkstate &state, double &v, autogkreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::autogkresults(const_cast(state.c_ptr()), &v, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - - - - -static ae_int_t autogk_maxsubintervals = 10000; -static void autogk_autogkinternalprepare(double a, - double b, - double eps, - double xwidth, - autogkinternalstate* state, - ae_state *_state); -static ae_bool autogk_autogkinternaliteration(autogkinternalstate* state, - ae_state *_state); -static void autogk_mheappop(/* Real */ ae_matrix* heap, - ae_int_t heapsize, - ae_int_t heapwidth, - ae_state *_state); -static void autogk_mheappush(/* Real */ ae_matrix* heap, - ae_int_t heapsize, - ae_int_t heapwidth, - ae_state *_state); -static void autogk_mheapresize(/* Real */ ae_matrix* heap, - ae_int_t* heapsize, - ae_int_t newheapsize, - ae_int_t heapwidth, - ae_state *_state); - - - - - -/************************************************************************* -Computation of nodes and weights for a Gauss quadrature formula - -The algorithm generates the N-point Gauss quadrature formula with weight -function given by coefficients alpha and beta of a recurrence relation -which generates a system of orthogonal polynomials: - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-1], alpha coefficients - Beta – array[0..N-1], beta coefficients - Zero-indexed element is not used and may be arbitrary. - Beta[I]>0. - Mu0 – zeroth moment of the weight function. - N – number of nodes of the quadrature formula, N>=1 - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgeneraterec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector d; - ae_vector e; - ae_matrix z; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z, 0, 0, DT_REAL, _state, ae_true); - - if( n<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * Initialize - */ - ae_vector_set_length(&d, n, _state); - ae_vector_set_length(&e, n, _state); - for(i=1; i<=n-1; i++) - { - d.ptr.p_double[i-1] = alpha->ptr.p_double[i-1]; - if( ae_fp_less_eq(beta->ptr.p_double[i],0) ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - e.ptr.p_double[i-1] = ae_sqrt(beta->ptr.p_double[i], _state); - } - d.ptr.p_double[n-1] = alpha->ptr.p_double[n-1]; - - /* - * EVD - */ - if( !smatrixtdevd(&d, &e, n, 3, &z, _state) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Generate - */ - ae_vector_set_length(x, n, _state); - ae_vector_set_length(w, n, _state); - for(i=1; i<=n; i++) - { - x->ptr.p_double[i-1] = d.ptr.p_double[i-1]; - w->ptr.p_double[i-1] = mu0*ae_sqr(z.ptr.pp_double[0][i-1], _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Computation of nodes and weights for a Gauss-Lobatto quadrature formula - -The algorithm generates the N-point Gauss-Lobatto quadrature formula with -weight function given by coefficients alpha and beta of a recurrence which -generates a system of orthogonal polynomials. - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-2], alpha coefficients - Beta – array[0..N-2], beta coefficients. - Zero-indexed element is not used, may be arbitrary. - Beta[I]>0 - Mu0 – zeroth moment of the weighting function. - A – left boundary of the integration interval. - B – right boundary of the integration interval. - N – number of nodes of the quadrature formula, N>=3 - (including the left and right boundary nodes). - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslobattorec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - double a, - double b, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _alpha; - ae_vector _beta; - ae_int_t i; - ae_vector d; - ae_vector e; - ae_matrix z; - double pim1a; - double pia; - double pim1b; - double pib; - double t; - double a11; - double a12; - double a21; - double a22; - double b1; - double b2; - double alph; - double bet; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_alpha, alpha, _state, ae_true); - alpha = &_alpha; - ae_vector_init_copy(&_beta, beta, _state, ae_true); - beta = &_beta; - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z, 0, 0, DT_REAL, _state, ae_true); - - if( n<=2 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * Initialize, D[1:N+1], E[1:N] - */ - n = n-2; - ae_vector_set_length(&d, n+2, _state); - ae_vector_set_length(&e, n+1, _state); - for(i=1; i<=n+1; i++) - { - d.ptr.p_double[i-1] = alpha->ptr.p_double[i-1]; - } - for(i=1; i<=n; i++) - { - if( ae_fp_less_eq(beta->ptr.p_double[i],0) ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - e.ptr.p_double[i-1] = ae_sqrt(beta->ptr.p_double[i], _state); - } - - /* - * Caclulate Pn(a), Pn+1(a), Pn(b), Pn+1(b) - */ - beta->ptr.p_double[0] = 0; - pim1a = 0; - pia = 1; - pim1b = 0; - pib = 1; - for(i=1; i<=n+1; i++) - { - - /* - * Pi(a) - */ - t = (a-alpha->ptr.p_double[i-1])*pia-beta->ptr.p_double[i-1]*pim1a; - pim1a = pia; - pia = t; - - /* - * Pi(b) - */ - t = (b-alpha->ptr.p_double[i-1])*pib-beta->ptr.p_double[i-1]*pim1b; - pim1b = pib; - pib = t; - } - - /* - * Calculate alpha'(n+1), beta'(n+1) - */ - a11 = pia; - a12 = pim1a; - a21 = pib; - a22 = pim1b; - b1 = a*pia; - b2 = b*pib; - if( ae_fp_greater(ae_fabs(a11, _state),ae_fabs(a21, _state)) ) - { - a22 = a22-a12*a21/a11; - b2 = b2-b1*a21/a11; - bet = b2/a22; - alph = (b1-bet*a12)/a11; - } - else - { - a12 = a12-a22*a11/a21; - b1 = b1-b2*a11/a21; - bet = b1/a12; - alph = (b2-bet*a22)/a21; - } - if( ae_fp_less(bet,0) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - d.ptr.p_double[n+1] = alph; - e.ptr.p_double[n] = ae_sqrt(bet, _state); - - /* - * EVD - */ - if( !smatrixtdevd(&d, &e, n+2, 3, &z, _state) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Generate - */ - ae_vector_set_length(x, n+2, _state); - ae_vector_set_length(w, n+2, _state); - for(i=1; i<=n+2; i++) - { - x->ptr.p_double[i-1] = d.ptr.p_double[i-1]; - w->ptr.p_double[i-1] = mu0*ae_sqr(z.ptr.pp_double[0][i-1], _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Computation of nodes and weights for a Gauss-Radau quadrature formula - -The algorithm generates the N-point Gauss-Radau quadrature formula with -weight function given by the coefficients alpha and beta of a recurrence -which generates a system of orthogonal polynomials. - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-2], alpha coefficients. - Beta – array[0..N-1], beta coefficients - Zero-indexed element is not used. - Beta[I]>0 - Mu0 – zeroth moment of the weighting function. - A – left boundary of the integration interval. - N – number of nodes of the quadrature formula, N>=2 - (including the left boundary node). - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategaussradaurec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - double a, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _alpha; - ae_vector _beta; - ae_int_t i; - ae_vector d; - ae_vector e; - ae_matrix z; - double polim1; - double poli; - double t; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_alpha, alpha, _state, ae_true); - alpha = &_alpha; - ae_vector_init_copy(&_beta, beta, _state, ae_true); - beta = &_beta; - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z, 0, 0, DT_REAL, _state, ae_true); - - if( n<2 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * Initialize, D[1:N], E[1:N] - */ - n = n-1; - ae_vector_set_length(&d, n+1, _state); - ae_vector_set_length(&e, n, _state); - for(i=1; i<=n; i++) - { - d.ptr.p_double[i-1] = alpha->ptr.p_double[i-1]; - if( ae_fp_less_eq(beta->ptr.p_double[i],0) ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - e.ptr.p_double[i-1] = ae_sqrt(beta->ptr.p_double[i], _state); - } - - /* - * Caclulate Pn(a), Pn-1(a), and D[N+1] - */ - beta->ptr.p_double[0] = 0; - polim1 = 0; - poli = 1; - for(i=1; i<=n; i++) - { - t = (a-alpha->ptr.p_double[i-1])*poli-beta->ptr.p_double[i-1]*polim1; - polim1 = poli; - poli = t; - } - d.ptr.p_double[n] = a-beta->ptr.p_double[n]*polim1/poli; - - /* - * EVD - */ - if( !smatrixtdevd(&d, &e, n+1, 3, &z, _state) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Generate - */ - ae_vector_set_length(x, n+1, _state); - ae_vector_set_length(w, n+1, _state); - for(i=1; i<=n+1; i++) - { - x->ptr.p_double[i-1] = d.ptr.p_double[i-1]; - w->ptr.p_double[i-1] = mu0*ae_sqr(z.ptr.pp_double[0][i-1], _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns nodes/weights for Gauss-Legendre quadrature on [-1,1] with N -nodes. - -INPUT PARAMETERS: - N - number of nodes, >=1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslegendre(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector alpha; - ae_vector beta; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&alpha, 0, DT_REAL, _state, ae_true); - ae_vector_init(&beta, 0, DT_REAL, _state, ae_true); - - if( n<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&alpha, n, _state); - ae_vector_set_length(&beta, n, _state); - for(i=0; i<=n-1; i++) - { - alpha.ptr.p_double[i] = 0; - } - beta.ptr.p_double[0] = 2; - for(i=1; i<=n-1; i++) - { - beta.ptr.p_double[i] = 1/(4-1/ae_sqr(i, _state)); - } - gqgeneraterec(&alpha, &beta, beta.ptr.p_double[0], n, info, x, w, _state); - - /* - * test basic properties to detect errors - */ - if( *info>0 ) - { - if( ae_fp_less(x->ptr.p_double[0],-1)||ae_fp_greater(x->ptr.p_double[n-1],1) ) - { - *info = -4; - } - for(i=0; i<=n-2; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns nodes/weights for Gauss-Jacobi quadrature on [-1,1] with weight -function W(x)=Power(1-x,Alpha)*Power(1+x,Beta). - -INPUT PARAMETERS: - N - number of nodes, >=1 - Alpha - power-law coefficient, Alpha>-1 - Beta - power-law coefficient, Beta>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. Alpha or Beta are too close - to -1 to obtain weights/nodes with high enough - accuracy, or, may be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha/Beta was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategaussjacobi(ae_int_t n, - double alpha, - double beta, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector a; - ae_vector b; - double alpha2; - double beta2; - double apb; - double t; - ae_int_t i; - double s; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&a, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - - if( (n<1||ae_fp_less_eq(alpha,-1))||ae_fp_less_eq(beta,-1) ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&a, n, _state); - ae_vector_set_length(&b, n, _state); - apb = alpha+beta; - a.ptr.p_double[0] = (beta-alpha)/(apb+2); - t = (apb+1)*ae_log(2, _state)+lngamma(alpha+1, &s, _state)+lngamma(beta+1, &s, _state)-lngamma(apb+2, &s, _state); - if( ae_fp_greater(t,ae_log(ae_maxrealnumber, _state)) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - b.ptr.p_double[0] = ae_exp(t, _state); - if( n>1 ) - { - alpha2 = ae_sqr(alpha, _state); - beta2 = ae_sqr(beta, _state); - a.ptr.p_double[1] = (beta2-alpha2)/((apb+2)*(apb+4)); - b.ptr.p_double[1] = 4*(alpha+1)*(beta+1)/((apb+3)*ae_sqr(apb+2, _state)); - for(i=2; i<=n-1; i++) - { - a.ptr.p_double[i] = 0.25*(beta2-alpha2)/(i*i*(1+0.5*apb/i)*(1+0.5*(apb+2)/i)); - b.ptr.p_double[i] = 0.25*(1+alpha/i)*(1+beta/i)*(1+apb/i)/((1+0.5*(apb+1)/i)*(1+0.5*(apb-1)/i)*ae_sqr(1+0.5*apb/i, _state)); - } - } - gqgeneraterec(&a, &b, b.ptr.p_double[0], n, info, x, w, _state); - - /* - * test basic properties to detect errors - */ - if( *info>0 ) - { - if( ae_fp_less(x->ptr.p_double[0],-1)||ae_fp_greater(x->ptr.p_double[n-1],1) ) - { - *info = -4; - } - for(i=0; i<=n-2; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns nodes/weights for Gauss-Laguerre quadrature on [0,+inf) with -weight function W(x)=Power(x,Alpha)*Exp(-x) - -INPUT PARAMETERS: - N - number of nodes, >=1 - Alpha - power-law coefficient, Alpha>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. Alpha is too close to -1 to - obtain weights/nodes with high enough accuracy - or, may be, N is too large. Try to use - multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslaguerre(ae_int_t n, - double alpha, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector a; - ae_vector b; - double t; - ae_int_t i; - double s; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&a, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - - if( n<1||ae_fp_less_eq(alpha,-1) ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&a, n, _state); - ae_vector_set_length(&b, n, _state); - a.ptr.p_double[0] = alpha+1; - t = lngamma(alpha+1, &s, _state); - if( ae_fp_greater_eq(t,ae_log(ae_maxrealnumber, _state)) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - b.ptr.p_double[0] = ae_exp(t, _state); - if( n>1 ) - { - for(i=1; i<=n-1; i++) - { - a.ptr.p_double[i] = 2*i+alpha+1; - b.ptr.p_double[i] = i*(i+alpha); - } - } - gqgeneraterec(&a, &b, b.ptr.p_double[0], n, info, x, w, _state); - - /* - * test basic properties to detect errors - */ - if( *info>0 ) - { - if( ae_fp_less(x->ptr.p_double[0],0) ) - { - *info = -4; - } - for(i=0; i<=n-2; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns nodes/weights for Gauss-Hermite quadrature on (-inf,+inf) with -weight function W(x)=Exp(-x*x) - -INPUT PARAMETERS: - N - number of nodes, >=1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. May be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausshermite(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector a; - ae_vector b; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(w); - ae_vector_init(&a, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - - if( n<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&a, n, _state); - ae_vector_set_length(&b, n, _state); - for(i=0; i<=n-1; i++) - { - a.ptr.p_double[i] = 0; - } - b.ptr.p_double[0] = ae_sqrt(4*ae_atan(1, _state), _state); - if( n>1 ) - { - for(i=1; i<=n-1; i++) - { - b.ptr.p_double[i] = 0.5*i; - } - } - gqgeneraterec(&a, &b, b.ptr.p_double[0], n, info, x, w, _state); - - /* - * test basic properties to detect errors - */ - if( *info>0 ) - { - for(i=0; i<=n-2; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -Computation of nodes and weights of a Gauss-Kronrod quadrature formula - -The algorithm generates the N-point Gauss-Kronrod quadrature formula with -weight function given by coefficients alpha and beta of a recurrence -relation which generates a system of orthogonal polynomials: - - P-1(x) = 0 - P0(x) = 1 - Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zero moment Mu0 - - Mu0 = integral(W(x)dx,a,b) - - -INPUT PARAMETERS: - Alpha – alpha coefficients, array[0..floor(3*K/2)]. - Beta – beta coefficients, array[0..ceil(3*K/2)]. - Beta[0] is not used and may be arbitrary. - Beta[I]>0. - Mu0 – zeroth moment of the weight function. - N – number of nodes of the Gauss-Kronrod quadrature formula, - N >= 3, - N = 2*K+1. - -OUTPUT PARAMETERS: - Info - error code: - * -5 no real and positive Gauss-Kronrod formula can - be created for such a weight function with a - given number of nodes. - * -4 N is too large, task may be ill conditioned - - x[i]=x[i+1] found. - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - -- ALGLIB -- - Copyright 08.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgeneraterec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _alpha; - ae_vector _beta; - ae_vector ta; - ae_int_t i; - ae_int_t j; - ae_vector t; - ae_vector s; - ae_int_t wlen; - ae_int_t woffs; - double u; - ae_int_t m; - ae_int_t l; - ae_int_t k; - ae_vector xgtmp; - ae_vector wgtmp; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_alpha, alpha, _state, ae_true); - alpha = &_alpha; - ae_vector_init_copy(&_beta, beta, _state, ae_true); - beta = &_beta; - *info = 0; - ae_vector_clear(x); - ae_vector_clear(wkronrod); - ae_vector_clear(wgauss); - ae_vector_init(&ta, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&s, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xgtmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wgtmp, 0, DT_REAL, _state, ae_true); - - if( n%2!=1||n<3 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=ae_iceil((double)(3*(n/2))/(double)2, _state); i++) - { - if( ae_fp_less_eq(beta->ptr.p_double[i],0) ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - } - *info = 1; - - /* - * from external conventions about N/Beta/Mu0 to internal - */ - n = n/2; - beta->ptr.p_double[0] = mu0; - - /* - * Calculate Gauss nodes/weights, save them for later processing - */ - gqgeneraterec(alpha, beta, mu0, n, info, &xgtmp, &wgtmp, _state); - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Resize: - * * A from 0..floor(3*n/2) to 0..2*n - * * B from 0..ceil(3*n/2) to 0..2*n - */ - ae_vector_set_length(&ta, ae_ifloor((double)(3*n)/(double)2, _state)+1, _state); - ae_v_move(&ta.ptr.p_double[0], 1, &alpha->ptr.p_double[0], 1, ae_v_len(0,ae_ifloor((double)(3*n)/(double)2, _state))); - ae_vector_set_length(alpha, 2*n+1, _state); - ae_v_move(&alpha->ptr.p_double[0], 1, &ta.ptr.p_double[0], 1, ae_v_len(0,ae_ifloor((double)(3*n)/(double)2, _state))); - for(i=ae_ifloor((double)(3*n)/(double)2, _state)+1; i<=2*n; i++) - { - alpha->ptr.p_double[i] = 0; - } - ae_vector_set_length(&ta, ae_iceil((double)(3*n)/(double)2, _state)+1, _state); - ae_v_move(&ta.ptr.p_double[0], 1, &beta->ptr.p_double[0], 1, ae_v_len(0,ae_iceil((double)(3*n)/(double)2, _state))); - ae_vector_set_length(beta, 2*n+1, _state); - ae_v_move(&beta->ptr.p_double[0], 1, &ta.ptr.p_double[0], 1, ae_v_len(0,ae_iceil((double)(3*n)/(double)2, _state))); - for(i=ae_iceil((double)(3*n)/(double)2, _state)+1; i<=2*n; i++) - { - beta->ptr.p_double[i] = 0; - } - - /* - * Initialize T, S - */ - wlen = 2+n/2; - ae_vector_set_length(&t, wlen, _state); - ae_vector_set_length(&s, wlen, _state); - ae_vector_set_length(&ta, wlen, _state); - woffs = 1; - for(i=0; i<=wlen-1; i++) - { - t.ptr.p_double[i] = 0; - s.ptr.p_double[i] = 0; - } - - /* - * Algorithm from Dirk P. Laurie, "Calculation of Gauss-Kronrod quadrature rules", 1997. - */ - t.ptr.p_double[woffs+0] = beta->ptr.p_double[n+1]; - for(m=0; m<=n-2; m++) - { - u = 0; - for(k=(m+1)/2; k>=0; k--) - { - l = m-k; - u = u+(alpha->ptr.p_double[k+n+1]-alpha->ptr.p_double[l])*t.ptr.p_double[woffs+k]+beta->ptr.p_double[k+n+1]*s.ptr.p_double[woffs+k-1]-beta->ptr.p_double[l]*s.ptr.p_double[woffs+k]; - s.ptr.p_double[woffs+k] = u; - } - ae_v_move(&ta.ptr.p_double[0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,wlen-1)); - ae_v_move(&t.ptr.p_double[0], 1, &s.ptr.p_double[0], 1, ae_v_len(0,wlen-1)); - ae_v_move(&s.ptr.p_double[0], 1, &ta.ptr.p_double[0], 1, ae_v_len(0,wlen-1)); - } - for(j=n/2; j>=0; j--) - { - s.ptr.p_double[woffs+j] = s.ptr.p_double[woffs+j-1]; - } - for(m=n-1; m<=2*n-3; m++) - { - u = 0; - for(k=m+1-n; k<=(m-1)/2; k++) - { - l = m-k; - j = n-1-l; - u = u-(alpha->ptr.p_double[k+n+1]-alpha->ptr.p_double[l])*t.ptr.p_double[woffs+j]-beta->ptr.p_double[k+n+1]*s.ptr.p_double[woffs+j]+beta->ptr.p_double[l]*s.ptr.p_double[woffs+j+1]; - s.ptr.p_double[woffs+j] = u; - } - if( m%2==0 ) - { - k = m/2; - alpha->ptr.p_double[k+n+1] = alpha->ptr.p_double[k]+(s.ptr.p_double[woffs+j]-beta->ptr.p_double[k+n+1]*s.ptr.p_double[woffs+j+1])/t.ptr.p_double[woffs+j+1]; - } - else - { - k = (m+1)/2; - beta->ptr.p_double[k+n+1] = s.ptr.p_double[woffs+j]/s.ptr.p_double[woffs+j+1]; - } - ae_v_move(&ta.ptr.p_double[0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,wlen-1)); - ae_v_move(&t.ptr.p_double[0], 1, &s.ptr.p_double[0], 1, ae_v_len(0,wlen-1)); - ae_v_move(&s.ptr.p_double[0], 1, &ta.ptr.p_double[0], 1, ae_v_len(0,wlen-1)); - } - alpha->ptr.p_double[2*n] = alpha->ptr.p_double[n-1]-beta->ptr.p_double[2*n]*s.ptr.p_double[woffs+0]/t.ptr.p_double[woffs+0]; - - /* - * calculation of Kronrod nodes and weights, unpacking of Gauss weights - */ - gqgeneraterec(alpha, beta, mu0, 2*n+1, info, x, wkronrod, _state); - if( *info==-2 ) - { - *info = -5; - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - for(i=0; i<=2*n-1; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - ae_vector_set_length(wgauss, 2*n+1, _state); - for(i=0; i<=2*n; i++) - { - wgauss->ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - wgauss->ptr.p_double[2*i+1] = wgtmp.ptr.p_double[i]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes/weights for Gauss-Legendre -quadrature with N points. - -GKQLegendreCalc (calculation) or GKQLegendreTbl (precomputed table) is -used depending on machine precision and number of nodes. - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgenerategausslegendre(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state) -{ - double eps; - - *info = 0; - ae_vector_clear(x); - ae_vector_clear(wkronrod); - ae_vector_clear(wgauss); - - if( ae_fp_greater(ae_machineepsilon,1.0E-32)&&(((((n==15||n==21)||n==31)||n==41)||n==51)||n==61) ) - { - *info = 1; - gkqlegendretbl(n, x, wkronrod, wgauss, &eps, _state); - } - else - { - gkqlegendrecalc(n, info, x, wkronrod, wgauss, _state); - } -} - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes/weights for Gauss-Jacobi -quadrature on [-1,1] with weight function - - W(x)=Power(1-x,Alpha)*Power(1+x,Beta). - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - Alpha - power-law coefficient, Alpha>-1 - Beta - power-law coefficient, Beta>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -5 no real and positive Gauss-Kronrod formula can - be created for such a weight function with a - given number of nodes. - * -4 an error was detected when calculating - weights/nodes. Alpha or Beta are too close - to -1 to obtain weights/nodes with high enough - accuracy, or, may be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - * +2 OK, but quadrature rule have exterior nodes, - x[0]<-1 or x[n-1]>+1 - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgenerategaussjacobi(ae_int_t n, - double alpha, - double beta, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t clen; - ae_vector a; - ae_vector b; - double alpha2; - double beta2; - double apb; - double t; - ae_int_t i; - double s; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(wkronrod); - ae_vector_clear(wgauss); - ae_vector_init(&a, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - - if( n%2!=1||n<3 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( ae_fp_less_eq(alpha,-1)||ae_fp_less_eq(beta,-1) ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - clen = ae_iceil((double)(3*(n/2))/(double)2, _state)+1; - ae_vector_set_length(&a, clen, _state); - ae_vector_set_length(&b, clen, _state); - for(i=0; i<=clen-1; i++) - { - a.ptr.p_double[i] = 0; - } - apb = alpha+beta; - a.ptr.p_double[0] = (beta-alpha)/(apb+2); - t = (apb+1)*ae_log(2, _state)+lngamma(alpha+1, &s, _state)+lngamma(beta+1, &s, _state)-lngamma(apb+2, &s, _state); - if( ae_fp_greater(t,ae_log(ae_maxrealnumber, _state)) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - b.ptr.p_double[0] = ae_exp(t, _state); - if( clen>1 ) - { - alpha2 = ae_sqr(alpha, _state); - beta2 = ae_sqr(beta, _state); - a.ptr.p_double[1] = (beta2-alpha2)/((apb+2)*(apb+4)); - b.ptr.p_double[1] = 4*(alpha+1)*(beta+1)/((apb+3)*ae_sqr(apb+2, _state)); - for(i=2; i<=clen-1; i++) - { - a.ptr.p_double[i] = 0.25*(beta2-alpha2)/(i*i*(1+0.5*apb/i)*(1+0.5*(apb+2)/i)); - b.ptr.p_double[i] = 0.25*(1+alpha/i)*(1+beta/i)*(1+apb/i)/((1+0.5*(apb+1)/i)*(1+0.5*(apb-1)/i)*ae_sqr(1+0.5*apb/i, _state)); - } - } - gkqgeneraterec(&a, &b, b.ptr.p_double[0], n, info, x, wkronrod, wgauss, _state); - - /* - * test basic properties to detect errors - */ - if( *info>0 ) - { - if( ae_fp_less(x->ptr.p_double[0],-1)||ae_fp_greater(x->ptr.p_double[n-1],1) ) - { - *info = 2; - } - for(i=0; i<=n-2; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes for quadrature with N points. - -Reduction to tridiagonal eigenproblem is used. - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqlegendrecalc(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector alpha; - ae_vector beta; - ae_int_t alen; - ae_int_t blen; - double mu0; - ae_int_t k; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(x); - ae_vector_clear(wkronrod); - ae_vector_clear(wgauss); - ae_vector_init(&alpha, 0, DT_REAL, _state, ae_true); - ae_vector_init(&beta, 0, DT_REAL, _state, ae_true); - - if( n%2!=1||n<3 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - mu0 = 2; - alen = ae_ifloor((double)(3*(n/2))/(double)2, _state)+1; - blen = ae_iceil((double)(3*(n/2))/(double)2, _state)+1; - ae_vector_set_length(&alpha, alen, _state); - ae_vector_set_length(&beta, blen, _state); - for(k=0; k<=alen-1; k++) - { - alpha.ptr.p_double[k] = 0; - } - beta.ptr.p_double[0] = 2; - for(k=1; k<=blen-1; k++) - { - beta.ptr.p_double[k] = 1/(4-1/ae_sqr(k, _state)); - } - gkqgeneraterec(&alpha, &beta, mu0, n, info, x, wkronrod, wgauss, _state); - - /* - * test basic properties to detect errors - */ - if( *info>0 ) - { - if( ae_fp_less(x->ptr.p_double[0],-1)||ae_fp_greater(x->ptr.p_double[n-1],1) ) - { - *info = -4; - } - for(i=0; i<=n-2; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],x->ptr.p_double[i+1]) ) - { - *info = -4; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes for quadrature with N points using -pre-calculated table. Nodes/weights were computed with accuracy up to -1.0E-32 (if MPFR version of ALGLIB is used). In standard double precision -accuracy reduces to something about 2.0E-16 (depending on your compiler's -handling of long floating point constants). - -INPUT PARAMETERS: - N - number of Kronrod nodes. - N can be 15, 21, 31, 41, 51, 61. - -OUTPUT PARAMETERS: - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqlegendretbl(ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - double* eps, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t ng; - ae_vector p1; - ae_vector p2; - double tmp; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(x); - ae_vector_clear(wkronrod); - ae_vector_clear(wgauss); - *eps = 0; - ae_vector_init(&p1, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - ng = 0; - - /* - * Process - */ - ae_assert(((((n==15||n==21)||n==31)||n==41)||n==51)||n==61, "GKQNodesTbl: incorrect N!", _state); - ae_vector_set_length(x, n, _state); - ae_vector_set_length(wkronrod, n, _state); - ae_vector_set_length(wgauss, n, _state); - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = 0; - wkronrod->ptr.p_double[i] = 0; - wgauss->ptr.p_double[i] = 0; - } - *eps = ae_maxreal(ae_machineepsilon, 1.0E-32, _state); - if( n==15 ) - { - ng = 4; - wgauss->ptr.p_double[0] = 0.129484966168869693270611432679082; - wgauss->ptr.p_double[1] = 0.279705391489276667901467771423780; - wgauss->ptr.p_double[2] = 0.381830050505118944950369775488975; - wgauss->ptr.p_double[3] = 0.417959183673469387755102040816327; - x->ptr.p_double[0] = 0.991455371120812639206854697526329; - x->ptr.p_double[1] = 0.949107912342758524526189684047851; - x->ptr.p_double[2] = 0.864864423359769072789712788640926; - x->ptr.p_double[3] = 0.741531185599394439863864773280788; - x->ptr.p_double[4] = 0.586087235467691130294144838258730; - x->ptr.p_double[5] = 0.405845151377397166906606412076961; - x->ptr.p_double[6] = 0.207784955007898467600689403773245; - x->ptr.p_double[7] = 0.000000000000000000000000000000000; - wkronrod->ptr.p_double[0] = 0.022935322010529224963732008058970; - wkronrod->ptr.p_double[1] = 0.063092092629978553290700663189204; - wkronrod->ptr.p_double[2] = 0.104790010322250183839876322541518; - wkronrod->ptr.p_double[3] = 0.140653259715525918745189590510238; - wkronrod->ptr.p_double[4] = 0.169004726639267902826583426598550; - wkronrod->ptr.p_double[5] = 0.190350578064785409913256402421014; - wkronrod->ptr.p_double[6] = 0.204432940075298892414161999234649; - wkronrod->ptr.p_double[7] = 0.209482141084727828012999174891714; - } - if( n==21 ) - { - ng = 5; - wgauss->ptr.p_double[0] = 0.066671344308688137593568809893332; - wgauss->ptr.p_double[1] = 0.149451349150580593145776339657697; - wgauss->ptr.p_double[2] = 0.219086362515982043995534934228163; - wgauss->ptr.p_double[3] = 0.269266719309996355091226921569469; - wgauss->ptr.p_double[4] = 0.295524224714752870173892994651338; - x->ptr.p_double[0] = 0.995657163025808080735527280689003; - x->ptr.p_double[1] = 0.973906528517171720077964012084452; - x->ptr.p_double[2] = 0.930157491355708226001207180059508; - x->ptr.p_double[3] = 0.865063366688984510732096688423493; - x->ptr.p_double[4] = 0.780817726586416897063717578345042; - x->ptr.p_double[5] = 0.679409568299024406234327365114874; - x->ptr.p_double[6] = 0.562757134668604683339000099272694; - x->ptr.p_double[7] = 0.433395394129247190799265943165784; - x->ptr.p_double[8] = 0.294392862701460198131126603103866; - x->ptr.p_double[9] = 0.148874338981631210884826001129720; - x->ptr.p_double[10] = 0.000000000000000000000000000000000; - wkronrod->ptr.p_double[0] = 0.011694638867371874278064396062192; - wkronrod->ptr.p_double[1] = 0.032558162307964727478818972459390; - wkronrod->ptr.p_double[2] = 0.054755896574351996031381300244580; - wkronrod->ptr.p_double[3] = 0.075039674810919952767043140916190; - wkronrod->ptr.p_double[4] = 0.093125454583697605535065465083366; - wkronrod->ptr.p_double[5] = 0.109387158802297641899210590325805; - wkronrod->ptr.p_double[6] = 0.123491976262065851077958109831074; - wkronrod->ptr.p_double[7] = 0.134709217311473325928054001771707; - wkronrod->ptr.p_double[8] = 0.142775938577060080797094273138717; - wkronrod->ptr.p_double[9] = 0.147739104901338491374841515972068; - wkronrod->ptr.p_double[10] = 0.149445554002916905664936468389821; - } - if( n==31 ) - { - ng = 8; - wgauss->ptr.p_double[0] = 0.030753241996117268354628393577204; - wgauss->ptr.p_double[1] = 0.070366047488108124709267416450667; - wgauss->ptr.p_double[2] = 0.107159220467171935011869546685869; - wgauss->ptr.p_double[3] = 0.139570677926154314447804794511028; - wgauss->ptr.p_double[4] = 0.166269205816993933553200860481209; - wgauss->ptr.p_double[5] = 0.186161000015562211026800561866423; - wgauss->ptr.p_double[6] = 0.198431485327111576456118326443839; - wgauss->ptr.p_double[7] = 0.202578241925561272880620199967519; - x->ptr.p_double[0] = 0.998002298693397060285172840152271; - x->ptr.p_double[1] = 0.987992518020485428489565718586613; - x->ptr.p_double[2] = 0.967739075679139134257347978784337; - x->ptr.p_double[3] = 0.937273392400705904307758947710209; - x->ptr.p_double[4] = 0.897264532344081900882509656454496; - x->ptr.p_double[5] = 0.848206583410427216200648320774217; - x->ptr.p_double[6] = 0.790418501442465932967649294817947; - x->ptr.p_double[7] = 0.724417731360170047416186054613938; - x->ptr.p_double[8] = 0.650996741297416970533735895313275; - x->ptr.p_double[9] = 0.570972172608538847537226737253911; - x->ptr.p_double[10] = 0.485081863640239680693655740232351; - x->ptr.p_double[11] = 0.394151347077563369897207370981045; - x->ptr.p_double[12] = 0.299180007153168812166780024266389; - x->ptr.p_double[13] = 0.201194093997434522300628303394596; - x->ptr.p_double[14] = 0.101142066918717499027074231447392; - x->ptr.p_double[15] = 0.000000000000000000000000000000000; - wkronrod->ptr.p_double[0] = 0.005377479872923348987792051430128; - wkronrod->ptr.p_double[1] = 0.015007947329316122538374763075807; - wkronrod->ptr.p_double[2] = 0.025460847326715320186874001019653; - wkronrod->ptr.p_double[3] = 0.035346360791375846222037948478360; - wkronrod->ptr.p_double[4] = 0.044589751324764876608227299373280; - wkronrod->ptr.p_double[5] = 0.053481524690928087265343147239430; - wkronrod->ptr.p_double[6] = 0.062009567800670640285139230960803; - wkronrod->ptr.p_double[7] = 0.069854121318728258709520077099147; - wkronrod->ptr.p_double[8] = 0.076849680757720378894432777482659; - wkronrod->ptr.p_double[9] = 0.083080502823133021038289247286104; - wkronrod->ptr.p_double[10] = 0.088564443056211770647275443693774; - wkronrod->ptr.p_double[11] = 0.093126598170825321225486872747346; - wkronrod->ptr.p_double[12] = 0.096642726983623678505179907627589; - wkronrod->ptr.p_double[13] = 0.099173598721791959332393173484603; - wkronrod->ptr.p_double[14] = 0.100769845523875595044946662617570; - wkronrod->ptr.p_double[15] = 0.101330007014791549017374792767493; - } - if( n==41 ) - { - ng = 10; - wgauss->ptr.p_double[0] = 0.017614007139152118311861962351853; - wgauss->ptr.p_double[1] = 0.040601429800386941331039952274932; - wgauss->ptr.p_double[2] = 0.062672048334109063569506535187042; - wgauss->ptr.p_double[3] = 0.083276741576704748724758143222046; - wgauss->ptr.p_double[4] = 0.101930119817240435036750135480350; - wgauss->ptr.p_double[5] = 0.118194531961518417312377377711382; - wgauss->ptr.p_double[6] = 0.131688638449176626898494499748163; - wgauss->ptr.p_double[7] = 0.142096109318382051329298325067165; - wgauss->ptr.p_double[8] = 0.149172986472603746787828737001969; - wgauss->ptr.p_double[9] = 0.152753387130725850698084331955098; - x->ptr.p_double[0] = 0.998859031588277663838315576545863; - x->ptr.p_double[1] = 0.993128599185094924786122388471320; - x->ptr.p_double[2] = 0.981507877450250259193342994720217; - x->ptr.p_double[3] = 0.963971927277913791267666131197277; - x->ptr.p_double[4] = 0.940822633831754753519982722212443; - x->ptr.p_double[5] = 0.912234428251325905867752441203298; - x->ptr.p_double[6] = 0.878276811252281976077442995113078; - x->ptr.p_double[7] = 0.839116971822218823394529061701521; - x->ptr.p_double[8] = 0.795041428837551198350638833272788; - x->ptr.p_double[9] = 0.746331906460150792614305070355642; - x->ptr.p_double[10] = 0.693237656334751384805490711845932; - x->ptr.p_double[11] = 0.636053680726515025452836696226286; - x->ptr.p_double[12] = 0.575140446819710315342946036586425; - x->ptr.p_double[13] = 0.510867001950827098004364050955251; - x->ptr.p_double[14] = 0.443593175238725103199992213492640; - x->ptr.p_double[15] = 0.373706088715419560672548177024927; - x->ptr.p_double[16] = 0.301627868114913004320555356858592; - x->ptr.p_double[17] = 0.227785851141645078080496195368575; - x->ptr.p_double[18] = 0.152605465240922675505220241022678; - x->ptr.p_double[19] = 0.076526521133497333754640409398838; - x->ptr.p_double[20] = 0.000000000000000000000000000000000; - wkronrod->ptr.p_double[0] = 0.003073583718520531501218293246031; - wkronrod->ptr.p_double[1] = 0.008600269855642942198661787950102; - wkronrod->ptr.p_double[2] = 0.014626169256971252983787960308868; - wkronrod->ptr.p_double[3] = 0.020388373461266523598010231432755; - wkronrod->ptr.p_double[4] = 0.025882133604951158834505067096153; - wkronrod->ptr.p_double[5] = 0.031287306777032798958543119323801; - wkronrod->ptr.p_double[6] = 0.036600169758200798030557240707211; - wkronrod->ptr.p_double[7] = 0.041668873327973686263788305936895; - wkronrod->ptr.p_double[8] = 0.046434821867497674720231880926108; - wkronrod->ptr.p_double[9] = 0.050944573923728691932707670050345; - wkronrod->ptr.p_double[10] = 0.055195105348285994744832372419777; - wkronrod->ptr.p_double[11] = 0.059111400880639572374967220648594; - wkronrod->ptr.p_double[12] = 0.062653237554781168025870122174255; - wkronrod->ptr.p_double[13] = 0.065834597133618422111563556969398; - wkronrod->ptr.p_double[14] = 0.068648672928521619345623411885368; - wkronrod->ptr.p_double[15] = 0.071054423553444068305790361723210; - wkronrod->ptr.p_double[16] = 0.073030690332786667495189417658913; - wkronrod->ptr.p_double[17] = 0.074582875400499188986581418362488; - wkronrod->ptr.p_double[18] = 0.075704497684556674659542775376617; - wkronrod->ptr.p_double[19] = 0.076377867672080736705502835038061; - wkronrod->ptr.p_double[20] = 0.076600711917999656445049901530102; - } - if( n==51 ) - { - ng = 13; - wgauss->ptr.p_double[0] = 0.011393798501026287947902964113235; - wgauss->ptr.p_double[1] = 0.026354986615032137261901815295299; - wgauss->ptr.p_double[2] = 0.040939156701306312655623487711646; - wgauss->ptr.p_double[3] = 0.054904695975835191925936891540473; - wgauss->ptr.p_double[4] = 0.068038333812356917207187185656708; - wgauss->ptr.p_double[5] = 0.080140700335001018013234959669111; - wgauss->ptr.p_double[6] = 0.091028261982963649811497220702892; - wgauss->ptr.p_double[7] = 0.100535949067050644202206890392686; - wgauss->ptr.p_double[8] = 0.108519624474263653116093957050117; - wgauss->ptr.p_double[9] = 0.114858259145711648339325545869556; - wgauss->ptr.p_double[10] = 0.119455763535784772228178126512901; - wgauss->ptr.p_double[11] = 0.122242442990310041688959518945852; - wgauss->ptr.p_double[12] = 0.123176053726715451203902873079050; - x->ptr.p_double[0] = 0.999262104992609834193457486540341; - x->ptr.p_double[1] = 0.995556969790498097908784946893902; - x->ptr.p_double[2] = 0.988035794534077247637331014577406; - x->ptr.p_double[3] = 0.976663921459517511498315386479594; - x->ptr.p_double[4] = 0.961614986425842512418130033660167; - x->ptr.p_double[5] = 0.942974571228974339414011169658471; - x->ptr.p_double[6] = 0.920747115281701561746346084546331; - x->ptr.p_double[7] = 0.894991997878275368851042006782805; - x->ptr.p_double[8] = 0.865847065293275595448996969588340; - x->ptr.p_double[9] = 0.833442628760834001421021108693570; - x->ptr.p_double[10] = 0.797873797998500059410410904994307; - x->ptr.p_double[11] = 0.759259263037357630577282865204361; - x->ptr.p_double[12] = 0.717766406813084388186654079773298; - x->ptr.p_double[13] = 0.673566368473468364485120633247622; - x->ptr.p_double[14] = 0.626810099010317412788122681624518; - x->ptr.p_double[15] = 0.577662930241222967723689841612654; - x->ptr.p_double[16] = 0.526325284334719182599623778158010; - x->ptr.p_double[17] = 0.473002731445714960522182115009192; - x->ptr.p_double[18] = 0.417885382193037748851814394594572; - x->ptr.p_double[19] = 0.361172305809387837735821730127641; - x->ptr.p_double[20] = 0.303089538931107830167478909980339; - x->ptr.p_double[21] = 0.243866883720988432045190362797452; - x->ptr.p_double[22] = 0.183718939421048892015969888759528; - x->ptr.p_double[23] = 0.122864692610710396387359818808037; - x->ptr.p_double[24] = 0.061544483005685078886546392366797; - x->ptr.p_double[25] = 0.000000000000000000000000000000000; - wkronrod->ptr.p_double[0] = 0.001987383892330315926507851882843; - wkronrod->ptr.p_double[1] = 0.005561932135356713758040236901066; - wkronrod->ptr.p_double[2] = 0.009473973386174151607207710523655; - wkronrod->ptr.p_double[3] = 0.013236229195571674813656405846976; - wkronrod->ptr.p_double[4] = 0.016847817709128298231516667536336; - wkronrod->ptr.p_double[5] = 0.020435371145882835456568292235939; - wkronrod->ptr.p_double[6] = 0.024009945606953216220092489164881; - wkronrod->ptr.p_double[7] = 0.027475317587851737802948455517811; - wkronrod->ptr.p_double[8] = 0.030792300167387488891109020215229; - wkronrod->ptr.p_double[9] = 0.034002130274329337836748795229551; - wkronrod->ptr.p_double[10] = 0.037116271483415543560330625367620; - wkronrod->ptr.p_double[11] = 0.040083825504032382074839284467076; - wkronrod->ptr.p_double[12] = 0.042872845020170049476895792439495; - wkronrod->ptr.p_double[13] = 0.045502913049921788909870584752660; - wkronrod->ptr.p_double[14] = 0.047982537138836713906392255756915; - wkronrod->ptr.p_double[15] = 0.050277679080715671963325259433440; - wkronrod->ptr.p_double[16] = 0.052362885806407475864366712137873; - wkronrod->ptr.p_double[17] = 0.054251129888545490144543370459876; - wkronrod->ptr.p_double[18] = 0.055950811220412317308240686382747; - wkronrod->ptr.p_double[19] = 0.057437116361567832853582693939506; - wkronrod->ptr.p_double[20] = 0.058689680022394207961974175856788; - wkronrod->ptr.p_double[21] = 0.059720340324174059979099291932562; - wkronrod->ptr.p_double[22] = 0.060539455376045862945360267517565; - wkronrod->ptr.p_double[23] = 0.061128509717053048305859030416293; - wkronrod->ptr.p_double[24] = 0.061471189871425316661544131965264; - wkronrod->ptr.p_double[25] = 0.061580818067832935078759824240055; - } - if( n==61 ) - { - ng = 15; - wgauss->ptr.p_double[0] = 0.007968192496166605615465883474674; - wgauss->ptr.p_double[1] = 0.018466468311090959142302131912047; - wgauss->ptr.p_double[2] = 0.028784707883323369349719179611292; - wgauss->ptr.p_double[3] = 0.038799192569627049596801936446348; - wgauss->ptr.p_double[4] = 0.048402672830594052902938140422808; - wgauss->ptr.p_double[5] = 0.057493156217619066481721689402056; - wgauss->ptr.p_double[6] = 0.065974229882180495128128515115962; - wgauss->ptr.p_double[7] = 0.073755974737705206268243850022191; - wgauss->ptr.p_double[8] = 0.080755895229420215354694938460530; - wgauss->ptr.p_double[9] = 0.086899787201082979802387530715126; - wgauss->ptr.p_double[10] = 0.092122522237786128717632707087619; - wgauss->ptr.p_double[11] = 0.096368737174644259639468626351810; - wgauss->ptr.p_double[12] = 0.099593420586795267062780282103569; - wgauss->ptr.p_double[13] = 0.101762389748405504596428952168554; - wgauss->ptr.p_double[14] = 0.102852652893558840341285636705415; - x->ptr.p_double[0] = 0.999484410050490637571325895705811; - x->ptr.p_double[1] = 0.996893484074649540271630050918695; - x->ptr.p_double[2] = 0.991630996870404594858628366109486; - x->ptr.p_double[3] = 0.983668123279747209970032581605663; - x->ptr.p_double[4] = 0.973116322501126268374693868423707; - x->ptr.p_double[5] = 0.960021864968307512216871025581798; - x->ptr.p_double[6] = 0.944374444748559979415831324037439; - x->ptr.p_double[7] = 0.926200047429274325879324277080474; - x->ptr.p_double[8] = 0.905573307699907798546522558925958; - x->ptr.p_double[9] = 0.882560535792052681543116462530226; - x->ptr.p_double[10] = 0.857205233546061098958658510658944; - x->ptr.p_double[11] = 0.829565762382768397442898119732502; - x->ptr.p_double[12] = 0.799727835821839083013668942322683; - x->ptr.p_double[13] = 0.767777432104826194917977340974503; - x->ptr.p_double[14] = 0.733790062453226804726171131369528; - x->ptr.p_double[15] = 0.697850494793315796932292388026640; - x->ptr.p_double[16] = 0.660061064126626961370053668149271; - x->ptr.p_double[17] = 0.620526182989242861140477556431189; - x->ptr.p_double[18] = 0.579345235826361691756024932172540; - x->ptr.p_double[19] = 0.536624148142019899264169793311073; - x->ptr.p_double[20] = 0.492480467861778574993693061207709; - x->ptr.p_double[21] = 0.447033769538089176780609900322854; - x->ptr.p_double[22] = 0.400401254830394392535476211542661; - x->ptr.p_double[23] = 0.352704725530878113471037207089374; - x->ptr.p_double[24] = 0.304073202273625077372677107199257; - x->ptr.p_double[25] = 0.254636926167889846439805129817805; - x->ptr.p_double[26] = 0.204525116682309891438957671002025; - x->ptr.p_double[27] = 0.153869913608583546963794672743256; - x->ptr.p_double[28] = 0.102806937966737030147096751318001; - x->ptr.p_double[29] = 0.051471842555317695833025213166723; - x->ptr.p_double[30] = 0.000000000000000000000000000000000; - wkronrod->ptr.p_double[0] = 0.001389013698677007624551591226760; - wkronrod->ptr.p_double[1] = 0.003890461127099884051267201844516; - wkronrod->ptr.p_double[2] = 0.006630703915931292173319826369750; - wkronrod->ptr.p_double[3] = 0.009273279659517763428441146892024; - wkronrod->ptr.p_double[4] = 0.011823015253496341742232898853251; - wkronrod->ptr.p_double[5] = 0.014369729507045804812451432443580; - wkronrod->ptr.p_double[6] = 0.016920889189053272627572289420322; - wkronrod->ptr.p_double[7] = 0.019414141193942381173408951050128; - wkronrod->ptr.p_double[8] = 0.021828035821609192297167485738339; - wkronrod->ptr.p_double[9] = 0.024191162078080601365686370725232; - wkronrod->ptr.p_double[10] = 0.026509954882333101610601709335075; - wkronrod->ptr.p_double[11] = 0.028754048765041292843978785354334; - wkronrod->ptr.p_double[12] = 0.030907257562387762472884252943092; - wkronrod->ptr.p_double[13] = 0.032981447057483726031814191016854; - wkronrod->ptr.p_double[14] = 0.034979338028060024137499670731468; - wkronrod->ptr.p_double[15] = 0.036882364651821229223911065617136; - wkronrod->ptr.p_double[16] = 0.038678945624727592950348651532281; - wkronrod->ptr.p_double[17] = 0.040374538951535959111995279752468; - wkronrod->ptr.p_double[18] = 0.041969810215164246147147541285970; - wkronrod->ptr.p_double[19] = 0.043452539701356069316831728117073; - wkronrod->ptr.p_double[20] = 0.044814800133162663192355551616723; - wkronrod->ptr.p_double[21] = 0.046059238271006988116271735559374; - wkronrod->ptr.p_double[22] = 0.047185546569299153945261478181099; - wkronrod->ptr.p_double[23] = 0.048185861757087129140779492298305; - wkronrod->ptr.p_double[24] = 0.049055434555029778887528165367238; - wkronrod->ptr.p_double[25] = 0.049795683427074206357811569379942; - wkronrod->ptr.p_double[26] = 0.050405921402782346840893085653585; - wkronrod->ptr.p_double[27] = 0.050881795898749606492297473049805; - wkronrod->ptr.p_double[28] = 0.051221547849258772170656282604944; - wkronrod->ptr.p_double[29] = 0.051426128537459025933862879215781; - wkronrod->ptr.p_double[30] = 0.051494729429451567558340433647099; - } - - /* - * copy nodes - */ - for(i=n-1; i>=n/2; i--) - { - x->ptr.p_double[i] = -x->ptr.p_double[n-1-i]; - } - - /* - * copy Kronrod weights - */ - for(i=n-1; i>=n/2; i--) - { - wkronrod->ptr.p_double[i] = wkronrod->ptr.p_double[n-1-i]; - } - - /* - * copy Gauss weights - */ - for(i=ng-1; i>=0; i--) - { - wgauss->ptr.p_double[n-2-2*i] = wgauss->ptr.p_double[i]; - wgauss->ptr.p_double[1+2*i] = wgauss->ptr.p_double[i]; - } - for(i=0; i<=n/2; i++) - { - wgauss->ptr.p_double[2*i] = 0; - } - - /* - * reorder - */ - tagsort(x, n, &p1, &p2, _state); - for(i=0; i<=n-1; i++) - { - tmp = wkronrod->ptr.p_double[i]; - wkronrod->ptr.p_double[i] = wkronrod->ptr.p_double[p2.ptr.p_int[i]]; - wkronrod->ptr.p_double[p2.ptr.p_int[i]] = tmp; - tmp = wgauss->ptr.p_double[i]; - wgauss->ptr.p_double[i] = wgauss->ptr.p_double[p2.ptr.p_int[i]]; - wgauss->ptr.p_double[p2.ptr.p_int[i]] = tmp; - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -Integration of a smooth function F(x) on a finite interval [a,b]. - -Fast-convergent algorithm based on a Gauss-Kronrod formula is used. Result -is calculated with accuracy close to the machine precision. - -Algorithm works well only with smooth integrands. It may be used with -continuous non-smooth integrands, but with less performance. - -It should never be used with integrands which have integrable singularities -at lower or upper limits - algorithm may crash. Use AutoGKSingular in such -cases. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksmooth(double a, - double b, - autogkstate* state, - ae_state *_state) -{ - - _autogkstate_clear(state); - - ae_assert(ae_isfinite(a, _state), "AutoGKSmooth: A is not finite!", _state); - ae_assert(ae_isfinite(b, _state), "AutoGKSmooth: B is not finite!", _state); - autogksmoothw(a, b, 0.0, state, _state); -} - - -/************************************************************************* -Integration of a smooth function F(x) on a finite interval [a,b]. - -This subroutine is same as AutoGKSmooth(), but it guarantees that interval -[a,b] is partitioned into subintervals which have width at most XWidth. - -Subroutine can be used when integrating nearly-constant function with -narrow "bumps" (about XWidth wide). If "bumps" are too narrow, AutoGKSmooth -subroutine can overlook them. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmooth, AutoGKSingular, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksmoothw(double a, - double b, - double xwidth, - autogkstate* state, - ae_state *_state) -{ - - _autogkstate_clear(state); - - ae_assert(ae_isfinite(a, _state), "AutoGKSmoothW: A is not finite!", _state); - ae_assert(ae_isfinite(b, _state), "AutoGKSmoothW: B is not finite!", _state); - ae_assert(ae_isfinite(xwidth, _state), "AutoGKSmoothW: XWidth is not finite!", _state); - state->wrappermode = 0; - state->a = a; - state->b = b; - state->xwidth = xwidth; - state->needf = ae_false; - ae_vector_set_length(&state->rstate.ra, 10+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Integration on a finite interval [A,B]. -Integrand have integrable singularities at A/B. - -F(X) must diverge as "(x-A)^alpha" at A, as "(B-x)^beta" at B, with known -alpha/beta (alpha>-1, beta>-1). If alpha/beta are not known, estimates -from below can be used (but these estimates should be greater than -1 too). - -One of alpha/beta variables (or even both alpha/beta) may be equal to 0, -which means than function F(x) is non-singular at A/B. Anyway (singular at -bounds or not), function F(x) is supposed to be continuous on (A,B). - -Fast-convergent algorithm based on a Gauss-Kronrod formula is used. Result -is calculated with accuracy close to the machine precision. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - Alpha - power-law coefficient of the F(x) at A, - Alpha>-1 - Beta - power-law coefficient of the F(x) at B, - Beta>-1 - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmooth, AutoGKSmoothW, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksingular(double a, - double b, - double alpha, - double beta, - autogkstate* state, - ae_state *_state) -{ - - _autogkstate_clear(state); - - ae_assert(ae_isfinite(a, _state), "AutoGKSingular: A is not finite!", _state); - ae_assert(ae_isfinite(b, _state), "AutoGKSingular: B is not finite!", _state); - ae_assert(ae_isfinite(alpha, _state), "AutoGKSingular: Alpha is not finite!", _state); - ae_assert(ae_isfinite(beta, _state), "AutoGKSingular: Beta is not finite!", _state); - state->wrappermode = 1; - state->a = a; - state->b = b; - state->alpha = alpha; - state->beta = beta; - state->xwidth = 0.0; - state->needf = ae_false; - ae_vector_set_length(&state->rstate.ra, 10+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 07.05.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool autogkiteration(autogkstate* state, ae_state *_state) -{ - double s; - double tmp; - double eps; - double a; - double b; - double x; - double t; - double alpha; - double beta; - double v1; - double v2; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - s = state->rstate.ra.ptr.p_double[0]; - tmp = state->rstate.ra.ptr.p_double[1]; - eps = state->rstate.ra.ptr.p_double[2]; - a = state->rstate.ra.ptr.p_double[3]; - b = state->rstate.ra.ptr.p_double[4]; - x = state->rstate.ra.ptr.p_double[5]; - t = state->rstate.ra.ptr.p_double[6]; - alpha = state->rstate.ra.ptr.p_double[7]; - beta = state->rstate.ra.ptr.p_double[8]; - v1 = state->rstate.ra.ptr.p_double[9]; - v2 = state->rstate.ra.ptr.p_double[10]; - } - else - { - s = -983; - tmp = -989; - eps = -834; - a = 900; - b = -287; - x = 364; - t = 214; - alpha = -338; - beta = -686; - v1 = 912; - v2 = 585; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - - /* - * Routine body - */ - eps = 0; - a = state->a; - b = state->b; - alpha = state->alpha; - beta = state->beta; - state->terminationtype = -1; - state->nfev = 0; - state->nintervals = 0; - - /* - * smooth function at a finite interval - */ - if( state->wrappermode!=0 ) - { - goto lbl_3; - } - - /* - * special case - */ - if( ae_fp_eq(a,b) ) - { - state->terminationtype = 1; - state->v = 0; - result = ae_false; - return result; - } - - /* - * general case - */ - autogk_autogkinternalprepare(a, b, eps, state->xwidth, &state->internalstate, _state); -lbl_5: - if( !autogk_autogkinternaliteration(&state->internalstate, _state) ) - { - goto lbl_6; - } - x = state->internalstate.x; - state->x = x; - state->xminusa = x-a; - state->bminusx = b-x; - state->needf = ae_true; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->needf = ae_false; - state->nfev = state->nfev+1; - state->internalstate.f = state->f; - goto lbl_5; -lbl_6: - state->v = state->internalstate.r; - state->terminationtype = state->internalstate.info; - state->nintervals = state->internalstate.heapused; - result = ae_false; - return result; -lbl_3: - - /* - * function with power-law singularities at the ends of a finite interval - */ - if( state->wrappermode!=1 ) - { - goto lbl_7; - } - - /* - * test coefficients - */ - if( ae_fp_less_eq(alpha,-1)||ae_fp_less_eq(beta,-1) ) - { - state->terminationtype = -1; - state->v = 0; - result = ae_false; - return result; - } - - /* - * special cases - */ - if( ae_fp_eq(a,b) ) - { - state->terminationtype = 1; - state->v = 0; - result = ae_false; - return result; - } - - /* - * reduction to general form - */ - if( ae_fp_less(a,b) ) - { - s = 1; - } - else - { - s = -1; - tmp = a; - a = b; - b = tmp; - tmp = alpha; - alpha = beta; - beta = tmp; - } - alpha = ae_minreal(alpha, 0, _state); - beta = ae_minreal(beta, 0, _state); - - /* - * first, integrate left half of [a,b]: - * integral(f(x)dx, a, (b+a)/2) = - * = 1/(1+alpha) * integral(t^(-alpha/(1+alpha))*f(a+t^(1/(1+alpha)))dt, 0, (0.5*(b-a))^(1+alpha)) - */ - autogk_autogkinternalprepare(0, ae_pow(0.5*(b-a), 1+alpha, _state), eps, state->xwidth, &state->internalstate, _state); -lbl_9: - if( !autogk_autogkinternaliteration(&state->internalstate, _state) ) - { - goto lbl_10; - } - - /* - * Fill State.X, State.XMinusA, State.BMinusX. - * Latter two are filled correctly even if Binternalstate.x; - t = ae_pow(x, 1/(1+alpha), _state); - state->x = a+t; - if( ae_fp_greater(s,0) ) - { - state->xminusa = t; - state->bminusx = b-(a+t); - } - else - { - state->xminusa = a+t-b; - state->bminusx = -t; - } - state->needf = ae_true; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->needf = ae_false; - if( ae_fp_neq(alpha,0) ) - { - state->internalstate.f = state->f*ae_pow(x, -alpha/(1+alpha), _state)/(1+alpha); - } - else - { - state->internalstate.f = state->f; - } - state->nfev = state->nfev+1; - goto lbl_9; -lbl_10: - v1 = state->internalstate.r; - state->nintervals = state->nintervals+state->internalstate.heapused; - - /* - * then, integrate right half of [a,b]: - * integral(f(x)dx, (b+a)/2, b) = - * = 1/(1+beta) * integral(t^(-beta/(1+beta))*f(b-t^(1/(1+beta)))dt, 0, (0.5*(b-a))^(1+beta)) - */ - autogk_autogkinternalprepare(0, ae_pow(0.5*(b-a), 1+beta, _state), eps, state->xwidth, &state->internalstate, _state); -lbl_11: - if( !autogk_autogkinternaliteration(&state->internalstate, _state) ) - { - goto lbl_12; - } - - /* - * Fill State.X, State.XMinusA, State.BMinusX. - * Latter two are filled correctly (X-A, B-X) even if Binternalstate.x; - t = ae_pow(x, 1/(1+beta), _state); - state->x = b-t; - if( ae_fp_greater(s,0) ) - { - state->xminusa = b-t-a; - state->bminusx = t; - } - else - { - state->xminusa = -t; - state->bminusx = a-(b-t); - } - state->needf = ae_true; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->needf = ae_false; - if( ae_fp_neq(beta,0) ) - { - state->internalstate.f = state->f*ae_pow(x, -beta/(1+beta), _state)/(1+beta); - } - else - { - state->internalstate.f = state->f; - } - state->nfev = state->nfev+1; - goto lbl_11; -lbl_12: - v2 = state->internalstate.r; - state->nintervals = state->nintervals+state->internalstate.heapused; - - /* - * final result - */ - state->v = s*(v1+v2); - state->terminationtype = 1; - result = ae_false; - return result; -lbl_7: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ra.ptr.p_double[0] = s; - state->rstate.ra.ptr.p_double[1] = tmp; - state->rstate.ra.ptr.p_double[2] = eps; - state->rstate.ra.ptr.p_double[3] = a; - state->rstate.ra.ptr.p_double[4] = b; - state->rstate.ra.ptr.p_double[5] = x; - state->rstate.ra.ptr.p_double[6] = t; - state->rstate.ra.ptr.p_double[7] = alpha; - state->rstate.ra.ptr.p_double[8] = beta; - state->rstate.ra.ptr.p_double[9] = v1; - state->rstate.ra.ptr.p_double[10] = v2; - return result; -} - - -/************************************************************************* -Adaptive integration results - -Called after AutoGKIteration returned False. - -Input parameters: - State - algorithm state (used by AutoGKIteration). - -Output parameters: - V - integral(f(x)dx,a,b) - Rep - optimization report (see AutoGKReport description) - - -- ALGLIB -- - Copyright 14.11.2007 by Bochkanov Sergey -*************************************************************************/ -void autogkresults(autogkstate* state, - double* v, - autogkreport* rep, - ae_state *_state) -{ - - *v = 0; - _autogkreport_clear(rep); - - *v = state->v; - rep->terminationtype = state->terminationtype; - rep->nfev = state->nfev; - rep->nintervals = state->nintervals; -} - - -/************************************************************************* -Internal AutoGK subroutine -eps<0 - error -eps=0 - automatic eps selection - -width<0 - error -width=0 - no width requirements -*************************************************************************/ -static void autogk_autogkinternalprepare(double a, - double b, - double eps, - double xwidth, - autogkinternalstate* state, - ae_state *_state) -{ - - - - /* - * Save settings - */ - state->a = a; - state->b = b; - state->eps = eps; - state->xwidth = xwidth; - - /* - * Prepare RComm structure - */ - ae_vector_set_length(&state->rstate.ia, 3+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Internal AutoGK subroutine -*************************************************************************/ -static ae_bool autogk_autogkinternaliteration(autogkinternalstate* state, - ae_state *_state) -{ - double c1; - double c2; - ae_int_t i; - ae_int_t j; - double intg; - double intk; - double inta; - double v; - double ta; - double tb; - ae_int_t ns; - double qeps; - ae_int_t info; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - i = state->rstate.ia.ptr.p_int[0]; - j = state->rstate.ia.ptr.p_int[1]; - ns = state->rstate.ia.ptr.p_int[2]; - info = state->rstate.ia.ptr.p_int[3]; - c1 = state->rstate.ra.ptr.p_double[0]; - c2 = state->rstate.ra.ptr.p_double[1]; - intg = state->rstate.ra.ptr.p_double[2]; - intk = state->rstate.ra.ptr.p_double[3]; - inta = state->rstate.ra.ptr.p_double[4]; - v = state->rstate.ra.ptr.p_double[5]; - ta = state->rstate.ra.ptr.p_double[6]; - tb = state->rstate.ra.ptr.p_double[7]; - qeps = state->rstate.ra.ptr.p_double[8]; - } - else - { - i = 497; - j = -271; - ns = -581; - info = 745; - c1 = -533; - c2 = -77; - intg = 678; - intk = -293; - inta = 316; - v = 647; - ta = -756; - tb = 830; - qeps = -871; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - - /* - * Routine body - */ - - /* - * initialize quadratures. - * use 15-point Gauss-Kronrod formula. - */ - state->n = 15; - gkqgenerategausslegendre(state->n, &info, &state->qn, &state->wk, &state->wg, _state); - if( info<0 ) - { - state->info = -5; - state->r = 0; - result = ae_false; - return result; - } - ae_vector_set_length(&state->wr, state->n, _state); - for(i=0; i<=state->n-1; i++) - { - if( i==0 ) - { - state->wr.ptr.p_double[i] = 0.5*ae_fabs(state->qn.ptr.p_double[1]-state->qn.ptr.p_double[0], _state); - continue; - } - if( i==state->n-1 ) - { - state->wr.ptr.p_double[state->n-1] = 0.5*ae_fabs(state->qn.ptr.p_double[state->n-1]-state->qn.ptr.p_double[state->n-2], _state); - continue; - } - state->wr.ptr.p_double[i] = 0.5*ae_fabs(state->qn.ptr.p_double[i-1]-state->qn.ptr.p_double[i+1], _state); - } - - /* - * special case - */ - if( ae_fp_eq(state->a,state->b) ) - { - state->info = 1; - state->r = 0; - result = ae_false; - return result; - } - - /* - * test parameters - */ - if( ae_fp_less(state->eps,0)||ae_fp_less(state->xwidth,0) ) - { - state->info = -1; - state->r = 0; - result = ae_false; - return result; - } - state->info = 1; - if( ae_fp_eq(state->eps,0) ) - { - state->eps = 100000*ae_machineepsilon; - } - - /* - * First, prepare heap - * * column 0 - absolute error - * * column 1 - integral of a F(x) (calculated using Kronrod extension nodes) - * * column 2 - integral of a |F(x)| (calculated using modified rect. method) - * * column 3 - left boundary of a subinterval - * * column 4 - right boundary of a subinterval - */ - if( ae_fp_neq(state->xwidth,0) ) - { - goto lbl_3; - } - - /* - * no maximum width requirements - * start from one big subinterval - */ - state->heapwidth = 5; - state->heapsize = 1; - state->heapused = 1; - ae_matrix_set_length(&state->heap, state->heapsize, state->heapwidth, _state); - c1 = 0.5*(state->b-state->a); - c2 = 0.5*(state->b+state->a); - intg = 0; - intk = 0; - inta = 0; - i = 0; -lbl_5: - if( i>state->n-1 ) - { - goto lbl_7; - } - - /* - * obtain F - */ - state->x = c1*state->qn.ptr.p_double[i]+c2; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - v = state->f; - - /* - * Gauss-Kronrod formula - */ - intk = intk+v*state->wk.ptr.p_double[i]; - if( i%2==1 ) - { - intg = intg+v*state->wg.ptr.p_double[i]; - } - - /* - * Integral |F(x)| - * Use rectangles method - */ - inta = inta+ae_fabs(v, _state)*state->wr.ptr.p_double[i]; - i = i+1; - goto lbl_5; -lbl_7: - intk = intk*(state->b-state->a)*0.5; - intg = intg*(state->b-state->a)*0.5; - inta = inta*(state->b-state->a)*0.5; - state->heap.ptr.pp_double[0][0] = ae_fabs(intg-intk, _state); - state->heap.ptr.pp_double[0][1] = intk; - state->heap.ptr.pp_double[0][2] = inta; - state->heap.ptr.pp_double[0][3] = state->a; - state->heap.ptr.pp_double[0][4] = state->b; - state->sumerr = state->heap.ptr.pp_double[0][0]; - state->sumabs = ae_fabs(inta, _state); - goto lbl_4; -lbl_3: - - /* - * maximum subinterval should be no more than XWidth. - * so we create Ceil((B-A)/XWidth)+1 small subintervals - */ - ns = ae_iceil(ae_fabs(state->b-state->a, _state)/state->xwidth, _state)+1; - state->heapsize = ns; - state->heapused = ns; - state->heapwidth = 5; - ae_matrix_set_length(&state->heap, state->heapsize, state->heapwidth, _state); - state->sumerr = 0; - state->sumabs = 0; - j = 0; -lbl_8: - if( j>ns-1 ) - { - goto lbl_10; - } - ta = state->a+j*(state->b-state->a)/ns; - tb = state->a+(j+1)*(state->b-state->a)/ns; - c1 = 0.5*(tb-ta); - c2 = 0.5*(tb+ta); - intg = 0; - intk = 0; - inta = 0; - i = 0; -lbl_11: - if( i>state->n-1 ) - { - goto lbl_13; - } - - /* - * obtain F - */ - state->x = c1*state->qn.ptr.p_double[i]+c2; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - v = state->f; - - /* - * Gauss-Kronrod formula - */ - intk = intk+v*state->wk.ptr.p_double[i]; - if( i%2==1 ) - { - intg = intg+v*state->wg.ptr.p_double[i]; - } - - /* - * Integral |F(x)| - * Use rectangles method - */ - inta = inta+ae_fabs(v, _state)*state->wr.ptr.p_double[i]; - i = i+1; - goto lbl_11; -lbl_13: - intk = intk*(tb-ta)*0.5; - intg = intg*(tb-ta)*0.5; - inta = inta*(tb-ta)*0.5; - state->heap.ptr.pp_double[j][0] = ae_fabs(intg-intk, _state); - state->heap.ptr.pp_double[j][1] = intk; - state->heap.ptr.pp_double[j][2] = inta; - state->heap.ptr.pp_double[j][3] = ta; - state->heap.ptr.pp_double[j][4] = tb; - state->sumerr = state->sumerr+state->heap.ptr.pp_double[j][0]; - state->sumabs = state->sumabs+ae_fabs(inta, _state); - j = j+1; - goto lbl_8; -lbl_10: -lbl_4: - - /* - * method iterations - */ -lbl_14: - if( ae_false ) - { - goto lbl_15; - } - - /* - * additional memory if needed - */ - if( state->heapused==state->heapsize ) - { - autogk_mheapresize(&state->heap, &state->heapsize, 4*state->heapsize, state->heapwidth, _state); - } - - /* - * TODO: every 20 iterations recalculate errors/sums - */ - if( ae_fp_less_eq(state->sumerr,state->eps*state->sumabs)||state->heapused>=autogk_maxsubintervals ) - { - state->r = 0; - for(j=0; j<=state->heapused-1; j++) - { - state->r = state->r+state->heap.ptr.pp_double[j][1]; - } - result = ae_false; - return result; - } - - /* - * Exclude interval with maximum absolute error - */ - autogk_mheappop(&state->heap, state->heapused, state->heapwidth, _state); - state->sumerr = state->sumerr-state->heap.ptr.pp_double[state->heapused-1][0]; - state->sumabs = state->sumabs-state->heap.ptr.pp_double[state->heapused-1][2]; - - /* - * Divide interval, create subintervals - */ - ta = state->heap.ptr.pp_double[state->heapused-1][3]; - tb = state->heap.ptr.pp_double[state->heapused-1][4]; - state->heap.ptr.pp_double[state->heapused-1][3] = ta; - state->heap.ptr.pp_double[state->heapused-1][4] = 0.5*(ta+tb); - state->heap.ptr.pp_double[state->heapused][3] = 0.5*(ta+tb); - state->heap.ptr.pp_double[state->heapused][4] = tb; - j = state->heapused-1; -lbl_16: - if( j>state->heapused ) - { - goto lbl_18; - } - c1 = 0.5*(state->heap.ptr.pp_double[j][4]-state->heap.ptr.pp_double[j][3]); - c2 = 0.5*(state->heap.ptr.pp_double[j][4]+state->heap.ptr.pp_double[j][3]); - intg = 0; - intk = 0; - inta = 0; - i = 0; -lbl_19: - if( i>state->n-1 ) - { - goto lbl_21; - } - - /* - * F(x) - */ - state->x = c1*state->qn.ptr.p_double[i]+c2; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - v = state->f; - - /* - * Gauss-Kronrod formula - */ - intk = intk+v*state->wk.ptr.p_double[i]; - if( i%2==1 ) - { - intg = intg+v*state->wg.ptr.p_double[i]; - } - - /* - * Integral |F(x)| - * Use rectangles method - */ - inta = inta+ae_fabs(v, _state)*state->wr.ptr.p_double[i]; - i = i+1; - goto lbl_19; -lbl_21: - intk = intk*(state->heap.ptr.pp_double[j][4]-state->heap.ptr.pp_double[j][3])*0.5; - intg = intg*(state->heap.ptr.pp_double[j][4]-state->heap.ptr.pp_double[j][3])*0.5; - inta = inta*(state->heap.ptr.pp_double[j][4]-state->heap.ptr.pp_double[j][3])*0.5; - state->heap.ptr.pp_double[j][0] = ae_fabs(intg-intk, _state); - state->heap.ptr.pp_double[j][1] = intk; - state->heap.ptr.pp_double[j][2] = inta; - state->sumerr = state->sumerr+state->heap.ptr.pp_double[j][0]; - state->sumabs = state->sumabs+state->heap.ptr.pp_double[j][2]; - j = j+1; - goto lbl_16; -lbl_18: - autogk_mheappush(&state->heap, state->heapused-1, state->heapwidth, _state); - autogk_mheappush(&state->heap, state->heapused, state->heapwidth, _state); - state->heapused = state->heapused+1; - goto lbl_14; -lbl_15: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = i; - state->rstate.ia.ptr.p_int[1] = j; - state->rstate.ia.ptr.p_int[2] = ns; - state->rstate.ia.ptr.p_int[3] = info; - state->rstate.ra.ptr.p_double[0] = c1; - state->rstate.ra.ptr.p_double[1] = c2; - state->rstate.ra.ptr.p_double[2] = intg; - state->rstate.ra.ptr.p_double[3] = intk; - state->rstate.ra.ptr.p_double[4] = inta; - state->rstate.ra.ptr.p_double[5] = v; - state->rstate.ra.ptr.p_double[6] = ta; - state->rstate.ra.ptr.p_double[7] = tb; - state->rstate.ra.ptr.p_double[8] = qeps; - return result; -} - - -static void autogk_mheappop(/* Real */ ae_matrix* heap, - ae_int_t heapsize, - ae_int_t heapwidth, - ae_state *_state) -{ - ae_int_t i; - ae_int_t p; - double t; - ae_int_t maxcp; - - - if( heapsize==1 ) - { - return; - } - for(i=0; i<=heapwidth-1; i++) - { - t = heap->ptr.pp_double[heapsize-1][i]; - heap->ptr.pp_double[heapsize-1][i] = heap->ptr.pp_double[0][i]; - heap->ptr.pp_double[0][i] = t; - } - p = 0; - while(2*p+1ptr.pp_double[2*p+2][0],heap->ptr.pp_double[2*p+1][0]) ) - { - maxcp = 2*p+2; - } - } - if( ae_fp_less(heap->ptr.pp_double[p][0],heap->ptr.pp_double[maxcp][0]) ) - { - for(i=0; i<=heapwidth-1; i++) - { - t = heap->ptr.pp_double[p][i]; - heap->ptr.pp_double[p][i] = heap->ptr.pp_double[maxcp][i]; - heap->ptr.pp_double[maxcp][i] = t; - } - p = maxcp; - } - else - { - break; - } - } -} - - -static void autogk_mheappush(/* Real */ ae_matrix* heap, - ae_int_t heapsize, - ae_int_t heapwidth, - ae_state *_state) -{ - ae_int_t i; - ae_int_t p; - double t; - ae_int_t parent; - - - if( heapsize==0 ) - { - return; - } - p = heapsize; - while(p!=0) - { - parent = (p-1)/2; - if( ae_fp_greater(heap->ptr.pp_double[p][0],heap->ptr.pp_double[parent][0]) ) - { - for(i=0; i<=heapwidth-1; i++) - { - t = heap->ptr.pp_double[p][i]; - heap->ptr.pp_double[p][i] = heap->ptr.pp_double[parent][i]; - heap->ptr.pp_double[parent][i] = t; - } - p = parent; - } - else - { - break; - } - } -} - - -static void autogk_mheapresize(/* Real */ ae_matrix* heap, - ae_int_t* heapsize, - ae_int_t newheapsize, - ae_int_t heapwidth, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix tmp; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init(&tmp, 0, 0, DT_REAL, _state, ae_true); - - ae_matrix_set_length(&tmp, *heapsize, heapwidth, _state); - for(i=0; i<=*heapsize-1; i++) - { - ae_v_move(&tmp.ptr.pp_double[i][0], 1, &heap->ptr.pp_double[i][0], 1, ae_v_len(0,heapwidth-1)); - } - ae_matrix_set_length(heap, newheapsize, heapwidth, _state); - for(i=0; i<=*heapsize-1; i++) - { - ae_v_move(&heap->ptr.pp_double[i][0], 1, &tmp.ptr.pp_double[i][0], 1, ae_v_len(0,heapwidth-1)); - } - *heapsize = newheapsize; - ae_frame_leave(_state); -} - - -ae_bool _autogkreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - autogkreport *p = (autogkreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _autogkreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - autogkreport *dst = (autogkreport*)_dst; - autogkreport *src = (autogkreport*)_src; - dst->terminationtype = src->terminationtype; - dst->nfev = src->nfev; - dst->nintervals = src->nintervals; - return ae_true; -} - - -void _autogkreport_clear(void* _p) -{ - autogkreport *p = (autogkreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _autogkreport_destroy(void* _p) -{ - autogkreport *p = (autogkreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _autogkinternalstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - autogkinternalstate *p = (autogkinternalstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->heap, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->qn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->wg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->wk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->wr, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _autogkinternalstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - autogkinternalstate *dst = (autogkinternalstate*)_dst; - autogkinternalstate *src = (autogkinternalstate*)_src; - dst->a = src->a; - dst->b = src->b; - dst->eps = src->eps; - dst->xwidth = src->xwidth; - dst->x = src->x; - dst->f = src->f; - dst->info = src->info; - dst->r = src->r; - if( !ae_matrix_init_copy(&dst->heap, &src->heap, _state, make_automatic) ) - return ae_false; - dst->heapsize = src->heapsize; - dst->heapwidth = src->heapwidth; - dst->heapused = src->heapused; - dst->sumerr = src->sumerr; - dst->sumabs = src->sumabs; - if( !ae_vector_init_copy(&dst->qn, &src->qn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->wg, &src->wg, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->wk, &src->wk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->wr, &src->wr, _state, make_automatic) ) - return ae_false; - dst->n = src->n; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _autogkinternalstate_clear(void* _p) -{ - autogkinternalstate *p = (autogkinternalstate*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->heap); - ae_vector_clear(&p->qn); - ae_vector_clear(&p->wg); - ae_vector_clear(&p->wk); - ae_vector_clear(&p->wr); - _rcommstate_clear(&p->rstate); -} - - -void _autogkinternalstate_destroy(void* _p) -{ - autogkinternalstate *p = (autogkinternalstate*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->heap); - ae_vector_destroy(&p->qn); - ae_vector_destroy(&p->wg); - ae_vector_destroy(&p->wk); - ae_vector_destroy(&p->wr); - _rcommstate_destroy(&p->rstate); -} - - -ae_bool _autogkstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - autogkstate *p = (autogkstate*)_p; - ae_touch_ptr((void*)p); - if( !_autogkinternalstate_init(&p->internalstate, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _autogkstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - autogkstate *dst = (autogkstate*)_dst; - autogkstate *src = (autogkstate*)_src; - dst->a = src->a; - dst->b = src->b; - dst->alpha = src->alpha; - dst->beta = src->beta; - dst->xwidth = src->xwidth; - dst->x = src->x; - dst->xminusa = src->xminusa; - dst->bminusx = src->bminusx; - dst->needf = src->needf; - dst->f = src->f; - dst->wrappermode = src->wrappermode; - if( !_autogkinternalstate_init_copy(&dst->internalstate, &src->internalstate, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - dst->v = src->v; - dst->terminationtype = src->terminationtype; - dst->nfev = src->nfev; - dst->nintervals = src->nintervals; - return ae_true; -} - - -void _autogkstate_clear(void* _p) -{ - autogkstate *p = (autogkstate*)_p; - ae_touch_ptr((void*)p); - _autogkinternalstate_clear(&p->internalstate); - _rcommstate_clear(&p->rstate); -} - - -void _autogkstate_destroy(void* _p) -{ - autogkstate *p = (autogkstate*)_p; - ae_touch_ptr((void*)p); - _autogkinternalstate_destroy(&p->internalstate); - _rcommstate_destroy(&p->rstate); -} - - - -} - diff --git a/src/inc/alglib/integration.h b/src/inc/alglib/integration.h deleted file mode 100644 index b0f25c3..0000000 --- a/src/inc/alglib/integration.h +++ /dev/null @@ -1,837 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _integration_pkg_h -#define _integration_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "linalg.h" -#include "specialfunctions.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - ae_int_t terminationtype; - ae_int_t nfev; - ae_int_t nintervals; -} autogkreport; -typedef struct -{ - double a; - double b; - double eps; - double xwidth; - double x; - double f; - ae_int_t info; - double r; - ae_matrix heap; - ae_int_t heapsize; - ae_int_t heapwidth; - ae_int_t heapused; - double sumerr; - double sumabs; - ae_vector qn; - ae_vector wg; - ae_vector wk; - ae_vector wr; - ae_int_t n; - rcommstate rstate; -} autogkinternalstate; -typedef struct -{ - double a; - double b; - double alpha; - double beta; - double xwidth; - double x; - double xminusa; - double bminusx; - ae_bool needf; - double f; - ae_int_t wrappermode; - autogkinternalstate internalstate; - rcommstate rstate; - double v; - ae_int_t terminationtype; - ae_int_t nfev; - ae_int_t nintervals; -} autogkstate; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - - - - -/************************************************************************* -Integration report: -* TerminationType = completetion code: - * -5 non-convergence of Gauss-Kronrod nodes - calculation subroutine. - * -1 incorrect parameters were specified - * 1 OK -* Rep.NFEV countains number of function calculations -* Rep.NIntervals contains number of intervals [a,b] - was partitioned into. -*************************************************************************/ -class _autogkreport_owner -{ -public: - _autogkreport_owner(); - _autogkreport_owner(const _autogkreport_owner &rhs); - _autogkreport_owner& operator=(const _autogkreport_owner &rhs); - virtual ~_autogkreport_owner(); - alglib_impl::autogkreport* c_ptr(); - alglib_impl::autogkreport* c_ptr() const; -protected: - alglib_impl::autogkreport *p_struct; -}; -class autogkreport : public _autogkreport_owner -{ -public: - autogkreport(); - autogkreport(const autogkreport &rhs); - autogkreport& operator=(const autogkreport &rhs); - virtual ~autogkreport(); - ae_int_t &terminationtype; - ae_int_t &nfev; - ae_int_t &nintervals; - -}; - - -/************************************************************************* -This structure stores state of the integration algorithm. - -Although this class has public fields, they are not intended for external -use. You should use ALGLIB functions to work with this class: -* autogksmooth()/AutoGKSmoothW()/... to create objects -* autogkintegrate() to begin integration -* autogkresults() to get results -*************************************************************************/ -class _autogkstate_owner -{ -public: - _autogkstate_owner(); - _autogkstate_owner(const _autogkstate_owner &rhs); - _autogkstate_owner& operator=(const _autogkstate_owner &rhs); - virtual ~_autogkstate_owner(); - alglib_impl::autogkstate* c_ptr(); - alglib_impl::autogkstate* c_ptr() const; -protected: - alglib_impl::autogkstate *p_struct; -}; -class autogkstate : public _autogkstate_owner -{ -public: - autogkstate(); - autogkstate(const autogkstate &rhs); - autogkstate& operator=(const autogkstate &rhs); - virtual ~autogkstate(); - ae_bool &needf; - double &x; - double &xminusa; - double &bminusx; - double &f; - -}; - -/************************************************************************* -Computation of nodes and weights for a Gauss quadrature formula - -The algorithm generates the N-point Gauss quadrature formula with weight -function given by coefficients alpha and beta of a recurrence relation -which generates a system of orthogonal polynomials: - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-1], alpha coefficients - Beta – array[0..N-1], beta coefficients - Zero-indexed element is not used and may be arbitrary. - Beta[I]>0. - Mu0 – zeroth moment of the weight function. - N – number of nodes of the quadrature formula, N>=1 - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgeneraterec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w); - - -/************************************************************************* -Computation of nodes and weights for a Gauss-Lobatto quadrature formula - -The algorithm generates the N-point Gauss-Lobatto quadrature formula with -weight function given by coefficients alpha and beta of a recurrence which -generates a system of orthogonal polynomials. - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-2], alpha coefficients - Beta – array[0..N-2], beta coefficients. - Zero-indexed element is not used, may be arbitrary. - Beta[I]>0 - Mu0 – zeroth moment of the weighting function. - A – left boundary of the integration interval. - B – right boundary of the integration interval. - N – number of nodes of the quadrature formula, N>=3 - (including the left and right boundary nodes). - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslobattorec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const double a, const double b, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w); - - -/************************************************************************* -Computation of nodes and weights for a Gauss-Radau quadrature formula - -The algorithm generates the N-point Gauss-Radau quadrature formula with -weight function given by the coefficients alpha and beta of a recurrence -which generates a system of orthogonal polynomials. - -P-1(x) = 0 -P0(x) = 1 -Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zeroth moment Mu0 - -Mu0 = integral(W(x)dx,a,b) - -INPUT PARAMETERS: - Alpha – array[0..N-2], alpha coefficients. - Beta – array[0..N-1], beta coefficients - Zero-indexed element is not used. - Beta[I]>0 - Mu0 – zeroth moment of the weighting function. - A – left boundary of the integration interval. - N – number of nodes of the quadrature formula, N>=2 - (including the left boundary node). - -OUTPUT PARAMETERS: - Info - error code: - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * 1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 2005-2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategaussradaurec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const double a, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w); - - -/************************************************************************* -Returns nodes/weights for Gauss-Legendre quadrature on [-1,1] with N -nodes. - -INPUT PARAMETERS: - N - number of nodes, >=1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslegendre(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w); - - -/************************************************************************* -Returns nodes/weights for Gauss-Jacobi quadrature on [-1,1] with weight -function W(x)=Power(1-x,Alpha)*Power(1+x,Beta). - -INPUT PARAMETERS: - N - number of nodes, >=1 - Alpha - power-law coefficient, Alpha>-1 - Beta - power-law coefficient, Beta>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. Alpha or Beta are too close - to -1 to obtain weights/nodes with high enough - accuracy, or, may be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha/Beta was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategaussjacobi(const ae_int_t n, const double alpha, const double beta, ae_int_t &info, real_1d_array &x, real_1d_array &w); - - -/************************************************************************* -Returns nodes/weights for Gauss-Laguerre quadrature on [0,+inf) with -weight function W(x)=Power(x,Alpha)*Exp(-x) - -INPUT PARAMETERS: - N - number of nodes, >=1 - Alpha - power-law coefficient, Alpha>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. Alpha is too close to -1 to - obtain weights/nodes with high enough accuracy - or, may be, N is too large. Try to use - multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausslaguerre(const ae_int_t n, const double alpha, ae_int_t &info, real_1d_array &x, real_1d_array &w); - - -/************************************************************************* -Returns nodes/weights for Gauss-Hermite quadrature on (-inf,+inf) with -weight function W(x)=Exp(-x*x) - -INPUT PARAMETERS: - N - number of nodes, >=1 - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. May be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N/Alpha was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - W - array[0..N-1] - array of quadrature weights. - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gqgenerategausshermite(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &w); - -/************************************************************************* -Computation of nodes and weights of a Gauss-Kronrod quadrature formula - -The algorithm generates the N-point Gauss-Kronrod quadrature formula with -weight function given by coefficients alpha and beta of a recurrence -relation which generates a system of orthogonal polynomials: - - P-1(x) = 0 - P0(x) = 1 - Pn+1(x) = (x-alpha(n))*Pn(x) - beta(n)*Pn-1(x) - -and zero moment Mu0 - - Mu0 = integral(W(x)dx,a,b) - - -INPUT PARAMETERS: - Alpha – alpha coefficients, array[0..floor(3*K/2)]. - Beta – beta coefficients, array[0..ceil(3*K/2)]. - Beta[0] is not used and may be arbitrary. - Beta[I]>0. - Mu0 – zeroth moment of the weight function. - N – number of nodes of the Gauss-Kronrod quadrature formula, - N >= 3, - N = 2*K+1. - -OUTPUT PARAMETERS: - Info - error code: - * -5 no real and positive Gauss-Kronrod formula can - be created for such a weight function with a - given number of nodes. - * -4 N is too large, task may be ill conditioned - - x[i]=x[i+1] found. - * -3 internal eigenproblem solver hasn't converged - * -2 Beta[i]<=0 - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, - in ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - -- ALGLIB -- - Copyright 08.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgeneraterec(const real_1d_array &alpha, const real_1d_array &beta, const double mu0, const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss); - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes/weights for Gauss-Legendre -quadrature with N points. - -GKQLegendreCalc (calculation) or GKQLegendreTbl (precomputed table) is -used depending on machine precision and number of nodes. - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgenerategausslegendre(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss); - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes/weights for Gauss-Jacobi -quadrature on [-1,1] with weight function - - W(x)=Power(1-x,Alpha)*Power(1+x,Beta). - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - Alpha - power-law coefficient, Alpha>-1 - Beta - power-law coefficient, Beta>-1 - -OUTPUT PARAMETERS: - Info - error code: - * -5 no real and positive Gauss-Kronrod formula can - be created for such a weight function with a - given number of nodes. - * -4 an error was detected when calculating - weights/nodes. Alpha or Beta are too close - to -1 to obtain weights/nodes with high enough - accuracy, or, may be, N is too large. Try to - use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - * +2 OK, but quadrature rule have exterior nodes, - x[0]<-1 or x[n-1]>+1 - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqgenerategaussjacobi(const ae_int_t n, const double alpha, const double beta, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss); - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes for quadrature with N points. - -Reduction to tridiagonal eigenproblem is used. - -INPUT PARAMETERS: - N - number of Kronrod nodes, must be odd number, >=3. - -OUTPUT PARAMETERS: - Info - error code: - * -4 an error was detected when calculating - weights/nodes. N is too large to obtain - weights/nodes with high enough accuracy. - Try to use multiple precision version. - * -3 internal eigenproblem solver hasn't converged - * -1 incorrect N was passed - * +1 OK - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqlegendrecalc(const ae_int_t n, ae_int_t &info, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss); - - -/************************************************************************* -Returns Gauss and Gauss-Kronrod nodes for quadrature with N points using -pre-calculated table. Nodes/weights were computed with accuracy up to -1.0E-32 (if MPFR version of ALGLIB is used). In standard double precision -accuracy reduces to something about 2.0E-16 (depending on your compiler's -handling of long floating point constants). - -INPUT PARAMETERS: - N - number of Kronrod nodes. - N can be 15, 21, 31, 41, 51, 61. - -OUTPUT PARAMETERS: - X - array[0..N-1] - array of quadrature nodes, ordered in - ascending order. - WKronrod - array[0..N-1] - Kronrod weights - WGauss - array[0..N-1] - Gauss weights (interleaved with zeros - corresponding to extended Kronrod nodes). - - - -- ALGLIB -- - Copyright 12.05.2009 by Bochkanov Sergey -*************************************************************************/ -void gkqlegendretbl(const ae_int_t n, real_1d_array &x, real_1d_array &wkronrod, real_1d_array &wgauss, double &eps); - -/************************************************************************* -Integration of a smooth function F(x) on a finite interval [a,b]. - -Fast-convergent algorithm based on a Gauss-Kronrod formula is used. Result -is calculated with accuracy close to the machine precision. - -Algorithm works well only with smooth integrands. It may be used with -continuous non-smooth integrands, but with less performance. - -It should never be used with integrands which have integrable singularities -at lower or upper limits - algorithm may crash. Use AutoGKSingular in such -cases. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmoothW, AutoGKSingular, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksmooth(const double a, const double b, autogkstate &state); - - -/************************************************************************* -Integration of a smooth function F(x) on a finite interval [a,b]. - -This subroutine is same as AutoGKSmooth(), but it guarantees that interval -[a,b] is partitioned into subintervals which have width at most XWidth. - -Subroutine can be used when integrating nearly-constant function with -narrow "bumps" (about XWidth wide). If "bumps" are too narrow, AutoGKSmooth -subroutine can overlook them. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmooth, AutoGKSingular, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksmoothw(const double a, const double b, const double xwidth, autogkstate &state); - - -/************************************************************************* -Integration on a finite interval [A,B]. -Integrand have integrable singularities at A/B. - -F(X) must diverge as "(x-A)^alpha" at A, as "(B-x)^beta" at B, with known -alpha/beta (alpha>-1, beta>-1). If alpha/beta are not known, estimates -from below can be used (but these estimates should be greater than -1 too). - -One of alpha/beta variables (or even both alpha/beta) may be equal to 0, -which means than function F(x) is non-singular at A/B. Anyway (singular at -bounds or not), function F(x) is supposed to be continuous on (A,B). - -Fast-convergent algorithm based on a Gauss-Kronrod formula is used. Result -is calculated with accuracy close to the machine precision. - -INPUT PARAMETERS: - A, B - interval boundaries (AB) - Alpha - power-law coefficient of the F(x) at A, - Alpha>-1 - Beta - power-law coefficient of the F(x) at B, - Beta>-1 - -OUTPUT PARAMETERS - State - structure which stores algorithm state - -SEE ALSO - AutoGKSmooth, AutoGKSmoothW, AutoGKResults. - - - -- ALGLIB -- - Copyright 06.05.2009 by Bochkanov Sergey -*************************************************************************/ -void autogksingular(const double a, const double b, const double alpha, const double beta, autogkstate &state); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool autogkiteration(const autogkstate &state); - - -/************************************************************************* -This function is used to launcn iterations of the 1-dimensional integrator - -It accepts following parameters: - func - callback which calculates f(x) for given x - ptr - optional pointer which is passed to func; can be NULL - - - -- ALGLIB -- - Copyright 07.05.2009 by Bochkanov Sergey - -*************************************************************************/ -void autogkintegrate(autogkstate &state, - void (*func)(double x, double xminusa, double bminusx, double &y, void *ptr), - void *ptr = NULL); - - -/************************************************************************* -Adaptive integration results - -Called after AutoGKIteration returned False. - -Input parameters: - State - algorithm state (used by AutoGKIteration). - -Output parameters: - V - integral(f(x)dx,a,b) - Rep - optimization report (see AutoGKReport description) - - -- ALGLIB -- - Copyright 14.11.2007 by Bochkanov Sergey -*************************************************************************/ -void autogkresults(const autogkstate &state, double &v, autogkreport &rep); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void gqgeneraterec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gqgenerategausslobattorec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - double a, - double b, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gqgenerategaussradaurec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - double a, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gqgenerategausslegendre(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gqgenerategaussjacobi(ae_int_t n, - double alpha, - double beta, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gqgenerategausslaguerre(ae_int_t n, - double alpha, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gqgenerategausshermite(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* w, - ae_state *_state); -void gkqgeneraterec(/* Real */ ae_vector* alpha, - /* Real */ ae_vector* beta, - double mu0, - ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state); -void gkqgenerategausslegendre(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state); -void gkqgenerategaussjacobi(ae_int_t n, - double alpha, - double beta, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state); -void gkqlegendrecalc(ae_int_t n, - ae_int_t* info, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - ae_state *_state); -void gkqlegendretbl(ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* wkronrod, - /* Real */ ae_vector* wgauss, - double* eps, - ae_state *_state); -void autogksmooth(double a, - double b, - autogkstate* state, - ae_state *_state); -void autogksmoothw(double a, - double b, - double xwidth, - autogkstate* state, - ae_state *_state); -void autogksingular(double a, - double b, - double alpha, - double beta, - autogkstate* state, - ae_state *_state); -ae_bool autogkiteration(autogkstate* state, ae_state *_state); -void autogkresults(autogkstate* state, - double* v, - autogkreport* rep, - ae_state *_state); -ae_bool _autogkreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _autogkreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _autogkreport_clear(void* _p); -void _autogkreport_destroy(void* _p); -ae_bool _autogkinternalstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _autogkinternalstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _autogkinternalstate_clear(void* _p); -void _autogkinternalstate_destroy(void* _p); -ae_bool _autogkstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _autogkstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _autogkstate_clear(void* _p); -void _autogkstate_destroy(void* _p); - -} -#endif - diff --git a/src/inc/alglib/interpolation.cpp b/src/inc/alglib/interpolation.cpp deleted file mode 100644 index 08ed432..0000000 --- a/src/inc/alglib/interpolation.cpp +++ /dev/null @@ -1,30715 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "interpolation.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -IDW interpolant. -*************************************************************************/ -_idwinterpolant_owner::_idwinterpolant_owner() -{ - p_struct = (alglib_impl::idwinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::idwinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_idwinterpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_idwinterpolant_owner::_idwinterpolant_owner(const _idwinterpolant_owner &rhs) -{ - p_struct = (alglib_impl::idwinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::idwinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_idwinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_idwinterpolant_owner& _idwinterpolant_owner::operator=(const _idwinterpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_idwinterpolant_clear(p_struct); - if( !alglib_impl::_idwinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_idwinterpolant_owner::~_idwinterpolant_owner() -{ - alglib_impl::_idwinterpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::idwinterpolant* _idwinterpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::idwinterpolant* _idwinterpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -idwinterpolant::idwinterpolant() : _idwinterpolant_owner() -{ -} - -idwinterpolant::idwinterpolant(const idwinterpolant &rhs):_idwinterpolant_owner(rhs) -{ -} - -idwinterpolant& idwinterpolant::operator=(const idwinterpolant &rhs) -{ - if( this==&rhs ) - return *this; - _idwinterpolant_owner::operator=(rhs); - return *this; -} - -idwinterpolant::~idwinterpolant() -{ -} - -/************************************************************************* -IDW interpolation - -INPUT PARAMETERS: - Z - IDW interpolant built with one of model building - subroutines. - X - array[0..NX-1], interpolation point - -Result: - IDW interpolant Z(X) - - -- ALGLIB -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -double idwcalc(const idwinterpolant &z, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::idwcalc(const_cast(z.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -IDW interpolant using modified Shepard method for uniform point -distributions. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - D - nodal function type, either: - * 0 constant model. Just for demonstration only, worst - model ever. - * 1 linear model, least squares fitting. Simpe model for - datasets too small for quadratic models - * 2 quadratic model, least squares fitting. Best model - available (if your dataset is large enough). - * -1 "fast" linear model, use with caution!!! It is - significantly faster than linear/quadratic and better - than constant model. But it is less robust (especially - in the presence of noise). - NQ - number of points used to calculate nodal functions (ignored - for constant models). NQ should be LARGER than: - * max(1.5*(1+NX),2^NX+1) for linear model, - * max(3/4*(NX+2)*(NX+1),2^NX+1) for quadratic model. - Values less than this threshold will be silently increased. - NW - number of points used to calculate weights and to interpolate. - Required: >=2^NX+1, values less than this threshold will be - silently increased. - Recommended value: about 2*NQ - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: - * best results are obtained with quadratic models, worst - with constant - models - * when N is large, NQ and NW must be significantly smaller than N both - to obtain optimal performance and to obtain optimal accuracy. In 2 or - 3-dimensional tasks NQ=15 and NW=25 are good values to start with. - * NQ and NW may be greater than N. In such cases they will be - automatically decreased. - * this subroutine is always succeeds (as long as correct parameters are - passed). - * see 'Multivariate Interpolation of Large Sets of Scattered Data' by - Robert J. Renka for more information on this algorithm. - * this subroutine assumes that point distribution is uniform at the small - scales. If it isn't - for example, points are concentrated along - "lines", but "lines" distribution is uniform at the larger scale - then - you should use IDWBuildModifiedShepardR() - - - -- ALGLIB PROJECT -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildmodifiedshepard(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t d, const ae_int_t nq, const ae_int_t nw, idwinterpolant &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::idwbuildmodifiedshepard(const_cast(xy.c_ptr()), n, nx, d, nq, nw, const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -IDW interpolant using modified Shepard method for non-uniform datasets. - -This type of model uses constant nodal functions and interpolates using -all nodes which are closer than user-specified radius R. It may be used -when points distribution is non-uniform at the small scale, but it is at -the distances as large as R. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - R - radius, R>0 - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: -* if there is less than IDWKMin points within R-ball, algorithm selects - IDWKMin closest ones, so that continuity properties of interpolant are - preserved even far from points. - - -- ALGLIB PROJECT -- - Copyright 11.04.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildmodifiedshepardr(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const double r, idwinterpolant &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::idwbuildmodifiedshepardr(const_cast(xy.c_ptr()), n, nx, r, const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -IDW model for noisy data. - -This subroutine may be used to handle noisy data, i.e. data with noise in -OUTPUT values. It differs from IDWBuildModifiedShepard() in the following -aspects: -* nodal functions are not constrained to pass through nodes: Qi(xi)<>yi, - i.e. we have fitting instead of interpolation. -* weights which are used during least squares fitting stage are all equal - to 1.0 (independently of distance) -* "fast"-linear or constant nodal functions are not supported (either not - robust enough or too rigid) - -This problem require far more complex tuning than interpolation problems. -Below you can find some recommendations regarding this problem: -* focus on tuning NQ; it controls noise reduction. As for NW, you can just - make it equal to 2*NQ. -* you can use cross-validation to determine optimal NQ. -* optimal NQ is a result of complex tradeoff between noise level (more - noise = larger NQ required) and underlying function complexity (given - fixed N, larger NQ means smoothing of compex features in the data). For - example, NQ=N will reduce noise to the minimum level possible, but you - will end up with just constant/linear/quadratic (depending on D) least - squares model for the whole dataset. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - D - nodal function degree, either: - * 1 linear model, least squares fitting. Simpe model for - datasets too small for quadratic models (or for very - noisy problems). - * 2 quadratic model, least squares fitting. Best model - available (if your dataset is large enough). - NQ - number of points used to calculate nodal functions. NQ should - be significantly larger than 1.5 times the number of - coefficients in a nodal function to overcome effects of noise: - * larger than 1.5*(1+NX) for linear model, - * larger than 3/4*(NX+2)*(NX+1) for quadratic model. - Values less than this threshold will be silently increased. - NW - number of points used to calculate weights and to interpolate. - Required: >=2^NX+1, values less than this threshold will be - silently increased. - Recommended value: about 2*NQ or larger - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: - * best results are obtained with quadratic models, linear models are not - recommended to use unless you are pretty sure that it is what you want - * this subroutine is always succeeds (as long as correct parameters are - passed). - * see 'Multivariate Interpolation of Large Sets of Scattered Data' by - Robert J. Renka for more information on this algorithm. - - - -- ALGLIB PROJECT -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildnoisy(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t d, const ae_int_t nq, const ae_int_t nw, idwinterpolant &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::idwbuildnoisy(const_cast(xy.c_ptr()), n, nx, d, nq, nw, const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Barycentric interpolant. -*************************************************************************/ -_barycentricinterpolant_owner::_barycentricinterpolant_owner() -{ - p_struct = (alglib_impl::barycentricinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_barycentricinterpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_barycentricinterpolant_owner::_barycentricinterpolant_owner(const _barycentricinterpolant_owner &rhs) -{ - p_struct = (alglib_impl::barycentricinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_barycentricinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_barycentricinterpolant_owner& _barycentricinterpolant_owner::operator=(const _barycentricinterpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_barycentricinterpolant_clear(p_struct); - if( !alglib_impl::_barycentricinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_barycentricinterpolant_owner::~_barycentricinterpolant_owner() -{ - alglib_impl::_barycentricinterpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::barycentricinterpolant* _barycentricinterpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::barycentricinterpolant* _barycentricinterpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -barycentricinterpolant::barycentricinterpolant() : _barycentricinterpolant_owner() -{ -} - -barycentricinterpolant::barycentricinterpolant(const barycentricinterpolant &rhs):_barycentricinterpolant_owner(rhs) -{ -} - -barycentricinterpolant& barycentricinterpolant::operator=(const barycentricinterpolant &rhs) -{ - if( this==&rhs ) - return *this; - _barycentricinterpolant_owner::operator=(rhs); - return *this; -} - -barycentricinterpolant::~barycentricinterpolant() -{ -} - -/************************************************************************* -Rational interpolation using barycentric formula - -F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i])) - -Input parameters: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -Result: - barycentric interpolant F(t) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -double barycentriccalc(const barycentricinterpolant &b, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::barycentriccalc(const_cast(b.c_ptr()), t, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Differentiation of barycentric interpolant: first derivative. - -Algorithm used in this subroutine is very robust and should not fail until -provided with values too close to MaxRealNumber (usually MaxRealNumber/N -or greater will overflow). - -INPUT PARAMETERS: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -OUTPUT PARAMETERS: - F - barycentric interpolant at T - DF - first derivative - -NOTE - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricdiff1(const barycentricinterpolant &b, const double t, double &f, double &df) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricdiff1(const_cast(b.c_ptr()), t, &f, &df, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Differentiation of barycentric interpolant: first/second derivatives. - -INPUT PARAMETERS: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -OUTPUT PARAMETERS: - F - barycentric interpolant at T - DF - first derivative - D2F - second derivative - -NOTE: this algorithm may fail due to overflow/underflor if used on data -whose values are close to MaxRealNumber or MinRealNumber. Use more robust -BarycentricDiff1() subroutine in such cases. - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricdiff2(const barycentricinterpolant &b, const double t, double &f, double &df, double &d2f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricdiff2(const_cast(b.c_ptr()), t, &f, &df, &d2f, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the argument. - -INPUT PARAMETERS: - B - rational interpolant in barycentric form - CA, CB - transformation coefficients: x = CA*t + CB - -OUTPUT PARAMETERS: - B - transformed interpolant with X replaced by T - - -- ALGLIB PROJECT -- - Copyright 19.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriclintransx(const barycentricinterpolant &b, const double ca, const double cb) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentriclintransx(const_cast(b.c_ptr()), ca, cb, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the barycentric -interpolant. - -INPUT PARAMETERS: - B - rational interpolant in barycentric form - CA, CB - transformation coefficients: B2(x) = CA*B(x) + CB - -OUTPUT PARAMETERS: - B - transformed interpolant - - -- ALGLIB PROJECT -- - Copyright 19.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriclintransy(const barycentricinterpolant &b, const double ca, const double cb) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentriclintransy(const_cast(b.c_ptr()), ca, cb, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Extracts X/Y/W arrays from rational interpolant - -INPUT PARAMETERS: - B - barycentric interpolant - -OUTPUT PARAMETERS: - N - nodes count, N>0 - X - interpolation nodes, array[0..N-1] - F - function values, array[0..N-1] - W - barycentric weights, array[0..N-1] - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricunpack(const barycentricinterpolant &b, ae_int_t &n, real_1d_array &x, real_1d_array &y, real_1d_array &w) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricunpack(const_cast(b.c_ptr()), &n, const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rational interpolant from X/Y/W arrays - -F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i])) - -INPUT PARAMETERS: - X - interpolation nodes, array[0..N-1] - F - function values, array[0..N-1] - W - barycentric weights, array[0..N-1] - N - nodes count, N>0 - -OUTPUT PARAMETERS: - B - barycentric interpolant built from (X, Y, W) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricbuildxyw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, barycentricinterpolant &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricbuildxyw(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rational interpolant without poles - -The subroutine constructs the rational interpolating function without real -poles (see 'Barycentric rational interpolation with no poles and high -rates of approximation', Michael S. Floater. and Kai Hormann, for more -information on this subject). - -Input parameters: - X - interpolation nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of nodes, N>0. - D - order of the interpolation scheme, 0 <= D <= N-1. - D<0 will cause an error. - D>=N it will be replaced with D=N-1. - if you don't know what D to choose, use small value about 3-5. - -Output parameters: - B - barycentric interpolant. - -Note: - this algorithm always succeeds and calculates the weights with close - to machine precision. - - -- ALGLIB PROJECT -- - Copyright 17.06.2007 by Bochkanov Sergey -*************************************************************************/ -void barycentricbuildfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t d, barycentricinterpolant &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricbuildfloaterhormann(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, d, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from barycentric representation to Chebyshev basis. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - P - polynomial in barycentric form - A,B - base interval for Chebyshev polynomials (see below) - A<>B - -OUTPUT PARAMETERS - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N-1 }, - where Ti - I-th Chebyshev polynomial. - -NOTES: - barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2cheb(const barycentricinterpolant &p, const double a, const double b, real_1d_array &t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbar2cheb(const_cast(p.c_ptr()), a, b, const_cast(t.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from Chebyshev basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N }, - where Ti - I-th Chebyshev polynomial. - N - number of coefficients: - * if given, only leading N elements of T are used - * if not given, automatically determined from size of T - A,B - base interval for Chebyshev polynomials (see above) - A(t.c_ptr()), n, a, b, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from Chebyshev basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N }, - where Ti - I-th Chebyshev polynomial. - N - number of coefficients: - * if given, only leading N elements of T are used - * if not given, automatically determined from size of T - A,B - base interval for Chebyshev polynomials (see above) - A(t.c_ptr()), n, a, b, const_cast(p.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from barycentric representation to power basis. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - P - polynomial in barycentric form - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if P was obtained as - result of interpolation on [-1,+1], you can set C=0 and S=1 and - represent P as sum of 1, x, x^2, x^3 and so on. In most cases you it - is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3 - will be better option. Such representation can be obtained by using - 1000.0 as offset C and 1.0 as scale S. - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return coefficients in - any case, but for N>8 they will become unreliable. However, N's - less than 5 are pretty safe. - -3. barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2pow(const barycentricinterpolant &p, const double c, const double s, real_1d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbar2pow(const_cast(p.c_ptr()), c, s, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from barycentric representation to power basis. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - P - polynomial in barycentric form - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if P was obtained as - result of interpolation on [-1,+1], you can set C=0 and S=1 and - represent P as sum of 1, x, x^2, x^3 and so on. In most cases you it - is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3 - will be better option. Such representation can be obtained by using - 1000.0 as offset C and 1.0 as scale S. - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return coefficients in - any case, but for N>8 they will become unreliable. However, N's - less than 5 are pretty safe. - -3. barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2pow(const barycentricinterpolant &p, real_1d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - double c; - double s; - - c = 0; - s = 1; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbar2pow(const_cast(p.c_ptr()), c, s, const_cast(a.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from power basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - * if given, only leading N elements of A are used - * if not given, automatically determined from size of A - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - P - polynomial in barycentric form - - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if you interpolate on - [-1,+1], you can set C=0 and S=1 and convert from sum of 1, x, x^2, - x^3 and so on. In most cases you it is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as input basis. Converting from sum of 1, (x-1000), (x-1000)^2, - (x-1000)^3 will be better option (you have to specify 1000.0 as offset - C and 1.0 as scale S). - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return barycentric model - in any case, but for N>8 accuracy well degrade. However, N's less than - 5 are pretty safe. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialpow2bar(const real_1d_array &a, const ae_int_t n, const double c, const double s, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialpow2bar(const_cast(a.c_ptr()), n, c, s, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion from power basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - * if given, only leading N elements of A are used - * if not given, automatically determined from size of A - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - P - polynomial in barycentric form - - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if you interpolate on - [-1,+1], you can set C=0 and S=1 and convert from sum of 1, x, x^2, - x^3 and so on. In most cases you it is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as input basis. Converting from sum of 1, (x-1000), (x-1000)^2, - (x-1000)^3 will be better option (you have to specify 1000.0 as offset - C and 1.0 as scale S). - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return barycentric model - in any case, but for N>8 accuracy well degrade. However, N's less than - 5 are pretty safe. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialpow2bar(const real_1d_array &a, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - double c; - double s; - - n = a.length(); - c = 0; - s = 1; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialpow2bar(const_cast(a.c_ptr()), n, c, s, const_cast(p.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant: generation of the model on the general grid. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - X - abscissas, array[0..N-1] - Y - function values, array[0..N-1] - N - number of points, N>=1 - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuild(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuild(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant: generation of the model on the general grid. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - X - abscissas, array[0..N-1] - Y - function values, array[0..N-1] - N - number of points, N>=1 - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuild(const real_1d_array &x, const real_1d_array &y, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'polynomialbuild': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuild(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant: generation of the model on equidistant grid. -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1] - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuildeqdist(a, b, const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant: generation of the model on equidistant grid. -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1] - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = y.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuildeqdist(a, b, const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant on Chebyshev grid (first kind). -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1], - Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n))) - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuildcheb1(a, b, const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant on Chebyshev grid (first kind). -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1], - Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n))) - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = y.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuildcheb1(a, b, const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant on Chebyshev grid (second kind). -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1], - Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1))) - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuildcheb2(a, b, const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Lagrange intepolant on Chebyshev grid (second kind). -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1], - Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1))) - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = y.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialbuildcheb2(a, b, const_cast(y.c_ptr()), n, const_cast(p.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fast equidistant polynomial interpolation function with O(N) complexity - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on equidistant grid, N>=1 - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolynomialBuildEqDist()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::polynomialcalceqdist(a, b, const_cast(f.c_ptr()), n, t, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fast equidistant polynomial interpolation function with O(N) complexity - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on equidistant grid, N>=1 - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolynomialBuildEqDist()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = f.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::polynomialcalceqdist(a, b, const_cast(f.c_ptr()), n, t, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (first kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (first kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb1()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::polynomialcalccheb1(a, b, const_cast(f.c_ptr()), n, t, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (first kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (first kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb1()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = f.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::polynomialcalccheb1(a, b, const_cast(f.c_ptr()), n, t, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (second kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (second kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb2()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::polynomialcalccheb2(a, b, const_cast(f.c_ptr()), n, t, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (second kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (second kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb2()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = f.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::polynomialcalccheb2(a, b, const_cast(f.c_ptr()), n, t, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -1-dimensional spline interpolant -*************************************************************************/ -_spline1dinterpolant_owner::_spline1dinterpolant_owner() -{ - p_struct = (alglib_impl::spline1dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline1dinterpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline1dinterpolant_owner::_spline1dinterpolant_owner(const _spline1dinterpolant_owner &rhs) -{ - p_struct = (alglib_impl::spline1dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline1dinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline1dinterpolant_owner& _spline1dinterpolant_owner::operator=(const _spline1dinterpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_spline1dinterpolant_clear(p_struct); - if( !alglib_impl::_spline1dinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_spline1dinterpolant_owner::~_spline1dinterpolant_owner() -{ - alglib_impl::_spline1dinterpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::spline1dinterpolant* _spline1dinterpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::spline1dinterpolant* _spline1dinterpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -spline1dinterpolant::spline1dinterpolant() : _spline1dinterpolant_owner() -{ -} - -spline1dinterpolant::spline1dinterpolant(const spline1dinterpolant &rhs):_spline1dinterpolant_owner(rhs) -{ -} - -spline1dinterpolant& spline1dinterpolant::operator=(const spline1dinterpolant &rhs) -{ - if( this==&rhs ) - return *this; - _spline1dinterpolant_owner::operator=(rhs); - return *this; -} - -spline1dinterpolant::~spline1dinterpolant() -{ -} - -/************************************************************************* -This subroutine builds linear spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildlinear(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds linear spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dbuildlinear': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildlinear(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds cubic spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - C - spline interpolant - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds cubic spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - C - spline interpolant - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t boundltype; - double boundl; - ae_int_t boundrtype; - double boundr; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dbuildcubic': looks like one of arguments has wrong size"); - n = x.length(); - boundltype = 0; - boundl = 0; - boundrtype = 0; - boundr = 0; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns table of function derivatives d[] -(calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D - derivative values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, real_1d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dgriddiffcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(d.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns table of function derivatives d[] -(calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D - derivative values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t boundltype; - double boundl; - ae_int_t boundrtype; - double boundr; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dgriddiffcubic': looks like one of arguments has wrong size"); - n = x.length(); - boundltype = 0; - boundl = 0; - boundrtype = 0; - boundr = 0; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dgriddiffcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(d.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns tables of first and second -function derivatives d1[] and d2[] (calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D1 - S' values at X[] - D2 - S'' values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, real_1d_array &d1, real_1d_array &d2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dgriddiff2cubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(d1.c_ptr()), const_cast(d2.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns tables of first and second -function derivatives d1[] and d2[] (calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D1 - S' values at X[] - D2 - S'' values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d1, real_1d_array &d2) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t boundltype; - double boundl; - ae_int_t boundrtype; - double boundr; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dgriddiff2cubic': looks like one of arguments has wrong size"); - n = x.length(); - boundltype = 0; - boundl = 0; - boundrtype = 0; - boundr = 0; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dgriddiff2cubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(d1.c_ptr()), const_cast(d2.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dconvcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(x2.c_ptr()), n2, const_cast(y2.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t boundltype; - double boundl; - ae_int_t boundrtype; - double boundr; - ae_int_t n2; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dconvcubic': looks like one of arguments has wrong size"); - n = x.length(); - boundltype = 0; - boundl = 0; - boundrtype = 0; - boundr = 0; - n2 = x2.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dconvcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(x2.c_ptr()), n2, const_cast(y2.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] and derivatives d2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiffcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2, real_1d_array &d2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dconvdiffcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(x2.c_ptr()), n2, const_cast(y2.c_ptr()), const_cast(d2.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] and derivatives d2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiffcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, real_1d_array &d2) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t boundltype; - double boundl; - ae_int_t boundrtype; - double boundr; - ae_int_t n2; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dconvdiffcubic': looks like one of arguments has wrong size"); - n = x.length(); - boundltype = 0; - boundl = 0; - boundrtype = 0; - boundr = 0; - n2 = x2.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dconvdiffcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(x2.c_ptr()), n2, const_cast(y2.c_ptr()), const_cast(d2.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[], first and second derivatives d2[] and dd2[] -(calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - DD2 - second derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiff2cubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2, real_1d_array &d2, real_1d_array &dd2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dconvdiff2cubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(x2.c_ptr()), n2, const_cast(y2.c_ptr()), const_cast(d2.c_ptr()), const_cast(dd2.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[], first and second derivatives d2[] and dd2[] -(calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - DD2 - second derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiff2cubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, real_1d_array &d2, real_1d_array &dd2) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t boundltype; - double boundl; - ae_int_t boundrtype; - double boundr; - ae_int_t n2; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dconvdiff2cubic': looks like one of arguments has wrong size"); - n = x.length(); - boundltype = 0; - boundl = 0; - boundrtype = 0; - boundr = 0; - n2 = x2.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dconvdiff2cubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, boundltype, boundl, boundrtype, boundr, const_cast(x2.c_ptr()), n2, const_cast(y2.c_ptr()), const_cast(d2.c_ptr()), const_cast(dd2.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds Catmull-Rom spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundType - boundary condition type: - * -1 for periodic boundary condition - * 0 for parabolically terminated spline (default) - Tension - tension parameter: - * tension=0 corresponds to classic Catmull-Rom spline (default) - * 0(x.c_ptr()), const_cast(y.c_ptr()), n, boundtype, tension, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds Catmull-Rom spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundType - boundary condition type: - * -1 for periodic boundary condition - * 0 for parabolically terminated spline (default) - Tension - tension parameter: - * tension=0 corresponds to classic Catmull-Rom spline (default) - * 0(x.c_ptr()), const_cast(y.c_ptr()), n, boundtype, tension, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds Hermite spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - D - derivatives, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, const ae_int_t n, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildhermite(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(d.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds Hermite spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - D - derivatives, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length()) || (x.length()!=d.length())) - throw ap_error("Error while calling 'spline1dbuildhermite': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildhermite(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(d.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds Akima spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildakima(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds Akima spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dbuildakima': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildakima(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine calculates the value of the spline at the given point X. - -INPUT PARAMETERS: - C - spline interpolant - X - point - -Result: - S(x) - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -double spline1dcalc(const spline1dinterpolant &c, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spline1dcalc(const_cast(c.c_ptr()), x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine differentiates the spline. - -INPUT PARAMETERS: - C - spline interpolant. - X - point - -Result: - S - S(x) - DS - S'(x) - D2S - S''(x) - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1ddiff(const spline1dinterpolant &c, const double x, double &s, double &ds, double &d2s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1ddiff(const_cast(c.c_ptr()), x, &s, &ds, &d2s, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine unpacks the spline into the coefficients table. - -INPUT PARAMETERS: - C - spline interpolant. - X - point - -OUTPUT PARAMETERS: - Tbl - coefficients table, unpacked format, array[0..N-2, 0..5]. - For I = 0...N-2: - Tbl[I,0] = X[i] - Tbl[I,1] = X[i+1] - Tbl[I,2] = C0 - Tbl[I,3] = C1 - Tbl[I,4] = C2 - Tbl[I,5] = C3 - On [x[i], x[i+1]] spline is equals to: - S(x) = C0 + C1*t + C2*t^2 + C3*t^3 - t = x-x[i] - -NOTE: - You can rebuild spline with Spline1DBuildHermite() function, which - accepts as inputs function values and derivatives at nodes, which are - easy to calculate when you have coefficients. - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dunpack(const spline1dinterpolant &c, ae_int_t &n, real_2d_array &tbl) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dunpack(const_cast(c.c_ptr()), &n, const_cast(tbl.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: x = A*t + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dlintransx(const spline1dinterpolant &c, const double a, const double b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dlintransx(const_cast(c.c_ptr()), a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: S2(x) = A*S(x) + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dlintransy(const spline1dinterpolant &c, const double a, const double b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dlintransy(const_cast(c.c_ptr()), a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine integrates the spline. - -INPUT PARAMETERS: - C - spline interpolant. - X - right bound of the integration interval [a, x], - here 'a' denotes min(x[]) -Result: - integral(S(t)dt,a,x) - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -double spline1dintegrate(const spline1dinterpolant &c, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spline1dintegrate(const_cast(c.c_ptr()), x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function builds monotone cubic Hermite interpolant. This interpolant -is monotonic in [x(0),x(n-1)] and is constant outside of this interval. - -In case y[] form non-monotonic sequence, interpolant is piecewise -monotonic. Say, for x=(0,1,2,3,4) and y=(0,1,2,1,0) interpolant will -monotonically grow at [0..2] and monotonically decrease at [2..4]. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. Subroutine automatically - sorts points, so caller may pass unsorted array. - Y - function values, array[0..N-1] - N - the number of points(N>=2). - -OUTPUT PARAMETERS: - C - spline interpolant. - - -- ALGLIB PROJECT -- - Copyright 21.06.2012 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildmonotone(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function builds monotone cubic Hermite interpolant. This interpolant -is monotonic in [x(0),x(n-1)] and is constant outside of this interval. - -In case y[] form non-monotonic sequence, interpolant is piecewise -monotonic. Say, for x=(0,1,2,3,4) and y=(0,1,2,1,0) interpolant will -monotonically grow at [0..2] and monotonically decrease at [2..4]. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. Subroutine automatically - sorts points, so caller may pass unsorted array. - Y - function values, array[0..N-1] - N - the number of points(N>=2). - -OUTPUT PARAMETERS: - C - spline interpolant. - - -- ALGLIB PROJECT -- - Copyright 21.06.2012 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dbuildmonotone': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dbuildmonotone(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Polynomial fitting report: - TaskRCond reciprocal of task's condition number - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error -*************************************************************************/ -_polynomialfitreport_owner::_polynomialfitreport_owner() -{ - p_struct = (alglib_impl::polynomialfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::polynomialfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_polynomialfitreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_polynomialfitreport_owner::_polynomialfitreport_owner(const _polynomialfitreport_owner &rhs) -{ - p_struct = (alglib_impl::polynomialfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::polynomialfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_polynomialfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_polynomialfitreport_owner& _polynomialfitreport_owner::operator=(const _polynomialfitreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_polynomialfitreport_clear(p_struct); - if( !alglib_impl::_polynomialfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_polynomialfitreport_owner::~_polynomialfitreport_owner() -{ - alglib_impl::_polynomialfitreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::polynomialfitreport* _polynomialfitreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::polynomialfitreport* _polynomialfitreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -polynomialfitreport::polynomialfitreport() : _polynomialfitreport_owner() ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror) -{ -} - -polynomialfitreport::polynomialfitreport(const polynomialfitreport &rhs):_polynomialfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror) -{ -} - -polynomialfitreport& polynomialfitreport::operator=(const polynomialfitreport &rhs) -{ - if( this==&rhs ) - return *this; - _polynomialfitreport_owner::operator=(rhs); - return *this; -} - -polynomialfitreport::~polynomialfitreport() -{ -} - - -/************************************************************************* -Barycentric fitting report: - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error - TaskRCond reciprocal of task's condition number -*************************************************************************/ -_barycentricfitreport_owner::_barycentricfitreport_owner() -{ - p_struct = (alglib_impl::barycentricfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_barycentricfitreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_barycentricfitreport_owner::_barycentricfitreport_owner(const _barycentricfitreport_owner &rhs) -{ - p_struct = (alglib_impl::barycentricfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::barycentricfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_barycentricfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_barycentricfitreport_owner& _barycentricfitreport_owner::operator=(const _barycentricfitreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_barycentricfitreport_clear(p_struct); - if( !alglib_impl::_barycentricfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_barycentricfitreport_owner::~_barycentricfitreport_owner() -{ - alglib_impl::_barycentricfitreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::barycentricfitreport* _barycentricfitreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::barycentricfitreport* _barycentricfitreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -barycentricfitreport::barycentricfitreport() : _barycentricfitreport_owner() ,taskrcond(p_struct->taskrcond),dbest(p_struct->dbest),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror) -{ -} - -barycentricfitreport::barycentricfitreport(const barycentricfitreport &rhs):_barycentricfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),dbest(p_struct->dbest),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror) -{ -} - -barycentricfitreport& barycentricfitreport::operator=(const barycentricfitreport &rhs) -{ - if( this==&rhs ) - return *this; - _barycentricfitreport_owner::operator=(rhs); - return *this; -} - -barycentricfitreport::~barycentricfitreport() -{ -} - - -/************************************************************************* -Spline fitting report: - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error - -Fields below are filled by obsolete functions (Spline1DFitCubic, -Spline1DFitHermite). Modern fitting functions do NOT fill these fields: - TaskRCond reciprocal of task's condition number -*************************************************************************/ -_spline1dfitreport_owner::_spline1dfitreport_owner() -{ - p_struct = (alglib_impl::spline1dfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline1dfitreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline1dfitreport_owner::_spline1dfitreport_owner(const _spline1dfitreport_owner &rhs) -{ - p_struct = (alglib_impl::spline1dfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline1dfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline1dfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline1dfitreport_owner& _spline1dfitreport_owner::operator=(const _spline1dfitreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_spline1dfitreport_clear(p_struct); - if( !alglib_impl::_spline1dfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_spline1dfitreport_owner::~_spline1dfitreport_owner() -{ - alglib_impl::_spline1dfitreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::spline1dfitreport* _spline1dfitreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::spline1dfitreport* _spline1dfitreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -spline1dfitreport::spline1dfitreport() : _spline1dfitreport_owner() ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror) -{ -} - -spline1dfitreport::spline1dfitreport(const spline1dfitreport &rhs):_spline1dfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror) -{ -} - -spline1dfitreport& spline1dfitreport::operator=(const spline1dfitreport &rhs) -{ - if( this==&rhs ) - return *this; - _spline1dfitreport_owner::operator=(rhs); - return *this; -} - -spline1dfitreport::~spline1dfitreport() -{ -} - - -/************************************************************************* -Least squares fitting report. This structure contains informational fields -which are set by fitting functions provided by this unit. - -Different functions initialize different sets of fields, so you should -read documentation on specific function you used in order to know which -fields are initialized. - - TaskRCond reciprocal of task's condition number - IterationsCount number of internal iterations - - VarIdx if user-supplied gradient contains errors which were - detected by nonlinear fitter, this field is set to - index of the first component of gradient which is - suspected to be spoiled by bugs. - - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error - - WRMSError weighted RMS error - - CovPar covariance matrix for parameters, filled by some solvers - ErrPar vector of errors in parameters, filled by some solvers - ErrCurve vector of fit errors - variability of the best-fit - curve, filled by some solvers. - Noise vector of per-point noise estimates, filled by - some solvers. - R2 coefficient of determination (non-weighted, non-adjusted), - filled by some solvers. -*************************************************************************/ -_lsfitreport_owner::_lsfitreport_owner() -{ - p_struct = (alglib_impl::lsfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lsfitreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lsfitreport_owner::_lsfitreport_owner(const _lsfitreport_owner &rhs) -{ - p_struct = (alglib_impl::lsfitreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lsfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lsfitreport_owner& _lsfitreport_owner::operator=(const _lsfitreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_lsfitreport_clear(p_struct); - if( !alglib_impl::_lsfitreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_lsfitreport_owner::~_lsfitreport_owner() -{ - alglib_impl::_lsfitreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::lsfitreport* _lsfitreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::lsfitreport* _lsfitreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -lsfitreport::lsfitreport() : _lsfitreport_owner() ,taskrcond(p_struct->taskrcond),iterationscount(p_struct->iterationscount),varidx(p_struct->varidx),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror),wrmserror(p_struct->wrmserror),covpar(&p_struct->covpar),errpar(&p_struct->errpar),errcurve(&p_struct->errcurve),noise(&p_struct->noise),r2(p_struct->r2) -{ -} - -lsfitreport::lsfitreport(const lsfitreport &rhs):_lsfitreport_owner(rhs) ,taskrcond(p_struct->taskrcond),iterationscount(p_struct->iterationscount),varidx(p_struct->varidx),rmserror(p_struct->rmserror),avgerror(p_struct->avgerror),avgrelerror(p_struct->avgrelerror),maxerror(p_struct->maxerror),wrmserror(p_struct->wrmserror),covpar(&p_struct->covpar),errpar(&p_struct->errpar),errcurve(&p_struct->errcurve),noise(&p_struct->noise),r2(p_struct->r2) -{ -} - -lsfitreport& lsfitreport::operator=(const lsfitreport &rhs) -{ - if( this==&rhs ) - return *this; - _lsfitreport_owner::operator=(rhs); - return *this; -} - -lsfitreport::~lsfitreport() -{ -} - - -/************************************************************************* -Nonlinear fitter. - -You should use ALGLIB functions to work with fitter. -Never try to access its fields directly! -*************************************************************************/ -_lsfitstate_owner::_lsfitstate_owner() -{ - p_struct = (alglib_impl::lsfitstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lsfitstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lsfitstate_owner::_lsfitstate_owner(const _lsfitstate_owner &rhs) -{ - p_struct = (alglib_impl::lsfitstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::lsfitstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lsfitstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lsfitstate_owner& _lsfitstate_owner::operator=(const _lsfitstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_lsfitstate_clear(p_struct); - if( !alglib_impl::_lsfitstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_lsfitstate_owner::~_lsfitstate_owner() -{ - alglib_impl::_lsfitstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::lsfitstate* _lsfitstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::lsfitstate* _lsfitstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -lsfitstate::lsfitstate() : _lsfitstate_owner() ,needf(p_struct->needf),needfg(p_struct->needfg),needfgh(p_struct->needfgh),xupdated(p_struct->xupdated),c(&p_struct->c),f(p_struct->f),g(&p_struct->g),h(&p_struct->h),x(&p_struct->x) -{ -} - -lsfitstate::lsfitstate(const lsfitstate &rhs):_lsfitstate_owner(rhs) ,needf(p_struct->needf),needfg(p_struct->needfg),needfgh(p_struct->needfgh),xupdated(p_struct->xupdated),c(&p_struct->c),f(p_struct->f),g(&p_struct->g),h(&p_struct->h),x(&p_struct->x) -{ -} - -lsfitstate& lsfitstate::operator=(const lsfitstate &rhs) -{ - if( this==&rhs ) - return *this; - _lsfitstate_owner::operator=(rhs); - return *this; -} - -lsfitstate::~lsfitstate() -{ -} - -/************************************************************************* -Fitting by polynomials in barycentric form. This function provides simple -unterface for unconstrained unweighted fitting. See PolynomialFitWC() if -you need constrained fitting. - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFitWC() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0 - * if given, only leading N elements of X/Y are used - * if not given, automatically determined from sizes of X/Y - M - number of basis functions (= polynomial_degree + 1), M>=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialfit(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(p.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fitting by polynomials in barycentric form. This function provides simple -unterface for unconstrained unweighted fitting. See PolynomialFitWC() if -you need constrained fitting. - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFitWC() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0 - * if given, only leading N elements of X/Y are used - * if not given, automatically determined from sizes of X/Y - M - number of basis functions (= polynomial_degree + 1), M>=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'polynomialfit': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialfit(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(p.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by polynomials in barycentric form, with constraints on -function values or first derivatives. - -Small regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFit() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points, N>0. - * if given, only leading N elements of X/Y/W are used - * if not given, automatically determined from sizes of X/Y/W - XC - points where polynomial values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that P(XC[i])=YC[i] - * DC[i]=1 means that P'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* even simple constraints can be inconsistent, see Wikipedia article on - this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the one special cases, however, we can guarantee consistency. This - case is: M>1 and constraints on the function values (NOT DERIVATIVES) - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialfitwc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(p.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by polynomials in barycentric form, with constraints on -function values or first derivatives. - -Small regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFit() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points, N>0. - * if given, only leading N elements of X/Y/W are used - * if not given, automatically determined from sizes of X/Y/W - XC - points where polynomial values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that P(XC[i])=YC[i] - * DC[i]=1 means that P'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* even simple constraints can be inconsistent, see Wikipedia article on - this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the one special cases, however, we can guarantee consistency. This - case is: M>1 and constraints on the function values (NOT DERIVATIVES) - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t k; - if( (x.length()!=y.length()) || (x.length()!=w.length())) - throw ap_error("Error while calling 'polynomialfitwc': looks like one of arguments has wrong size"); - if( (xc.length()!=yc.length()) || (xc.length()!=dc.length())) - throw ap_error("Error while calling 'polynomialfitwc': looks like one of arguments has wrong size"); - n = x.length(); - k = xc.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::polynomialfitwc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(p.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weghted rational least squares fitting using Floater-Hormann rational -functions with optimal D chosen from [0,9], with constraints and -individual weights. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least WEIGHTED root -mean square error) is chosen. Task is linear, so linear least squares -solver is used. Complexity of this computational scheme is O(N*M^2) -(mostly dominated by the least squares solver). - -SEE ALSO -* BarycentricFitFloaterHormann(), "lightweight" fitting without invididual - weights and constraints. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points, N>0. - XC - points where function values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -1 means another errors in parameters passed - (N<=0, for example) - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroutine doesn't calculate task's condition number for K<>0. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained barycentric interpolants: -* excessive constraints can be inconsistent. Floater-Hormann basis - functions aren't as flexible as splines (although they are very smooth). -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function VALUES at the interval - boundaries. Note that consustency of the constraints on the function - DERIVATIVES is NOT guaranteed (you can use in such cases cubic splines - which are more flexible). -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricfitfloaterhormannwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricfitfloaterhormannwc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(b.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rational least squares fitting using Floater-Hormann rational functions -with optimal D chosen from [0,9]. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least root mean -square error) is chosen. Task is linear, so linear least squares solver -is used. Complexity of this computational scheme is O(N*M^2) (mostly -dominated by the least squares solver). - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricfitfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::barycentricfitfloaterhormann(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(b.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rational least squares fitting using Floater-Hormann rational functions -with optimal D chosen from [0,9]. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least root mean -square error) is chosen. Task is linear, so linear least squares solver -is used. Complexity of this computational scheme is O(N*M^2) (mostly -dominated by the least squares solver). - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitpenalized(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, rho, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rational least squares fitting using Floater-Hormann rational functions -with optimal D chosen from [0,9]. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least root mean -square error) is chosen. Task is linear, so linear least squares solver -is used. Complexity of this computational scheme is O(N*M^2) (mostly -dominated by the least squares solver). - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dfitpenalized': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitpenalized(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, rho, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by penalized cubic spline. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with natural boundary -conditions. Problem is regularized by adding non-linearity penalty to the -usual least squares penalty function: - - S(x) = arg min { LS + P }, where - LS = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty - P = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty - rho - tunable constant given by user - C - automatically determined scale parameter, - makes penalty invariant with respect to scaling of X, Y, W. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - problem. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - M - number of basis functions ( = number_of_nodes), M>=4. - Rho - regularization constant passed by user. It penalizes - nonlinearity in the regression spline. It is logarithmically - scaled, i.e. actual value of regularization constant is - calculated as 10^Rho. It is automatically scaled so that: - * Rho=2.0 corresponds to moderate amount of nonlinearity - * generally, it should be somewhere in the [-8.0,+8.0] - If you do not want to penalize nonlineary, - pass small Rho. Values as low as -15 should work. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD or - Cholesky decomposition; problem may be - too ill-conditioned (very rare) - S - spline interpolant. - Rep - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTE 1: additional nodes are added to the spline outside of the fitting -interval to force linearity when xmax(x,xc). It is done -for consistency - we penalize non-linearity at [min(x,xc),max(x,xc)], so -it is natural to force linearity outside of this interval. - -NOTE 2: function automatically sorts points, so caller may pass unsorted -array. - - -- ALGLIB PROJECT -- - Copyright 19.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitpenalizedw(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, m, rho, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by penalized cubic spline. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with natural boundary -conditions. Problem is regularized by adding non-linearity penalty to the -usual least squares penalty function: - - S(x) = arg min { LS + P }, where - LS = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty - P = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty - rho - tunable constant given by user - C - automatically determined scale parameter, - makes penalty invariant with respect to scaling of X, Y, W. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - problem. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - M - number of basis functions ( = number_of_nodes), M>=4. - Rho - regularization constant passed by user. It penalizes - nonlinearity in the regression spline. It is logarithmically - scaled, i.e. actual value of regularization constant is - calculated as 10^Rho. It is automatically scaled so that: - * Rho=2.0 corresponds to moderate amount of nonlinearity - * generally, it should be somewhere in the [-8.0,+8.0] - If you do not want to penalize nonlineary, - pass small Rho. Values as low as -15 should work. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD or - Cholesky decomposition; problem may be - too ill-conditioned (very rare) - S - spline interpolant. - Rep - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTE 1: additional nodes are added to the spline outside of the fitting -interval to force linearity when xmax(x,xc). It is done -for consistency - we penalize non-linearity at [min(x,xc),max(x,xc)], so -it is natural to force linearity outside of this interval. - -NOTE 2: function automatically sorts points, so caller may pass unsorted -array. - - -- ALGLIB PROJECT -- - Copyright 19.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length()) || (x.length()!=w.length())) - throw ap_error("Error while calling 'spline1dfitpenalizedw': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitpenalizedw(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, m, rho, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by cubic spline, with constraints on function values or -derivatives. - -Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with continuous second -derivatives and non-fixed first derivatives at interval ends. Small -regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitHermiteWC() - fitting by Hermite splines (more flexible, - less smooth) - Spline1DFitCubic() - "lightweight" fitting by cubic splines, - without invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - S - spline interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function values AND/OR its - derivatives at the interval boundaries. -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitcubicwc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by cubic spline, with constraints on function values or -derivatives. - -Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with continuous second -derivatives and non-fixed first derivatives at interval ends. Small -regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitHermiteWC() - fitting by Hermite splines (more flexible, - less smooth) - Spline1DFitCubic() - "lightweight" fitting by cubic splines, - without invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - S - spline interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function values AND/OR its - derivatives at the interval boundaries. -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t k; - if( (x.length()!=y.length()) || (x.length()!=w.length())) - throw ap_error("Error while calling 'spline1dfitcubicwc': looks like one of arguments has wrong size"); - if( (xc.length()!=yc.length()) || (xc.length()!=dc.length())) - throw ap_error("Error while calling 'spline1dfitcubicwc': looks like one of arguments has wrong size"); - n = x.length(); - k = xc.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitcubicwc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by Hermite spline, with constraints on function values -or first derivatives. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are Hermite splines. Small regularizing -term is used when solving constrained tasks (to improve stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitCubicWC() - fitting by Cubic splines (less flexible, - more smooth) - Spline1DFitHermite() - "lightweight" Hermite fitting, without - invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4, - M IS EVEN! - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -2 means odd M was passed (which is not supported) - -1 means another errors in parameters passed - (N<=0, for example) - S - spline interpolant. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -IMPORTANT: - this subroitine supports only even M's - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the several special cases, however, we can guarantee consistency. -* one of this cases is M>=4 and constraints on the function value - (AND/OR its derivative) at the interval boundaries. -* another special case is M>=4 and ONE constraint on the function value - (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)] - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfithermitewc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted fitting by Hermite spline, with constraints on function values -or first derivatives. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are Hermite splines. Small regularizing -term is used when solving constrained tasks (to improve stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitCubicWC() - fitting by Cubic splines (less flexible, - more smooth) - Spline1DFitHermite() - "lightweight" Hermite fitting, without - invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4, - M IS EVEN! - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -2 means odd M was passed (which is not supported) - -1 means another errors in parameters passed - (N<=0, for example) - S - spline interpolant. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -IMPORTANT: - this subroitine supports only even M's - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the several special cases, however, we can guarantee consistency. -* one of this cases is M>=4 and constraints on the function value - (AND/OR its derivative) at the interval boundaries. -* another special case is M>=4 and ONE constraint on the function value - (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)] - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t k; - if( (x.length()!=y.length()) || (x.length()!=w.length())) - throw ap_error("Error while calling 'spline1dfithermitewc': looks like one of arguments has wrong size"); - if( (xc.length()!=yc.length()) || (xc.length()!=dc.length())) - throw ap_error("Error while calling 'spline1dfithermitewc': looks like one of arguments has wrong size"); - n = x.length(); - k = xc.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfithermitewc(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), n, const_cast(xc.c_ptr()), const_cast(yc.c_ptr()), const_cast(dc.c_ptr()), k, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Least squares fitting by cubic spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitCubicWC(). See Spline1DFitCubicWC() for more information -about subroutine parameters (we don't duplicate it here because of length) - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Least squares fitting by cubic spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitCubicWC(). See Spline1DFitCubicWC() for more information -about subroutine parameters (we don't duplicate it here because of length) - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dfitcubic': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfitcubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Least squares fitting by Hermite spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitHermiteWC(). See Spline1DFitHermiteWC() description for -more information about subroutine parameters (we don't duplicate it here -because of length). - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfithermite(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Least squares fitting by Hermite spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitHermiteWC(). See Spline1DFitHermiteWC() description for -more information about subroutine parameters (we don't duplicate it here -because of length). - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spline1dfithermite': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline1dfithermite(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m, &info, const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - W - array[0..N-1] Weights corresponding to function values. - Each summand in square sum of approximation deviations - from given values is multiplied by the square of - corresponding weight. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -1 incorrect N/M were specified - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinearw(const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(fmatrix.c_ptr()), n, m, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - W - array[0..N-1] Weights corresponding to function values. - Each summand in square sum of approximation deviations - from given values is multiplied by the square of - corresponding weight. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -1 incorrect N/M were specified - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - if( (y.length()!=w.length()) || (y.length()!=fmatrix.rows())) - throw ap_error("Error while calling 'lsfitlinearw': looks like one of arguments has wrong size"); - n = y.length(); - m = fmatrix.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinearw(const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(fmatrix.c_ptr()), n, m, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted constained linear least squares fitting. - -This is variation of LSFitLinearW(), which searchs for min|A*x=b| given -that K additional constaints C*x=bc are satisfied. It reduces original -task to modified one: min|B*y-d| WITHOUT constraints, then LSFitLinearW() -is called. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - W - array[0..N-1] Weights corresponding to function values. - Each summand in square sum of approximation deviations - from given values is multiplied by the square of - corresponding weight. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I,J] - value of J-th basis function in I-th point. - CMatrix - a table of constaints, array[0..K-1,0..M]. - I-th row of CMatrix corresponds to I-th linear constraint: - CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M] - N - number of points used. N>=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinearwc(const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(fmatrix.c_ptr()), const_cast(cmatrix.c_ptr()), n, m, k, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted constained linear least squares fitting. - -This is variation of LSFitLinearW(), which searchs for min|A*x=b| given -that K additional constaints C*x=bc are satisfied. It reduces original -task to modified one: min|B*y-d| WITHOUT constraints, then LSFitLinearW() -is called. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - W - array[0..N-1] Weights corresponding to function values. - Each summand in square sum of approximation deviations - from given values is multiplied by the square of - corresponding weight. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I,J] - value of J-th basis function in I-th point. - CMatrix - a table of constaints, array[0..K-1,0..M]. - I-th row of CMatrix corresponds to I-th linear constraint: - CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M] - N - number of points used. N>=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (y.length()!=w.length()) || (y.length()!=fmatrix.rows())) - throw ap_error("Error while calling 'lsfitlinearwc': looks like one of arguments has wrong size"); - if( (fmatrix.cols()!=cmatrix.cols()-1)) - throw ap_error("Error while calling 'lsfitlinearwc': looks like one of arguments has wrong size"); - n = y.length(); - m = fmatrix.cols(); - k = cmatrix.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinearwc(const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(fmatrix.c_ptr()), const_cast(cmatrix.c_ptr()), n, m, k, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinear(const_cast(y.c_ptr()), const_cast(fmatrix.c_ptr()), n, m, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - if( (y.length()!=fmatrix.rows())) - throw ap_error("Error while calling 'lsfitlinear': looks like one of arguments has wrong size"); - n = y.length(); - m = fmatrix.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinear(const_cast(y.c_ptr()), const_cast(fmatrix.c_ptr()), n, m, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Constained linear least squares fitting. - -This is variation of LSFitLinear(), which searchs for min|A*x=b| given -that K additional constaints C*x=bc are satisfied. It reduces original -task to modified one: min|B*y-d| WITHOUT constraints, then LSFitLinear() -is called. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I,J] - value of J-th basis function in I-th point. - CMatrix - a table of constaints, array[0..K-1,0..M]. - I-th row of CMatrix corresponds to I-th linear constraint: - CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M] - N - number of points used. N>=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinearc(const_cast(y.c_ptr()), const_cast(fmatrix.c_ptr()), const_cast(cmatrix.c_ptr()), n, m, k, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Constained linear least squares fitting. - -This is variation of LSFitLinear(), which searchs for min|A*x=b| given -that K additional constaints C*x=bc are satisfied. It reduces original -task to modified one: min|B*y-d| WITHOUT constraints, then LSFitLinear() -is called. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I,J] - value of J-th basis function in I-th point. - CMatrix - a table of constaints, array[0..K-1,0..M]. - I-th row of CMatrix corresponds to I-th linear constraint: - CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M] - N - number of points used. N>=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (y.length()!=fmatrix.rows())) - throw ap_error("Error while calling 'lsfitlinearc': looks like one of arguments has wrong size"); - if( (fmatrix.cols()!=cmatrix.cols()-1)) - throw ap_error("Error while calling 'lsfitlinearc': looks like one of arguments has wrong size"); - n = y.length(); - m = fmatrix.cols(); - k = cmatrix.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitlinearc(const_cast(y.c_ptr()), const_cast(fmatrix.c_ptr()), const_cast(cmatrix.c_ptr()), n, m, k, &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewf(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const double diffstep, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatewf(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(c.c_ptr()), n, m, k, diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewf(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const double diffstep, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (x.rows()!=y.length()) || (x.rows()!=w.length())) - throw ap_error("Error while calling 'lsfitcreatewf': looks like one of arguments has wrong size"); - n = x.rows(); - m = x.cols(); - k = c.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatewf(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(c.c_ptr()), n, m, k, diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const double diffstep, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatef(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(c.c_ptr()), n, m, k, diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const double diffstep, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (x.rows()!=y.length())) - throw ap_error("Error while calling 'lsfitcreatef': looks like one of arguments has wrong size"); - n = x.rows(); - m = x.cols(); - k = c.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatef(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(c.c_ptr()), n, m, k, diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient only. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also: - LSFitResults - LSFitCreateFG (fitting without weights) - LSFitCreateWFGH (fitting using Hessian) - LSFitCreateFGH (fitting using Hessian, without weights) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const bool cheapfg, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatewfg(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(c.c_ptr()), n, m, k, cheapfg, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient only. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also: - LSFitResults - LSFitCreateFG (fitting without weights) - LSFitCreateWFGH (fitting using Hessian) - LSFitCreateFGH (fitting using Hessian, without weights) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const bool cheapfg, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (x.rows()!=y.length()) || (x.rows()!=w.length())) - throw ap_error("Error while calling 'lsfitcreatewfg': looks like one of arguments has wrong size"); - n = x.rows(); - m = x.cols(); - k = c.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatewfg(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(c.c_ptr()), n, m, k, cheapfg, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Nonlinear least squares fitting using gradient only, without individual -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const bool cheapfg, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatefg(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(c.c_ptr()), n, m, k, cheapfg, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Nonlinear least squares fitting using gradient only, without individual -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const bool cheapfg, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (x.rows()!=y.length())) - throw ap_error("Error while calling 'lsfitcreatefg': looks like one of arguments has wrong size"); - n = x.rows(); - m = x.cols(); - k = c.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatefg(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(c.c_ptr()), n, m, k, cheapfg, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient/Hessian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatewfgh(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(c.c_ptr()), n, m, k, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient/Hessian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (x.rows()!=y.length()) || (x.rows()!=w.length())) - throw ap_error("Error while calling 'lsfitcreatewfgh': looks like one of arguments has wrong size"); - n = x.rows(); - m = x.cols(); - k = c.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatewfgh(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(w.c_ptr()), const_cast(c.c_ptr()), n, m, k, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Nonlinear least squares fitting using gradient/Hessian, without individial -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatefgh(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(c.c_ptr()), n, m, k, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Nonlinear least squares fitting using gradient/Hessian, without individial -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - ae_int_t k; - if( (x.rows()!=y.length())) - throw ap_error("Error while calling 'lsfitcreatefgh': looks like one of arguments has wrong size"); - n = x.rows(); - m = x.cols(); - k = c.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitcreatefgh(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(c.c_ptr()), n, m, k, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Stopping conditions for nonlinear least squares fitting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsF - stopping criterion. Algorithm stops if - |F(k+1)-F(k)| <= EpsF*max{|F(k)|, |F(k+1)|, 1} - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by LSFitSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. Only Levenberg-Marquardt - iterations are counted (L-BFGS/CG iterations are NOT - counted because their cost is very low compared to that of - LM). - -NOTE - -Passing EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to automatic -stopping criterion selection (according to the scheme used by MINLM unit). - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetcond(const lsfitstate &state, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitsetcond(const_cast(state.c_ptr()), epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - -NOTE: non-zero StpMax leads to moderate performance degradation because -intermediate step of preconditioned L-BFGS optimization is incompatible -with limits on step size. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetstpmax(const lsfitstate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitsetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -When reports are needed, State.C (current parameters) and State.F (current -value of fitting function) are reported. - - - -- ALGLIB -- - Copyright 15.08.2010 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetxrep(const lsfitstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets scaling coefficients for underlying optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Generally, scale is NOT considered to be a form of preconditioner. But LM -optimizer is unique in that it uses scaling matrix both in the stopping -condition tests and as Marquardt damping factor. - -Proper scaling is very important for the algorithm performance. It is less -important for the quality of results, but still has some influence (it is -easier to converge when variables are properly scaled, so premature -stopping is possible when very badly scalled variables are combined with -relaxed stopping conditions). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetscale(const lsfitstate &state, const real_1d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitsetscale(const_cast(state.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets boundary constraints for underlying optimizer - -Boundary constraints are inactive by default (after initial creation). -They are preserved until explicitly turned off with another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[K]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[K]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: unlike other constrained optimization algorithms, this solver has -following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetbc(const lsfitstate &state, const real_1d_array &bndl, const real_1d_array &bndu) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitsetbc(const_cast(state.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool lsfititeration(const lsfitstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::lsfititeration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void lsfitfit(lsfitstate &state, - void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &c, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'lsfitfit()' (func is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::lsfititeration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.c, state.x, state.f, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.c, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'lsfitfit' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void lsfitfit(lsfitstate &state, - void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &c, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'lsfitfit()' (func is NULL)"); - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'lsfitfit()' (grad is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::lsfititeration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.c, state.x, state.f, ptr); - continue; - } - if( state.needfg ) - { - grad(state.c, state.x, state.f, state.g, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.c, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'lsfitfit' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void lsfitfit(lsfitstate &state, - void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*hess)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, real_2d_array &hess, void *ptr), - void (*rep)(const real_1d_array &c, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'lsfitfit()' (func is NULL)"); - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'lsfitfit()' (grad is NULL)"); - if( hess==NULL ) - throw ap_error("ALGLIB: error in 'lsfitfit()' (hess is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::lsfititeration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.c, state.x, state.f, ptr); - continue; - } - if( state.needfg ) - { - grad(state.c, state.x, state.f, state.g, ptr); - continue; - } - if( state.needfgh ) - { - hess(state.c, state.x, state.f, state.g, state.h, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.c, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'lsfitfit' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -Nonlinear least squares fitting results. - -Called after return from LSFitFit(). - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - Info - completion code: - * -7 gradient verification failed. - See LSFitSetGradientCheck() for more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - C - array[0..K-1], solution - Rep - optimization report. On success following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - * WRMSError weighted rms error on the (X,Y). - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(J*CovPar*J')), - where J is Jacobian matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitresults(const lsfitstate &state, ae_int_t &info, real_1d_array &c, lsfitreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitresults(const_cast(state.c_ptr()), &info, const_cast(c.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before fitting begins -* LSFitFit() is called -* prior to actual fitting, for each point in data set X_i and each - component of parameters being fited C_j algorithm performs following - steps: - * two trial steps are made to C_j-TestStep*S[j] and C_j+TestStep*S[j], - where C_j is j-th parameter and S[j] is a scale of j-th parameter - * if needed, steps are bounded with respect to constraints on C[] - * F(X_i|C) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N*K (points count * parameters count) gradient - evaluations. It is very costly and you should use it only for low - dimensional problems, when you want to be sure that you've - correctly calculated analytic derivatives. You should not use it - in the production code (unless you want to check derivatives - provided by some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with LSFitSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -NOTE 4: this function works only for optimizers created with LSFitCreateWFG() - or LSFitCreateFG() constructors. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetgradientcheck(const lsfitstate &state, const double teststep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lsfitsetgradientcheck(const_cast(state.c_ptr()), teststep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Parametric spline inteprolant: 2-dimensional curve. - -You should not try to access its members directly - use PSpline2XXXXXXXX() -functions instead. -*************************************************************************/ -_pspline2interpolant_owner::_pspline2interpolant_owner() -{ - p_struct = (alglib_impl::pspline2interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline2interpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_pspline2interpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_pspline2interpolant_owner::_pspline2interpolant_owner(const _pspline2interpolant_owner &rhs) -{ - p_struct = (alglib_impl::pspline2interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline2interpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_pspline2interpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_pspline2interpolant_owner& _pspline2interpolant_owner::operator=(const _pspline2interpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_pspline2interpolant_clear(p_struct); - if( !alglib_impl::_pspline2interpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_pspline2interpolant_owner::~_pspline2interpolant_owner() -{ - alglib_impl::_pspline2interpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::pspline2interpolant* _pspline2interpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::pspline2interpolant* _pspline2interpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -pspline2interpolant::pspline2interpolant() : _pspline2interpolant_owner() -{ -} - -pspline2interpolant::pspline2interpolant(const pspline2interpolant &rhs):_pspline2interpolant_owner(rhs) -{ -} - -pspline2interpolant& pspline2interpolant::operator=(const pspline2interpolant &rhs) -{ - if( this==&rhs ) - return *this; - _pspline2interpolant_owner::operator=(rhs); - return *this; -} - -pspline2interpolant::~pspline2interpolant() -{ -} - - -/************************************************************************* -Parametric spline inteprolant: 3-dimensional curve. - -You should not try to access its members directly - use PSpline3XXXXXXXX() -functions instead. -*************************************************************************/ -_pspline3interpolant_owner::_pspline3interpolant_owner() -{ - p_struct = (alglib_impl::pspline3interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline3interpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_pspline3interpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_pspline3interpolant_owner::_pspline3interpolant_owner(const _pspline3interpolant_owner &rhs) -{ - p_struct = (alglib_impl::pspline3interpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::pspline3interpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_pspline3interpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_pspline3interpolant_owner& _pspline3interpolant_owner::operator=(const _pspline3interpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_pspline3interpolant_clear(p_struct); - if( !alglib_impl::_pspline3interpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_pspline3interpolant_owner::~_pspline3interpolant_owner() -{ - alglib_impl::_pspline3interpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::pspline3interpolant* _pspline3interpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::pspline3interpolant* _pspline3interpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -pspline3interpolant::pspline3interpolant() : _pspline3interpolant_owner() -{ -} - -pspline3interpolant::pspline3interpolant(const pspline3interpolant &rhs):_pspline3interpolant_owner(rhs) -{ -} - -pspline3interpolant& pspline3interpolant::operator=(const pspline3interpolant &rhs) -{ - if( this==&rhs ) - return *this; - _pspline3interpolant_owner::operator=(rhs); - return *this; -} - -pspline3interpolant::~pspline3interpolant() -{ -} - -/************************************************************************* -This function builds non-periodic 2-dimensional parametric spline which -starts at (X[0],Y[0]) and ends at (X[N-1],Y[N-1]). - -INPUT PARAMETERS: - XY - points, array[0..N-1,0..1]. - XY[I,0:1] corresponds to the Ith point. - Order of points is important! - N - points count, N>=5 for Akima splines, N>=2 for other types of - splines. - ST - spline type: - * 0 Akima spline - * 1 parabolically terminated Catmull-Rom spline (Tension=0) - * 2 parabolically terminated cubic spline - PT - parameterization type: - * 0 uniform - * 1 chord length - * 2 centripetal - -OUTPUT PARAMETERS: - P - parametric spline interpolant - - -NOTES: -* this function assumes that there all consequent points are distinct. - I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2), (x2,y2)<>(x3,y3) and so on. - However, non-consequent points may coincide, i.e. we can have (x0,y0)= - =(x2,y2). - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2build(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline2interpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline2build(const_cast(xy.c_ptr()), n, st, pt, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function builds non-periodic 3-dimensional parametric spline which -starts at (X[0],Y[0],Z[0]) and ends at (X[N-1],Y[N-1],Z[N-1]). - -Same as PSpline2Build() function, but for 3D, so we won't duplicate its -description here. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3build(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline3interpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3build(const_cast(xy.c_ptr()), n, st, pt, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function builds periodic 2-dimensional parametric spline which -starts at (X[0],Y[0]), goes through all points to (X[N-1],Y[N-1]) and then -back to (X[0],Y[0]). - -INPUT PARAMETERS: - XY - points, array[0..N-1,0..1]. - XY[I,0:1] corresponds to the Ith point. - XY[N-1,0:1] must be different from XY[0,0:1]. - Order of points is important! - N - points count, N>=3 for other types of splines. - ST - spline type: - * 1 Catmull-Rom spline (Tension=0) with cyclic boundary conditions - * 2 cubic spline with cyclic boundary conditions - PT - parameterization type: - * 0 uniform - * 1 chord length - * 2 centripetal - -OUTPUT PARAMETERS: - P - parametric spline interpolant - - -NOTES: -* this function assumes that there all consequent points are distinct. - I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2), (x2,y2)<>(x3,y3) and so on. - However, non-consequent points may coincide, i.e. we can have (x0,y0)= - =(x2,y2). -* last point of sequence is NOT equal to the first point. You shouldn't - make curve "explicitly periodic" by making them equal. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2buildperiodic(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline2interpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline2buildperiodic(const_cast(xy.c_ptr()), n, st, pt, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function builds periodic 3-dimensional parametric spline which -starts at (X[0],Y[0],Z[0]), goes through all points to (X[N-1],Y[N-1],Z[N-1]) -and then back to (X[0],Y[0],Z[0]). - -Same as PSpline2Build() function, but for 3D, so we won't duplicate its -description here. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3buildperiodic(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline3interpolant &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3buildperiodic(const_cast(xy.c_ptr()), n, st, pt, const_cast(p.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns vector of parameter values correspoding to points. - -I.e. for P created from (X[0],Y[0])...(X[N-1],Y[N-1]) and U=TValues(P) we -have - (X[0],Y[0]) = PSpline2Calc(P,U[0]), - (X[1],Y[1]) = PSpline2Calc(P,U[1]), - (X[2],Y[2]) = PSpline2Calc(P,U[2]), - ... - -INPUT PARAMETERS: - P - parametric spline interpolant - -OUTPUT PARAMETERS: - N - array size - T - array[0..N-1] - - -NOTES: -* for non-periodic splines U[0]=0, U[0](p.c_ptr()), &n, const_cast(t.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns vector of parameter values correspoding to points. - -Same as PSpline2ParameterValues(), but for 3D. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3parametervalues(const pspline3interpolant &p, ae_int_t &n, real_1d_array &t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3parametervalues(const_cast(p.c_ptr()), &n, const_cast(t.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates the value of the parametric spline for a given -value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-position - Y - Y-position - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2calc(const pspline2interpolant &p, const double t, double &x, double &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline2calc(const_cast(p.c_ptr()), t, &x, &y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates the value of the parametric spline for a given -value of parameter T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-position - Y - Y-position - Z - Z-position - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3calc(const pspline3interpolant &p, const double t, double &x, double &y, double &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3calc(const_cast(p.c_ptr()), t, &x, &y, &z, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates tangent vector for a given value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-component of tangent vector (normalized) - Y - Y-component of tangent vector (normalized) - -NOTE: - X^2+Y^2 is either 1 (for non-zero tangent vector) or 0. - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2tangent(const pspline2interpolant &p, const double t, double &x, double &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline2tangent(const_cast(p.c_ptr()), t, &x, &y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates tangent vector for a given value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-component of tangent vector (normalized) - Y - Y-component of tangent vector (normalized) - Z - Z-component of tangent vector (normalized) - -NOTE: - X^2+Y^2+Z^2 is either 1 (for non-zero tangent vector) or 0. - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3tangent(const pspline3interpolant &p, const double t, double &x, double &y, double &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3tangent(const_cast(p.c_ptr()), t, &x, &y, &z, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates derivative, i.e. it returns (dX/dT,dY/dT). - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - X-derivative - Y - Y-value - DY - Y-derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2diff(const pspline2interpolant &p, const double t, double &x, double &dx, double &y, double &dy) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline2diff(const_cast(p.c_ptr()), t, &x, &dx, &y, &dy, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates derivative, i.e. it returns (dX/dT,dY/dT,dZ/dT). - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - X-derivative - Y - Y-value - DY - Y-derivative - Z - Z-value - DZ - Z-derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3diff(const pspline3interpolant &p, const double t, double &x, double &dx, double &y, double &dy, double &z, double &dz) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3diff(const_cast(p.c_ptr()), t, &x, &dx, &y, &dy, &z, &dz, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates first and second derivative with respect to T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - derivative - D2X - second derivative - Y - Y-value - DY - derivative - D2Y - second derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2diff2(const pspline2interpolant &p, const double t, double &x, double &dx, double &d2x, double &y, double &dy, double &d2y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline2diff2(const_cast(p.c_ptr()), t, &x, &dx, &d2x, &y, &dy, &d2y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates first and second derivative with respect to T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - derivative - D2X - second derivative - Y - Y-value - DY - derivative - D2Y - second derivative - Z - Z-value - DZ - derivative - D2Z - second derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3diff2(const pspline3interpolant &p, const double t, double &x, double &dx, double &d2x, double &y, double &dy, double &d2y, double &z, double &dz, double &d2z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pspline3diff2(const_cast(p.c_ptr()), t, &x, &dx, &d2x, &y, &dy, &d2y, &z, &dz, &d2z, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates arc length, i.e. length of curve between t=a -and t=b. - -INPUT PARAMETERS: - P - parametric spline interpolant - A,B - parameter values corresponding to arc ends: - * B>A will result in positive length returned - * B(p.c_ptr()), a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates arc length, i.e. length of curve between t=a -and t=b. - -INPUT PARAMETERS: - P - parametric spline interpolant - A,B - parameter values corresponding to arc ends: - * B>A will result in positive length returned - * B(p.c_ptr()), a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -RBF model. - -Never try to directly work with fields of this object - always use ALGLIB -functions to use this object. -*************************************************************************/ -_rbfmodel_owner::_rbfmodel_owner() -{ - p_struct = (alglib_impl::rbfmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfmodel), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_rbfmodel_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_rbfmodel_owner::_rbfmodel_owner(const _rbfmodel_owner &rhs) -{ - p_struct = (alglib_impl::rbfmodel*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfmodel), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_rbfmodel_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_rbfmodel_owner& _rbfmodel_owner::operator=(const _rbfmodel_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_rbfmodel_clear(p_struct); - if( !alglib_impl::_rbfmodel_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_rbfmodel_owner::~_rbfmodel_owner() -{ - alglib_impl::_rbfmodel_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::rbfmodel* _rbfmodel_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::rbfmodel* _rbfmodel_owner::c_ptr() const -{ - return const_cast(p_struct); -} -rbfmodel::rbfmodel() : _rbfmodel_owner() -{ -} - -rbfmodel::rbfmodel(const rbfmodel &rhs):_rbfmodel_owner(rhs) -{ -} - -rbfmodel& rbfmodel::operator=(const rbfmodel &rhs) -{ - if( this==&rhs ) - return *this; - _rbfmodel_owner::operator=(rhs); - return *this; -} - -rbfmodel::~rbfmodel() -{ -} - - -/************************************************************************* -RBF solution report: -* TerminationType - termination type, positive values - success, - non-positive - failure. -*************************************************************************/ -_rbfreport_owner::_rbfreport_owner() -{ - p_struct = (alglib_impl::rbfreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_rbfreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_rbfreport_owner::_rbfreport_owner(const _rbfreport_owner &rhs) -{ - p_struct = (alglib_impl::rbfreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::rbfreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_rbfreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_rbfreport_owner& _rbfreport_owner::operator=(const _rbfreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_rbfreport_clear(p_struct); - if( !alglib_impl::_rbfreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_rbfreport_owner::~_rbfreport_owner() -{ - alglib_impl::_rbfreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::rbfreport* _rbfreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::rbfreport* _rbfreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -rbfreport::rbfreport() : _rbfreport_owner() ,arows(p_struct->arows),acols(p_struct->acols),annz(p_struct->annz),iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype) -{ -} - -rbfreport::rbfreport(const rbfreport &rhs):_rbfreport_owner(rhs) ,arows(p_struct->arows),acols(p_struct->acols),annz(p_struct->annz),iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype) -{ -} - -rbfreport& rbfreport::operator=(const rbfreport &rhs) -{ - if( this==&rhs ) - return *this; - _rbfreport_owner::operator=(rhs); - return *this; -} - -rbfreport::~rbfreport() -{ -} - - -/************************************************************************* -This function serializes data structure to string. - -Important properties of s_out: -* it contains alphanumeric characters, dots, underscores, minus signs -* these symbols are grouped into words, which are separated by spaces - and Windows-style (CR+LF) newlines -* although serializer uses spaces and CR+LF as separators, you can - replace any separator character by arbitrary combination of spaces, - tabs, Windows or Unix newlines. It allows flexible reformatting of - the string in case you want to include it into text or XML file. - But you should not insert separators into the middle of the "words" - nor you should change case of letters. -* s_out can be freely moved between 32-bit and 64-bit systems, little - and big endian machines, and so on. You can serialize structure on - 32-bit machine and unserialize it on 64-bit one (or vice versa), or - serialize it on SPARC and unserialize on x86. You can also - serialize it in C++ version of ALGLIB and unserialize in C# one, - and vice versa. -*************************************************************************/ -void rbfserialize(rbfmodel &obj, std::string &s_out) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - alglib_impl::ae_int_t ssize; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_alloc_start(&serializer); - alglib_impl::rbfalloc(&serializer, obj.c_ptr(), &state); - ssize = alglib_impl::ae_serializer_get_alloc_size(&serializer); - s_out.clear(); - s_out.reserve((size_t)(ssize+1)); - alglib_impl::ae_serializer_sstart_str(&serializer, &s_out); - alglib_impl::rbfserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - if( s_out.length()>(size_t)ssize ) - throw ap_error("ALGLIB: serialization integrity error"); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} -/************************************************************************* -This function unserializes data structure from string. -*************************************************************************/ -void rbfunserialize(std::string &s_in, rbfmodel &obj) -{ - alglib_impl::ae_state state; - alglib_impl::ae_serializer serializer; - - alglib_impl::ae_state_init(&state); - try - { - alglib_impl::ae_serializer_init(&serializer); - alglib_impl::ae_serializer_ustart_str(&serializer, &s_in); - alglib_impl::rbfunserialize(&serializer, obj.c_ptr(), &state); - alglib_impl::ae_serializer_stop(&serializer); - alglib_impl::ae_serializer_clear(&serializer); - alglib_impl::ae_state_clear(&state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(state.error_msg); - } -} - -/************************************************************************* -This function creates RBF model for a scalar (NY=1) or vector (NY>1) -function in a NX-dimensional space (NX=2 or NX=3). - -Newly created model is empty. It can be used for interpolation right after -creation, but it just returns zeros. You have to add points to the model, -tune interpolation settings, and then call model construction function -RBFBuildModel() which will update model according to your specification. - -USAGE: -1. User creates model with RBFCreate() -2. User adds dataset with RBFSetPoints() (points do NOT have to be on a - regular grid) -3. (OPTIONAL) User chooses polynomial term by calling: - * RBFLinTerm() to set linear term - * RBFConstTerm() to set constant term - * RBFZeroTerm() to set zero term - By default, linear term is used. -4. User chooses specific RBF algorithm to use: either QNN (RBFSetAlgoQNN) - or ML (RBFSetAlgoMultiLayer). -5. User calls RBFBuildModel() function which rebuilds model according to - the specification -6. User may call RBFCalc() to calculate model value at the specified point, - RBFGridCalc() to calculate model values at the points of the regular - grid. User may extract model coefficients with RBFUnpack() call. - -INPUT PARAMETERS: - NX - dimension of the space, NX=2 or NX=3 - NY - function dimension, NY>=1 - -OUTPUT PARAMETERS: - S - RBF model (initially equals to zero) - -NOTE 1: memory requirements. RBF models require amount of memory which is - proportional to the number of data points. Memory is allocated - during model construction, but most of this memory is freed after - model coefficients are calculated. - - Some approximate estimates for N centers with default settings are - given below: - * about 250*N*(sizeof(double)+2*sizeof(int)) bytes of memory is - needed during model construction stage. - * about 15*N*sizeof(double) bytes is needed after model is built. - For example, for N=100000 we may need 0.6 GB of memory to build - model, but just about 0.012 GB to store it. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcreate(const ae_int_t nx, const ae_int_t ny, rbfmodel &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfcreate(nx, ny, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds dataset. - -This function overrides results of the previous calls, i.e. multiple calls -of this function will result in only the last set being added. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call. - XY - points, array[N,NX+NY]. One row corresponds to one point - in the dataset. First NX elements are coordinates, next - NY elements are function values. Array may be larger than - specific, in this case only leading [N,NX+NY] elements - will be used. - N - number of points in the dataset - -After you've added dataset and (optionally) tuned algorithm settings you -should call RBFBuildModel() in order to build a model for you. - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetpoints(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds dataset. - -This function overrides results of the previous calls, i.e. multiple calls -of this function will result in only the last set being added. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call. - XY - points, array[N,NX+NY]. One row corresponds to one point - in the dataset. First NX elements are coordinates, next - NY elements are function values. Array may be larger than - specific, in this case only leading [N,NX+NY] elements - will be used. - N - number of points in the dataset - -After you've added dataset and (optionally) tuned algorithm settings you -should call RBFBuildModel() in order to build a model for you. - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = xy.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetpoints(const_cast(s.c_ptr()), const_cast(xy.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-QNN and it is good for point sets with -following properties: -a) all points are distinct -b) all points are well separated. -c) points distribution is approximately uniform. There is no "contour - lines", clusters of points, or other small-scale structures. - -Algorithm description: -1) interpolation centers are allocated to data points -2) interpolation radii are calculated as distances to the nearest centers - times Q coefficient (where Q is a value from [0.75,1.50]). -3) after performing (2) radii are transformed in order to avoid situation - when single outlier has very large radius and influences many points - across all dataset. Transformation has following form: - new_r[i] = min(r[i],Z*median(r[])) - where r[i] is I-th radius, median() is a median radius across entire - dataset, Z is user-specified value which controls amount of deviation - from median radius. - -When (a) is violated, we will be unable to build RBF model. When (b) or -(c) are violated, model will be built, but interpolation quality will be -low. See http://www.alglib.net/interpolation/ for more information on this -subject. - -This algorithm is used by default. - -Additional Q parameter controls smoothness properties of the RBF basis: -* Q<0.75 will give perfectly conditioned basis, but terrible smoothness - properties (RBF interpolant will have sharp peaks around function values) -* Q around 1.0 gives good balance between smoothness and condition number -* Q>1.5 will lead to badly conditioned systems and slow convergence of the - underlying linear solver (although smoothness will be very good) -* Q>2.0 will effectively make optimizer useless because it won't converge - within reasonable amount of iterations. It is possible to set such large - Q, but it is advised not to do so. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Q - Q parameter, Q>0, recommended value - 1.0 - Z - Z parameter, Z>0, recommended value - 5.0 - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgoqnn(const rbfmodel &s, const double q, const double z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetalgoqnn(const_cast(s.c_ptr()), q, z, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-QNN and it is good for point sets with -following properties: -a) all points are distinct -b) all points are well separated. -c) points distribution is approximately uniform. There is no "contour - lines", clusters of points, or other small-scale structures. - -Algorithm description: -1) interpolation centers are allocated to data points -2) interpolation radii are calculated as distances to the nearest centers - times Q coefficient (where Q is a value from [0.75,1.50]). -3) after performing (2) radii are transformed in order to avoid situation - when single outlier has very large radius and influences many points - across all dataset. Transformation has following form: - new_r[i] = min(r[i],Z*median(r[])) - where r[i] is I-th radius, median() is a median radius across entire - dataset, Z is user-specified value which controls amount of deviation - from median radius. - -When (a) is violated, we will be unable to build RBF model. When (b) or -(c) are violated, model will be built, but interpolation quality will be -low. See http://www.alglib.net/interpolation/ for more information on this -subject. - -This algorithm is used by default. - -Additional Q parameter controls smoothness properties of the RBF basis: -* Q<0.75 will give perfectly conditioned basis, but terrible smoothness - properties (RBF interpolant will have sharp peaks around function values) -* Q around 1.0 gives good balance between smoothness and condition number -* Q>1.5 will lead to badly conditioned systems and slow convergence of the - underlying linear solver (although smoothness will be very good) -* Q>2.0 will effectively make optimizer useless because it won't converge - within reasonable amount of iterations. It is possible to set such large - Q, but it is advised not to do so. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Q - Q parameter, Q>0, recommended value - 1.0 - Z - Z parameter, Z>0, recommended value - 5.0 - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgoqnn(const rbfmodel &s) -{ - alglib_impl::ae_state _alglib_env_state; - double q; - double z; - - q = 1.0; - z = 5.0; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetalgoqnn(const_cast(s.c_ptr()), q, z, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-ML. It builds multilayer RBF model, i.e. -model with subsequently decreasing radii, which allows us to combine -smoothness (due to large radii of the first layers) with exactness (due -to small radii of the last layers) and fast convergence. - -Internally RBF-ML uses many different means of acceleration, from sparse -matrices to KD-trees, which results in algorithm whose working time is -roughly proportional to N*log(N)*Density*RBase^2*NLayers, where N is a -number of points, Density is an average density if points per unit of the -interpolation space, RBase is an initial radius, NLayers is a number of -layers. - -RBF-ML is good for following kinds of interpolation problems: -1. "exact" problems (perfect fit) with well separated points -2. least squares problems with arbitrary distribution of points (algorithm - gives perfect fit where it is possible, and resorts to least squares - fit in the hard areas). -3. noisy problems where we want to apply some controlled amount of - smoothing. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - RBase - RBase parameter, RBase>0 - NLayers - NLayers parameter, NLayers>0, recommended value to start - with - about 5. - LambdaV - regularization value, can be useful when solving problem - in the least squares sense. Optimal lambda is problem- - dependent and require trial and error. In our experience, - good lambda can be as large as 0.1, and you can use 0.001 - as initial guess. - Default value - 0.01, which is used when LambdaV is not - given. You can specify zero value, but it is not - recommended to do so. - -TUNING ALGORITHM - -In order to use this algorithm you have to choose three parameters: -* initial radius RBase -* number of layers in the model NLayers -* regularization coefficient LambdaV - -Initial radius is easy to choose - you can pick any number several times -larger than the average distance between points. Algorithm won't break -down if you choose radius which is too large (model construction time will -increase, but model will be built correctly). - -Choose such number of layers that RLast=RBase/2^(NLayers-1) (radius used -by the last layer) will be smaller than the typical distance between -points. In case model error is too large, you can increase number of -layers. Having more layers will make model construction and evaluation -proportionally slower, but it will allow you to have model which precisely -fits your data. From the other side, if you want to suppress noise, you -can DECREASE number of layers to make your model less flexible. - -Regularization coefficient LambdaV controls smoothness of the individual -models built for each layer. We recommend you to use default value in case -you don't want to tune this parameter, because having non-zero LambdaV -accelerates and stabilizes internal iterative algorithm. In case you want -to suppress noise you can use LambdaV as additional parameter (larger -value = more smoothness) to tune. - -TYPICAL ERRORS - -1. Using initial radius which is too large. Memory requirements of the - RBF-ML are roughly proportional to N*Density*RBase^2 (where Density is - an average density of points per unit of the interpolation space). In - the extreme case of the very large RBase we will need O(N^2) units of - memory - and many layers in order to decrease radius to some reasonably - small value. - -2. Using too small number of layers - RBF models with large radius are not - flexible enough to reproduce small variations in the target function. - You need many layers with different radii, from large to small, in - order to have good model. - -3. Using initial radius which is too small. You will get model with - "holes" in the areas which are too far away from interpolation centers. - However, algorithm will work correctly (and quickly) in this case. - -4. Using too many layers - you will get too large and too slow model. This - model will perfectly reproduce your function, but maybe you will be - able to achieve similar results with less layers (and less memory). - - -- ALGLIB -- - Copyright 02.03.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers, const double lambdav) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetalgomultilayer(const_cast(s.c_ptr()), rbase, nlayers, lambdav, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-ML. It builds multilayer RBF model, i.e. -model with subsequently decreasing radii, which allows us to combine -smoothness (due to large radii of the first layers) with exactness (due -to small radii of the last layers) and fast convergence. - -Internally RBF-ML uses many different means of acceleration, from sparse -matrices to KD-trees, which results in algorithm whose working time is -roughly proportional to N*log(N)*Density*RBase^2*NLayers, where N is a -number of points, Density is an average density if points per unit of the -interpolation space, RBase is an initial radius, NLayers is a number of -layers. - -RBF-ML is good for following kinds of interpolation problems: -1. "exact" problems (perfect fit) with well separated points -2. least squares problems with arbitrary distribution of points (algorithm - gives perfect fit where it is possible, and resorts to least squares - fit in the hard areas). -3. noisy problems where we want to apply some controlled amount of - smoothing. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - RBase - RBase parameter, RBase>0 - NLayers - NLayers parameter, NLayers>0, recommended value to start - with - about 5. - LambdaV - regularization value, can be useful when solving problem - in the least squares sense. Optimal lambda is problem- - dependent and require trial and error. In our experience, - good lambda can be as large as 0.1, and you can use 0.001 - as initial guess. - Default value - 0.01, which is used when LambdaV is not - given. You can specify zero value, but it is not - recommended to do so. - -TUNING ALGORITHM - -In order to use this algorithm you have to choose three parameters: -* initial radius RBase -* number of layers in the model NLayers -* regularization coefficient LambdaV - -Initial radius is easy to choose - you can pick any number several times -larger than the average distance between points. Algorithm won't break -down if you choose radius which is too large (model construction time will -increase, but model will be built correctly). - -Choose such number of layers that RLast=RBase/2^(NLayers-1) (radius used -by the last layer) will be smaller than the typical distance between -points. In case model error is too large, you can increase number of -layers. Having more layers will make model construction and evaluation -proportionally slower, but it will allow you to have model which precisely -fits your data. From the other side, if you want to suppress noise, you -can DECREASE number of layers to make your model less flexible. - -Regularization coefficient LambdaV controls smoothness of the individual -models built for each layer. We recommend you to use default value in case -you don't want to tune this parameter, because having non-zero LambdaV -accelerates and stabilizes internal iterative algorithm. In case you want -to suppress noise you can use LambdaV as additional parameter (larger -value = more smoothness) to tune. - -TYPICAL ERRORS - -1. Using initial radius which is too large. Memory requirements of the - RBF-ML are roughly proportional to N*Density*RBase^2 (where Density is - an average density of points per unit of the interpolation space). In - the extreme case of the very large RBase we will need O(N^2) units of - memory - and many layers in order to decrease radius to some reasonably - small value. - -2. Using too small number of layers - RBF models with large radius are not - flexible enough to reproduce small variations in the target function. - You need many layers with different radii, from large to small, in - order to have good model. - -3. Using initial radius which is too small. You will get model with - "holes" in the areas which are too far away from interpolation centers. - However, algorithm will work correctly (and quickly) in this case. - -4. Using too many layers - you will get too large and too slow model. This - model will perfectly reproduce your function, but maybe you will be - able to achieve similar results with less layers (and less memory). - - -- ALGLIB -- - Copyright 02.03.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers) -{ - alglib_impl::ae_state _alglib_env_state; - double lambdav; - - lambdav = 0.01; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetalgomultilayer(const_cast(s.c_ptr()), rbase, nlayers, lambdav, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets linear term (model is a sum of radial basis functions -plus linear polynomial). This function won't have effect until next call -to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetlinterm(const rbfmodel &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetlinterm(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets constant term (model is a sum of radial basis functions -plus constant). This function won't have effect until next call to -RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetconstterm(const rbfmodel &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetconstterm(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets zero term (model is a sum of radial basis functions -without polynomial term). This function won't have effect until next call -to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetzeroterm(const rbfmodel &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfsetzeroterm(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function builds RBF model and returns report (contains some -information which can be used for evaluation of the algorithm properties). - -Call to this function modifies RBF model by calculating its centers/radii/ -weights and saving them into RBFModel structure. Initially RBFModel -contain zero coefficients, but after call to this function we will have -coefficients which were calculated in order to fit our dataset. - -After you called this function you can call RBFCalc(), RBFGridCalc() and -other model calculation functions. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Rep - report: - * Rep.TerminationType: - * -5 - non-distinct basis function centers were detected, - interpolation aborted - * -4 - nonconvergence of the internal SVD solver - * 1 - successful termination - Fields are used for debugging purposes: - * Rep.IterationsCount - iterations count of the LSQR solver - * Rep.NMV - number of matrix-vector products - * Rep.ARows - rows count for the system matrix - * Rep.ACols - columns count for the system matrix - * Rep.ANNZ - number of significantly non-zero elements - (elements above some algorithm-determined threshold) - -NOTE: failure to build model will leave current state of the structure -unchanged. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfbuildmodel(const rbfmodel &s, rbfreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfbuildmodel(const_cast(s.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates values of the RBF model in the given point. - -This function should be used when we have NY=1 (scalar function) and NX=2 -(2-dimensional space). If you have 3-dimensional space, use RBFCalc3(). If -you have general situation (NX-dimensional space, NY-dimensional function) -you should use general, less efficient implementation RBFCalc(). - -If you want to calculate function values many times, consider using -RBFGridCalc2(), which is far more efficient than many subsequent calls to -RBFCalc2(). - -This function returns 0.0 when: -* model is not initialized -* NX<>2 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - first coordinate, finite number - X1 - second coordinate, finite number - -RESULT: - value of the model or 0.0 (as defined above) - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -double rbfcalc2(const rbfmodel &s, const double x0, const double x1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rbfcalc2(const_cast(s.c_ptr()), x0, x1, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates values of the RBF model in the given point. - -This function should be used when we have NY=1 (scalar function) and NX=3 -(3-dimensional space). If you have 2-dimensional space, use RBFCalc2(). If -you have general situation (NX-dimensional space, NY-dimensional function) -you should use general, less efficient implementation RBFCalc(). - -This function returns 0.0 when: -* model is not initialized -* NX<>3 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - first coordinate, finite number - X1 - second coordinate, finite number - X2 - third coordinate, finite number - -RESULT: - value of the model or 0.0 (as defined above) - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -double rbfcalc3(const rbfmodel &s, const double x0, const double x1, const double x2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rbfcalc3(const_cast(s.c_ptr()), x0, x1, x2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates values of the RBF model at the given point. - -This is general function which can be used for arbitrary NX (dimension of -the space of arguments) and NY (dimension of the function itself). However -when you have NY=1 you may find more convenient to use RBFCalc2() or -RBFCalc3(). - -This function returns 0.0 when model is not initialized. - -INPUT PARAMETERS: - S - RBF model - X - coordinates, array[NX]. - X may have more than NX elements, in this case only - leading NX will be used. - -OUTPUT PARAMETERS: - Y - function value, array[NY]. Y is out-parameter and - reallocated after call to this function. In case you want - to reuse previously allocated Y, you may use RBFCalcBuf(), - which reallocates Y only when it is too small. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcalc(const rbfmodel &s, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfcalc(const_cast(s.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates values of the RBF model at the given point. - -Same as RBFCalc(), but does not reallocate Y when in is large enough to -store function values. - -INPUT PARAMETERS: - S - RBF model - X - coordinates, array[NX]. - X may have more than NX elements, in this case only - leading NX will be used. - Y - possibly preallocated array - -OUTPUT PARAMETERS: - Y - function value, array[NY]. Y is not reallocated when it - is larger than NY. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcalcbuf(const rbfmodel &s, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfcalcbuf(const_cast(s.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates values of the RBF model at the regular grid. - -Grid have N0*N1 points, with Point[I,J] = (X0[I], X1[J]) - -This function returns 0.0 when: -* model is not initialized -* NX<>2 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - array of grid nodes, first coordinates, array[N0] - N0 - grid size (number of nodes) in the first dimension - X1 - array of grid nodes, second coordinates, array[N1] - N1 - grid size (number of nodes) in the second dimension - -OUTPUT PARAMETERS: - Y - function values, array[N0,N1]. Y is out-variable and - is reallocated by this function. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfgridcalc2(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, real_2d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfgridcalc2(const_cast(s.c_ptr()), const_cast(x0.c_ptr()), n0, const_cast(x1.c_ptr()), n1, const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function "unpacks" RBF model by extracting its coefficients. - -INPUT PARAMETERS: - S - RBF model - -OUTPUT PARAMETERS: - NX - dimensionality of argument - NY - dimensionality of the target function - XWR - model information, array[NC,NX+NY+1]. - One row of the array corresponds to one basis function: - * first NX columns - coordinates of the center - * next NY columns - weights, one per dimension of the - function being modelled - * last column - radius, same for all dimensions of - the function being modelled - NC - number of the centers - V - polynomial term , array[NY,NX+1]. One row per one - dimension of the function being modelled. First NX - elements are linear coefficients, V[NX] is equal to the - constant part. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfunpack(const rbfmodel &s, ae_int_t &nx, ae_int_t &ny, real_2d_array &xwr, ae_int_t &nc, real_2d_array &v) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rbfunpack(const_cast(s.c_ptr()), &nx, &ny, const_cast(xwr.c_ptr()), &nc, const_cast(v.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -2-dimensional spline inteprolant -*************************************************************************/ -_spline2dinterpolant_owner::_spline2dinterpolant_owner() -{ - p_struct = (alglib_impl::spline2dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline2dinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline2dinterpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline2dinterpolant_owner::_spline2dinterpolant_owner(const _spline2dinterpolant_owner &rhs) -{ - p_struct = (alglib_impl::spline2dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline2dinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline2dinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline2dinterpolant_owner& _spline2dinterpolant_owner::operator=(const _spline2dinterpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_spline2dinterpolant_clear(p_struct); - if( !alglib_impl::_spline2dinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_spline2dinterpolant_owner::~_spline2dinterpolant_owner() -{ - alglib_impl::_spline2dinterpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::spline2dinterpolant* _spline2dinterpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::spline2dinterpolant* _spline2dinterpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -spline2dinterpolant::spline2dinterpolant() : _spline2dinterpolant_owner() -{ -} - -spline2dinterpolant::spline2dinterpolant(const spline2dinterpolant &rhs):_spline2dinterpolant_owner(rhs) -{ -} - -spline2dinterpolant& spline2dinterpolant::operator=(const spline2dinterpolant &rhs) -{ - if( this==&rhs ) - return *this; - _spline2dinterpolant_owner::operator=(rhs); - return *this; -} - -spline2dinterpolant::~spline2dinterpolant() -{ -} - -/************************************************************************* -This subroutine calculates the value of the bilinear or bicubic spline at -the given point X. - -Input parameters: - C - coefficients table. - Built by BuildBilinearSpline or BuildBicubicSpline. - X, Y- point - -Result: - S(x,y) - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -double spline2dcalc(const spline2dinterpolant &c, const double x, const double y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spline2dcalc(const_cast(c.c_ptr()), x, y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine calculates the value of the bilinear or bicubic spline at -the given point X and its derivatives. - -Input parameters: - C - spline interpolant. - X, Y- point - -Output parameters: - F - S(x,y) - FX - dS(x,y)/dX - FY - dS(x,y)/dY - FXY - d2S(x,y)/dXdY - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2ddiff(const spline2dinterpolant &c, const double x, const double y, double &f, double &fx, double &fy, double &fxy) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2ddiff(const_cast(c.c_ptr()), x, y, &f, &fx, &fy, &fxy, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -Input parameters: - C - spline interpolant - AX, BX - transformation coefficients: x = A*t + B - AY, BY - transformation coefficients: y = A*u + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dlintransxy(const spline2dinterpolant &c, const double ax, const double bx, const double ay, const double by) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dlintransxy(const_cast(c.c_ptr()), ax, bx, ay, by, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -Input parameters: - C - spline interpolant. - A, B- transformation coefficients: S2(x,y) = A*S(x,y) + B - -Output parameters: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dlintransf(const spline2dinterpolant &c, const double a, const double b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dlintransf(const_cast(c.c_ptr()), a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine makes the copy of the spline model. - -Input parameters: - C - spline interpolant - -Output parameters: - CC - spline copy - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dcopy(const spline2dinterpolant &c, spline2dinterpolant &cc) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dcopy(const_cast(c.c_ptr()), const_cast(cc.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bicubic spline resampling - -Input parameters: - A - function values at the old grid, - array[0..OldHeight-1, 0..OldWidth-1] - OldHeight - old grid height, OldHeight>1 - OldWidth - old grid width, OldWidth>1 - NewHeight - new grid height, NewHeight>1 - NewWidth - new grid width, NewWidth>1 - -Output parameters: - B - function values at the new grid, - array[0..NewHeight-1, 0..NewWidth-1] - - -- ALGLIB routine -- - 15 May, 2007 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline2dresamplebicubic(const real_2d_array &a, const ae_int_t oldheight, const ae_int_t oldwidth, real_2d_array &b, const ae_int_t newheight, const ae_int_t newwidth) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dresamplebicubic(const_cast(a.c_ptr()), oldheight, oldwidth, const_cast(b.c_ptr()), newheight, newwidth, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bilinear spline resampling - -Input parameters: - A - function values at the old grid, - array[0..OldHeight-1, 0..OldWidth-1] - OldHeight - old grid height, OldHeight>1 - OldWidth - old grid width, OldWidth>1 - NewHeight - new grid height, NewHeight>1 - NewWidth - new grid width, NewWidth>1 - -Output parameters: - B - function values at the new grid, - array[0..NewHeight-1, 0..NewWidth-1] - - -- ALGLIB routine -- - 09.07.2007 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline2dresamplebilinear(const real_2d_array &a, const ae_int_t oldheight, const ae_int_t oldwidth, real_2d_array &b, const ae_int_t newheight, const ae_int_t newwidth) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dresamplebilinear(const_cast(a.c_ptr()), oldheight, oldwidth, const_cast(b.c_ptr()), newheight, newwidth, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds bilinear vector-valued spline. - -Input parameters: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - F - function values, array[0..M*N*D-1]: - * first D elements store D values at (X[0],Y[0]) - * next D elements store D values at (X[1],Y[0]) - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(J*N+I)...D*(J*N+I)+D-1]. - M,N - grid size, M>=2, N>=2 - D - vector dimension, D>=1 - -Output parameters: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbilinearv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &f, const ae_int_t d, spline2dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dbuildbilinearv(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, const_cast(f.c_ptr()), d, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds bicubic vector-valued spline. - -Input parameters: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - F - function values, array[0..M*N*D-1]: - * first D elements store D values at (X[0],Y[0]) - * next D elements store D values at (X[1],Y[0]) - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(J*N+I)...D*(J*N+I)+D-1]. - M,N - grid size, M>=2, N>=2 - D - vector dimension, D>=1 - -Output parameters: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbicubicv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &f, const ae_int_t d, spline2dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dbuildbicubicv(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, const_cast(f.c_ptr()), d, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y- point - F - output buffer, possibly preallocated array. In case array size - is large enough to store result, it is not reallocated. Array - which is too short will be reallocated - -OUTPUT PARAMETERS: - F - array[D] (or larger) which stores function values - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dcalcvbuf(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dcalcvbuf(const_cast(c.c_ptr()), x, y, const_cast(f.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y- point - -OUTPUT PARAMETERS: - F - array[D] which stores function values. F is out-parameter and - it is reallocated after call to this function. In case you - want to reuse previously allocated F, you may use - Spline2DCalcVBuf(), which reallocates F only when it is too - small. - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dcalcv(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dcalcv(const_cast(c.c_ptr()), x, y, const_cast(f.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine unpacks two-dimensional spline into the coefficients table - -Input parameters: - C - spline interpolant. - -Result: - M, N- grid size (x-axis and y-axis) - D - number of components - Tbl - coefficients table, unpacked format, - D - components: [0..(N-1)*(M-1)*D-1, 0..19]. - For T=0..D-1 (component index), I = 0...N-2 (x index), - J=0..M-2 (y index): - K := T + I*D + J*D*(N-1) - - K-th row stores decomposition for T-th component of the - vector-valued function - - Tbl[K,0] = X[i] - Tbl[K,1] = X[i+1] - Tbl[K,2] = Y[j] - Tbl[K,3] = Y[j+1] - Tbl[K,4] = C00 - Tbl[K,5] = C01 - Tbl[K,6] = C02 - Tbl[K,7] = C03 - Tbl[K,8] = C10 - Tbl[K,9] = C11 - ... - Tbl[K,19] = C33 - On each grid square spline is equals to: - S(x) = SUM(c[i,j]*(t^i)*(u^j), i=0..3, j=0..3) - t = x-x[j] - u = y-y[i] - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dunpackv(const spline2dinterpolant &c, ae_int_t &m, ae_int_t &n, ae_int_t &d, real_2d_array &tbl) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dunpackv(const_cast(c.c_ptr()), &m, &n, &d, const_cast(tbl.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DBuildBilinearV(), which is more -flexible and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbilinear(const real_1d_array &x, const real_1d_array &y, const real_2d_array &f, const ae_int_t m, const ae_int_t n, spline2dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dbuildbilinear(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(f.c_ptr()), m, n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DBuildBicubicV(), which is more -flexible and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbicubic(const real_1d_array &x, const real_1d_array &y, const real_2d_array &f, const ae_int_t m, const ae_int_t n, spline2dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dbuildbicubic(const_cast(x.c_ptr()), const_cast(y.c_ptr()), const_cast(f.c_ptr()), m, n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DUnpackV(), which is more flexible -and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dunpack(const spline2dinterpolant &c, ae_int_t &m, ae_int_t &n, real_2d_array &tbl) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline2dunpack(const_cast(c.c_ptr()), &m, &n, const_cast(tbl.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -3-dimensional spline inteprolant -*************************************************************************/ -_spline3dinterpolant_owner::_spline3dinterpolant_owner() -{ - p_struct = (alglib_impl::spline3dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline3dinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline3dinterpolant_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline3dinterpolant_owner::_spline3dinterpolant_owner(const _spline3dinterpolant_owner &rhs) -{ - p_struct = (alglib_impl::spline3dinterpolant*)alglib_impl::ae_malloc(sizeof(alglib_impl::spline3dinterpolant), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_spline3dinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_spline3dinterpolant_owner& _spline3dinterpolant_owner::operator=(const _spline3dinterpolant_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_spline3dinterpolant_clear(p_struct); - if( !alglib_impl::_spline3dinterpolant_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_spline3dinterpolant_owner::~_spline3dinterpolant_owner() -{ - alglib_impl::_spline3dinterpolant_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::spline3dinterpolant* _spline3dinterpolant_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::spline3dinterpolant* _spline3dinterpolant_owner::c_ptr() const -{ - return const_cast(p_struct); -} -spline3dinterpolant::spline3dinterpolant() : _spline3dinterpolant_owner() -{ -} - -spline3dinterpolant::spline3dinterpolant(const spline3dinterpolant &rhs):_spline3dinterpolant_owner(rhs) -{ -} - -spline3dinterpolant& spline3dinterpolant::operator=(const spline3dinterpolant &rhs) -{ - if( this==&rhs ) - return *this; - _spline3dinterpolant_owner::operator=(rhs); - return *this; -} - -spline3dinterpolant::~spline3dinterpolant() -{ -} - -/************************************************************************* -This subroutine calculates the value of the trilinear or tricubic spline at -the given point (X,Y,Z). - -INPUT PARAMETERS: - C - coefficients table. - Built by BuildBilinearSpline or BuildBicubicSpline. - X, Y, - Z - point - -Result: - S(x,y,z) - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -double spline3dcalc(const spline3dinterpolant &c, const double x, const double y, const double z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spline3dcalc(const_cast(c.c_ptr()), x, y, z, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -INPUT PARAMETERS: - C - spline interpolant - AX, BX - transformation coefficients: x = A*u + B - AY, BY - transformation coefficients: y = A*v + B - AZ, BZ - transformation coefficients: z = A*w + B - -OUTPUT PARAMETERS: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dlintransxyz(const spline3dinterpolant &c, const double ax, const double bx, const double ay, const double by, const double az, const double bz) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dlintransxyz(const_cast(c.c_ptr()), ax, bx, ay, by, az, bz, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: S2(x,y) = A*S(x,y,z) + B - -OUTPUT PARAMETERS: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dlintransf(const spline3dinterpolant &c, const double a, const double b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dlintransf(const_cast(c.c_ptr()), a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Trilinear spline resampling - -INPUT PARAMETERS: - A - array[0..OldXCount*OldYCount*OldZCount-1], function - values at the old grid, : - A[0] x=0,y=0,z=0 - A[1] x=1,y=0,z=0 - A[..] ... - A[..] x=oldxcount-1,y=0,z=0 - A[..] x=0,y=1,z=0 - A[..] ... - ... - OldZCount - old Z-count, OldZCount>1 - OldYCount - old Y-count, OldYCount>1 - OldXCount - old X-count, OldXCount>1 - NewZCount - new Z-count, NewZCount>1 - NewYCount - new Y-count, NewYCount>1 - NewXCount - new X-count, NewXCount>1 - -OUTPUT PARAMETERS: - B - array[0..NewXCount*NewYCount*NewZCount-1], function - values at the new grid: - B[0] x=0,y=0,z=0 - B[1] x=1,y=0,z=0 - B[..] ... - B[..] x=newxcount-1,y=0,z=0 - B[..] x=0,y=1,z=0 - B[..] ... - ... - - -- ALGLIB routine -- - 26.04.2012 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline3dresampletrilinear(const real_1d_array &a, const ae_int_t oldzcount, const ae_int_t oldycount, const ae_int_t oldxcount, const ae_int_t newzcount, const ae_int_t newycount, const ae_int_t newxcount, real_1d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dresampletrilinear(const_cast(a.c_ptr()), oldzcount, oldycount, oldxcount, newzcount, newycount, newxcount, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine builds trilinear vector-valued spline. - -INPUT PARAMETERS: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - Z - spline applicates, array[0..L-1] - F - function values, array[0..M*N*L*D-1]: - * first D elements store D values at (X[0],Y[0],Z[0]) - * next D elements store D values at (X[1],Y[0],Z[0]) - * next D elements store D values at (X[2],Y[0],Z[0]) - * ... - * next D elements store D values at (X[0],Y[1],Z[0]) - * next D elements store D values at (X[1],Y[1],Z[0]) - * next D elements store D values at (X[2],Y[1],Z[0]) - * ... - * next D elements store D values at (X[0],Y[0],Z[1]) - * next D elements store D values at (X[1],Y[0],Z[1]) - * next D elements store D values at (X[2],Y[0],Z[1]) - * ... - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(N*(M*K+J)+I)...D*(N*(M*K+J)+I)+D-1]. - M,N, - L - grid size, M>=2, N>=2, L>=2 - D - vector dimension, D>=1 - -OUTPUT PARAMETERS: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dbuildtrilinearv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &z, const ae_int_t l, const real_1d_array &f, const ae_int_t d, spline3dinterpolant &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dbuildtrilinearv(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, const_cast(z.c_ptr()), l, const_cast(f.c_ptr()), d, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y,Z). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, - Z - point - F - output buffer, possibly preallocated array. In case array size - is large enough to store result, it is not reallocated. Array - which is too short will be reallocated - -OUTPUT PARAMETERS: - F - array[D] (or larger) which stores function values - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcalcvbuf(const spline3dinterpolant &c, const double x, const double y, const double z, real_1d_array &f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dcalcvbuf(const_cast(c.c_ptr()), x, y, z, const_cast(f.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine calculates trilinear or tricubic vector-valued spline at the -given point (X,Y,Z). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, - Z - point - -OUTPUT PARAMETERS: - F - array[D] which stores function values. F is out-parameter and - it is reallocated after call to this function. In case you - want to reuse previously allocated F, you may use - Spline2DCalcVBuf(), which reallocates F only when it is too - small. - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcalcv(const spline3dinterpolant &c, const double x, const double y, const double z, real_1d_array &f) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dcalcv(const_cast(c.c_ptr()), x, y, z, const_cast(f.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine unpacks tri-dimensional spline into the coefficients table - -INPUT PARAMETERS: - C - spline interpolant. - -Result: - N - grid size (X) - M - grid size (Y) - L - grid size (Z) - D - number of components - SType- spline type. Currently, only one spline type is supported: - trilinear spline, as indicated by SType=1. - Tbl - spline coefficients: [0..(N-1)*(M-1)*(L-1)*D-1, 0..13]. - For T=0..D-1 (component index), I = 0...N-2 (x index), - J=0..M-2 (y index), K=0..L-2 (z index): - Q := T + I*D + J*D*(N-1) + K*D*(N-1)*(M-1), - - Q-th row stores decomposition for T-th component of the - vector-valued function - - Tbl[Q,0] = X[i] - Tbl[Q,1] = X[i+1] - Tbl[Q,2] = Y[j] - Tbl[Q,3] = Y[j+1] - Tbl[Q,4] = Z[k] - Tbl[Q,5] = Z[k+1] - - Tbl[Q,6] = C000 - Tbl[Q,7] = C100 - Tbl[Q,8] = C010 - Tbl[Q,9] = C110 - Tbl[Q,10]= C001 - Tbl[Q,11]= C101 - Tbl[Q,12]= C011 - Tbl[Q,13]= C111 - On each grid square spline is equals to: - S(x) = SUM(c[i,j,k]*(x^i)*(y^j)*(z^k), i=0..1, j=0..1, k=0..1) - t = x-x[j] - u = y-y[i] - v = z-z[k] - - NOTE: format of Tbl is given for SType=1. Future versions of - ALGLIB can use different formats for different values of - SType. - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dunpackv(const spline3dinterpolant &c, ae_int_t &n, ae_int_t &m, ae_int_t &l, ae_int_t &d, ae_int_t &stype, real_2d_array &tbl) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spline3dunpackv(const_cast(c.c_ptr()), &n, &m, &l, &d, &stype, const_cast(tbl.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static double idwint_idwqfactor = 1.5; -static ae_int_t idwint_idwkmin = 5; -static double idwint_idwcalcq(idwinterpolant* z, - /* Real */ ae_vector* x, - ae_int_t k, - ae_state *_state); -static void idwint_idwinit1(ae_int_t n, - ae_int_t nx, - ae_int_t d, - ae_int_t nq, - ae_int_t nw, - idwinterpolant* z, - ae_state *_state); -static void idwint_idwinternalsolver(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - /* Real */ ae_vector* temp, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* x, - double* taskrcond, - ae_state *_state); - - -static void ratint_barycentricnormalize(barycentricinterpolant* b, - ae_state *_state); - - - - -static void spline1d_spline1dgriddiffcubicinternal(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* d, - /* Real */ ae_vector* a1, - /* Real */ ae_vector* a2, - /* Real */ ae_vector* a3, - /* Real */ ae_vector* b, - /* Real */ ae_vector* dt, - ae_state *_state); -static void spline1d_heapsortpoints(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state); -static void spline1d_heapsortppoints(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Integer */ ae_vector* p, - ae_int_t n, - ae_state *_state); -static void spline1d_solvetridiagonal(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - /* Real */ ae_vector* d, - ae_int_t n, - /* Real */ ae_vector* x, - ae_state *_state); -static void spline1d_solvecyclictridiagonal(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - /* Real */ ae_vector* d, - ae_int_t n, - /* Real */ ae_vector* x, - ae_state *_state); -static double spline1d_diffthreepoint(double t, - double x0, - double f0, - double x1, - double f1, - double x2, - double f2, - ae_state *_state); -static void spline1d_hermitecalc(double p0, - double m0, - double p1, - double m1, - double t, - double* s, - double* ds, - ae_state *_state); -static double spline1d_rescaleval(double a0, - double b0, - double a1, - double b1, - double t, - ae_state *_state); - - -static void lsfit_spline1dfitinternal(ae_int_t st, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -static void lsfit_lsfitlinearinternal(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -static void lsfit_lsfitclearrequestfields(lsfitstate* state, - ae_state *_state); -static void lsfit_barycentriccalcbasis(barycentricinterpolant* b, - double t, - /* Real */ ae_vector* y, - ae_state *_state); -static void lsfit_internalchebyshevfit(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -static void lsfit_barycentricfitwcfixedd(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t d, - ae_int_t* info, - barycentricinterpolant* b, - barycentricfitreport* rep, - ae_state *_state); -static void lsfit_clearreport(lsfitreport* rep, ae_state *_state); -static void lsfit_estimateerrors(/* Real */ ae_matrix* f1, - /* Real */ ae_vector* f0, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* x, - /* Real */ ae_vector* s, - ae_int_t n, - ae_int_t k, - lsfitreport* rep, - /* Real */ ae_matrix* z, - ae_int_t zkind, - ae_state *_state); - - -static void pspline_pspline2par(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t pt, - /* Real */ ae_vector* p, - ae_state *_state); -static void pspline_pspline3par(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t pt, - /* Real */ ae_vector* p, - ae_state *_state); - - -static double rbf_eps = 1.0E-6; -static ae_int_t rbf_mxnx = 3; -static double rbf_rbffarradius = 6; -static double rbf_rbfnearradius = 2.1; -static double rbf_rbfmlradius = 3; -static ae_int_t rbf_rbffirstversion = 0; -static void rbf_rbfgridpoints(rbfmodel* s, ae_state *_state); -static void rbf_rbfradnn(rbfmodel* s, - double q, - double z, - ae_state *_state); -static ae_bool rbf_buildlinearmodel(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t ny, - ae_int_t modeltype, - /* Real */ ae_matrix* v, - ae_state *_state); -static void rbf_buildrbfmodellsqr(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - /* Real */ ae_matrix* xc, - /* Real */ ae_vector* r, - ae_int_t n, - ae_int_t nc, - ae_int_t ny, - kdtree* pointstree, - kdtree* centerstree, - double epsort, - double epserr, - ae_int_t maxits, - ae_int_t* gnnz, - ae_int_t* snnz, - /* Real */ ae_matrix* w, - ae_int_t* info, - ae_int_t* iterationscount, - ae_int_t* nmv, - ae_state *_state); -static void rbf_buildrbfmlayersmodellsqr(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - /* Real */ ae_matrix* xc, - double rval, - /* Real */ ae_vector* r, - ae_int_t n, - ae_int_t* nc, - ae_int_t ny, - ae_int_t nlayers, - kdtree* centerstree, - double epsort, - double epserr, - ae_int_t maxits, - double lambdav, - ae_int_t* annz, - /* Real */ ae_matrix* w, - ae_int_t* info, - ae_int_t* iterationscount, - ae_int_t* nmv, - ae_state *_state); - - -static void spline2d_bicubiccalcderivatives(/* Real */ ae_matrix* a, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* dx, - /* Real */ ae_matrix* dy, - /* Real */ ae_matrix* dxy, - ae_state *_state); - - -static void spline3d_spline3ddiff(spline3dinterpolant* c, - double x, - double y, - double z, - double* f, - double* fx, - double* fy, - double* fxy, - ae_state *_state); - - - - - -/************************************************************************* -IDW interpolation - -INPUT PARAMETERS: - Z - IDW interpolant built with one of model building - subroutines. - X - array[0..NX-1], interpolation point - -Result: - IDW interpolant Z(X) - - -- ALGLIB -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -double idwcalc(idwinterpolant* z, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - double r; - double s; - double w; - double v1; - double v2; - double d0; - double di; - double result; - - - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - k = 0; - - /* - * Query - */ - if( z->modeltype==0 ) - { - - /* - * NQ/NW-based model - */ - k = kdtreequeryknn(&z->tree, x, z->nw, ae_true, _state); - kdtreequeryresultsdistances(&z->tree, &z->rbuf, _state); - kdtreequeryresultstags(&z->tree, &z->tbuf, _state); - } - if( z->modeltype==1 ) - { - - /* - * R-based model - */ - k = kdtreequeryrnn(&z->tree, x, z->r, ae_true, _state); - kdtreequeryresultsdistances(&z->tree, &z->rbuf, _state); - kdtreequeryresultstags(&z->tree, &z->tbuf, _state); - if( ktree, x, idwint_idwkmin, ae_true, _state); - kdtreequeryresultsdistances(&z->tree, &z->rbuf, _state); - kdtreequeryresultstags(&z->tree, &z->tbuf, _state); - } - } - - /* - * initialize weights for linear/quadratic members calculation. - * - * NOTE 1: weights are calculated using NORMALIZED modified - * Shepard's formula. Original formula gives w(i) = sqr((R-di)/(R*di)), - * where di is i-th distance, R is max(di). Modified formula have - * following form: - * w_mod(i) = 1, if di=d0 - * w_mod(i) = w(i)/w(0), if di<>d0 - * - * NOTE 2: self-match is USED for this query - * - * NOTE 3: last point almost always gain zero weight, but it MUST - * be used for fitting because sometimes it will gain NON-ZERO - * weight - for example, when all distances are equal. - */ - r = z->rbuf.ptr.p_double[k-1]; - d0 = z->rbuf.ptr.p_double[0]; - result = 0; - s = 0; - for(i=0; i<=k-1; i++) - { - di = z->rbuf.ptr.p_double[i]; - if( ae_fp_eq(di,d0) ) - { - - /* - * distance is equal to shortest, set it 1.0 - * without explicitly calculating (which would give - * us same result, but 'll expose us to the risk of - * division by zero). - */ - w = 1; - } - else - { - - /* - * use normalized formula - */ - v1 = (r-di)/(r-d0); - v2 = d0/di; - w = ae_sqr(v1*v2, _state); - } - result = result+w*idwint_idwcalcq(z, x, z->tbuf.ptr.p_int[i], _state); - s = s+w; - } - result = result/s; - return result; -} - - -/************************************************************************* -IDW interpolant using modified Shepard method for uniform point -distributions. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - D - nodal function type, either: - * 0 constant model. Just for demonstration only, worst - model ever. - * 1 linear model, least squares fitting. Simpe model for - datasets too small for quadratic models - * 2 quadratic model, least squares fitting. Best model - available (if your dataset is large enough). - * -1 "fast" linear model, use with caution!!! It is - significantly faster than linear/quadratic and better - than constant model. But it is less robust (especially - in the presence of noise). - NQ - number of points used to calculate nodal functions (ignored - for constant models). NQ should be LARGER than: - * max(1.5*(1+NX),2^NX+1) for linear model, - * max(3/4*(NX+2)*(NX+1),2^NX+1) for quadratic model. - Values less than this threshold will be silently increased. - NW - number of points used to calculate weights and to interpolate. - Required: >=2^NX+1, values less than this threshold will be - silently increased. - Recommended value: about 2*NQ - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: - * best results are obtained with quadratic models, worst - with constant - models - * when N is large, NQ and NW must be significantly smaller than N both - to obtain optimal performance and to obtain optimal accuracy. In 2 or - 3-dimensional tasks NQ=15 and NW=25 are good values to start with. - * NQ and NW may be greater than N. In such cases they will be - automatically decreased. - * this subroutine is always succeeds (as long as correct parameters are - passed). - * see 'Multivariate Interpolation of Large Sets of Scattered Data' by - Robert J. Renka for more information on this algorithm. - * this subroutine assumes that point distribution is uniform at the small - scales. If it isn't - for example, points are concentrated along - "lines", but "lines" distribution is uniform at the larger scale - then - you should use IDWBuildModifiedShepardR() - - - -- ALGLIB PROJECT -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildmodifiedshepard(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - ae_int_t d, - ae_int_t nq, - ae_int_t nw, - idwinterpolant* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t j2; - ae_int_t j3; - double v; - double r; - double s; - double d0; - double di; - double v1; - double v2; - ae_int_t nc; - ae_int_t offs; - ae_vector x; - ae_vector qrbuf; - ae_matrix qxybuf; - ae_vector y; - ae_matrix fmatrix; - ae_vector w; - ae_vector qsol; - ae_vector temp; - ae_vector tags; - ae_int_t info; - double taskrcond; - - ae_frame_make(_state, &_frame_block); - _idwinterpolant_clear(z); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&qrbuf, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&qxybuf, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&qsol, 0, DT_REAL, _state, ae_true); - ae_vector_init(&temp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tags, 0, DT_INT, _state, ae_true); - - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - nc = 0; - - /* - * assertions - */ - ae_assert(n>0, "IDWBuildModifiedShepard: N<=0!", _state); - ae_assert(nx>=1, "IDWBuildModifiedShepard: NX<1!", _state); - ae_assert(d>=-1&&d<=2, "IDWBuildModifiedShepard: D<>-1 and D<>0 and D<>1 and D<>2!", _state); - - /* - * Correct parameters if needed - */ - if( d==1 ) - { - nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(1+nx), _state)+1, _state); - nq = ae_maxint(nq, ae_round(ae_pow(2, nx, _state), _state)+1, _state); - } - if( d==2 ) - { - nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(nx+2)*(nx+1)/2, _state)+1, _state); - nq = ae_maxint(nq, ae_round(ae_pow(2, nx, _state), _state)+1, _state); - } - nw = ae_maxint(nw, ae_round(ae_pow(2, nx, _state), _state)+1, _state); - nq = ae_minint(nq, n, _state); - nw = ae_minint(nw, n, _state); - - /* - * primary initialization of Z - */ - idwint_idwinit1(n, nx, d, nq, nw, z, _state); - z->modeltype = 0; - - /* - * Create KD-tree - */ - ae_vector_set_length(&tags, n, _state); - for(i=0; i<=n-1; i++) - { - tags.ptr.p_int[i] = i; - } - kdtreebuildtagged(xy, &tags, n, nx, 1, 2, &z->tree, _state); - - /* - * build nodal functions - */ - ae_vector_set_length(&temp, nq+1, _state); - ae_vector_set_length(&x, nx, _state); - ae_vector_set_length(&qrbuf, nq, _state); - ae_matrix_set_length(&qxybuf, nq, nx+1, _state); - if( d==-1 ) - { - ae_vector_set_length(&w, nq, _state); - } - if( d==1 ) - { - ae_vector_set_length(&y, nq, _state); - ae_vector_set_length(&w, nq, _state); - ae_vector_set_length(&qsol, nx, _state); - - /* - * NX for linear members, - * 1 for temporary storage - */ - ae_matrix_set_length(&fmatrix, nq, nx+1, _state); - } - if( d==2 ) - { - ae_vector_set_length(&y, nq, _state); - ae_vector_set_length(&w, nq, _state); - ae_vector_set_length(&qsol, nx+ae_round(nx*(nx+1)*0.5, _state), _state); - - /* - * NX for linear members, - * Round(NX*(NX+1)*0.5) for quadratic model, - * 1 for temporary storage - */ - ae_matrix_set_length(&fmatrix, nq, nx+ae_round(nx*(nx+1)*0.5, _state)+1, _state); - } - for(i=0; i<=n-1; i++) - { - - /* - * Initialize center and function value. - * If D=0 it is all what we need - */ - ae_v_move(&z->q.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx)); - if( d==0 ) - { - continue; - } - - /* - * calculate weights for linear/quadratic members calculation. - * - * NOTE 1: weights are calculated using NORMALIZED modified - * Shepard's formula. Original formula is w(i) = sqr((R-di)/(R*di)), - * where di is i-th distance, R is max(di). Modified formula have - * following form: - * w_mod(i) = 1, if di=d0 - * w_mod(i) = w(i)/w(0), if di<>d0 - * - * NOTE 2: self-match is NOT used for this query - * - * NOTE 3: last point almost always gain zero weight, but it MUST - * be used for fitting because sometimes it will gain NON-ZERO - * weight - for example, when all distances are equal. - */ - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1)); - k = kdtreequeryknn(&z->tree, &x, nq, ae_false, _state); - kdtreequeryresultsxy(&z->tree, &qxybuf, _state); - kdtreequeryresultsdistances(&z->tree, &qrbuf, _state); - r = qrbuf.ptr.p_double[k-1]; - d0 = qrbuf.ptr.p_double[0]; - for(j=0; j<=k-1; j++) - { - di = qrbuf.ptr.p_double[j]; - if( ae_fp_eq(di,d0) ) - { - - /* - * distance is equal to shortest, set it 1.0 - * without explicitly calculating (which would give - * us same result, but 'll expose us to the risk of - * division by zero). - */ - w.ptr.p_double[j] = 1; - } - else - { - - /* - * use normalized formula - */ - v1 = (r-di)/(r-d0); - v2 = d0/di; - w.ptr.p_double[j] = ae_sqr(v1*v2, _state); - } - } - - /* - * calculate linear/quadratic members - */ - if( d==-1 ) - { - - /* - * "Fast" linear nodal function calculated using - * inverse distance weighting - */ - for(j=0; j<=nx-1; j++) - { - x.ptr.p_double[j] = 0; - } - s = 0; - for(j=0; j<=k-1; j++) - { - - /* - * calculate J-th inverse distance weighted gradient: - * grad_k = (y_j-y_k)*(x_j-x_k)/sqr(norm(x_j-x_k)) - * grad = sum(wk*grad_k)/sum(w_k) - */ - v = 0; - for(j2=0; j2<=nx-1; j2++) - { - v = v+ae_sqr(qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2], _state); - } - - /* - * Although x_j<>x_k, sqr(norm(x_j-x_k)) may be zero due to - * underflow. If it is, we assume than J-th gradient is zero - * (i.e. don't add anything) - */ - if( ae_fp_neq(v,0) ) - { - for(j2=0; j2<=nx-1; j2++) - { - x.ptr.p_double[j2] = x.ptr.p_double[j2]+w.ptr.p_double[j]*(qxybuf.ptr.pp_double[j][nx]-xy->ptr.pp_double[i][nx])*(qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2])/v; - } - } - s = s+w.ptr.p_double[j]; - } - for(j=0; j<=nx-1; j++) - { - z->q.ptr.pp_double[i][nx+1+j] = x.ptr.p_double[j]/s; - } - } - else - { - - /* - * Least squares models: build - */ - if( d==1 ) - { - - /* - * Linear nodal function calculated using - * least squares fitting to its neighbors - */ - for(j=0; j<=k-1; j++) - { - for(j2=0; j2<=nx-1; j2++) - { - fmatrix.ptr.pp_double[j][j2] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2]; - } - y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx]-xy->ptr.pp_double[i][nx]; - } - nc = nx; - } - if( d==2 ) - { - - /* - * Quadratic nodal function calculated using - * least squares fitting to its neighbors - */ - for(j=0; j<=k-1; j++) - { - offs = 0; - for(j2=0; j2<=nx-1; j2++) - { - fmatrix.ptr.pp_double[j][offs] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2]; - offs = offs+1; - } - for(j2=0; j2<=nx-1; j2++) - { - for(j3=j2; j3<=nx-1; j3++) - { - fmatrix.ptr.pp_double[j][offs] = (qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2])*(qxybuf.ptr.pp_double[j][j3]-xy->ptr.pp_double[i][j3]); - offs = offs+1; - } - } - y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx]-xy->ptr.pp_double[i][nx]; - } - nc = nx+ae_round(nx*(nx+1)*0.5, _state); - } - idwint_idwinternalsolver(&y, &w, &fmatrix, &temp, k, nc, &info, &qsol, &taskrcond, _state); - - /* - * Least squares models: copy results - */ - if( info>0 ) - { - - /* - * LLS task is solved, copy results - */ - z->debugworstrcond = ae_minreal(z->debugworstrcond, taskrcond, _state); - z->debugbestrcond = ae_maxreal(z->debugbestrcond, taskrcond, _state); - for(j=0; j<=nc-1; j++) - { - z->q.ptr.pp_double[i][nx+1+j] = qsol.ptr.p_double[j]; - } - } - else - { - - /* - * Solver failure, very strange, but we will use - * zero values to handle it. - */ - z->debugsolverfailures = z->debugsolverfailures+1; - for(j=0; j<=nc-1; j++) - { - z->q.ptr.pp_double[i][nx+1+j] = 0; - } - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -IDW interpolant using modified Shepard method for non-uniform datasets. - -This type of model uses constant nodal functions and interpolates using -all nodes which are closer than user-specified radius R. It may be used -when points distribution is non-uniform at the small scale, but it is at -the distances as large as R. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - R - radius, R>0 - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: -* if there is less than IDWKMin points within R-ball, algorithm selects - IDWKMin closest ones, so that continuity properties of interpolant are - preserved even far from points. - - -- ALGLIB PROJECT -- - Copyright 11.04.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildmodifiedshepardr(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - double r, - idwinterpolant* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector tags; - - ae_frame_make(_state, &_frame_block); - _idwinterpolant_clear(z); - ae_vector_init(&tags, 0, DT_INT, _state, ae_true); - - - /* - * assertions - */ - ae_assert(n>0, "IDWBuildModifiedShepardR: N<=0!", _state); - ae_assert(nx>=1, "IDWBuildModifiedShepardR: NX<1!", _state); - ae_assert(ae_fp_greater(r,0), "IDWBuildModifiedShepardR: R<=0!", _state); - - /* - * primary initialization of Z - */ - idwint_idwinit1(n, nx, 0, 0, n, z, _state); - z->modeltype = 1; - z->r = r; - - /* - * Create KD-tree - */ - ae_vector_set_length(&tags, n, _state); - for(i=0; i<=n-1; i++) - { - tags.ptr.p_int[i] = i; - } - kdtreebuildtagged(xy, &tags, n, nx, 1, 2, &z->tree, _state); - - /* - * build nodal functions - */ - for(i=0; i<=n-1; i++) - { - ae_v_move(&z->q.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -IDW model for noisy data. - -This subroutine may be used to handle noisy data, i.e. data with noise in -OUTPUT values. It differs from IDWBuildModifiedShepard() in the following -aspects: -* nodal functions are not constrained to pass through nodes: Qi(xi)<>yi, - i.e. we have fitting instead of interpolation. -* weights which are used during least squares fitting stage are all equal - to 1.0 (independently of distance) -* "fast"-linear or constant nodal functions are not supported (either not - robust enough or too rigid) - -This problem require far more complex tuning than interpolation problems. -Below you can find some recommendations regarding this problem: -* focus on tuning NQ; it controls noise reduction. As for NW, you can just - make it equal to 2*NQ. -* you can use cross-validation to determine optimal NQ. -* optimal NQ is a result of complex tradeoff between noise level (more - noise = larger NQ required) and underlying function complexity (given - fixed N, larger NQ means smoothing of compex features in the data). For - example, NQ=N will reduce noise to the minimum level possible, but you - will end up with just constant/linear/quadratic (depending on D) least - squares model for the whole dataset. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - D - nodal function degree, either: - * 1 linear model, least squares fitting. Simpe model for - datasets too small for quadratic models (or for very - noisy problems). - * 2 quadratic model, least squares fitting. Best model - available (if your dataset is large enough). - NQ - number of points used to calculate nodal functions. NQ should - be significantly larger than 1.5 times the number of - coefficients in a nodal function to overcome effects of noise: - * larger than 1.5*(1+NX) for linear model, - * larger than 3/4*(NX+2)*(NX+1) for quadratic model. - Values less than this threshold will be silently increased. - NW - number of points used to calculate weights and to interpolate. - Required: >=2^NX+1, values less than this threshold will be - silently increased. - Recommended value: about 2*NQ or larger - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: - * best results are obtained with quadratic models, linear models are not - recommended to use unless you are pretty sure that it is what you want - * this subroutine is always succeeds (as long as correct parameters are - passed). - * see 'Multivariate Interpolation of Large Sets of Scattered Data' by - Robert J. Renka for more information on this algorithm. - - - -- ALGLIB PROJECT -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildnoisy(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - ae_int_t d, - ae_int_t nq, - ae_int_t nw, - idwinterpolant* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t j2; - ae_int_t j3; - double v; - ae_int_t nc; - ae_int_t offs; - double taskrcond; - ae_vector x; - ae_vector qrbuf; - ae_matrix qxybuf; - ae_vector y; - ae_vector w; - ae_matrix fmatrix; - ae_vector qsol; - ae_vector tags; - ae_vector temp; - ae_int_t info; - - ae_frame_make(_state, &_frame_block); - _idwinterpolant_clear(z); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&qrbuf, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&qxybuf, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&qsol, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tags, 0, DT_INT, _state, ae_true); - ae_vector_init(&temp, 0, DT_REAL, _state, ae_true); - - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - nc = 0; - - /* - * assertions - */ - ae_assert(n>0, "IDWBuildNoisy: N<=0!", _state); - ae_assert(nx>=1, "IDWBuildNoisy: NX<1!", _state); - ae_assert(d>=1&&d<=2, "IDWBuildNoisy: D<>1 and D<>2!", _state); - - /* - * Correct parameters if needed - */ - if( d==1 ) - { - nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(1+nx), _state)+1, _state); - } - if( d==2 ) - { - nq = ae_maxint(nq, ae_iceil(idwint_idwqfactor*(nx+2)*(nx+1)/2, _state)+1, _state); - } - nw = ae_maxint(nw, ae_round(ae_pow(2, nx, _state), _state)+1, _state); - nq = ae_minint(nq, n, _state); - nw = ae_minint(nw, n, _state); - - /* - * primary initialization of Z - */ - idwint_idwinit1(n, nx, d, nq, nw, z, _state); - z->modeltype = 0; - - /* - * Create KD-tree - */ - ae_vector_set_length(&tags, n, _state); - for(i=0; i<=n-1; i++) - { - tags.ptr.p_int[i] = i; - } - kdtreebuildtagged(xy, &tags, n, nx, 1, 2, &z->tree, _state); - - /* - * build nodal functions - * (special algorithm for noisy data is used) - */ - ae_vector_set_length(&temp, nq+1, _state); - ae_vector_set_length(&x, nx, _state); - ae_vector_set_length(&qrbuf, nq, _state); - ae_matrix_set_length(&qxybuf, nq, nx+1, _state); - if( d==1 ) - { - ae_vector_set_length(&y, nq, _state); - ae_vector_set_length(&w, nq, _state); - ae_vector_set_length(&qsol, 1+nx, _state); - - /* - * 1 for constant member, - * NX for linear members, - * 1 for temporary storage - */ - ae_matrix_set_length(&fmatrix, nq, 1+nx+1, _state); - } - if( d==2 ) - { - ae_vector_set_length(&y, nq, _state); - ae_vector_set_length(&w, nq, _state); - ae_vector_set_length(&qsol, 1+nx+ae_round(nx*(nx+1)*0.5, _state), _state); - - /* - * 1 for constant member, - * NX for linear members, - * Round(NX*(NX+1)*0.5) for quadratic model, - * 1 for temporary storage - */ - ae_matrix_set_length(&fmatrix, nq, 1+nx+ae_round(nx*(nx+1)*0.5, _state)+1, _state); - } - for(i=0; i<=n-1; i++) - { - - /* - * Initialize center. - */ - ae_v_move(&z->q.ptr.pp_double[i][0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1)); - - /* - * Calculate linear/quadratic members - * using least squares fit - * NOTE 1: all weight are equal to 1.0 - * NOTE 2: self-match is USED for this query - */ - ae_v_move(&x.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nx-1)); - k = kdtreequeryknn(&z->tree, &x, nq, ae_true, _state); - kdtreequeryresultsxy(&z->tree, &qxybuf, _state); - kdtreequeryresultsdistances(&z->tree, &qrbuf, _state); - if( d==1 ) - { - - /* - * Linear nodal function calculated using - * least squares fitting to its neighbors - */ - for(j=0; j<=k-1; j++) - { - fmatrix.ptr.pp_double[j][0] = 1.0; - for(j2=0; j2<=nx-1; j2++) - { - fmatrix.ptr.pp_double[j][1+j2] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2]; - } - y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx]; - w.ptr.p_double[j] = 1; - } - nc = 1+nx; - } - if( d==2 ) - { - - /* - * Quadratic nodal function calculated using - * least squares fitting to its neighbors - */ - for(j=0; j<=k-1; j++) - { - fmatrix.ptr.pp_double[j][0] = 1; - offs = 1; - for(j2=0; j2<=nx-1; j2++) - { - fmatrix.ptr.pp_double[j][offs] = qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2]; - offs = offs+1; - } - for(j2=0; j2<=nx-1; j2++) - { - for(j3=j2; j3<=nx-1; j3++) - { - fmatrix.ptr.pp_double[j][offs] = (qxybuf.ptr.pp_double[j][j2]-xy->ptr.pp_double[i][j2])*(qxybuf.ptr.pp_double[j][j3]-xy->ptr.pp_double[i][j3]); - offs = offs+1; - } - } - y.ptr.p_double[j] = qxybuf.ptr.pp_double[j][nx]; - w.ptr.p_double[j] = 1; - } - nc = 1+nx+ae_round(nx*(nx+1)*0.5, _state); - } - idwint_idwinternalsolver(&y, &w, &fmatrix, &temp, k, nc, &info, &qsol, &taskrcond, _state); - - /* - * Least squares models: copy results - */ - if( info>0 ) - { - - /* - * LLS task is solved, copy results - */ - z->debugworstrcond = ae_minreal(z->debugworstrcond, taskrcond, _state); - z->debugbestrcond = ae_maxreal(z->debugbestrcond, taskrcond, _state); - for(j=0; j<=nc-1; j++) - { - z->q.ptr.pp_double[i][nx+j] = qsol.ptr.p_double[j]; - } - } - else - { - - /* - * Solver failure, very strange, but we will use - * zero values to handle it. - */ - z->debugsolverfailures = z->debugsolverfailures+1; - v = 0; - for(j=0; j<=k-1; j++) - { - v = v+qxybuf.ptr.pp_double[j][nx]; - } - z->q.ptr.pp_double[i][nx] = v/k; - for(j=0; j<=nc-2; j++) - { - z->q.ptr.pp_double[i][nx+1+j] = 0; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine: K-th nodal function calculation - - -- ALGLIB -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -static double idwint_idwcalcq(idwinterpolant* z, - /* Real */ ae_vector* x, - ae_int_t k, - ae_state *_state) -{ - ae_int_t nx; - ae_int_t i; - ae_int_t j; - ae_int_t offs; - double result; - - - nx = z->nx; - - /* - * constant member - */ - result = z->q.ptr.pp_double[k][nx]; - - /* - * linear members - */ - if( z->d>=1 ) - { - for(i=0; i<=nx-1; i++) - { - result = result+z->q.ptr.pp_double[k][nx+1+i]*(x->ptr.p_double[i]-z->q.ptr.pp_double[k][i]); - } - } - - /* - * quadratic members - */ - if( z->d>=2 ) - { - offs = nx+1+nx; - for(i=0; i<=nx-1; i++) - { - for(j=i; j<=nx-1; j++) - { - result = result+z->q.ptr.pp_double[k][offs]*(x->ptr.p_double[i]-z->q.ptr.pp_double[k][i])*(x->ptr.p_double[j]-z->q.ptr.pp_double[k][j]); - offs = offs+1; - } - } - } - return result; -} - - -/************************************************************************* -Initialization of internal structures. - -It assumes correctness of all parameters. - - -- ALGLIB -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -static void idwint_idwinit1(ae_int_t n, - ae_int_t nx, - ae_int_t d, - ae_int_t nq, - ae_int_t nw, - idwinterpolant* z, - ae_state *_state) -{ - - - z->debugsolverfailures = 0; - z->debugworstrcond = 1.0; - z->debugbestrcond = 0; - z->n = n; - z->nx = nx; - z->d = 0; - if( d==1 ) - { - z->d = 1; - } - if( d==2 ) - { - z->d = 2; - } - if( d==-1 ) - { - z->d = 1; - } - z->nw = nw; - if( d==-1 ) - { - ae_matrix_set_length(&z->q, n, nx+1+nx, _state); - } - if( d==0 ) - { - ae_matrix_set_length(&z->q, n, nx+1, _state); - } - if( d==1 ) - { - ae_matrix_set_length(&z->q, n, nx+1+nx, _state); - } - if( d==2 ) - { - ae_matrix_set_length(&z->q, n, nx+1+nx+ae_round(nx*(nx+1)*0.5, _state), _state); - } - ae_vector_set_length(&z->tbuf, nw, _state); - ae_vector_set_length(&z->rbuf, nw, _state); - ae_matrix_set_length(&z->xybuf, nw, nx+1, _state); - ae_vector_set_length(&z->xbuf, nx, _state); -} - - -/************************************************************************* -Linear least squares solver for small tasks. - -Works faster than standard ALGLIB solver in non-degenerate cases (due to -absense of internal allocations and optimized row/colums). In degenerate -cases it calls standard solver, which results in small performance penalty -associated with preliminary steps. - -INPUT PARAMETERS: - Y array[0..N-1] - W array[0..N-1] - FMatrix array[0..N-1,0..M], have additional column for temporary - values - Temp array[0..N] -*************************************************************************/ -static void idwint_idwinternalsolver(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - /* Real */ ae_vector* temp, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* x, - double* taskrcond, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double tau; - ae_vector b; - densesolverlsreport srep; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - _densesolverlsreport_init(&srep, _state, ae_true); - - - /* - * set up info - */ - *info = 1; - - /* - * prepare matrix - */ - for(i=0; i<=n-1; i++) - { - fmatrix->ptr.pp_double[i][m] = y->ptr.p_double[i]; - v = w->ptr.p_double[i]; - ae_v_muld(&fmatrix->ptr.pp_double[i][0], 1, ae_v_len(0,m), v); - } - - /* - * use either fast algorithm or general algorithm - */ - if( m<=n ) - { - - /* - * QR decomposition - * We assume that M<=N (we would have called LSFit() otherwise) - */ - for(i=0; i<=m-1; i++) - { - if( iptr.p_double[1], 1, &fmatrix->ptr.pp_double[i][i], fmatrix->stride, ae_v_len(1,n-i)); - generatereflection(temp, n-i, &tau, _state); - fmatrix->ptr.pp_double[i][i] = temp->ptr.p_double[1]; - temp->ptr.p_double[1] = 1; - for(j=i+1; j<=m; j++) - { - v = ae_v_dotproduct(&fmatrix->ptr.pp_double[i][j], fmatrix->stride, &temp->ptr.p_double[1], 1, ae_v_len(i,n-1)); - v = tau*v; - ae_v_subd(&fmatrix->ptr.pp_double[i][j], fmatrix->stride, &temp->ptr.p_double[1], 1, ae_v_len(i,n-1), v); - } - } - } - - /* - * Check condition number - */ - *taskrcond = rmatrixtrrcondinf(fmatrix, m, ae_true, ae_false, _state); - - /* - * use either fast algorithm for non-degenerate cases - * or slow algorithm for degenerate cases - */ - if( ae_fp_greater(*taskrcond,10000*n*ae_machineepsilon) ) - { - - /* - * solve triangular system R*x = FMatrix[0:M-1,M] - * using fast algorithm, then exit - */ - x->ptr.p_double[m-1] = fmatrix->ptr.pp_double[m-1][m]/fmatrix->ptr.pp_double[m-1][m-1]; - for(i=m-2; i>=0; i--) - { - v = ae_v_dotproduct(&fmatrix->ptr.pp_double[i][i+1], 1, &x->ptr.p_double[i+1], 1, ae_v_len(i+1,m-1)); - x->ptr.p_double[i] = (fmatrix->ptr.pp_double[i][m]-v)/fmatrix->ptr.pp_double[i][i]; - } - } - else - { - - /* - * use more general algorithm - */ - ae_vector_set_length(&b, m, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=i-1; j++) - { - fmatrix->ptr.pp_double[i][j] = 0.0; - } - b.ptr.p_double[i] = fmatrix->ptr.pp_double[i][m]; - } - rmatrixsolvels(fmatrix, m, m, &b, 10000*ae_machineepsilon, info, &srep, x, _state); - } - } - else - { - - /* - * use more general algorithm - */ - ae_vector_set_length(&b, n, _state); - for(i=0; i<=n-1; i++) - { - b.ptr.p_double[i] = fmatrix->ptr.pp_double[i][m]; - } - rmatrixsolvels(fmatrix, n, m, &b, 10000*ae_machineepsilon, info, &srep, x, _state); - *taskrcond = srep.r2; - } - ae_frame_leave(_state); -} - - -ae_bool _idwinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - idwinterpolant *p = (idwinterpolant*)_p; - ae_touch_ptr((void*)p); - if( !_kdtree_init(&p->tree, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->q, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tbuf, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->xybuf, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _idwinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - idwinterpolant *dst = (idwinterpolant*)_dst; - idwinterpolant *src = (idwinterpolant*)_src; - dst->n = src->n; - dst->nx = src->nx; - dst->d = src->d; - dst->r = src->r; - dst->nw = src->nw; - if( !_kdtree_init_copy(&dst->tree, &src->tree, _state, make_automatic) ) - return ae_false; - dst->modeltype = src->modeltype; - if( !ae_matrix_init_copy(&dst->q, &src->q, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xbuf, &src->xbuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tbuf, &src->tbuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rbuf, &src->rbuf, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->xybuf, &src->xybuf, _state, make_automatic) ) - return ae_false; - dst->debugsolverfailures = src->debugsolverfailures; - dst->debugworstrcond = src->debugworstrcond; - dst->debugbestrcond = src->debugbestrcond; - return ae_true; -} - - -void _idwinterpolant_clear(void* _p) -{ - idwinterpolant *p = (idwinterpolant*)_p; - ae_touch_ptr((void*)p); - _kdtree_clear(&p->tree); - ae_matrix_clear(&p->q); - ae_vector_clear(&p->xbuf); - ae_vector_clear(&p->tbuf); - ae_vector_clear(&p->rbuf); - ae_matrix_clear(&p->xybuf); -} - - -void _idwinterpolant_destroy(void* _p) -{ - idwinterpolant *p = (idwinterpolant*)_p; - ae_touch_ptr((void*)p); - _kdtree_destroy(&p->tree); - ae_matrix_destroy(&p->q); - ae_vector_destroy(&p->xbuf); - ae_vector_destroy(&p->tbuf); - ae_vector_destroy(&p->rbuf); - ae_matrix_destroy(&p->xybuf); -} - - - - -/************************************************************************* -Rational interpolation using barycentric formula - -F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i])) - -Input parameters: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -Result: - barycentric interpolant F(t) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -double barycentriccalc(barycentricinterpolant* b, - double t, - ae_state *_state) -{ - double s1; - double s2; - double s; - double v; - ae_int_t i; - double result; - - - ae_assert(!ae_isinf(t, _state), "BarycentricCalc: infinite T!", _state); - - /* - * special case: NaN - */ - if( ae_isnan(t, _state) ) - { - result = _state->v_nan; - return result; - } - - /* - * special case: N=1 - */ - if( b->n==1 ) - { - result = b->sy*b->y.ptr.p_double[0]; - return result; - } - - /* - * Here we assume that task is normalized, i.e.: - * 1. abs(Y[i])<=1 - * 2. abs(W[i])<=1 - * 3. X[] is ordered - */ - s = ae_fabs(t-b->x.ptr.p_double[0], _state); - for(i=0; i<=b->n-1; i++) - { - v = b->x.ptr.p_double[i]; - if( ae_fp_eq(v,t) ) - { - result = b->sy*b->y.ptr.p_double[i]; - return result; - } - v = ae_fabs(t-v, _state); - if( ae_fp_less(v,s) ) - { - s = v; - } - } - s1 = 0; - s2 = 0; - for(i=0; i<=b->n-1; i++) - { - v = s/(t-b->x.ptr.p_double[i]); - v = v*b->w.ptr.p_double[i]; - s1 = s1+v*b->y.ptr.p_double[i]; - s2 = s2+v; - } - result = b->sy*s1/s2; - return result; -} - - -/************************************************************************* -Differentiation of barycentric interpolant: first derivative. - -Algorithm used in this subroutine is very robust and should not fail until -provided with values too close to MaxRealNumber (usually MaxRealNumber/N -or greater will overflow). - -INPUT PARAMETERS: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -OUTPUT PARAMETERS: - F - barycentric interpolant at T - DF - first derivative - -NOTE - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricdiff1(barycentricinterpolant* b, - double t, - double* f, - double* df, - ae_state *_state) -{ - double v; - double vv; - ae_int_t i; - ae_int_t k; - double n0; - double n1; - double d0; - double d1; - double s0; - double s1; - double xk; - double xi; - double xmin; - double xmax; - double xscale1; - double xoffs1; - double xscale2; - double xoffs2; - double xprev; - - *f = 0; - *df = 0; - - ae_assert(!ae_isinf(t, _state), "BarycentricDiff1: infinite T!", _state); - - /* - * special case: NaN - */ - if( ae_isnan(t, _state) ) - { - *f = _state->v_nan; - *df = _state->v_nan; - return; - } - - /* - * special case: N=1 - */ - if( b->n==1 ) - { - *f = b->sy*b->y.ptr.p_double[0]; - *df = 0; - return; - } - if( ae_fp_eq(b->sy,0) ) - { - *f = 0; - *df = 0; - return; - } - ae_assert(ae_fp_greater(b->sy,0), "BarycentricDiff1: internal error", _state); - - /* - * We assume than N>1 and B.SY>0. Find: - * 1. pivot point (X[i] closest to T) - * 2. width of interval containing X[i] - */ - v = ae_fabs(b->x.ptr.p_double[0]-t, _state); - k = 0; - xmin = b->x.ptr.p_double[0]; - xmax = b->x.ptr.p_double[0]; - for(i=1; i<=b->n-1; i++) - { - vv = b->x.ptr.p_double[i]; - if( ae_fp_less(ae_fabs(vv-t, _state),v) ) - { - v = ae_fabs(vv-t, _state); - k = i; - } - xmin = ae_minreal(xmin, vv, _state); - xmax = ae_maxreal(xmax, vv, _state); - } - - /* - * pivot point found, calculate dNumerator and dDenominator - */ - xscale1 = 1/(xmax-xmin); - xoffs1 = -xmin/(xmax-xmin)+1; - xscale2 = 2; - xoffs2 = -3; - t = t*xscale1+xoffs1; - t = t*xscale2+xoffs2; - xk = b->x.ptr.p_double[k]; - xk = xk*xscale1+xoffs1; - xk = xk*xscale2+xoffs2; - v = t-xk; - n0 = 0; - n1 = 0; - d0 = 0; - d1 = 0; - xprev = -2; - for(i=0; i<=b->n-1; i++) - { - xi = b->x.ptr.p_double[i]; - xi = xi*xscale1+xoffs1; - xi = xi*xscale2+xoffs2; - ae_assert(ae_fp_greater(xi,xprev), "BarycentricDiff1: points are too close!", _state); - xprev = xi; - if( i!=k ) - { - vv = ae_sqr(t-xi, _state); - s0 = (t-xk)/(t-xi); - s1 = (xk-xi)/vv; - } - else - { - s0 = 1; - s1 = 0; - } - vv = b->w.ptr.p_double[i]*b->y.ptr.p_double[i]; - n0 = n0+s0*vv; - n1 = n1+s1*vv; - vv = b->w.ptr.p_double[i]; - d0 = d0+s0*vv; - d1 = d1+s1*vv; - } - *f = b->sy*n0/d0; - *df = (n1*d0-n0*d1)/ae_sqr(d0, _state); - if( ae_fp_neq(*df,0) ) - { - *df = ae_sign(*df, _state)*ae_exp(ae_log(ae_fabs(*df, _state), _state)+ae_log(b->sy, _state)+ae_log(xscale1, _state)+ae_log(xscale2, _state), _state); - } -} - - -/************************************************************************* -Differentiation of barycentric interpolant: first/second derivatives. - -INPUT PARAMETERS: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -OUTPUT PARAMETERS: - F - barycentric interpolant at T - DF - first derivative - D2F - second derivative - -NOTE: this algorithm may fail due to overflow/underflor if used on data -whose values are close to MaxRealNumber or MinRealNumber. Use more robust -BarycentricDiff1() subroutine in such cases. - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricdiff2(barycentricinterpolant* b, - double t, - double* f, - double* df, - double* d2f, - ae_state *_state) -{ - double v; - double vv; - ae_int_t i; - ae_int_t k; - double n0; - double n1; - double n2; - double d0; - double d1; - double d2; - double s0; - double s1; - double s2; - double xk; - double xi; - - *f = 0; - *df = 0; - *d2f = 0; - - ae_assert(!ae_isinf(t, _state), "BarycentricDiff1: infinite T!", _state); - - /* - * special case: NaN - */ - if( ae_isnan(t, _state) ) - { - *f = _state->v_nan; - *df = _state->v_nan; - *d2f = _state->v_nan; - return; - } - - /* - * special case: N=1 - */ - if( b->n==1 ) - { - *f = b->sy*b->y.ptr.p_double[0]; - *df = 0; - *d2f = 0; - return; - } - if( ae_fp_eq(b->sy,0) ) - { - *f = 0; - *df = 0; - *d2f = 0; - return; - } - - /* - * We assume than N>1 and B.SY>0. Find: - * 1. pivot point (X[i] closest to T) - * 2. width of interval containing X[i] - */ - ae_assert(ae_fp_greater(b->sy,0), "BarycentricDiff: internal error", _state); - *f = 0; - *df = 0; - *d2f = 0; - v = ae_fabs(b->x.ptr.p_double[0]-t, _state); - k = 0; - for(i=1; i<=b->n-1; i++) - { - vv = b->x.ptr.p_double[i]; - if( ae_fp_less(ae_fabs(vv-t, _state),v) ) - { - v = ae_fabs(vv-t, _state); - k = i; - } - } - - /* - * pivot point found, calculate dNumerator and dDenominator - */ - xk = b->x.ptr.p_double[k]; - v = t-xk; - n0 = 0; - n1 = 0; - n2 = 0; - d0 = 0; - d1 = 0; - d2 = 0; - for(i=0; i<=b->n-1; i++) - { - if( i!=k ) - { - xi = b->x.ptr.p_double[i]; - vv = ae_sqr(t-xi, _state); - s0 = (t-xk)/(t-xi); - s1 = (xk-xi)/vv; - s2 = -2*(xk-xi)/(vv*(t-xi)); - } - else - { - s0 = 1; - s1 = 0; - s2 = 0; - } - vv = b->w.ptr.p_double[i]*b->y.ptr.p_double[i]; - n0 = n0+s0*vv; - n1 = n1+s1*vv; - n2 = n2+s2*vv; - vv = b->w.ptr.p_double[i]; - d0 = d0+s0*vv; - d1 = d1+s1*vv; - d2 = d2+s2*vv; - } - *f = b->sy*n0/d0; - *df = b->sy*(n1*d0-n0*d1)/ae_sqr(d0, _state); - *d2f = b->sy*((n2*d0-n0*d2)*ae_sqr(d0, _state)-(n1*d0-n0*d1)*2*d0*d1)/ae_sqr(ae_sqr(d0, _state), _state); -} - - -/************************************************************************* -This subroutine performs linear transformation of the argument. - -INPUT PARAMETERS: - B - rational interpolant in barycentric form - CA, CB - transformation coefficients: x = CA*t + CB - -OUTPUT PARAMETERS: - B - transformed interpolant with X replaced by T - - -- ALGLIB PROJECT -- - Copyright 19.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriclintransx(barycentricinterpolant* b, - double ca, - double cb, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - - - - /* - * special case, replace by constant F(CB) - */ - if( ae_fp_eq(ca,0) ) - { - b->sy = barycentriccalc(b, cb, _state); - v = 1; - for(i=0; i<=b->n-1; i++) - { - b->y.ptr.p_double[i] = 1; - b->w.ptr.p_double[i] = v; - v = -v; - } - return; - } - - /* - * general case: CA<>0 - */ - for(i=0; i<=b->n-1; i++) - { - b->x.ptr.p_double[i] = (b->x.ptr.p_double[i]-cb)/ca; - } - if( ae_fp_less(ca,0) ) - { - for(i=0; i<=b->n-1; i++) - { - if( in-1-i ) - { - j = b->n-1-i; - v = b->x.ptr.p_double[i]; - b->x.ptr.p_double[i] = b->x.ptr.p_double[j]; - b->x.ptr.p_double[j] = v; - v = b->y.ptr.p_double[i]; - b->y.ptr.p_double[i] = b->y.ptr.p_double[j]; - b->y.ptr.p_double[j] = v; - v = b->w.ptr.p_double[i]; - b->w.ptr.p_double[i] = b->w.ptr.p_double[j]; - b->w.ptr.p_double[j] = v; - } - else - { - break; - } - } - } -} - - -/************************************************************************* -This subroutine performs linear transformation of the barycentric -interpolant. - -INPUT PARAMETERS: - B - rational interpolant in barycentric form - CA, CB - transformation coefficients: B2(x) = CA*B(x) + CB - -OUTPUT PARAMETERS: - B - transformed interpolant - - -- ALGLIB PROJECT -- - Copyright 19.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriclintransy(barycentricinterpolant* b, - double ca, - double cb, - ae_state *_state) -{ - ae_int_t i; - double v; - - - for(i=0; i<=b->n-1; i++) - { - b->y.ptr.p_double[i] = ca*b->sy*b->y.ptr.p_double[i]+cb; - } - b->sy = 0; - for(i=0; i<=b->n-1; i++) - { - b->sy = ae_maxreal(b->sy, ae_fabs(b->y.ptr.p_double[i], _state), _state); - } - if( ae_fp_greater(b->sy,0) ) - { - v = 1/b->sy; - ae_v_muld(&b->y.ptr.p_double[0], 1, ae_v_len(0,b->n-1), v); - } -} - - -/************************************************************************* -Extracts X/Y/W arrays from rational interpolant - -INPUT PARAMETERS: - B - barycentric interpolant - -OUTPUT PARAMETERS: - N - nodes count, N>0 - X - interpolation nodes, array[0..N-1] - F - function values, array[0..N-1] - W - barycentric weights, array[0..N-1] - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricunpack(barycentricinterpolant* b, - ae_int_t* n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_state *_state) -{ - double v; - - *n = 0; - ae_vector_clear(x); - ae_vector_clear(y); - ae_vector_clear(w); - - *n = b->n; - ae_vector_set_length(x, *n, _state); - ae_vector_set_length(y, *n, _state); - ae_vector_set_length(w, *n, _state); - v = b->sy; - ae_v_move(&x->ptr.p_double[0], 1, &b->x.ptr.p_double[0], 1, ae_v_len(0,*n-1)); - ae_v_moved(&y->ptr.p_double[0], 1, &b->y.ptr.p_double[0], 1, ae_v_len(0,*n-1), v); - ae_v_move(&w->ptr.p_double[0], 1, &b->w.ptr.p_double[0], 1, ae_v_len(0,*n-1)); -} - - -/************************************************************************* -Rational interpolant from X/Y/W arrays - -F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i])) - -INPUT PARAMETERS: - X - interpolation nodes, array[0..N-1] - F - function values, array[0..N-1] - W - barycentric weights, array[0..N-1] - N - nodes count, N>0 - -OUTPUT PARAMETERS: - B - barycentric interpolant built from (X, Y, W) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricbuildxyw(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - barycentricinterpolant* b, - ae_state *_state) -{ - - _barycentricinterpolant_clear(b); - - ae_assert(n>0, "BarycentricBuildXYW: incorrect N!", _state); - - /* - * fill X/Y/W - */ - ae_vector_set_length(&b->x, n, _state); - ae_vector_set_length(&b->y, n, _state); - ae_vector_set_length(&b->w, n, _state); - ae_v_move(&b->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&b->y.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&b->w.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1)); - b->n = n; - - /* - * Normalize - */ - ratint_barycentricnormalize(b, _state); -} - - -/************************************************************************* -Rational interpolant without poles - -The subroutine constructs the rational interpolating function without real -poles (see 'Barycentric rational interpolation with no poles and high -rates of approximation', Michael S. Floater. and Kai Hormann, for more -information on this subject). - -Input parameters: - X - interpolation nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of nodes, N>0. - D - order of the interpolation scheme, 0 <= D <= N-1. - D<0 will cause an error. - D>=N it will be replaced with D=N-1. - if you don't know what D to choose, use small value about 3-5. - -Output parameters: - B - barycentric interpolant. - -Note: - this algorithm always succeeds and calculates the weights with close - to machine precision. - - -- ALGLIB PROJECT -- - Copyright 17.06.2007 by Bochkanov Sergey -*************************************************************************/ -void barycentricbuildfloaterhormann(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t d, - barycentricinterpolant* b, - ae_state *_state) -{ - ae_frame _frame_block; - double s0; - double s; - double v; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_vector perm; - ae_vector wtemp; - ae_vector sortrbuf; - ae_vector sortrbuf2; - - ae_frame_make(_state, &_frame_block); - _barycentricinterpolant_clear(b); - ae_vector_init(&perm, 0, DT_INT, _state, ae_true); - ae_vector_init(&wtemp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sortrbuf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sortrbuf2, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "BarycentricFloaterHormann: N<=0!", _state); - ae_assert(d>=0, "BarycentricFloaterHormann: incorrect D!", _state); - - /* - * Prepare - */ - if( d>n-1 ) - { - d = n-1; - } - b->n = n; - - /* - * special case: N=1 - */ - if( n==1 ) - { - ae_vector_set_length(&b->x, n, _state); - ae_vector_set_length(&b->y, n, _state); - ae_vector_set_length(&b->w, n, _state); - b->x.ptr.p_double[0] = x->ptr.p_double[0]; - b->y.ptr.p_double[0] = y->ptr.p_double[0]; - b->w.ptr.p_double[0] = 1; - ratint_barycentricnormalize(b, _state); - ae_frame_leave(_state); - return; - } - - /* - * Fill X/Y - */ - ae_vector_set_length(&b->x, n, _state); - ae_vector_set_length(&b->y, n, _state); - ae_v_move(&b->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&b->y.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - tagsortfastr(&b->x, &b->y, &sortrbuf, &sortrbuf2, n, _state); - - /* - * Calculate Wk - */ - ae_vector_set_length(&b->w, n, _state); - s0 = 1; - for(k=1; k<=d; k++) - { - s0 = -s0; - } - for(k=0; k<=n-1; k++) - { - - /* - * Wk - */ - s = 0; - for(i=ae_maxint(k-d, 0, _state); i<=ae_minint(k, n-1-d, _state); i++) - { - v = 1; - for(j=i; j<=i+d; j++) - { - if( j!=k ) - { - v = v/ae_fabs(b->x.ptr.p_double[k]-b->x.ptr.p_double[j], _state); - } - } - s = s+v; - } - b->w.ptr.p_double[k] = s0*s; - - /* - * Next S0 - */ - s0 = -s0; - } - - /* - * Normalize - */ - ratint_barycentricnormalize(b, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Copying of the barycentric interpolant (for internal use only) - -INPUT PARAMETERS: - B - barycentric interpolant - -OUTPUT PARAMETERS: - B2 - copy(B1) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriccopy(barycentricinterpolant* b, - barycentricinterpolant* b2, - ae_state *_state) -{ - - _barycentricinterpolant_clear(b2); - - b2->n = b->n; - b2->sy = b->sy; - ae_vector_set_length(&b2->x, b2->n, _state); - ae_vector_set_length(&b2->y, b2->n, _state); - ae_vector_set_length(&b2->w, b2->n, _state); - ae_v_move(&b2->x.ptr.p_double[0], 1, &b->x.ptr.p_double[0], 1, ae_v_len(0,b2->n-1)); - ae_v_move(&b2->y.ptr.p_double[0], 1, &b->y.ptr.p_double[0], 1, ae_v_len(0,b2->n-1)); - ae_v_move(&b2->w.ptr.p_double[0], 1, &b->w.ptr.p_double[0], 1, ae_v_len(0,b2->n-1)); -} - - -/************************************************************************* -Normalization of barycentric interpolant: -* B.N, B.X, B.Y and B.W are initialized -* B.SY is NOT initialized -* Y[] is normalized, scaling coefficient is stored in B.SY -* W[] is normalized, no scaling coefficient is stored -* X[] is sorted - -Internal subroutine. -*************************************************************************/ -static void ratint_barycentricnormalize(barycentricinterpolant* b, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector p1; - ae_vector p2; - ae_int_t i; - ae_int_t j; - ae_int_t j2; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&p1, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - - /* - * Normalize task: |Y|<=1, |W|<=1, sort X[] - */ - b->sy = 0; - for(i=0; i<=b->n-1; i++) - { - b->sy = ae_maxreal(b->sy, ae_fabs(b->y.ptr.p_double[i], _state), _state); - } - if( ae_fp_greater(b->sy,0)&&ae_fp_greater(ae_fabs(b->sy-1, _state),10*ae_machineepsilon) ) - { - v = 1/b->sy; - ae_v_muld(&b->y.ptr.p_double[0], 1, ae_v_len(0,b->n-1), v); - } - v = 0; - for(i=0; i<=b->n-1; i++) - { - v = ae_maxreal(v, ae_fabs(b->w.ptr.p_double[i], _state), _state); - } - if( ae_fp_greater(v,0)&&ae_fp_greater(ae_fabs(v-1, _state),10*ae_machineepsilon) ) - { - v = 1/v; - ae_v_muld(&b->w.ptr.p_double[0], 1, ae_v_len(0,b->n-1), v); - } - for(i=0; i<=b->n-2; i++) - { - if( ae_fp_less(b->x.ptr.p_double[i+1],b->x.ptr.p_double[i]) ) - { - tagsort(&b->x, b->n, &p1, &p2, _state); - for(j=0; j<=b->n-1; j++) - { - j2 = p2.ptr.p_int[j]; - v = b->y.ptr.p_double[j]; - b->y.ptr.p_double[j] = b->y.ptr.p_double[j2]; - b->y.ptr.p_double[j2] = v; - v = b->w.ptr.p_double[j]; - b->w.ptr.p_double[j] = b->w.ptr.p_double[j2]; - b->w.ptr.p_double[j2] = v; - } - break; - } - } - ae_frame_leave(_state); -} - - -ae_bool _barycentricinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - barycentricinterpolant *p = (barycentricinterpolant*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->w, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _barycentricinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - barycentricinterpolant *dst = (barycentricinterpolant*)_dst; - barycentricinterpolant *src = (barycentricinterpolant*)_src; - dst->n = src->n; - dst->sy = src->sy; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->w, &src->w, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _barycentricinterpolant_clear(void* _p) -{ - barycentricinterpolant *p = (barycentricinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->y); - ae_vector_clear(&p->w); -} - - -void _barycentricinterpolant_destroy(void* _p) -{ - barycentricinterpolant *p = (barycentricinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->y); - ae_vector_destroy(&p->w); -} - - - - -/************************************************************************* -Conversion from barycentric representation to Chebyshev basis. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - P - polynomial in barycentric form - A,B - base interval for Chebyshev polynomials (see below) - A<>B - -OUTPUT PARAMETERS - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N-1 }, - where Ti - I-th Chebyshev polynomial. - -NOTES: - barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2cheb(barycentricinterpolant* p, - double a, - double b, - /* Real */ ae_vector* t, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t k; - ae_vector vp; - ae_vector vx; - ae_vector tk; - ae_vector tk1; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(t); - ae_vector_init(&vp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&vx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tk, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tk1, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_isfinite(a, _state), "PolynomialBar2Cheb: A is not finite!", _state); - ae_assert(ae_isfinite(b, _state), "PolynomialBar2Cheb: B is not finite!", _state); - ae_assert(ae_fp_neq(a,b), "PolynomialBar2Cheb: A=B!", _state); - ae_assert(p->n>0, "PolynomialBar2Cheb: P is not correctly initialized barycentric interpolant!", _state); - - /* - * Calculate function values on a Chebyshev grid - */ - ae_vector_set_length(&vp, p->n, _state); - ae_vector_set_length(&vx, p->n, _state); - for(i=0; i<=p->n-1; i++) - { - vx.ptr.p_double[i] = ae_cos(ae_pi*(i+0.5)/p->n, _state); - vp.ptr.p_double[i] = barycentriccalc(p, 0.5*(vx.ptr.p_double[i]+1)*(b-a)+a, _state); - } - - /* - * T[0] - */ - ae_vector_set_length(t, p->n, _state); - v = 0; - for(i=0; i<=p->n-1; i++) - { - v = v+vp.ptr.p_double[i]; - } - t->ptr.p_double[0] = v/p->n; - - /* - * other T's. - * - * NOTES: - * 1. TK stores T{k} on VX, TK1 stores T{k-1} on VX - * 2. we can do same calculations with fast DCT, but it - * * adds dependencies - * * still leaves us with O(N^2) algorithm because - * preparation of function values is O(N^2) process - */ - if( p->n>1 ) - { - ae_vector_set_length(&tk, p->n, _state); - ae_vector_set_length(&tk1, p->n, _state); - for(i=0; i<=p->n-1; i++) - { - tk.ptr.p_double[i] = vx.ptr.p_double[i]; - tk1.ptr.p_double[i] = 1; - } - for(k=1; k<=p->n-1; k++) - { - - /* - * calculate discrete product of function vector and TK - */ - v = ae_v_dotproduct(&tk.ptr.p_double[0], 1, &vp.ptr.p_double[0], 1, ae_v_len(0,p->n-1)); - t->ptr.p_double[k] = v/(0.5*p->n); - - /* - * Update TK and TK1 - */ - for(i=0; i<=p->n-1; i++) - { - v = 2*vx.ptr.p_double[i]*tk.ptr.p_double[i]-tk1.ptr.p_double[i]; - tk1.ptr.p_double[i] = tk.ptr.p_double[i]; - tk.ptr.p_double[i] = v; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Conversion from Chebyshev basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N }, - where Ti - I-th Chebyshev polynomial. - N - number of coefficients: - * if given, only leading N elements of T are used - * if not given, automatically determined from size of T - A,B - base interval for Chebyshev polynomials (see above) - A=1, "PolynomialBar2Cheb: N<1", _state); - ae_assert(t->cnt>=n, "PolynomialBar2Cheb: Length(T)ptr.p_double[0]; - tk1 = 1; - tk = vx; - for(k=1; k<=n-1; k++) - { - vy = vy+t->ptr.p_double[k]*tk; - v = 2*vx*tk-tk1; - tk1 = tk; - tk = v; - } - y.ptr.p_double[i] = vy; - } - - /* - * Build barycentric interpolant, map grid from [-1,+1] to [A,B] - */ - polynomialbuildcheb1(a, b, &y, n, p, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Conversion from barycentric representation to power basis. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - P - polynomial in barycentric form - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if P was obtained as - result of interpolation on [-1,+1], you can set C=0 and S=1 and - represent P as sum of 1, x, x^2, x^3 and so on. In most cases you it - is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3 - will be better option. Such representation can be obtained by using - 1000.0 as offset C and 1.0 as scale S. - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return coefficients in - any case, but for N>8 they will become unreliable. However, N's - less than 5 are pretty safe. - -3. barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2pow(barycentricinterpolant* p, - double c, - double s, - /* Real */ ae_vector* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t k; - double e; - double d; - ae_vector vp; - ae_vector vx; - ae_vector tk; - ae_vector tk1; - ae_vector t; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(a); - ae_vector_init(&vp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&vx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tk, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tk1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_isfinite(c, _state), "PolynomialBar2Pow: C is not finite!", _state); - ae_assert(ae_isfinite(s, _state), "PolynomialBar2Pow: S is not finite!", _state); - ae_assert(ae_fp_neq(s,0), "PolynomialBar2Pow: S=0!", _state); - ae_assert(p->n>0, "PolynomialBar2Pow: P is not correctly initialized barycentric interpolant!", _state); - - /* - * Calculate function values on a Chebyshev grid - */ - ae_vector_set_length(&vp, p->n, _state); - ae_vector_set_length(&vx, p->n, _state); - for(i=0; i<=p->n-1; i++) - { - vx.ptr.p_double[i] = ae_cos(ae_pi*(i+0.5)/p->n, _state); - vp.ptr.p_double[i] = barycentriccalc(p, s*vx.ptr.p_double[i]+c, _state); - } - - /* - * T[0] - */ - ae_vector_set_length(&t, p->n, _state); - v = 0; - for(i=0; i<=p->n-1; i++) - { - v = v+vp.ptr.p_double[i]; - } - t.ptr.p_double[0] = v/p->n; - - /* - * other T's. - * - * NOTES: - * 1. TK stores T{k} on VX, TK1 stores T{k-1} on VX - * 2. we can do same calculations with fast DCT, but it - * * adds dependencies - * * still leaves us with O(N^2) algorithm because - * preparation of function values is O(N^2) process - */ - if( p->n>1 ) - { - ae_vector_set_length(&tk, p->n, _state); - ae_vector_set_length(&tk1, p->n, _state); - for(i=0; i<=p->n-1; i++) - { - tk.ptr.p_double[i] = vx.ptr.p_double[i]; - tk1.ptr.p_double[i] = 1; - } - for(k=1; k<=p->n-1; k++) - { - - /* - * calculate discrete product of function vector and TK - */ - v = ae_v_dotproduct(&tk.ptr.p_double[0], 1, &vp.ptr.p_double[0], 1, ae_v_len(0,p->n-1)); - t.ptr.p_double[k] = v/(0.5*p->n); - - /* - * Update TK and TK1 - */ - for(i=0; i<=p->n-1; i++) - { - v = 2*vx.ptr.p_double[i]*tk.ptr.p_double[i]-tk1.ptr.p_double[i]; - tk1.ptr.p_double[i] = tk.ptr.p_double[i]; - tk.ptr.p_double[i] = v; - } - } - } - - /* - * Convert from Chebyshev basis to power basis - */ - ae_vector_set_length(a, p->n, _state); - for(i=0; i<=p->n-1; i++) - { - a->ptr.p_double[i] = 0; - } - d = 0; - for(i=0; i<=p->n-1; i++) - { - for(k=i; k<=p->n-1; k++) - { - e = a->ptr.p_double[k]; - a->ptr.p_double[k] = 0; - if( i<=1&&k==i ) - { - a->ptr.p_double[k] = 1; - } - else - { - if( i!=0 ) - { - a->ptr.p_double[k] = 2*d; - } - if( k>i+1 ) - { - a->ptr.p_double[k] = a->ptr.p_double[k]-a->ptr.p_double[k-2]; - } - } - d = e; - } - d = a->ptr.p_double[i]; - e = 0; - k = i; - while(k<=p->n-1) - { - e = e+a->ptr.p_double[k]*t.ptr.p_double[k]; - k = k+2; - } - a->ptr.p_double[i] = e; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Conversion from power basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - * if given, only leading N elements of A are used - * if not given, automatically determined from size of A - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - P - polynomial in barycentric form - - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if you interpolate on - [-1,+1], you can set C=0 and S=1 and convert from sum of 1, x, x^2, - x^3 and so on. In most cases you it is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as input basis. Converting from sum of 1, (x-1000), (x-1000)^2, - (x-1000)^3 will be better option (you have to specify 1000.0 as offset - C and 1.0 as scale S). - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return barycentric model - in any case, but for N>8 accuracy well degrade. However, N's less than - 5 are pretty safe. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialpow2bar(/* Real */ ae_vector* a, - ae_int_t n, - double c, - double s, - barycentricinterpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t k; - ae_vector y; - double vx; - double vy; - double px; - - ae_frame_make(_state, &_frame_block); - _barycentricinterpolant_clear(p); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_isfinite(c, _state), "PolynomialPow2Bar: C is not finite!", _state); - ae_assert(ae_isfinite(s, _state), "PolynomialPow2Bar: S is not finite!", _state); - ae_assert(ae_fp_neq(s,0), "PolynomialPow2Bar: S is zero!", _state); - ae_assert(n>=1, "PolynomialPow2Bar: N<1", _state); - ae_assert(a->cnt>=n, "PolynomialPow2Bar: Length(A)ptr.p_double[0]; - px = vx; - for(k=1; k<=n-1; k++) - { - vy = vy+px*a->ptr.p_double[k]; - px = px*vx; - } - y.ptr.p_double[i] = vy; - } - - /* - * Build barycentric interpolant, map grid from [-1,+1] to [A,B] - */ - polynomialbuildcheb1(c-s, c+s, &y, n, p, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Lagrange intepolant: generation of the model on the general grid. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - X - abscissas, array[0..N-1] - Y - function values, array[0..N-1] - N - number of points, N>=1 - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuild(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_int_t j; - ae_int_t k; - ae_vector w; - double b; - double a; - double v; - double mx; - ae_vector sortrbuf; - ae_vector sortrbuf2; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - _barycentricinterpolant_clear(p); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sortrbuf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sortrbuf2, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "PolynomialBuild: N<=0!", _state); - ae_assert(x->cnt>=n, "PolynomialBuild: Length(X)cnt>=n, "PolynomialBuild: Length(Y)ptr.p_double[0]; - b = x->ptr.p_double[0]; - for(j=0; j<=n-1; j++) - { - w.ptr.p_double[j] = 1; - a = ae_minreal(a, x->ptr.p_double[j], _state); - b = ae_maxreal(b, x->ptr.p_double[j], _state); - } - for(k=0; k<=n-1; k++) - { - - /* - * W[K] is used instead of 0.0 because - * cycle on J does not touch K-th element - * and we MUST get maximum from ALL elements - */ - mx = ae_fabs(w.ptr.p_double[k], _state); - for(j=0; j<=n-1; j++) - { - if( j!=k ) - { - v = (b-a)/(x->ptr.p_double[j]-x->ptr.p_double[k]); - w.ptr.p_double[j] = w.ptr.p_double[j]*v; - mx = ae_maxreal(mx, ae_fabs(w.ptr.p_double[j], _state), _state); - } - } - if( k%5==0 ) - { - - /* - * every 5-th run we renormalize W[] - */ - v = 1/mx; - ae_v_muld(&w.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - } - } - barycentricbuildxyw(x, y, &w, n, p, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Lagrange intepolant: generation of the model on equidistant grid. -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1] - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildeqdist(double a, - double b, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector w; - ae_vector x; - double v; - - ae_frame_make(_state, &_frame_block); - _barycentricinterpolant_clear(p); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "PolynomialBuildEqDist: N<=0!", _state); - ae_assert(y->cnt>=n, "PolynomialBuildEqDist: Length(Y)=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb1(double a, - double b, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector w; - ae_vector x; - double v; - double t; - - ae_frame_make(_state, &_frame_block); - _barycentricinterpolant_clear(p); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "PolynomialBuildCheb1: N<=0!", _state); - ae_assert(y->cnt>=n, "PolynomialBuildCheb1: Length(Y)=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb2(double a, - double b, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector w; - ae_vector x; - double v; - - ae_frame_make(_state, &_frame_block); - _barycentricinterpolant_clear(p); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "PolynomialBuildCheb2: N<=0!", _state); - ae_assert(y->cnt>=n, "PolynomialBuildCheb2: Length(Y)=1 - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolynomialBuildEqDist()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalceqdist(double a, - double b, - /* Real */ ae_vector* f, - ae_int_t n, - double t, - ae_state *_state) -{ - double s1; - double s2; - double v; - double threshold; - double s; - double h; - ae_int_t i; - ae_int_t j; - double w; - double x; - double result; - - - ae_assert(n>0, "PolynomialCalcEqDist: N<=0!", _state); - ae_assert(f->cnt>=n, "PolynomialCalcEqDist: Length(F)v_nan; - return result; - } - - /* - * Special case: N=1 - */ - if( n==1 ) - { - result = f->ptr.p_double[0]; - return result; - } - - /* - * First, decide: should we use "safe" formula (guarded - * against overflow) or fast one? - */ - threshold = ae_sqrt(ae_minrealnumber, _state); - j = 0; - s = t-a; - for(i=1; i<=n-1; i++) - { - x = a+(double)i/(double)(n-1)*(b-a); - if( ae_fp_less(ae_fabs(t-x, _state),ae_fabs(s, _state)) ) - { - s = t-x; - j = i; - } - } - if( ae_fp_eq(s,0) ) - { - result = f->ptr.p_double[j]; - return result; - } - if( ae_fp_greater(ae_fabs(s, _state),threshold) ) - { - - /* - * use fast formula - */ - j = -1; - s = 1.0; - } - - /* - * Calculate using safe or fast barycentric formula - */ - s1 = 0; - s2 = 0; - w = 1.0; - h = (b-a)/(n-1); - for(i=0; i<=n-1; i++) - { - if( i!=j ) - { - v = s*w/(t-(a+i*h)); - s1 = s1+v*f->ptr.p_double[i]; - s2 = s2+v; - } - else - { - v = w; - s1 = s1+v*f->ptr.p_double[i]; - s2 = s2+v; - } - w = -w*(n-1-i); - w = w/(i+1); - } - result = s1/s2; - return result; -} - - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (first kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (first kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb1()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb1(double a, - double b, - /* Real */ ae_vector* f, - ae_int_t n, - double t, - ae_state *_state) -{ - double s1; - double s2; - double v; - double threshold; - double s; - ae_int_t i; - ae_int_t j; - double a0; - double delta; - double alpha; - double beta; - double ca; - double sa; - double tempc; - double temps; - double x; - double w; - double p1; - double result; - - - ae_assert(n>0, "PolynomialCalcCheb1: N<=0!", _state); - ae_assert(f->cnt>=n, "PolynomialCalcCheb1: Length(F)v_nan; - return result; - } - - /* - * Special case: N=1 - */ - if( n==1 ) - { - result = f->ptr.p_double[0]; - return result; - } - - /* - * Prepare information for the recurrence formula - * used to calculate sin(pi*(2j+1)/(2n+2)) and - * cos(pi*(2j+1)/(2n+2)): - * - * A0 = pi/(2n+2) - * Delta = pi/(n+1) - * Alpha = 2 sin^2 (Delta/2) - * Beta = sin(Delta) - * - * so that sin(..) = sin(A0+j*delta) and cos(..) = cos(A0+j*delta). - * Then we use - * - * sin(x+delta) = sin(x) - (alpha*sin(x) - beta*cos(x)) - * cos(x+delta) = cos(x) - (alpha*cos(x) - beta*sin(x)) - * - * to repeatedly calculate sin(..) and cos(..). - */ - threshold = ae_sqrt(ae_minrealnumber, _state); - t = (t-0.5*(a+b))/(0.5*(b-a)); - a0 = ae_pi/(2*(n-1)+2); - delta = 2*ae_pi/(2*(n-1)+2); - alpha = 2*ae_sqr(ae_sin(delta/2, _state), _state); - beta = ae_sin(delta, _state); - - /* - * First, decide: should we use "safe" formula (guarded - * against overflow) or fast one? - */ - ca = ae_cos(a0, _state); - sa = ae_sin(a0, _state); - j = 0; - x = ca; - s = t-x; - for(i=1; i<=n-1; i++) - { - - /* - * Next X[i] - */ - temps = sa-(alpha*sa-beta*ca); - tempc = ca-(alpha*ca+beta*sa); - sa = temps; - ca = tempc; - x = ca; - - /* - * Use X[i] - */ - if( ae_fp_less(ae_fabs(t-x, _state),ae_fabs(s, _state)) ) - { - s = t-x; - j = i; - } - } - if( ae_fp_eq(s,0) ) - { - result = f->ptr.p_double[j]; - return result; - } - if( ae_fp_greater(ae_fabs(s, _state),threshold) ) - { - - /* - * use fast formula - */ - j = -1; - s = 1.0; - } - - /* - * Calculate using safe or fast barycentric formula - */ - s1 = 0; - s2 = 0; - ca = ae_cos(a0, _state); - sa = ae_sin(a0, _state); - p1 = 1.0; - for(i=0; i<=n-1; i++) - { - - /* - * Calculate X[i], W[i] - */ - x = ca; - w = p1*sa; - - /* - * Proceed - */ - if( i!=j ) - { - v = s*w/(t-x); - s1 = s1+v*f->ptr.p_double[i]; - s2 = s2+v; - } - else - { - v = w; - s1 = s1+v*f->ptr.p_double[i]; - s2 = s2+v; - } - - /* - * Next CA, SA, P1 - */ - temps = sa-(alpha*sa-beta*ca); - tempc = ca-(alpha*ca+beta*sa); - sa = temps; - ca = tempc; - p1 = -p1; - } - result = s1/s2; - return result; -} - - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (second kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (second kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb2()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb2(double a, - double b, - /* Real */ ae_vector* f, - ae_int_t n, - double t, - ae_state *_state) -{ - double s1; - double s2; - double v; - double threshold; - double s; - ae_int_t i; - ae_int_t j; - double a0; - double delta; - double alpha; - double beta; - double ca; - double sa; - double tempc; - double temps; - double x; - double w; - double p1; - double result; - - - ae_assert(n>0, "PolynomialCalcCheb2: N<=0!", _state); - ae_assert(f->cnt>=n, "PolynomialCalcCheb2: Length(F)v_nan; - return result; - } - - /* - * Special case: N=1 - */ - if( n==1 ) - { - result = f->ptr.p_double[0]; - return result; - } - - /* - * Prepare information for the recurrence formula - * used to calculate sin(pi*i/n) and - * cos(pi*i/n): - * - * A0 = 0 - * Delta = pi/n - * Alpha = 2 sin^2 (Delta/2) - * Beta = sin(Delta) - * - * so that sin(..) = sin(A0+j*delta) and cos(..) = cos(A0+j*delta). - * Then we use - * - * sin(x+delta) = sin(x) - (alpha*sin(x) - beta*cos(x)) - * cos(x+delta) = cos(x) - (alpha*cos(x) - beta*sin(x)) - * - * to repeatedly calculate sin(..) and cos(..). - */ - threshold = ae_sqrt(ae_minrealnumber, _state); - t = (t-0.5*(a+b))/(0.5*(b-a)); - a0 = 0.0; - delta = ae_pi/(n-1); - alpha = 2*ae_sqr(ae_sin(delta/2, _state), _state); - beta = ae_sin(delta, _state); - - /* - * First, decide: should we use "safe" formula (guarded - * against overflow) or fast one? - */ - ca = ae_cos(a0, _state); - sa = ae_sin(a0, _state); - j = 0; - x = ca; - s = t-x; - for(i=1; i<=n-1; i++) - { - - /* - * Next X[i] - */ - temps = sa-(alpha*sa-beta*ca); - tempc = ca-(alpha*ca+beta*sa); - sa = temps; - ca = tempc; - x = ca; - - /* - * Use X[i] - */ - if( ae_fp_less(ae_fabs(t-x, _state),ae_fabs(s, _state)) ) - { - s = t-x; - j = i; - } - } - if( ae_fp_eq(s,0) ) - { - result = f->ptr.p_double[j]; - return result; - } - if( ae_fp_greater(ae_fabs(s, _state),threshold) ) - { - - /* - * use fast formula - */ - j = -1; - s = 1.0; - } - - /* - * Calculate using safe or fast barycentric formula - */ - s1 = 0; - s2 = 0; - ca = ae_cos(a0, _state); - sa = ae_sin(a0, _state); - p1 = 1.0; - for(i=0; i<=n-1; i++) - { - - /* - * Calculate X[i], W[i] - */ - x = ca; - if( i==0||i==n-1 ) - { - w = 0.5*p1; - } - else - { - w = 1.0*p1; - } - - /* - * Proceed - */ - if( i!=j ) - { - v = s*w/(t-x); - s1 = s1+v*f->ptr.p_double[i]; - s2 = s2+v; - } - else - { - v = w; - s1 = s1+v*f->ptr.p_double[i]; - s2 = s2+v; - } - - /* - * Next CA, SA, P1 - */ - temps = sa-(alpha*sa-beta*ca); - tempc = ca-(alpha*ca+beta*sa); - sa = temps; - ca = tempc; - p1 = -p1; - } - result = s1/s2; - return result; -} - - - - -/************************************************************************* -This subroutine builds linear spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildlinear(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - _spline1dinterpolant_clear(c); - - ae_assert(n>1, "Spline1DBuildLinear: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DBuildLinear: Length(X)cnt>=n, "Spline1DBuildLinear: Length(Y)periodic = ae_false; - c->n = n; - c->k = 3; - c->continuity = 0; - ae_vector_set_length(&c->x, n, _state); - ae_vector_set_length(&c->c, 4*(n-1)+2, _state); - for(i=0; i<=n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=n-2; i++) - { - c->c.ptr.p_double[4*i+0] = y->ptr.p_double[i]; - c->c.ptr.p_double[4*i+1] = (y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i]); - c->c.ptr.p_double[4*i+2] = 0; - c->c.ptr.p_double[4*i+3] = 0; - } - c->c.ptr.p_double[4*(n-1)+0] = y->ptr.p_double[n-1]; - c->c.ptr.p_double[4*(n-1)+1] = c->c.ptr.p_double[4*(n-2)+1]; - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine builds cubic spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - C - spline interpolant - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - spline1dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector a1; - ae_vector a2; - ae_vector a3; - ae_vector b; - ae_vector dt; - ae_vector d; - ae_vector p; - ae_int_t ylen; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - _spline1dinterpolant_clear(c); - ae_vector_init(&a1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of boundary conditions - */ - ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DBuildCubic: incorrect BoundLType!", _state); - ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DBuildCubic: incorrect BoundRType!", _state); - ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DBuildCubic: incorrect BoundLType/BoundRType!", _state); - if( boundltype==1||boundltype==2 ) - { - ae_assert(ae_isfinite(boundl, _state), "Spline1DBuildCubic: BoundL is infinite or NAN!", _state); - } - if( boundrtype==1||boundrtype==2 ) - { - ae_assert(ae_isfinite(boundr, _state), "Spline1DBuildCubic: BoundR is infinite or NAN!", _state); - } - - /* - * check lengths of arguments - */ - ae_assert(n>=2, "Spline1DBuildCubic: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DBuildCubic: Length(X)cnt>=n, "Spline1DBuildCubic: Length(Y)ptr.p_double[n-1] = y->ptr.p_double[0]; - } - spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state); - spline1dbuildhermite(x, y, &d, n, c, _state); - c->periodic = boundltype==-1||boundrtype==-1; - c->continuity = 2; - ae_frame_leave(_state); -} - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns table of function derivatives d[] -(calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D - derivative values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiffcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector a1; - ae_vector a2; - ae_vector a3; - ae_vector b; - ae_vector dt; - ae_vector p; - ae_int_t i; - ae_int_t ylen; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_clear(d); - ae_vector_init(&a1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of boundary conditions - */ - ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DGridDiffCubic: incorrect BoundLType!", _state); - ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DGridDiffCubic: incorrect BoundRType!", _state); - ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DGridDiffCubic: incorrect BoundLType/BoundRType!", _state); - if( boundltype==1||boundltype==2 ) - { - ae_assert(ae_isfinite(boundl, _state), "Spline1DGridDiffCubic: BoundL is infinite or NAN!", _state); - } - if( boundrtype==1||boundrtype==2 ) - { - ae_assert(ae_isfinite(boundr, _state), "Spline1DGridDiffCubic: BoundR is infinite or NAN!", _state); - } - - /* - * check lengths of arguments - */ - ae_assert(n>=2, "Spline1DGridDiffCubic: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DGridDiffCubic: Length(X)cnt>=n, "Spline1DGridDiffCubic: Length(Y)ptr.p_double[i]; - } - ae_v_move(&d->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns tables of first and second -function derivatives d1[] and d2[] (calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D1 - S' values at X[] - D2 - S'' values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiff2cubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* d1, - /* Real */ ae_vector* d2, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector a1; - ae_vector a2; - ae_vector a3; - ae_vector b; - ae_vector dt; - ae_vector p; - ae_int_t i; - ae_int_t ylen; - double delta; - double delta2; - double delta3; - double s2; - double s3; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_clear(d1); - ae_vector_clear(d2); - ae_vector_init(&a1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of boundary conditions - */ - ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DGridDiff2Cubic: incorrect BoundLType!", _state); - ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DGridDiff2Cubic: incorrect BoundRType!", _state); - ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DGridDiff2Cubic: incorrect BoundLType/BoundRType!", _state); - if( boundltype==1||boundltype==2 ) - { - ae_assert(ae_isfinite(boundl, _state), "Spline1DGridDiff2Cubic: BoundL is infinite or NAN!", _state); - } - if( boundrtype==1||boundrtype==2 ) - { - ae_assert(ae_isfinite(boundr, _state), "Spline1DGridDiff2Cubic: BoundR is infinite or NAN!", _state); - } - - /* - * check lengths of arguments - */ - ae_assert(n>=2, "Spline1DGridDiff2Cubic: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DGridDiff2Cubic: Length(X)cnt>=n, "Spline1DGridDiff2Cubic: Length(Y)ptr.p_double[i+1]-x->ptr.p_double[i]; - delta2 = ae_sqr(delta, _state); - delta3 = delta*delta2; - s2 = (3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])-2*d1->ptr.p_double[i]*delta-d1->ptr.p_double[i+1]*delta)/delta2; - s3 = (2*(y->ptr.p_double[i]-y->ptr.p_double[i+1])+d1->ptr.p_double[i]*delta+d1->ptr.p_double[i+1]*delta)/delta3; - d2->ptr.p_double[i] = 2*s2; - } - d2->ptr.p_double[n-1] = 2*s2+6*s3*delta; - - /* - * Remember that HeapSortPPoints() call? - * Now we have to reorder them back. - */ - if( dt.cntptr.p_double[i]; - } - ae_v_move(&d1->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - dt.ptr.p_double[p.ptr.p_int[i]] = d2->ptr.p_double[i]; - } - ae_v_move(&d2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y2, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _x2; - ae_vector a1; - ae_vector a2; - ae_vector a3; - ae_vector b; - ae_vector d; - ae_vector dt; - ae_vector d1; - ae_vector d2; - ae_vector p; - ae_vector p2; - ae_int_t i; - ae_int_t ylen; - double t; - double t2; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_x2, x2, _state, ae_true); - x2 = &_x2; - ae_vector_clear(y2); - ae_vector_init(&a1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of boundary conditions - */ - ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DConvCubic: incorrect BoundLType!", _state); - ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DConvCubic: incorrect BoundRType!", _state); - ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DConvCubic: incorrect BoundLType/BoundRType!", _state); - if( boundltype==1||boundltype==2 ) - { - ae_assert(ae_isfinite(boundl, _state), "Spline1DConvCubic: BoundL is infinite or NAN!", _state); - } - if( boundrtype==1||boundrtype==2 ) - { - ae_assert(ae_isfinite(boundr, _state), "Spline1DConvCubic: BoundR is infinite or NAN!", _state); - } - - /* - * check lengths of arguments - */ - ae_assert(n>=2, "Spline1DConvCubic: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DConvCubic: Length(X)cnt>=n, "Spline1DConvCubic: Length(Y)=2, "Spline1DConvCubic: N2<2!", _state); - ae_assert(x2->cnt>=n2, "Spline1DConvCubic: Length(X2)ptr.p_double[i]; - apperiodicmap(&t, x->ptr.p_double[0], x->ptr.p_double[n-1], &t2, _state); - x2->ptr.p_double[i] = t; - } - } - spline1d_heapsortppoints(x2, &dt, &p2, n2, _state); - - /* - * Now we've checked and preordered everything, so we: - * * call internal GridDiff() function to get Hermite form of spline - * * convert using internal Conv() function - * * convert Y2 back to original order - */ - spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state); - spline1dconvdiffinternal(x, y, &d, n, x2, n2, y2, ae_true, &d1, ae_false, &d2, ae_false, _state); - ae_assert(dt.cnt>=n2, "Spline1DConvCubic: internal error!", _state); - for(i=0; i<=n2-1; i++) - { - dt.ptr.p_double[p2.ptr.p_int[i]] = y2->ptr.p_double[i]; - } - ae_v_move(&y2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] and derivatives d2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiffcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y2, - /* Real */ ae_vector* d2, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _x2; - ae_vector a1; - ae_vector a2; - ae_vector a3; - ae_vector b; - ae_vector d; - ae_vector dt; - ae_vector rt1; - ae_vector p; - ae_vector p2; - ae_int_t i; - ae_int_t ylen; - double t; - double t2; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_x2, x2, _state, ae_true); - x2 = &_x2; - ae_vector_clear(y2); - ae_vector_clear(d2); - ae_vector_init(&a1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&rt1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of boundary conditions - */ - ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DConvDiffCubic: incorrect BoundLType!", _state); - ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DConvDiffCubic: incorrect BoundRType!", _state); - ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DConvDiffCubic: incorrect BoundLType/BoundRType!", _state); - if( boundltype==1||boundltype==2 ) - { - ae_assert(ae_isfinite(boundl, _state), "Spline1DConvDiffCubic: BoundL is infinite or NAN!", _state); - } - if( boundrtype==1||boundrtype==2 ) - { - ae_assert(ae_isfinite(boundr, _state), "Spline1DConvDiffCubic: BoundR is infinite or NAN!", _state); - } - - /* - * check lengths of arguments - */ - ae_assert(n>=2, "Spline1DConvDiffCubic: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DConvDiffCubic: Length(X)cnt>=n, "Spline1DConvDiffCubic: Length(Y)=2, "Spline1DConvDiffCubic: N2<2!", _state); - ae_assert(x2->cnt>=n2, "Spline1DConvDiffCubic: Length(X2)ptr.p_double[i]; - apperiodicmap(&t, x->ptr.p_double[0], x->ptr.p_double[n-1], &t2, _state); - x2->ptr.p_double[i] = t; - } - } - spline1d_heapsortppoints(x2, &dt, &p2, n2, _state); - - /* - * Now we've checked and preordered everything, so we: - * * call internal GridDiff() function to get Hermite form of spline - * * convert using internal Conv() function - * * convert Y2 back to original order - */ - spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state); - spline1dconvdiffinternal(x, y, &d, n, x2, n2, y2, ae_true, d2, ae_true, &rt1, ae_false, _state); - ae_assert(dt.cnt>=n2, "Spline1DConvDiffCubic: internal error!", _state); - for(i=0; i<=n2-1; i++) - { - dt.ptr.p_double[p2.ptr.p_int[i]] = y2->ptr.p_double[i]; - } - ae_v_move(&y2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1)); - for(i=0; i<=n2-1; i++) - { - dt.ptr.p_double[p2.ptr.p_int[i]] = d2->ptr.p_double[i]; - } - ae_v_move(&d2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[], first and second derivatives d2[] and dd2[] -(calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - DD2 - second derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiff2cubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y2, - /* Real */ ae_vector* d2, - /* Real */ ae_vector* dd2, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _x2; - ae_vector a1; - ae_vector a2; - ae_vector a3; - ae_vector b; - ae_vector d; - ae_vector dt; - ae_vector p; - ae_vector p2; - ae_int_t i; - ae_int_t ylen; - double t; - double t2; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_x2, x2, _state, ae_true); - x2 = &_x2; - ae_vector_clear(y2); - ae_vector_clear(d2); - ae_vector_clear(dd2); - ae_vector_init(&a1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&a3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - - /* - * check correctness of boundary conditions - */ - ae_assert(((boundltype==-1||boundltype==0)||boundltype==1)||boundltype==2, "Spline1DConvDiff2Cubic: incorrect BoundLType!", _state); - ae_assert(((boundrtype==-1||boundrtype==0)||boundrtype==1)||boundrtype==2, "Spline1DConvDiff2Cubic: incorrect BoundRType!", _state); - ae_assert((boundrtype==-1&&boundltype==-1)||(boundrtype!=-1&&boundltype!=-1), "Spline1DConvDiff2Cubic: incorrect BoundLType/BoundRType!", _state); - if( boundltype==1||boundltype==2 ) - { - ae_assert(ae_isfinite(boundl, _state), "Spline1DConvDiff2Cubic: BoundL is infinite or NAN!", _state); - } - if( boundrtype==1||boundrtype==2 ) - { - ae_assert(ae_isfinite(boundr, _state), "Spline1DConvDiff2Cubic: BoundR is infinite or NAN!", _state); - } - - /* - * check lengths of arguments - */ - ae_assert(n>=2, "Spline1DConvDiff2Cubic: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DConvDiff2Cubic: Length(X)cnt>=n, "Spline1DConvDiff2Cubic: Length(Y)=2, "Spline1DConvDiff2Cubic: N2<2!", _state); - ae_assert(x2->cnt>=n2, "Spline1DConvDiff2Cubic: Length(X2)ptr.p_double[i]; - apperiodicmap(&t, x->ptr.p_double[0], x->ptr.p_double[n-1], &t2, _state); - x2->ptr.p_double[i] = t; - } - } - spline1d_heapsortppoints(x2, &dt, &p2, n2, _state); - - /* - * Now we've checked and preordered everything, so we: - * * call internal GridDiff() function to get Hermite form of spline - * * convert using internal Conv() function - * * convert Y2 back to original order - */ - spline1d_spline1dgriddiffcubicinternal(x, y, n, boundltype, boundl, boundrtype, boundr, &d, &a1, &a2, &a3, &b, &dt, _state); - spline1dconvdiffinternal(x, y, &d, n, x2, n2, y2, ae_true, d2, ae_true, dd2, ae_true, _state); - ae_assert(dt.cnt>=n2, "Spline1DConvDiff2Cubic: internal error!", _state); - for(i=0; i<=n2-1; i++) - { - dt.ptr.p_double[p2.ptr.p_int[i]] = y2->ptr.p_double[i]; - } - ae_v_move(&y2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1)); - for(i=0; i<=n2-1; i++) - { - dt.ptr.p_double[p2.ptr.p_int[i]] = d2->ptr.p_double[i]; - } - ae_v_move(&d2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1)); - for(i=0; i<=n2-1; i++) - { - dt.ptr.p_double[p2.ptr.p_int[i]] = dd2->ptr.p_double[i]; - } - ae_v_move(&dd2->ptr.p_double[0], 1, &dt.ptr.p_double[0], 1, ae_v_len(0,n2-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine builds Catmull-Rom spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundType - boundary condition type: - * -1 for periodic boundary condition - * 0 for parabolically terminated spline (default) - Tension - tension parameter: - * tension=0 corresponds to classic Catmull-Rom spline (default) - * 0=2, "Spline1DBuildCatmullRom: N<2!", _state); - ae_assert(boundtype==-1||boundtype==0, "Spline1DBuildCatmullRom: incorrect BoundType!", _state); - ae_assert(ae_fp_greater_eq(tension,0), "Spline1DBuildCatmullRom: Tension<0!", _state); - ae_assert(ae_fp_less_eq(tension,1), "Spline1DBuildCatmullRom: Tension>1!", _state); - ae_assert(x->cnt>=n, "Spline1DBuildCatmullRom: Length(X)cnt>=n, "Spline1DBuildCatmullRom: Length(Y)ptr.p_double[n-1] = y->ptr.p_double[0]; - ae_vector_set_length(&d, n, _state); - d.ptr.p_double[0] = (y->ptr.p_double[1]-y->ptr.p_double[n-2])/(2*(x->ptr.p_double[1]-x->ptr.p_double[0]+x->ptr.p_double[n-1]-x->ptr.p_double[n-2])); - for(i=1; i<=n-2; i++) - { - d.ptr.p_double[i] = (1-tension)*(y->ptr.p_double[i+1]-y->ptr.p_double[i-1])/(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]); - } - d.ptr.p_double[n-1] = d.ptr.p_double[0]; - - /* - * Now problem is reduced to the cubic Hermite spline - */ - spline1dbuildhermite(x, y, &d, n, c, _state); - c->periodic = ae_true; - } - else - { - - /* - * Non-periodic boundary conditions - */ - ae_vector_set_length(&d, n, _state); - for(i=1; i<=n-2; i++) - { - d.ptr.p_double[i] = (1-tension)*(y->ptr.p_double[i+1]-y->ptr.p_double[i-1])/(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]); - } - d.ptr.p_double[0] = 2*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0])-d.ptr.p_double[1]; - d.ptr.p_double[n-1] = 2*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2])-d.ptr.p_double[n-2]; - - /* - * Now problem is reduced to the cubic Hermite spline - */ - spline1dbuildhermite(x, y, &d, n, c, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine builds Hermite spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - D - derivatives, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildhermite(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* d, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _d; - ae_int_t i; - double delta; - double delta2; - double delta3; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_d, d, _state, ae_true); - d = &_d; - _spline1dinterpolant_clear(c); - - ae_assert(n>=2, "Spline1DBuildHermite: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DBuildHermite: Length(X)cnt>=n, "Spline1DBuildHermite: Length(Y)cnt>=n, "Spline1DBuildHermite: Length(D)x, n, _state); - ae_vector_set_length(&c->c, 4*(n-1)+2, _state); - c->periodic = ae_false; - c->k = 3; - c->n = n; - c->continuity = 1; - for(i=0; i<=n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=n-2; i++) - { - delta = x->ptr.p_double[i+1]-x->ptr.p_double[i]; - delta2 = ae_sqr(delta, _state); - delta3 = delta*delta2; - c->c.ptr.p_double[4*i+0] = y->ptr.p_double[i]; - c->c.ptr.p_double[4*i+1] = d->ptr.p_double[i]; - c->c.ptr.p_double[4*i+2] = (3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])-2*d->ptr.p_double[i]*delta-d->ptr.p_double[i+1]*delta)/delta2; - c->c.ptr.p_double[4*i+3] = (2*(y->ptr.p_double[i]-y->ptr.p_double[i+1])+d->ptr.p_double[i]*delta+d->ptr.p_double[i+1]*delta)/delta3; - } - c->c.ptr.p_double[4*(n-1)+0] = y->ptr.p_double[n-1]; - c->c.ptr.p_double[4*(n-1)+1] = d->ptr.p_double[n-1]; - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine builds Akima spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildakima(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_int_t i; - ae_vector d; - ae_vector w; - ae_vector diff; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - _spline1dinterpolant_clear(c); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&diff, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=2, "Spline1DBuildAkima: N<2!", _state); - ae_assert(x->cnt>=n, "Spline1DBuildAkima: Length(X)cnt>=n, "Spline1DBuildAkima: Length(Y)ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i]); - } - for(i=1; i<=n-2; i++) - { - w.ptr.p_double[i] = ae_fabs(diff.ptr.p_double[i]-diff.ptr.p_double[i-1], _state); - } - - /* - * Prepare Hermite interpolation scheme - */ - ae_vector_set_length(&d, n, _state); - for(i=2; i<=n-3; i++) - { - if( ae_fp_neq(ae_fabs(w.ptr.p_double[i-1], _state)+ae_fabs(w.ptr.p_double[i+1], _state),0) ) - { - d.ptr.p_double[i] = (w.ptr.p_double[i+1]*diff.ptr.p_double[i-1]+w.ptr.p_double[i-1]*diff.ptr.p_double[i])/(w.ptr.p_double[i+1]+w.ptr.p_double[i-1]); - } - else - { - d.ptr.p_double[i] = ((x->ptr.p_double[i+1]-x->ptr.p_double[i])*diff.ptr.p_double[i-1]+(x->ptr.p_double[i]-x->ptr.p_double[i-1])*diff.ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]); - } - } - d.ptr.p_double[0] = spline1d_diffthreepoint(x->ptr.p_double[0], x->ptr.p_double[0], y->ptr.p_double[0], x->ptr.p_double[1], y->ptr.p_double[1], x->ptr.p_double[2], y->ptr.p_double[2], _state); - d.ptr.p_double[1] = spline1d_diffthreepoint(x->ptr.p_double[1], x->ptr.p_double[0], y->ptr.p_double[0], x->ptr.p_double[1], y->ptr.p_double[1], x->ptr.p_double[2], y->ptr.p_double[2], _state); - d.ptr.p_double[n-2] = spline1d_diffthreepoint(x->ptr.p_double[n-2], x->ptr.p_double[n-3], y->ptr.p_double[n-3], x->ptr.p_double[n-2], y->ptr.p_double[n-2], x->ptr.p_double[n-1], y->ptr.p_double[n-1], _state); - d.ptr.p_double[n-1] = spline1d_diffthreepoint(x->ptr.p_double[n-1], x->ptr.p_double[n-3], y->ptr.p_double[n-3], x->ptr.p_double[n-2], y->ptr.p_double[n-2], x->ptr.p_double[n-1], y->ptr.p_double[n-1], _state); - - /* - * Build Akima spline using Hermite interpolation scheme - */ - spline1dbuildhermite(x, y, &d, n, c, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine calculates the value of the spline at the given point X. - -INPUT PARAMETERS: - C - spline interpolant - X - point - -Result: - S(x) - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -double spline1dcalc(spline1dinterpolant* c, double x, ae_state *_state) -{ - ae_int_t l; - ae_int_t r; - ae_int_t m; - double t; - double result; - - - ae_assert(c->k==3, "Spline1DCalc: internal error", _state); - ae_assert(!ae_isinf(x, _state), "Spline1DCalc: infinite X!", _state); - - /* - * special case: NaN - */ - if( ae_isnan(x, _state) ) - { - result = _state->v_nan; - return result; - } - - /* - * correct if periodic - */ - if( c->periodic ) - { - apperiodicmap(&x, c->x.ptr.p_double[0], c->x.ptr.p_double[c->n-1], &t, _state); - } - - /* - * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included) - */ - l = 0; - r = c->n-2+1; - while(l!=r-1) - { - m = (l+r)/2; - if( c->x.ptr.p_double[m]>=x ) - { - r = m; - } - else - { - l = m; - } - } - - /* - * Interpolation - */ - x = x-c->x.ptr.p_double[l]; - m = 4*l; - result = c->c.ptr.p_double[m]+x*(c->c.ptr.p_double[m+1]+x*(c->c.ptr.p_double[m+2]+x*c->c.ptr.p_double[m+3])); - return result; -} - - -/************************************************************************* -This subroutine differentiates the spline. - -INPUT PARAMETERS: - C - spline interpolant. - X - point - -Result: - S - S(x) - DS - S'(x) - D2S - S''(x) - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1ddiff(spline1dinterpolant* c, - double x, - double* s, - double* ds, - double* d2s, - ae_state *_state) -{ - ae_int_t l; - ae_int_t r; - ae_int_t m; - double t; - - *s = 0; - *ds = 0; - *d2s = 0; - - ae_assert(c->k==3, "Spline1DDiff: internal error", _state); - ae_assert(!ae_isinf(x, _state), "Spline1DDiff: infinite X!", _state); - - /* - * special case: NaN - */ - if( ae_isnan(x, _state) ) - { - *s = _state->v_nan; - *ds = _state->v_nan; - *d2s = _state->v_nan; - return; - } - - /* - * correct if periodic - */ - if( c->periodic ) - { - apperiodicmap(&x, c->x.ptr.p_double[0], c->x.ptr.p_double[c->n-1], &t, _state); - } - - /* - * Binary search - */ - l = 0; - r = c->n-2+1; - while(l!=r-1) - { - m = (l+r)/2; - if( c->x.ptr.p_double[m]>=x ) - { - r = m; - } - else - { - l = m; - } - } - - /* - * Differentiation - */ - x = x-c->x.ptr.p_double[l]; - m = 4*l; - *s = c->c.ptr.p_double[m]+x*(c->c.ptr.p_double[m+1]+x*(c->c.ptr.p_double[m+2]+x*c->c.ptr.p_double[m+3])); - *ds = c->c.ptr.p_double[m+1]+2*x*c->c.ptr.p_double[m+2]+3*ae_sqr(x, _state)*c->c.ptr.p_double[m+3]; - *d2s = 2*c->c.ptr.p_double[m+2]+6*x*c->c.ptr.p_double[m+3]; -} - - -/************************************************************************* -This subroutine makes the copy of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - -Result: - CC - spline copy - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dcopy(spline1dinterpolant* c, - spline1dinterpolant* cc, - ae_state *_state) -{ - ae_int_t s; - - _spline1dinterpolant_clear(cc); - - cc->periodic = c->periodic; - cc->n = c->n; - cc->k = c->k; - cc->continuity = c->continuity; - ae_vector_set_length(&cc->x, cc->n, _state); - ae_v_move(&cc->x.ptr.p_double[0], 1, &c->x.ptr.p_double[0], 1, ae_v_len(0,cc->n-1)); - s = c->c.cnt; - ae_vector_set_length(&cc->c, s, _state); - ae_v_move(&cc->c.ptr.p_double[0], 1, &c->c.ptr.p_double[0], 1, ae_v_len(0,s-1)); -} - - -/************************************************************************* -This subroutine unpacks the spline into the coefficients table. - -INPUT PARAMETERS: - C - spline interpolant. - X - point - -OUTPUT PARAMETERS: - Tbl - coefficients table, unpacked format, array[0..N-2, 0..5]. - For I = 0...N-2: - Tbl[I,0] = X[i] - Tbl[I,1] = X[i+1] - Tbl[I,2] = C0 - Tbl[I,3] = C1 - Tbl[I,4] = C2 - Tbl[I,5] = C3 - On [x[i], x[i+1]] spline is equals to: - S(x) = C0 + C1*t + C2*t^2 + C3*t^3 - t = x-x[i] - -NOTE: - You can rebuild spline with Spline1DBuildHermite() function, which - accepts as inputs function values and derivatives at nodes, which are - easy to calculate when you have coefficients. - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dunpack(spline1dinterpolant* c, - ae_int_t* n, - /* Real */ ae_matrix* tbl, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - *n = 0; - ae_matrix_clear(tbl); - - ae_matrix_set_length(tbl, c->n-2+1, 2+c->k+1, _state); - *n = c->n; - - /* - * Fill - */ - for(i=0; i<=*n-2; i++) - { - tbl->ptr.pp_double[i][0] = c->x.ptr.p_double[i]; - tbl->ptr.pp_double[i][1] = c->x.ptr.p_double[i+1]; - for(j=0; j<=c->k; j++) - { - tbl->ptr.pp_double[i][2+j] = c->c.ptr.p_double[(c->k+1)*i+j]; - } - } -} - - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: x = A*t + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dlintransx(spline1dinterpolant* c, - double a, - double b, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t n; - double v; - double dv; - double d2v; - ae_vector x; - ae_vector y; - ae_vector d; - ae_bool isperiodic; - ae_int_t contval; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - - ae_assert(c->k==3, "Spline1DLinTransX: internal error", _state); - n = c->n; - ae_vector_set_length(&x, n, _state); - ae_vector_set_length(&y, n, _state); - ae_vector_set_length(&d, n, _state); - - /* - * Unpack, X, Y, dY/dX. - * Scale and pack with Spline1DBuildHermite again. - */ - if( ae_fp_eq(a,0) ) - { - - /* - * Special case: A=0 - */ - v = spline1dcalc(c, b, _state); - for(i=0; i<=n-1; i++) - { - x.ptr.p_double[i] = c->x.ptr.p_double[i]; - y.ptr.p_double[i] = v; - d.ptr.p_double[i] = 0.0; - } - } - else - { - - /* - * General case, A<>0 - */ - for(i=0; i<=n-1; i++) - { - x.ptr.p_double[i] = c->x.ptr.p_double[i]; - spline1ddiff(c, x.ptr.p_double[i], &v, &dv, &d2v, _state); - x.ptr.p_double[i] = (x.ptr.p_double[i]-b)/a; - y.ptr.p_double[i] = v; - d.ptr.p_double[i] = a*dv; - } - } - isperiodic = c->periodic; - contval = c->continuity; - if( contval>0 ) - { - spline1dbuildhermite(&x, &y, &d, n, c, _state); - } - else - { - spline1dbuildlinear(&x, &y, n, c, _state); - } - c->periodic = isperiodic; - c->continuity = contval; - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: S2(x) = A*S(x) + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dlintransy(spline1dinterpolant* c, - double a, - double b, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - - - ae_assert(c->k==3, "Spline1DLinTransX: internal error", _state); - n = c->n; - for(i=0; i<=n-2; i++) - { - c->c.ptr.p_double[4*i] = a*c->c.ptr.p_double[4*i]+b; - for(j=1; j<=3; j++) - { - c->c.ptr.p_double[4*i+j] = a*c->c.ptr.p_double[4*i+j]; - } - } - c->c.ptr.p_double[4*(n-1)+0] = a*c->c.ptr.p_double[4*(n-1)+0]+b; - c->c.ptr.p_double[4*(n-1)+1] = a*c->c.ptr.p_double[4*(n-1)+1]; -} - - -/************************************************************************* -This subroutine integrates the spline. - -INPUT PARAMETERS: - C - spline interpolant. - X - right bound of the integration interval [a, x], - here 'a' denotes min(x[]) -Result: - integral(S(t)dt,a,x) - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -double spline1dintegrate(spline1dinterpolant* c, - double x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - ae_int_t l; - ae_int_t r; - ae_int_t m; - double w; - double v; - double t; - double intab; - double additionalterm; - double result; - - - n = c->n; - - /* - * Periodic splines require special treatment. We make - * following transformation: - * - * integral(S(t)dt,A,X) = integral(S(t)dt,A,Z)+AdditionalTerm - * - * here X may lie outside of [A,B], Z lies strictly in [A,B], - * AdditionalTerm is equals to integral(S(t)dt,A,B) times some - * integer number (may be zero). - */ - if( c->periodic&&(ae_fp_less(x,c->x.ptr.p_double[0])||ae_fp_greater(x,c->x.ptr.p_double[c->n-1])) ) - { - - /* - * compute integral(S(x)dx,A,B) - */ - intab = 0; - for(i=0; i<=c->n-2; i++) - { - w = c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i]; - m = (c->k+1)*i; - intab = intab+c->c.ptr.p_double[m]*w; - v = w; - for(j=1; j<=c->k; j++) - { - v = v*w; - intab = intab+c->c.ptr.p_double[m+j]*v/(j+1); - } - } - - /* - * map X into [A,B] - */ - apperiodicmap(&x, c->x.ptr.p_double[0], c->x.ptr.p_double[c->n-1], &t, _state); - additionalterm = t*intab; - } - else - { - additionalterm = 0; - } - - /* - * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included) - */ - l = 0; - r = n-2+1; - while(l!=r-1) - { - m = (l+r)/2; - if( ae_fp_greater_eq(c->x.ptr.p_double[m],x) ) - { - r = m; - } - else - { - l = m; - } - } - - /* - * Integration - */ - result = 0; - for(i=0; i<=l-1; i++) - { - w = c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i]; - m = (c->k+1)*i; - result = result+c->c.ptr.p_double[m]*w; - v = w; - for(j=1; j<=c->k; j++) - { - v = v*w; - result = result+c->c.ptr.p_double[m+j]*v/(j+1); - } - } - w = x-c->x.ptr.p_double[l]; - m = (c->k+1)*l; - v = w; - result = result+c->c.ptr.p_double[m]*w; - for(j=1; j<=c->k; j++) - { - v = v*w; - result = result+c->c.ptr.p_double[m+j]*v/(j+1); - } - result = result+additionalterm; - return result; -} - - -/************************************************************************* -Internal version of Spline1DConvDiff - -Converts from Hermite spline given by grid XOld to new grid X2 - -INPUT PARAMETERS: - XOld - old grid - YOld - values at old grid - DOld - first derivative at old grid - N - grid size - X2 - new grid - N2 - new grid size - Y - possibly preallocated output array - (reallocate if too small) - NeedY - do we need Y? - D1 - possibly preallocated output array - (reallocate if too small) - NeedD1 - do we need D1? - D2 - possibly preallocated output array - (reallocate if too small) - NeedD2 - do we need D1? - -OUTPUT ARRAYS: - Y - values, if needed - D1 - first derivative, if needed - D2 - second derivative, if needed - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiffinternal(/* Real */ ae_vector* xold, - /* Real */ ae_vector* yold, - /* Real */ ae_vector* dold, - ae_int_t n, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y, - ae_bool needy, - /* Real */ ae_vector* d1, - ae_bool needd1, - /* Real */ ae_vector* d2, - ae_bool needd2, - ae_state *_state) -{ - ae_int_t intervalindex; - ae_int_t pointindex; - ae_bool havetoadvance; - double c0; - double c1; - double c2; - double c3; - double a; - double b; - double w; - double w2; - double w3; - double fa; - double fb; - double da; - double db; - double t; - - - - /* - * Prepare space - */ - if( needy&&y->cntcntcnt=n2 ) - { - break; - } - t = x2->ptr.p_double[pointindex]; - - /* - * do we need to advance interval? - */ - havetoadvance = ae_false; - if( intervalindex==-1 ) - { - havetoadvance = ae_true; - } - else - { - if( intervalindexptr.p_double[intervalindex]; - b = xold->ptr.p_double[intervalindex+1]; - w = b-a; - w2 = w*w; - w3 = w*w2; - fa = yold->ptr.p_double[intervalindex]; - fb = yold->ptr.p_double[intervalindex+1]; - da = dold->ptr.p_double[intervalindex]; - db = dold->ptr.p_double[intervalindex+1]; - c0 = fa; - c1 = da; - c2 = (3*(fb-fa)-2*da*w-db*w)/w2; - c3 = (2*(fa-fb)+da*w+db*w)/w3; - continue; - } - - /* - * Calculate spline and its derivatives using power basis - */ - t = t-a; - if( needy ) - { - y->ptr.p_double[pointindex] = c0+t*(c1+t*(c2+t*c3)); - } - if( needd1 ) - { - d1->ptr.p_double[pointindex] = c1+2*t*c2+3*t*t*c3; - } - if( needd2 ) - { - d2->ptr.p_double[pointindex] = 2*c2+6*t*c3; - } - pointindex = pointindex+1; - } -} - - -/************************************************************************* -This function finds all roots and extrema of the spline S(x) defined at -[A,B] (interval which contains spline nodes). - -It does not extrapolates function, so roots and extrema located outside -of [A,B] will not be found. It returns all isolated (including multiple) -roots and extrema. - -INPUT PARAMETERS - C - spline interpolant - -OUTPUT PARAMETERS - R - array[NR], contains roots of the spline. - In case there is no roots, this array has zero length. - NR - number of roots, >=0 - DR - is set to True in case there is at least one interval - where spline is just a zero constant. Such degenerate - cases are not reported in the R/NR - E - array[NE], contains extrema (maximums/minimums) of - the spline. In case there is no extrema, this array - has zero length. - ET - array[NE], extrema types: - * ET[i]>0 in case I-th extrema is a minimum - * ET[i]<0 in case I-th extrema is a maximum - NE - number of extrema, >=0 - DE - is set to True in case there is at least one interval - where spline is a constant. Such degenerate cases are - not reported in the E/NE. - -NOTES: - -1. This function does NOT report following kinds of roots: - * intervals where function is constantly zero - * roots which are outside of [A,B] (note: it CAN return A or B) - -2. This function does NOT report following kinds of extrema: - * intervals where function is a constant - * extrema which are outside of (A,B) (note: it WON'T return A or B) - - -- ALGLIB PROJECT -- - Copyright 26.09.2011 by Bochkanov Sergey -*************************************************************************/ -void spline1drootsandextrema(spline1dinterpolant* c, - /* Real */ ae_vector* r, - ae_int_t* nr, - ae_bool* dr, - /* Real */ ae_vector* e, - /* Integer */ ae_vector* et, - ae_int_t* ne, - ae_bool* de, - ae_state *_state) -{ - ae_frame _frame_block; - double pl; - double ml; - double pll; - double pr; - double mr; - ae_vector tr; - ae_vector tmpr; - ae_vector tmpe; - ae_vector tmpet; - ae_vector tmpc; - double x0; - double x1; - double x2; - double ex0; - double ex1; - ae_int_t tne; - ae_int_t tnr; - ae_int_t i; - ae_int_t j; - ae_bool nstep; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(r); - *nr = 0; - *dr = ae_false; - ae_vector_clear(e); - ae_vector_clear(et); - *ne = 0; - *de = ae_false; - ae_vector_init(&tr, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpr, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpe, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpet, 0, DT_INT, _state, ae_true); - ae_vector_init(&tmpc, 0, DT_REAL, _state, ae_true); - - - /* - *exception handling - */ - ae_assert(c->k==3, "Spline1DRootsAndExtrema : incorrect parameter C.K!", _state); - ae_assert(c->continuity>=0, "Spline1DRootsAndExtrema : parameter C.Continuity must not be less than 0!", _state); - - /* - *initialization of variable - */ - *nr = 0; - *ne = 0; - *dr = ae_false; - *de = ae_false; - nstep = ae_true; - - /* - *consider case, when C.Continuty=0 - */ - if( c->continuity==0 ) - { - - /* - *allocation for auxiliary arrays - *'TmpR ' - it stores a time value for roots - *'TmpE ' - it stores a time value for extremums - *'TmpET '- it stores a time value for extremums type - */ - rvectorsetlengthatleast(&tmpr, 3*(c->n-1), _state); - rvectorsetlengthatleast(&tmpe, 2*(c->n-1), _state); - ivectorsetlengthatleast(&tmpet, 2*(c->n-1), _state); - - /* - *start calculating - */ - for(i=0; i<=c->n-2; i++) - { - - /* - *initialization pL, mL, pR, mR - */ - pl = c->c.ptr.p_double[4*i]; - ml = c->c.ptr.p_double[4*i+1]; - pr = c->c.ptr.p_double[4*(i+1)]; - mr = c->c.ptr.p_double[4*i+1]+2*c->c.ptr.p_double[4*i+2]*(c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i])+3*c->c.ptr.p_double[4*i+3]*(c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i])*(c->x.ptr.p_double[i+1]-c->x.ptr.p_double[i]); - - /* - *pre-searching roots and extremums - */ - solvecubicpolinom(pl, ml, pr, mr, c->x.ptr.p_double[i], c->x.ptr.p_double[i+1], &x0, &x1, &x2, &ex0, &ex1, &tnr, &tne, &tr, _state); - *dr = *dr||tnr==-1; - *de = *de||tne==-1; - - /* - *searching of roots - */ - if( tnr==1&&nstep ) - { - - /* - *is there roots? - */ - if( *nr>0 ) - { - - /* - *is a next root equal a previous root? - *if is't, then write new root - */ - if( ae_fp_neq(x0,tmpr.ptr.p_double[*nr-1]) ) - { - tmpr.ptr.p_double[*nr] = x0; - *nr = *nr+1; - } - } - else - { - - /* - *write a first root - */ - tmpr.ptr.p_double[*nr] = x0; - *nr = *nr+1; - } - } - else - { - - /* - *case when function at a segment identically to zero - *then we have to clear a root, if the one located on a - *constant segment - */ - if( tnr==-1 ) - { - - /* - *safe state variable as constant - */ - if( nstep ) - { - nstep = ae_false; - } - - /* - *clear the root, if there is - */ - if( *nr>0 ) - { - if( ae_fp_eq(c->x.ptr.p_double[i],tmpr.ptr.p_double[*nr-1]) ) - { - *nr = *nr-1; - } - } - - /* - *change state for 'DR' - */ - if( !*dr ) - { - *dr = ae_true; - } - } - else - { - nstep = ae_true; - } - } - - /* - *searching of extremums - */ - if( i>0 ) - { - pll = c->c.ptr.p_double[4*(i-1)]; - - /* - *if pL=pLL or pL=pR then - */ - if( tne==-1 ) - { - if( !*de ) - { - *de = ae_true; - } - } - else - { - if( ae_fp_greater(pl,pll)&&ae_fp_greater(pl,pr) ) - { - - /* - *maximum - */ - tmpet.ptr.p_int[*ne] = -1; - tmpe.ptr.p_double[*ne] = c->x.ptr.p_double[i]; - *ne = *ne+1; - } - else - { - if( ae_fp_less(pl,pll)&&ae_fp_less(pl,pr) ) - { - - /* - *minimum - */ - tmpet.ptr.p_int[*ne] = 1; - tmpe.ptr.p_double[*ne] = c->x.ptr.p_double[i]; - *ne = *ne+1; - } - } - } - } - } - - /* - *write final result - */ - rvectorsetlengthatleast(r, *nr, _state); - rvectorsetlengthatleast(e, *ne, _state); - ivectorsetlengthatleast(et, *ne, _state); - - /* - *write roots - */ - for(i=0; i<=*nr-1; i++) - { - r->ptr.p_double[i] = tmpr.ptr.p_double[i]; - } - - /* - *write extremums and their types - */ - for(i=0; i<=*ne-1; i++) - { - e->ptr.p_double[i] = tmpe.ptr.p_double[i]; - et->ptr.p_int[i] = tmpet.ptr.p_int[i]; - } - } - else - { - - /* - *case, when C.Continuity>=1 - *'TmpR ' - it stores a time value for roots - *'TmpC' - it stores a time value for extremums and - *their function value (TmpC={EX0,F(EX0), EX1,F(EX1), ..., EXn,F(EXn)};) - *'TmpE' - it stores a time value for extremums only - *'TmpET'- it stores a time value for extremums type - */ - rvectorsetlengthatleast(&tmpr, 2*c->n-1, _state); - rvectorsetlengthatleast(&tmpc, 4*c->n, _state); - rvectorsetlengthatleast(&tmpe, 2*c->n, _state); - ivectorsetlengthatleast(&tmpet, 2*c->n, _state); - - /* - *start calculating - */ - for(i=0; i<=c->n-2; i++) - { - - /* - *we calculate pL,mL, pR,mR as Fi+1(F'i+1) at left border - */ - pl = c->c.ptr.p_double[4*i]; - ml = c->c.ptr.p_double[4*i+1]; - pr = c->c.ptr.p_double[4*(i+1)]; - mr = c->c.ptr.p_double[4*(i+1)+1]; - - /* - *calculating roots and extremums at [X[i],X[i+1]] - */ - solvecubicpolinom(pl, ml, pr, mr, c->x.ptr.p_double[i], c->x.ptr.p_double[i+1], &x0, &x1, &x2, &ex0, &ex1, &tnr, &tne, &tr, _state); - - /* - *searching roots - */ - if( tnr>0 ) - { - - /* - *re-init tR - */ - if( tnr>=1 ) - { - tr.ptr.p_double[0] = x0; - } - if( tnr>=2 ) - { - tr.ptr.p_double[1] = x1; - } - if( tnr==3 ) - { - tr.ptr.p_double[2] = x2; - } - - /* - *start root selection - */ - if( *nr>0 ) - { - if( ae_fp_neq(tmpr.ptr.p_double[*nr-1],x0) ) - { - - /* - *previous segment was't constant identical zero - */ - if( nstep ) - { - for(j=0; j<=tnr-1; j++) - { - tmpr.ptr.p_double[*nr+j] = tr.ptr.p_double[j]; - } - *nr = *nr+tnr; - } - else - { - - /* - *previous segment was constant identical zero - *and we must ignore [NR+j-1] root - */ - for(j=1; j<=tnr-1; j++) - { - tmpr.ptr.p_double[*nr+j-1] = tr.ptr.p_double[j]; - } - *nr = *nr+tnr-1; - nstep = ae_true; - } - } - else - { - for(j=1; j<=tnr-1; j++) - { - tmpr.ptr.p_double[*nr+j-1] = tr.ptr.p_double[j]; - } - *nr = *nr+tnr-1; - } - } - else - { - - /* - *write first root - */ - for(j=0; j<=tnr-1; j++) - { - tmpr.ptr.p_double[*nr+j] = tr.ptr.p_double[j]; - } - *nr = *nr+tnr; - } - } - else - { - if( tnr==-1 ) - { - - /* - *decrement 'NR' if at previous step was writen a root - *(previous segment identical zero) - */ - if( *nr>0&&nstep ) - { - *nr = *nr-1; - } - - /* - *previous segment is't constant - */ - if( nstep ) - { - nstep = ae_false; - } - - /* - *rewrite 'DR' - */ - if( !*dr ) - { - *dr = ae_true; - } - } - } - - /* - *searching extremums - *write all term like extremums - */ - if( tne==1 ) - { - if( *ne>0 ) - { - - /* - *just ignore identical extremums - *because he must be one - */ - if( ae_fp_neq(tmpc.ptr.p_double[*ne-2],ex0) ) - { - tmpc.ptr.p_double[*ne] = ex0; - tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]); - *ne = *ne+2; - } - } - else - { - - /* - *write first extremum and it function value - */ - tmpc.ptr.p_double[*ne] = ex0; - tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]); - *ne = *ne+2; - } - } - else - { - if( tne==2 ) - { - if( *ne>0 ) - { - - /* - *ignore identical extremum - */ - if( ae_fp_neq(tmpc.ptr.p_double[*ne-2],ex0) ) - { - tmpc.ptr.p_double[*ne] = ex0; - tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]); - *ne = *ne+2; - } - } - else - { - - /* - *write first extremum - */ - tmpc.ptr.p_double[*ne] = ex0; - tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i])*(ex0-c->x.ptr.p_double[i]); - *ne = *ne+2; - } - - /* - *write second extremum - */ - tmpc.ptr.p_double[*ne] = ex1; - tmpc.ptr.p_double[*ne+1] = c->c.ptr.p_double[4*i]+c->c.ptr.p_double[4*i+1]*(ex1-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+2]*(ex1-c->x.ptr.p_double[i])*(ex1-c->x.ptr.p_double[i])+c->c.ptr.p_double[4*i+3]*(ex1-c->x.ptr.p_double[i])*(ex1-c->x.ptr.p_double[i])*(ex1-c->x.ptr.p_double[i]); - *ne = *ne+2; - } - else - { - if( tne==-1 ) - { - if( !*de ) - { - *de = ae_true; - } - } - } - } - } - - /* - *checking of arrays - *get number of extremums (tNe=NE/2) - *initialize pL as value F0(X[0]) and - *initialize pR as value Fn-1(X[N]) - */ - tne = *ne/2; - *ne = 0; - pl = c->c.ptr.p_double[0]; - pr = c->c.ptr.p_double[4*(c->n-1)]; - for(i=0; i<=tne-1; i++) - { - if( i>0&&ix.ptr.p_double[0]) ) - { - if( ae_fp_greater(tmpc.ptr.p_double[2*i+1],pl)&&ae_fp_greater(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i+1)+1]) ) - { - - /* - *maximum - */ - tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i]; - tmpet.ptr.p_int[*ne] = -1; - *ne = *ne+1; - } - else - { - if( ae_fp_less(tmpc.ptr.p_double[2*i+1],pl)&&ae_fp_less(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i+1)+1]) ) - { - - /* - *minimum - */ - tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i]; - tmpet.ptr.p_int[*ne] = 1; - *ne = *ne+1; - } - } - } - } - else - { - if( i==tne-1 ) - { - if( ae_fp_neq(tmpc.ptr.p_double[2*i],c->x.ptr.p_double[c->n-1]) ) - { - if( ae_fp_greater(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i-1)+1])&&ae_fp_greater(tmpc.ptr.p_double[2*i+1],pr) ) - { - - /* - *maximum - */ - tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i]; - tmpet.ptr.p_int[*ne] = -1; - *ne = *ne+1; - } - else - { - if( ae_fp_less(tmpc.ptr.p_double[2*i+1],tmpc.ptr.p_double[2*(i-1)+1])&&ae_fp_less(tmpc.ptr.p_double[2*i+1],pr) ) - { - - /* - *minimum - */ - tmpe.ptr.p_double[*ne] = tmpc.ptr.p_double[2*i]; - tmpet.ptr.p_int[*ne] = 1; - *ne = *ne+1; - } - } - } - } - } - } - } - - /* - *final results - *allocate R, E, ET - */ - rvectorsetlengthatleast(r, *nr, _state); - rvectorsetlengthatleast(e, *ne, _state); - ivectorsetlengthatleast(et, *ne, _state); - - /* - *write result for extremus and their types - */ - for(i=0; i<=*ne-1; i++) - { - e->ptr.p_double[i] = tmpe.ptr.p_double[i]; - et->ptr.p_int[i] = tmpet.ptr.p_int[i]; - } - - /* - *write result for roots - */ - for(i=0; i<=*nr-1; i++) - { - r->ptr.p_double[i] = tmpr.ptr.p_double[i]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. Heap sort. -*************************************************************************/ -void heapsortdpoints(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* d, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector rbuf; - ae_vector ibuf; - ae_vector rbuf2; - ae_vector ibuf2; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&rbuf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ibuf, 0, DT_INT, _state, ae_true); - ae_vector_init(&rbuf2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ibuf2, 0, DT_INT, _state, ae_true); - - ae_vector_set_length(&ibuf, n, _state); - ae_vector_set_length(&rbuf, n, _state); - for(i=0; i<=n-1; i++) - { - ibuf.ptr.p_int[i] = i; - } - tagsortfasti(x, &ibuf, &rbuf2, &ibuf2, n, _state); - for(i=0; i<=n-1; i++) - { - rbuf.ptr.p_double[i] = y->ptr.p_double[ibuf.ptr.p_int[i]]; - } - ae_v_move(&y->ptr.p_double[0], 1, &rbuf.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - rbuf.ptr.p_double[i] = d->ptr.p_double[ibuf.ptr.p_int[i]]; - } - ae_v_move(&d->ptr.p_double[0], 1, &rbuf.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -This procedure search roots of an quadratic equation inside [0;1] and it number of roots. - -INPUT PARAMETERS: - P0 - value of a function at 0 - M0 - value of a derivative at 0 - P1 - value of a function at 1 - M1 - value of a derivative at 1 - -OUTPUT PARAMETERS: - X0 - first root of an equation - X1 - second root of an equation - NR - number of roots - -RESTRICTIONS OF PARAMETERS: - -Parameters for this procedure has't to be zero simultaneously. Is expected, -that input polinom is't degenerate or constant identicaly ZERO. - - -REMARK: - -The procedure always fill value for X1 and X2, even if it is't belongs to [0;1]. -But first true root(even if existing one) is in X1. -Number of roots is NR. - - -- ALGLIB PROJECT -- - Copyright 26.09.2011 by Bochkanov Sergey -*************************************************************************/ -void solvepolinom2(double p0, - double m0, - double p1, - double m1, - double* x0, - double* x1, - ae_int_t* nr, - ae_state *_state) -{ - double a; - double b; - double c; - double dd; - double tmp; - double exf; - double extr; - - *x0 = 0; - *x1 = 0; - *nr = 0; - - - /* - *calculate parameters for equation: A, B and C - */ - a = 6*p0+3*m0-6*p1+3*m1; - b = -6*p0-4*m0+6*p1-2*m1; - c = m0; - - /* - *check case, when A=0 - *we are considering the linear equation - */ - if( ae_fp_eq(a,0) ) - { - - /* - *B<>0 and root inside [0;1] - *one root - */ - if( (ae_fp_neq(b,0)&&ae_sign(c, _state)*ae_sign(b, _state)<=0)&&ae_fp_greater_eq(ae_fabs(b, _state),ae_fabs(c, _state)) ) - { - *x0 = -c/b; - *nr = 1; - return; - } - else - { - *nr = 0; - return; - } - } - - /* - *consider case, when extremumu outside (0;1) - *exist one root only - */ - if( ae_fp_less_eq(ae_fabs(2*a, _state),ae_fabs(b, _state))||ae_sign(b, _state)*ae_sign(a, _state)>=0 ) - { - if( ae_sign(m0, _state)*ae_sign(m1, _state)>0 ) - { - *nr = 0; - return; - } - - /* - *consider case, when the one exist - *same sign of derivative - */ - if( ae_sign(m0, _state)*ae_sign(m1, _state)<0 ) - { - *nr = 1; - extr = -b/(2*a); - dd = b*b-4*a*c; - if( ae_fp_less(dd,0) ) - { - return; - } - *x0 = (-b-ae_sqrt(dd, _state))/(2*a); - *x1 = (-b+ae_sqrt(dd, _state))/(2*a); - if( (ae_fp_greater_eq(extr,1)&&ae_fp_less_eq(*x1,extr))||(ae_fp_less_eq(extr,0)&&ae_fp_greater_eq(*x1,extr)) ) - { - *x0 = *x1; - } - return; - } - - /* - *consider case, when the one is 0 - */ - if( ae_fp_eq(m0,0) ) - { - *x0 = 0; - *nr = 1; - return; - } - if( ae_fp_eq(m1,0) ) - { - *x0 = 1; - *nr = 1; - return; - } - } - else - { - - /* - *consider case, when both of derivatives is 0 - */ - if( ae_fp_eq(m0,0)&&ae_fp_eq(m1,0) ) - { - *x0 = 0; - *x1 = 1; - *nr = 2; - return; - } - - /* - *consider case, when derivative at 0 is 0, and derivative at 1 is't 0 - */ - if( ae_fp_eq(m0,0)&&ae_fp_neq(m1,0) ) - { - dd = b*b-4*a*c; - if( ae_fp_less(dd,0) ) - { - *x0 = 0; - *nr = 1; - return; - } - *x0 = (-b-ae_sqrt(dd, _state))/(2*a); - *x1 = (-b+ae_sqrt(dd, _state))/(2*a); - extr = -b/(2*a); - exf = a*extr*extr+b*extr+c; - if( ae_sign(exf, _state)*ae_sign(m1, _state)>0 ) - { - *x0 = 0; - *nr = 1; - return; - } - else - { - if( ae_fp_greater(extr,*x0) ) - { - *x0 = 0; - } - else - { - *x1 = 0; - } - *nr = 2; - - /* - *roots must placed ascending - */ - if( ae_fp_greater(*x0,*x1) ) - { - tmp = *x0; - *x0 = *x1; - *x1 = tmp; - } - return; - } - } - if( ae_fp_eq(m1,0)&&ae_fp_neq(m0,0) ) - { - dd = b*b-4*a*c; - if( ae_fp_less(dd,0) ) - { - *x0 = 1; - *nr = 1; - return; - } - *x0 = (-b-ae_sqrt(dd, _state))/(2*a); - *x1 = (-b+ae_sqrt(dd, _state))/(2*a); - extr = -b/(2*a); - exf = a*extr*extr+b*extr+c; - if( ae_sign(exf, _state)*ae_sign(m0, _state)>0 ) - { - *x0 = 1; - *nr = 1; - return; - } - else - { - if( ae_fp_less(extr,*x0) ) - { - *x0 = 1; - } - else - { - *x1 = 1; - } - *nr = 2; - - /* - *roots must placed ascending - */ - if( ae_fp_greater(*x0,*x1) ) - { - tmp = *x0; - *x0 = *x1; - *x1 = tmp; - } - return; - } - } - else - { - extr = -b/(2*a); - exf = a*extr*extr+b*extr+c; - if( ae_sign(exf, _state)*ae_sign(m0, _state)>0&&ae_sign(exf, _state)*ae_sign(m1, _state)>0 ) - { - *nr = 0; - return; - } - dd = b*b-4*a*c; - if( ae_fp_less(dd,0) ) - { - *nr = 0; - return; - } - *x0 = (-b-ae_sqrt(dd, _state))/(2*a); - *x1 = (-b+ae_sqrt(dd, _state))/(2*a); - - /* - *if EXF and m0, EXF and m1 has different signs, then equation has two roots - */ - if( ae_sign(exf, _state)*ae_sign(m0, _state)<0&&ae_sign(exf, _state)*ae_sign(m1, _state)<0 ) - { - *nr = 2; - - /* - *roots must placed ascending - */ - if( ae_fp_greater(*x0,*x1) ) - { - tmp = *x0; - *x0 = *x1; - *x1 = tmp; - } - return; - } - else - { - *nr = 1; - if( ae_sign(exf, _state)*ae_sign(m0, _state)<0 ) - { - if( ae_fp_less(*x1,extr) ) - { - *x0 = *x1; - } - return; - } - if( ae_sign(exf, _state)*ae_sign(m1, _state)<0 ) - { - if( ae_fp_greater(*x1,extr) ) - { - *x0 = *x1; - } - return; - } - } - } - } -} - - -/************************************************************************* -This procedure search roots of an cubic equation inside [A;B], it number of roots -and number of extremums. - -INPUT PARAMETERS: - pA - value of a function at A - mA - value of a derivative at A - pB - value of a function at B - mB - value of a derivative at B - A0 - left border [A0;B0] - B0 - right border [A0;B0] - -OUTPUT PARAMETERS: - X0 - first root of an equation - X1 - second root of an equation - X2 - third root of an equation - EX0 - first extremum of a function - EX0 - second extremum of a function - NR - number of roots - NR - number of extrmums - -RESTRICTIONS OF PARAMETERS: - -Length of [A;B] must be positive and is't zero, i.e. A<>B and AB - */ - ae_assert(ae_fp_less(a,b), "\nSolveCubicPolinom: incorrect borders for [A;B]!\n", _state); - - /* - *case 1 - *function can be identicaly to ZERO - */ - if( ((ae_fp_eq(ma,0)&&ae_fp_eq(mb,0))&&ae_fp_eq(pa,pb))&&ae_fp_eq(pa,0) ) - { - *nr = -1; - *ne = -1; - return; - } - if( (ae_fp_eq(ma,0)&&ae_fp_eq(mb,0))&&ae_fp_eq(pa,pb) ) - { - *nr = 0; - *ne = -1; - return; - } - tmpma = ma*(b-a); - tmpmb = mb*(b-a); - solvepolinom2(pa, tmpma, pb, tmpmb, ex0, ex1, ne, _state); - *ex0 = spline1d_rescaleval(0, 1, a, b, *ex0, _state); - *ex1 = spline1d_rescaleval(0, 1, a, b, *ex1, _state); - - /* - *case 3.1 - *no extremums at [A;B] - */ - if( *ne==0 ) - { - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, 1, x0, _state); - if( *nr==1 ) - { - *x0 = spline1d_rescaleval(0, 1, a, b, *x0, _state); - } - return; - } - - /* - *case 3.2 - *one extremum - */ - if( *ne==1 ) - { - if( ae_fp_eq(*ex0,a)||ae_fp_eq(*ex0,b) ) - { - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, 1, x0, _state); - if( *nr==1 ) - { - *x0 = spline1d_rescaleval(0, 1, a, b, *x0, _state); - } - return; - } - else - { - *nr = 0; - i = 0; - tex0 = spline1d_rescaleval(a, b, 0, 1, *ex0, _state); - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, tex0, x0, _state)+(*nr); - if( *nr>i ) - { - tempdata->ptr.p_double[i] = spline1d_rescaleval(0, tex0, a, *ex0, *x0, _state); - i = i+1; - } - *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex0, 1, x0, _state)+(*nr); - if( *nr>i ) - { - *x0 = spline1d_rescaleval(tex0, 1, *ex0, b, *x0, _state); - if( i>0 ) - { - if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) ) - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - else - { - *nr = *nr-1; - } - } - else - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - } - if( *nr>0 ) - { - *x0 = tempdata->ptr.p_double[0]; - if( *nr>1 ) - { - *x1 = tempdata->ptr.p_double[1]; - } - return; - } - } - return; - } - else - { - - /* - *case 3.3 - *two extremums(or more, but it's impossible) - * - * - *case 3.3.0 - *both extremums at the border - */ - if( ae_fp_eq(*ex0,a)&&ae_fp_eq(*ex1,b) ) - { - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, 1, x0, _state); - if( *nr==1 ) - { - *x0 = spline1d_rescaleval(0, 1, a, b, *x0, _state); - } - return; - } - if( ae_fp_eq(*ex0,a)&&ae_fp_neq(*ex1,b) ) - { - *nr = 0; - i = 0; - tex1 = spline1d_rescaleval(a, b, 0, 1, *ex1, _state); - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, tex1, x0, _state)+(*nr); - if( *nr>i ) - { - tempdata->ptr.p_double[i] = spline1d_rescaleval(0, tex1, a, *ex1, *x0, _state); - i = i+1; - } - *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex1, 1, x0, _state)+(*nr); - if( *nr>i ) - { - *x0 = spline1d_rescaleval(tex1, 1, *ex1, b, *x0, _state); - if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) ) - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - else - { - *nr = *nr-1; - } - } - if( *nr>0 ) - { - *x0 = tempdata->ptr.p_double[0]; - if( *nr>1 ) - { - *x1 = tempdata->ptr.p_double[1]; - } - return; - } - } - if( ae_fp_eq(*ex1,b)&&ae_fp_neq(*ex0,a) ) - { - *nr = 0; - i = 0; - tex0 = spline1d_rescaleval(a, b, 0, 1, *ex0, _state); - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, tex0, x0, _state)+(*nr); - if( *nr>i ) - { - tempdata->ptr.p_double[i] = spline1d_rescaleval(0, tex0, a, *ex0, *x0, _state); - i = i+1; - } - *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex0, 1, x0, _state)+(*nr); - if( *nr>i ) - { - *x0 = spline1d_rescaleval(tex0, 1, *ex0, b, *x0, _state); - if( i>0 ) - { - if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) ) - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - else - { - *nr = *nr-1; - } - } - else - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - } - if( *nr>0 ) - { - *x0 = tempdata->ptr.p_double[0]; - if( *nr>1 ) - { - *x1 = tempdata->ptr.p_double[1]; - } - return; - } - } - else - { - - /* - *case 3.3.2 - *both extremums inside (0;1) - */ - *nr = 0; - i = 0; - tex0 = spline1d_rescaleval(a, b, 0, 1, *ex0, _state); - tex1 = spline1d_rescaleval(a, b, 0, 1, *ex1, _state); - *nr = bisectmethod(pa, tmpma, pb, tmpmb, 0, tex0, x0, _state)+(*nr); - if( *nr>i ) - { - tempdata->ptr.p_double[i] = spline1d_rescaleval(0, tex0, a, *ex0, *x0, _state); - i = i+1; - } - *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex0, tex1, x0, _state)+(*nr); - if( *nr>i ) - { - *x0 = spline1d_rescaleval(tex0, tex1, *ex0, *ex1, *x0, _state); - if( i>0 ) - { - if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) ) - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - else - { - *nr = *nr-1; - } - } - else - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - } - *nr = bisectmethod(pa, tmpma, pb, tmpmb, tex1, 1, x0, _state)+(*nr); - if( *nr>i ) - { - *x0 = spline1d_rescaleval(tex1, 1, *ex1, b, *x0, _state); - if( i>0 ) - { - if( ae_fp_neq(*x0,tempdata->ptr.p_double[i-1]) ) - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - else - { - *nr = *nr-1; - } - } - else - { - tempdata->ptr.p_double[i] = *x0; - i = i+1; - } - } - - /* - *write are found roots - */ - if( *nr>0 ) - { - *x0 = tempdata->ptr.p_double[0]; - if( *nr>1 ) - { - *x1 = tempdata->ptr.p_double[1]; - } - if( *nr>2 ) - { - *x2 = tempdata->ptr.p_double[2]; - } - return; - } - } - } -} - - -/************************************************************************* -Function for searching a root at [A;B] by bisection method and return number of roots -(0 or 1) - -INPUT PARAMETERS: - pA - value of a function at A - mA - value of a derivative at A - pB - value of a function at B - mB - value of a derivative at B - A0 - left border [A0;B0] - B0 - right border [A0;B0] - -RESTRICTIONS OF PARAMETERS: - -We assume, that B0>A0. - - -REMARK: - -Assume, that exist one root only at [A;B], else -function may be work incorrectly. -The function dont check value A0,B0! - - -- ALGLIB PROJECT -- - Copyright 26.09.2011 by Bochkanov Sergey -*************************************************************************/ -ae_int_t bisectmethod(double pa, - double ma, - double pb, - double mb, - double a, - double b, - double* x, - ae_state *_state) -{ - double vacuum; - double eps; - double a0; - double b0; - double m; - double lf; - double rf; - double mf; - ae_int_t result; - - *x = 0; - - - /* - *accuracy - */ - eps = 1000*(b-a)*ae_machineepsilon; - - /* - *initialization left and right borders - */ - a0 = a; - b0 = b; - - /* - *initialize function value at 'A' and 'B' - */ - spline1d_hermitecalc(pa, ma, pb, mb, a, &lf, &vacuum, _state); - spline1d_hermitecalc(pa, ma, pb, mb, b, &rf, &vacuum, _state); - - /* - *check, that 'A' and 'B' are't roots, - *and that root exist - */ - if( ae_sign(lf, _state)*ae_sign(rf, _state)>0 ) - { - result = 0; - return result; - } - else - { - if( ae_fp_eq(lf,0) ) - { - *x = a; - result = 1; - return result; - } - else - { - if( ae_fp_eq(rf,0) ) - { - *x = b; - result = 1; - return result; - } - } - } - - /* - *searching a root - */ - do - { - m = (b0+a0)/2; - spline1d_hermitecalc(pa, ma, pb, mb, a0, &lf, &vacuum, _state); - spline1d_hermitecalc(pa, ma, pb, mb, b0, &rf, &vacuum, _state); - spline1d_hermitecalc(pa, ma, pb, mb, m, &mf, &vacuum, _state); - if( ae_sign(mf, _state)*ae_sign(lf, _state)<0 ) - { - b0 = m; - } - else - { - if( ae_sign(mf, _state)*ae_sign(rf, _state)<0 ) - { - a0 = m; - } - else - { - if( ae_fp_eq(lf,0) ) - { - *x = a0; - result = 1; - return result; - } - if( ae_fp_eq(rf,0) ) - { - *x = b0; - result = 1; - return result; - } - if( ae_fp_eq(mf,0) ) - { - *x = m; - result = 1; - return result; - } - } - } - } - while(ae_fp_greater_eq(ae_fabs(b0-a0, _state),eps)); - *x = m; - result = 1; - return result; -} - - -/************************************************************************* -This function builds monotone cubic Hermite interpolant. This interpolant -is monotonic in [x(0),x(n-1)] and is constant outside of this interval. - -In case y[] form non-monotonic sequence, interpolant is piecewise -monotonic. Say, for x=(0,1,2,3,4) and y=(0,1,2,1,0) interpolant will -monotonically grow at [0..2] and monotonically decrease at [2..4]. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. Subroutine automatically - sorts points, so caller may pass unsorted array. - Y - function values, array[0..N-1] - N - the number of points(N>=2). - -OUTPUT PARAMETERS: - C - spline interpolant. - - -- ALGLIB PROJECT -- - Copyright 21.06.2012 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildmonotone(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector d; - ae_vector ex; - ae_vector ey; - ae_vector p; - double delta; - double alpha; - double beta; - ae_int_t tmpn; - ae_int_t sn; - double ca; - double cb; - double epsilon; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - _spline1dinterpolant_clear(c); - ae_vector_init(&d, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ex, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ey, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - - - /* - * Check lengths of arguments - */ - ae_assert(n>=2, "Spline1DBuildMonotone: N<2", _state); - ae_assert(x->cnt>=n, "Spline1DBuildMonotone: Length(X)cnt>=n, "Spline1DBuildMonotone: Length(Y)ptr.p_double[0]-ae_fabs(x->ptr.p_double[1]-x->ptr.p_double[0], _state); - ex.ptr.p_double[n-1] = x->ptr.p_double[n-3]+ae_fabs(x->ptr.p_double[n-3]-x->ptr.p_double[n-4], _state); - ey.ptr.p_double[0] = y->ptr.p_double[0]; - ey.ptr.p_double[n-1] = y->ptr.p_double[n-3]; - for(i=1; i<=n-2; i++) - { - ex.ptr.p_double[i] = x->ptr.p_double[i-1]; - ey.ptr.p_double[i] = y->ptr.p_double[i-1]; - } - - /* - * Init sign of the function for first segment - */ - i = 0; - ca = 0; - do - { - ca = ey.ptr.p_double[i+1]-ey.ptr.p_double[i]; - i = i+1; - } - while(!(ae_fp_neq(ca,0)||i>n-2)); - if( ae_fp_neq(ca,0) ) - { - ca = ca/ae_fabs(ca, _state); - } - i = 0; - while(i=2, "Spline1DBuildMonotone: internal error", _state); - - /* - * Calculate derivatives for current segment - */ - d.ptr.p_double[i] = 0; - d.ptr.p_double[sn-1] = 0; - for(j=i+1; j<=sn-2; j++) - { - d.ptr.p_double[j] = ((ey.ptr.p_double[j]-ey.ptr.p_double[j-1])/(ex.ptr.p_double[j]-ex.ptr.p_double[j-1])+(ey.ptr.p_double[j+1]-ey.ptr.p_double[j])/(ex.ptr.p_double[j+1]-ex.ptr.p_double[j]))/2; - } - for(j=i; j<=sn-2; j++) - { - delta = (ey.ptr.p_double[j+1]-ey.ptr.p_double[j])/(ex.ptr.p_double[j+1]-ex.ptr.p_double[j]); - if( ae_fp_less_eq(ae_fabs(delta, _state),epsilon) ) - { - d.ptr.p_double[j] = 0; - d.ptr.p_double[j+1] = 0; - } - else - { - alpha = d.ptr.p_double[j]/delta; - beta = d.ptr.p_double[j+1]/delta; - if( ae_fp_neq(alpha,0) ) - { - cb = alpha*ae_sqrt(1+ae_sqr(beta/alpha, _state), _state); - } - else - { - if( ae_fp_neq(beta,0) ) - { - cb = beta; - } - else - { - continue; - } - } - if( ae_fp_greater(cb,3) ) - { - d.ptr.p_double[j] = 3*alpha*delta/cb; - d.ptr.p_double[j+1] = 3*beta*delta/cb; - } - } - } - - /* - * Transition to next segment - */ - i = sn-1; - } - spline1dbuildhermite(&ex, &ey, &d, n, c, _state); - c->continuity = 2; - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal version of Spline1DGridDiffCubic. - -Accepts pre-ordered X/Y, temporary arrays (which may be preallocated, if -you want to save time, or not) and output array (which may be preallocated -too). - -Y is passed as var-parameter because we may need to force last element to -be equal to the first one (if periodic boundary conditions are specified). - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -static void spline1d_spline1dgriddiffcubicinternal(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* d, - /* Real */ ae_vector* a1, - /* Real */ ae_vector* a2, - /* Real */ ae_vector* a3, - /* Real */ ae_vector* b, - /* Real */ ae_vector* dt, - ae_state *_state) -{ - ae_int_t i; - - - - /* - * allocate arrays - */ - if( d->cntcntcntcntcntcntptr.p_double[0] = (y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0]); - d->ptr.p_double[1] = d->ptr.p_double[0]; - return; - } - if( (n==2&&boundltype==-1)&&boundrtype==-1 ) - { - d->ptr.p_double[0] = 0; - d->ptr.p_double[1] = 0; - return; - } - - /* - * Periodic and non-periodic boundary conditions are - * two separate classes - */ - if( boundrtype==-1&&boundltype==-1 ) - { - - /* - * Periodic boundary conditions - */ - y->ptr.p_double[n-1] = y->ptr.p_double[0]; - - /* - * Boundary conditions at N-1 points - * (one point less because last point is the same as first point). - */ - a1->ptr.p_double[0] = x->ptr.p_double[1]-x->ptr.p_double[0]; - a2->ptr.p_double[0] = 2*(x->ptr.p_double[1]-x->ptr.p_double[0]+x->ptr.p_double[n-1]-x->ptr.p_double[n-2]); - a3->ptr.p_double[0] = x->ptr.p_double[n-1]-x->ptr.p_double[n-2]; - b->ptr.p_double[0] = 3*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2])*(x->ptr.p_double[1]-x->ptr.p_double[0])+3*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0])*(x->ptr.p_double[n-1]-x->ptr.p_double[n-2]); - for(i=1; i<=n-2; i++) - { - - /* - * Altough last point is [N-2], we use X[N-1] and Y[N-1] - * (because of periodicity) - */ - a1->ptr.p_double[i] = x->ptr.p_double[i+1]-x->ptr.p_double[i]; - a2->ptr.p_double[i] = 2*(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]); - a3->ptr.p_double[i] = x->ptr.p_double[i]-x->ptr.p_double[i-1]; - b->ptr.p_double[i] = 3*(y->ptr.p_double[i]-y->ptr.p_double[i-1])/(x->ptr.p_double[i]-x->ptr.p_double[i-1])*(x->ptr.p_double[i+1]-x->ptr.p_double[i])+3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i])*(x->ptr.p_double[i]-x->ptr.p_double[i-1]); - } - - /* - * Solve, add last point (with index N-1) - */ - spline1d_solvecyclictridiagonal(a1, a2, a3, b, n-1, dt, _state); - ae_v_move(&d->ptr.p_double[0], 1, &dt->ptr.p_double[0], 1, ae_v_len(0,n-2)); - d->ptr.p_double[n-1] = d->ptr.p_double[0]; - } - else - { - - /* - * Non-periodic boundary condition. - * Left boundary conditions. - */ - if( boundltype==0 ) - { - a1->ptr.p_double[0] = 0; - a2->ptr.p_double[0] = 1; - a3->ptr.p_double[0] = 1; - b->ptr.p_double[0] = 2*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0]); - } - if( boundltype==1 ) - { - a1->ptr.p_double[0] = 0; - a2->ptr.p_double[0] = 1; - a3->ptr.p_double[0] = 0; - b->ptr.p_double[0] = boundl; - } - if( boundltype==2 ) - { - a1->ptr.p_double[0] = 0; - a2->ptr.p_double[0] = 2; - a3->ptr.p_double[0] = 1; - b->ptr.p_double[0] = 3*(y->ptr.p_double[1]-y->ptr.p_double[0])/(x->ptr.p_double[1]-x->ptr.p_double[0])-0.5*boundl*(x->ptr.p_double[1]-x->ptr.p_double[0]); - } - - /* - * Central conditions - */ - for(i=1; i<=n-2; i++) - { - a1->ptr.p_double[i] = x->ptr.p_double[i+1]-x->ptr.p_double[i]; - a2->ptr.p_double[i] = 2*(x->ptr.p_double[i+1]-x->ptr.p_double[i-1]); - a3->ptr.p_double[i] = x->ptr.p_double[i]-x->ptr.p_double[i-1]; - b->ptr.p_double[i] = 3*(y->ptr.p_double[i]-y->ptr.p_double[i-1])/(x->ptr.p_double[i]-x->ptr.p_double[i-1])*(x->ptr.p_double[i+1]-x->ptr.p_double[i])+3*(y->ptr.p_double[i+1]-y->ptr.p_double[i])/(x->ptr.p_double[i+1]-x->ptr.p_double[i])*(x->ptr.p_double[i]-x->ptr.p_double[i-1]); - } - - /* - * Right boundary conditions - */ - if( boundrtype==0 ) - { - a1->ptr.p_double[n-1] = 1; - a2->ptr.p_double[n-1] = 1; - a3->ptr.p_double[n-1] = 0; - b->ptr.p_double[n-1] = 2*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2]); - } - if( boundrtype==1 ) - { - a1->ptr.p_double[n-1] = 0; - a2->ptr.p_double[n-1] = 1; - a3->ptr.p_double[n-1] = 0; - b->ptr.p_double[n-1] = boundr; - } - if( boundrtype==2 ) - { - a1->ptr.p_double[n-1] = 1; - a2->ptr.p_double[n-1] = 2; - a3->ptr.p_double[n-1] = 0; - b->ptr.p_double[n-1] = 3*(y->ptr.p_double[n-1]-y->ptr.p_double[n-2])/(x->ptr.p_double[n-1]-x->ptr.p_double[n-2])+0.5*boundr*(x->ptr.p_double[n-1]-x->ptr.p_double[n-2]); - } - - /* - * Solve - */ - spline1d_solvetridiagonal(a1, a2, a3, b, n, d, _state); - } -} - - -/************************************************************************* -Internal subroutine. Heap sort. -*************************************************************************/ -static void spline1d_heapsortpoints(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector bufx; - ae_vector bufy; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&bufx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bufy, 0, DT_REAL, _state, ae_true); - - tagsortfastr(x, y, &bufx, &bufy, n, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. Heap sort. - -Accepts: - X, Y - points - P - empty or preallocated array - -Returns: - X, Y - sorted by X - P - array of permutations; I-th position of output - arrays X/Y contains (X[P[I]],Y[P[I]]) -*************************************************************************/ -static void spline1d_heapsortppoints(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Integer */ ae_vector* p, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector rbuf; - ae_vector ibuf; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&rbuf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ibuf, 0, DT_INT, _state, ae_true); - - if( p->cntptr.p_int[i] = i; - } - tagsortfasti(x, p, &rbuf, &ibuf, n, _state); - for(i=0; i<=n-1; i++) - { - rbuf.ptr.p_double[i] = y->ptr.p_double[p->ptr.p_int[i]]; - } - ae_v_move(&y->ptr.p_double[0], 1, &rbuf.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. Tridiagonal solver. Solves - -( B[0] C[0] -( A[1] B[1] C[1] ) -( A[2] B[2] C[2] ) -( .......... ) * X = D -( .......... ) -( A[N-2] B[N-2] C[N-2] ) -( A[N-1] B[N-1] ) - -*************************************************************************/ -static void spline1d_solvetridiagonal(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - /* Real */ ae_vector* d, - ae_int_t n, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _b; - ae_vector _d; - ae_int_t k; - double t; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_b, b, _state, ae_true); - b = &_b; - ae_vector_init_copy(&_d, d, _state, ae_true); - d = &_d; - - if( x->cntptr.p_double[k]/b->ptr.p_double[k-1]; - b->ptr.p_double[k] = b->ptr.p_double[k]-t*c->ptr.p_double[k-1]; - d->ptr.p_double[k] = d->ptr.p_double[k]-t*d->ptr.p_double[k-1]; - } - x->ptr.p_double[n-1] = d->ptr.p_double[n-1]/b->ptr.p_double[n-1]; - for(k=n-2; k>=0; k--) - { - x->ptr.p_double[k] = (d->ptr.p_double[k]-c->ptr.p_double[k]*x->ptr.p_double[k+1])/b->ptr.p_double[k]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. Cyclic tridiagonal solver. Solves - -( B[0] C[0] A[0] ) -( A[1] B[1] C[1] ) -( A[2] B[2] C[2] ) -( .......... ) * X = D -( .......... ) -( A[N-2] B[N-2] C[N-2] ) -( C[N-1] A[N-1] B[N-1] ) -*************************************************************************/ -static void spline1d_solvecyclictridiagonal(/* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - /* Real */ ae_vector* d, - ae_int_t n, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _b; - ae_int_t k; - double alpha; - double beta; - double gamma; - ae_vector y; - ae_vector z; - ae_vector u; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_b, b, _state, ae_true); - b = &_b; - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&z, 0, DT_REAL, _state, ae_true); - ae_vector_init(&u, 0, DT_REAL, _state, ae_true); - - if( x->cntptr.p_double[0]; - alpha = c->ptr.p_double[n-1]; - gamma = -b->ptr.p_double[0]; - b->ptr.p_double[0] = 2*b->ptr.p_double[0]; - b->ptr.p_double[n-1] = b->ptr.p_double[n-1]-alpha*beta/gamma; - ae_vector_set_length(&u, n, _state); - for(k=0; k<=n-1; k++) - { - u.ptr.p_double[k] = 0; - } - u.ptr.p_double[0] = gamma; - u.ptr.p_double[n-1] = alpha; - spline1d_solvetridiagonal(a, b, c, d, n, &y, _state); - spline1d_solvetridiagonal(a, b, c, &u, n, &z, _state); - for(k=0; k<=n-1; k++) - { - x->ptr.p_double[k] = y.ptr.p_double[k]-(y.ptr.p_double[0]+beta/gamma*y.ptr.p_double[n-1])/(1+z.ptr.p_double[0]+beta/gamma*z.ptr.p_double[n-1])*z.ptr.p_double[k]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. Three-point differentiation -*************************************************************************/ -static double spline1d_diffthreepoint(double t, - double x0, - double f0, - double x1, - double f1, - double x2, - double f2, - ae_state *_state) -{ - double a; - double b; - double result; - - - t = t-x0; - x1 = x1-x0; - x2 = x2-x0; - a = (f2-f0-x2/x1*(f1-f0))/(ae_sqr(x2, _state)-x1*x2); - b = (f1-f0-a*ae_sqr(x1, _state))/x1; - result = 2*a*t+b; - return result; -} - - -/************************************************************************* -Procedure for calculating value of a function is providet in the form of -Hermite polinom - -INPUT PARAMETERS: - P0 - value of a function at 0 - M0 - value of a derivative at 0 - P1 - value of a function at 1 - M1 - value of a derivative at 1 - T - point inside [0;1] - -OUTPUT PARAMETERS: - S - value of a function at T - B0 - value of a derivative function at T - - -- ALGLIB PROJECT -- - Copyright 26.09.2011 by Bochkanov Sergey -*************************************************************************/ -static void spline1d_hermitecalc(double p0, - double m0, - double p1, - double m1, - double t, - double* s, - double* ds, - ae_state *_state) -{ - - *s = 0; - *ds = 0; - - *s = p0*(1+2*t)*(1-t)*(1-t)+m0*t*(1-t)*(1-t)+p1*(3-2*t)*t*t+m1*t*t*(t-1); - *ds = -p0*6*t*(1-t)+m0*(1-t)*(1-3*t)+p1*6*t*(1-t)+m1*t*(3*t-2); -} - - -/************************************************************************* -Function for mapping from [A0;B0] to [A1;B1] - -INPUT PARAMETERS: - A0 - left border [A0;B0] - B0 - right border [A0;B0] - A1 - left border [A1;B1] - B1 - right border [A1;B1] - T - value inside [A0;B0] - -RESTRICTIONS OF PARAMETERS: - -We assume, that B0>A0 and B1>A1. But we chech, that T is inside [A0;B0], -and if TB0 then T - B1. - -INPUT PARAMETERS: - A0 - left border for segment [A0;B0] from 'T' is converted to [A1;B1] - B0 - right border for segment [A0;B0] from 'T' is converted to [A1;B1] - A1 - left border for segment [A1;B1] to 'T' is converted from [A0;B0] - B1 - right border for segment [A1;B1] to 'T' is converted from [A0;B0] - T - the parameter is mapped from [A0;B0] to [A1;B1] - -Result: - is converted value for 'T' from [A0;B0] to [A1;B1] - -REMARK: - -The function dont check value A0,B0 and A1,B1! - - -- ALGLIB PROJECT -- - Copyright 26.09.2011 by Bochkanov Sergey -*************************************************************************/ -static double spline1d_rescaleval(double a0, - double b0, - double a1, - double b1, - double t, - ae_state *_state) -{ - double result; - - - - /* - *return left border - */ - if( ae_fp_less_eq(t,a0) ) - { - result = a1; - return result; - } - - /* - *return right border - */ - if( ae_fp_greater_eq(t,b0) ) - { - result = b1; - return result; - } - - /* - *return value between left and right borders - */ - result = (b1-a1)*(t-a0)/(b0-a0)+a1; - return result; -} - - -ae_bool _spline1dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - spline1dinterpolant *p = (spline1dinterpolant*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->c, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _spline1dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - spline1dinterpolant *dst = (spline1dinterpolant*)_dst; - spline1dinterpolant *src = (spline1dinterpolant*)_src; - dst->periodic = src->periodic; - dst->n = src->n; - dst->k = src->k; - dst->continuity = src->continuity; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->c, &src->c, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _spline1dinterpolant_clear(void* _p) -{ - spline1dinterpolant *p = (spline1dinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->c); -} - - -void _spline1dinterpolant_destroy(void* _p) -{ - spline1dinterpolant *p = (spline1dinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->c); -} - - - - -/************************************************************************* -Fitting by polynomials in barycentric form. This function provides simple -unterface for unconstrained unweighted fitting. See PolynomialFitWC() if -you need constrained fitting. - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFitWC() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0 - * if given, only leading N elements of X/Y are used - * if not given, automatically determined from sizes of X/Y - M - number of basis functions (= polynomial_degree + 1), M>=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfit(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* p, - polynomialfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector w; - ae_vector xc; - ae_vector yc; - ae_vector dc; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _barycentricinterpolant_clear(p); - _polynomialfitreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dc, 0, DT_INT, _state, ae_true); - - ae_assert(n>0, "PolynomialFit: N<=0!", _state); - ae_assert(m>0, "PolynomialFit: M<=0!", _state); - ae_assert(x->cnt>=n, "PolynomialFit: Length(X)cnt>=n, "PolynomialFit: Length(Y)0. - * if given, only leading N elements of X/Y/W are used - * if not given, automatically determined from sizes of X/Y/W - XC - points where polynomial values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that P(XC[i])=YC[i] - * DC[i]=1 means that P'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* even simple constraints can be inconsistent, see Wikipedia article on - this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the one special cases, however, we can guarantee consistency. This - case is: M>1 and constraints on the function values (NOT DERIVATIVES) - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfitwc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* p, - polynomialfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _w; - ae_vector _xc; - ae_vector _yc; - double xa; - double xb; - double sa; - double sb; - ae_vector xoriginal; - ae_vector yoriginal; - ae_vector y2; - ae_vector w2; - ae_vector tmp; - ae_vector tmp2; - ae_vector bx; - ae_vector by; - ae_vector bw; - ae_int_t i; - ae_int_t j; - double u; - double v; - double s; - ae_int_t relcnt; - lsfitreport lrep; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_w, w, _state, ae_true); - w = &_w; - ae_vector_init_copy(&_xc, xc, _state, ae_true); - xc = &_xc; - ae_vector_init_copy(&_yc, yc, _state, ae_true); - yc = &_yc; - *info = 0; - _barycentricinterpolant_clear(p); - _polynomialfitreport_clear(rep); - ae_vector_init(&xoriginal, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yoriginal, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&by, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bw, 0, DT_REAL, _state, ae_true); - _lsfitreport_init(&lrep, _state, ae_true); - - ae_assert(n>0, "PolynomialFitWC: N<=0!", _state); - ae_assert(m>0, "PolynomialFitWC: M<=0!", _state); - ae_assert(k>=0, "PolynomialFitWC: K<0!", _state); - ae_assert(k=M!", _state); - ae_assert(x->cnt>=n, "PolynomialFitWC: Length(X)cnt>=n, "PolynomialFitWC: Length(Y)cnt>=n, "PolynomialFitWC: Length(W)cnt>=k, "PolynomialFitWC: Length(XC)cnt>=k, "PolynomialFitWC: Length(YC)cnt>=k, "PolynomialFitWC: Length(DC)ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "PolynomialFitWC: one of DC[] is not 0 or 1!", _state); - } - - /* - * Scale X, Y, XC, YC. - * Solve scaled problem using internal Chebyshev fitting function. - */ - lsfitscalexy(x, y, w, n, xc, yc, dc, k, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state); - lsfit_internalchebyshevfit(x, y, w, n, xc, yc, dc, k, m, info, &tmp, &lrep, _state); - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Generate barycentric model and scale it - * * BX, BY store barycentric model nodes - * * FMatrix is reused (remember - it is at least MxM, what we need) - * - * Model intialization is done in O(M^2). In principle, it can be - * done in O(M*log(M)), but before it we solved task with O(N*M^2) - * complexity, so it is only a small amount of total time spent. - */ - ae_vector_set_length(&bx, m, _state); - ae_vector_set_length(&by, m, _state); - ae_vector_set_length(&bw, m, _state); - ae_vector_set_length(&tmp2, m, _state); - s = 1; - for(i=0; i<=m-1; i++) - { - if( m!=1 ) - { - u = ae_cos(ae_pi*i/(m-1), _state); - } - else - { - u = 0; - } - v = 0; - for(j=0; j<=m-1; j++) - { - if( j==0 ) - { - tmp2.ptr.p_double[j] = 1; - } - else - { - if( j==1 ) - { - tmp2.ptr.p_double[j] = u; - } - else - { - tmp2.ptr.p_double[j] = 2*u*tmp2.ptr.p_double[j-1]-tmp2.ptr.p_double[j-2]; - } - } - v = v+tmp.ptr.p_double[j]*tmp2.ptr.p_double[j]; - } - bx.ptr.p_double[i] = u; - by.ptr.p_double[i] = v; - bw.ptr.p_double[i] = s; - if( i==0||i==m-1 ) - { - bw.ptr.p_double[i] = 0.5*bw.ptr.p_double[i]; - } - s = -s; - } - barycentricbuildxyw(&bx, &by, &bw, m, p, _state); - barycentriclintransx(p, 2/(xb-xa), -(xa+xb)/(xb-xa), _state); - barycentriclintransy(p, sb-sa, sa, _state); - - /* - * Scale absolute errors obtained from LSFitLinearW. - * Relative error should be calculated separately - * (because of shifting/scaling of the task) - */ - rep->taskrcond = lrep.taskrcond; - rep->rmserror = lrep.rmserror*(sb-sa); - rep->avgerror = lrep.avgerror*(sb-sa); - rep->maxerror = lrep.maxerror*(sb-sa); - rep->avgrelerror = 0; - relcnt = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(yoriginal.ptr.p_double[i],0) ) - { - rep->avgrelerror = rep->avgrelerror+ae_fabs(barycentriccalc(p, xoriginal.ptr.p_double[i], _state)-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state); - relcnt = relcnt+1; - } - } - if( relcnt!=0 ) - { - rep->avgrelerror = rep->avgrelerror/relcnt; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Weghted rational least squares fitting using Floater-Hormann rational -functions with optimal D chosen from [0,9], with constraints and -individual weights. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least WEIGHTED root -mean square error) is chosen. Task is linear, so linear least squares -solver is used. Complexity of this computational scheme is O(N*M^2) -(mostly dominated by the least squares solver). - -SEE ALSO -* BarycentricFitFloaterHormann(), "lightweight" fitting without invididual - weights and constraints. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points, N>0. - XC - points where function values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -1 means another errors in parameters passed - (N<=0, for example) - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroutine doesn't calculate task's condition number for K<>0. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained barycentric interpolants: -* excessive constraints can be inconsistent. Floater-Hormann basis - functions aren't as flexible as splines (although they are very smooth). -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function VALUES at the interval - boundaries. Note that consustency of the constraints on the function - DERIVATIVES is NOT guaranteed (you can use in such cases cubic splines - which are more flexible). -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricfitfloaterhormannwc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* b, - barycentricfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t d; - ae_int_t i; - double wrmscur; - double wrmsbest; - barycentricinterpolant locb; - barycentricfitreport locrep; - ae_int_t locinfo; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _barycentricinterpolant_clear(b); - _barycentricfitreport_clear(rep); - _barycentricinterpolant_init(&locb, _state, ae_true); - _barycentricfitreport_init(&locrep, _state, ae_true); - - ae_assert(n>0, "BarycentricFitFloaterHormannWC: N<=0!", _state); - ae_assert(m>0, "BarycentricFitFloaterHormannWC: M<=0!", _state); - ae_assert(k>=0, "BarycentricFitFloaterHormannWC: K<0!", _state); - ae_assert(k=M!", _state); - ae_assert(x->cnt>=n, "BarycentricFitFloaterHormannWC: Length(X)cnt>=n, "BarycentricFitFloaterHormannWC: Length(Y)cnt>=n, "BarycentricFitFloaterHormannWC: Length(W)cnt>=k, "BarycentricFitFloaterHormannWC: Length(XC)cnt>=k, "BarycentricFitFloaterHormannWC: Length(YC)cnt>=k, "BarycentricFitFloaterHormannWC: Length(DC)ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "BarycentricFitFloaterHormannWC: one of DC[] is not 0 or 1!", _state); - } - - /* - * Find optimal D - * - * Info is -3 by default (degenerate constraints). - * If LocInfo will always be equal to -3, Info will remain equal to -3. - * If at least once LocInfo will be -4, Info will be -4. - */ - wrmsbest = ae_maxrealnumber; - rep->dbest = -1; - *info = -3; - for(d=0; d<=ae_minint(9, n-1, _state); d++) - { - lsfit_barycentricfitwcfixedd(x, y, w, n, xc, yc, dc, k, m, d, &locinfo, &locb, &locrep, _state); - ae_assert((locinfo==-4||locinfo==-3)||locinfo>0, "BarycentricFitFloaterHormannWC: unexpected result from BarycentricFitWCFixedD!", _state); - if( locinfo>0 ) - { - - /* - * Calculate weghted RMS - */ - wrmscur = 0; - for(i=0; i<=n-1; i++) - { - wrmscur = wrmscur+ae_sqr(w->ptr.p_double[i]*(y->ptr.p_double[i]-barycentriccalc(&locb, x->ptr.p_double[i], _state)), _state); - } - wrmscur = ae_sqrt(wrmscur/n, _state); - if( ae_fp_less(wrmscur,wrmsbest)||rep->dbest<0 ) - { - barycentriccopy(&locb, b, _state); - rep->dbest = d; - *info = 1; - rep->rmserror = locrep.rmserror; - rep->avgerror = locrep.avgerror; - rep->avgrelerror = locrep.avgrelerror; - rep->maxerror = locrep.maxerror; - rep->taskrcond = locrep.taskrcond; - wrmsbest = wrmscur; - } - } - else - { - if( locinfo!=-3&&*info<0 ) - { - *info = locinfo; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Rational least squares fitting using Floater-Hormann rational functions -with optimal D chosen from [0,9]. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least root mean -square error) is chosen. Task is linear, so linear least squares solver -is used. Complexity of this computational scheme is O(N*M^2) (mostly -dominated by the least squares solver). - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricfitfloaterhormann(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* b, - barycentricfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector w; - ae_vector xc; - ae_vector yc; - ae_vector dc; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _barycentricinterpolant_clear(b); - _barycentricfitreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dc, 0, DT_INT, _state, ae_true); - - ae_assert(n>0, "BarycentricFitFloaterHormann: N<=0!", _state); - ae_assert(m>0, "BarycentricFitFloaterHormann: M<=0!", _state); - ae_assert(x->cnt>=n, "BarycentricFitFloaterHormann: Length(X)cnt>=n, "BarycentricFitFloaterHormann: Length(Y)0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalized(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - double rho, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector w; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - *info = 0; - _spline1dinterpolant_clear(s); - _spline1dfitreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "Spline1DFitPenalized: N<1!", _state); - ae_assert(m>=4, "Spline1DFitPenalized: M<4!", _state); - ae_assert(x->cnt>=n, "Spline1DFitPenalized: Length(X)cnt>=n, "Spline1DFitPenalized: Length(Y)0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - M - number of basis functions ( = number_of_nodes), M>=4. - Rho - regularization constant passed by user. It penalizes - nonlinearity in the regression spline. It is logarithmically - scaled, i.e. actual value of regularization constant is - calculated as 10^Rho. It is automatically scaled so that: - * Rho=2.0 corresponds to moderate amount of nonlinearity - * generally, it should be somewhere in the [-8.0,+8.0] - If you do not want to penalize nonlineary, - pass small Rho. Values as low as -15 should work. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD or - Cholesky decomposition; problem may be - too ill-conditioned (very rare) - S - spline interpolant. - Rep - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTE 1: additional nodes are added to the spline outside of the fitting -interval to force linearity when xmax(x,xc). It is done -for consistency - we penalize non-linearity at [min(x,xc),max(x,xc)], so -it is natural to force linearity outside of this interval. - -NOTE 2: function automatically sorts points, so caller may pass unsorted -array. - - -- ALGLIB PROJECT -- - Copyright 19.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalizedw(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - ae_int_t m, - double rho, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _w; - ae_int_t i; - ae_int_t j; - ae_int_t b; - double v; - double relcnt; - double xa; - double xb; - double sa; - double sb; - ae_vector xoriginal; - ae_vector yoriginal; - double pdecay; - double tdecay; - ae_matrix fmatrix; - ae_vector fcolumn; - ae_vector y2; - ae_vector w2; - ae_vector xc; - ae_vector yc; - ae_vector dc; - double fdmax; - double admax; - ae_matrix amatrix; - ae_matrix d2matrix; - double fa; - double ga; - double fb; - double gb; - double lambdav; - ae_vector bx; - ae_vector by; - ae_vector bd1; - ae_vector bd2; - ae_vector tx; - ae_vector ty; - ae_vector td; - spline1dinterpolant bs; - ae_matrix nmatrix; - ae_vector rightpart; - fblslincgstate cgstate; - ae_vector c; - ae_vector tmp0; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_w, w, _state, ae_true); - w = &_w; - *info = 0; - _spline1dinterpolant_clear(s); - _spline1dfitreport_clear(rep); - ae_vector_init(&xoriginal, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yoriginal, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&fcolumn, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dc, 0, DT_INT, _state, ae_true); - ae_matrix_init(&amatrix, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&d2matrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&by, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bd1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bd2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ty, 0, DT_REAL, _state, ae_true); - ae_vector_init(&td, 0, DT_REAL, _state, ae_true); - _spline1dinterpolant_init(&bs, _state, ae_true); - ae_matrix_init(&nmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&rightpart, 0, DT_REAL, _state, ae_true); - _fblslincgstate_init(&cgstate, _state, ae_true); - ae_vector_init(&c, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp0, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "Spline1DFitPenalizedW: N<1!", _state); - ae_assert(m>=4, "Spline1DFitPenalizedW: M<4!", _state); - ae_assert(x->cnt>=n, "Spline1DFitPenalizedW: Length(X)cnt>=n, "Spline1DFitPenalizedW: Length(Y)cnt>=n, "Spline1DFitPenalizedW: Length(W)ptr.p_double[i]*fcolumn.ptr.p_double[i], _state); - } - fdmax = ae_maxreal(fdmax, v, _state); - - /* - * Fill temporary with second derivatives of basis function - */ - ae_v_move(&d2matrix.ptr.pp_double[b][0], 1, &bd2.ptr.p_double[0], 1, ae_v_len(0,m-1)); - } - - /* - * * calculate penalty matrix A - * * calculate max of diagonal elements of A - * * calculate PDecay - coefficient before penalty matrix - */ - for(i=0; i<=m-1; i++) - { - for(j=i; j<=m-1; j++) - { - - /* - * calculate integral(B_i''*B_j'') where B_i and B_j are - * i-th and j-th basis splines. - * B_i and B_j are piecewise linear functions. - */ - v = 0; - for(b=0; b<=m-2; b++) - { - fa = d2matrix.ptr.pp_double[i][b]; - fb = d2matrix.ptr.pp_double[i][b+1]; - ga = d2matrix.ptr.pp_double[j][b]; - gb = d2matrix.ptr.pp_double[j][b+1]; - v = v+(bx.ptr.p_double[b+1]-bx.ptr.p_double[b])*(fa*ga+(fa*(gb-ga)+ga*(fb-fa))/2+(fb-fa)*(gb-ga)/3); - } - amatrix.ptr.pp_double[i][j] = v; - amatrix.ptr.pp_double[j][i] = v; - } - } - admax = 0; - for(i=0; i<=m-1; i++) - { - admax = ae_maxreal(admax, ae_fabs(amatrix.ptr.pp_double[i][i], _state), _state); - } - pdecay = lambdav*fdmax/admax; - - /* - * Calculate TDecay for Tikhonov regularization - */ - tdecay = fdmax*(1+pdecay)*10*ae_machineepsilon; - - /* - * Prepare system - * - * NOTE: FMatrix is spoiled during this process - */ - for(i=0; i<=n-1; i++) - { - v = w->ptr.p_double[i]; - ae_v_muld(&fmatrix.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - rmatrixgemm(m, m, n, 1.0, &fmatrix, 0, 0, 1, &fmatrix, 0, 0, 0, 0.0, &nmatrix, 0, 0, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=m-1; j++) - { - nmatrix.ptr.pp_double[i][j] = nmatrix.ptr.pp_double[i][j]+pdecay*amatrix.ptr.pp_double[i][j]; - } - } - for(i=0; i<=m-1; i++) - { - nmatrix.ptr.pp_double[i][i] = nmatrix.ptr.pp_double[i][i]+tdecay; - } - for(i=0; i<=m-1; i++) - { - rightpart.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = y->ptr.p_double[i]*w->ptr.p_double[i]; - ae_v_addd(&rightpart.ptr.p_double[0], 1, &fmatrix.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - - /* - * Solve system - */ - if( !spdmatrixcholesky(&nmatrix, m, ae_true, _state) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - fblscholeskysolve(&nmatrix, 1.0, m, ae_true, &rightpart, &tmp0, _state); - ae_v_move(&c.ptr.p_double[0], 1, &rightpart.ptr.p_double[0], 1, ae_v_len(0,m-1)); - - /* - * add nodes to force linearity outside of the fitting interval - */ - spline1dgriddiffcubic(&bx, &c, m, 2, 0.0, 2, 0.0, &bd1, _state); - ae_vector_set_length(&tx, m+2, _state); - ae_vector_set_length(&ty, m+2, _state); - ae_vector_set_length(&td, m+2, _state); - ae_v_move(&tx.ptr.p_double[1], 1, &bx.ptr.p_double[0], 1, ae_v_len(1,m)); - ae_v_move(&ty.ptr.p_double[1], 1, &rightpart.ptr.p_double[0], 1, ae_v_len(1,m)); - ae_v_move(&td.ptr.p_double[1], 1, &bd1.ptr.p_double[0], 1, ae_v_len(1,m)); - tx.ptr.p_double[0] = tx.ptr.p_double[1]-(tx.ptr.p_double[2]-tx.ptr.p_double[1]); - ty.ptr.p_double[0] = ty.ptr.p_double[1]-td.ptr.p_double[1]*(tx.ptr.p_double[2]-tx.ptr.p_double[1]); - td.ptr.p_double[0] = td.ptr.p_double[1]; - tx.ptr.p_double[m+1] = tx.ptr.p_double[m]+(tx.ptr.p_double[m]-tx.ptr.p_double[m-1]); - ty.ptr.p_double[m+1] = ty.ptr.p_double[m]+td.ptr.p_double[m]*(tx.ptr.p_double[m]-tx.ptr.p_double[m-1]); - td.ptr.p_double[m+1] = td.ptr.p_double[m]; - spline1dbuildhermite(&tx, &ty, &td, m+2, s, _state); - spline1dlintransx(s, 2/(xb-xa), -(xa+xb)/(xb-xa), _state); - spline1dlintransy(s, sb-sa, sa, _state); - *info = 1; - - /* - * Fill report - */ - rep->rmserror = 0; - rep->avgerror = 0; - rep->avgrelerror = 0; - rep->maxerror = 0; - relcnt = 0; - spline1dconvcubic(&bx, &rightpart, m, 2, 0.0, 2, 0.0, x, n, &fcolumn, _state); - for(i=0; i<=n-1; i++) - { - v = (sb-sa)*fcolumn.ptr.p_double[i]+sa; - rep->rmserror = rep->rmserror+ae_sqr(v-yoriginal.ptr.p_double[i], _state); - rep->avgerror = rep->avgerror+ae_fabs(v-yoriginal.ptr.p_double[i], _state); - if( ae_fp_neq(yoriginal.ptr.p_double[i],0) ) - { - rep->avgrelerror = rep->avgrelerror+ae_fabs(v-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state); - relcnt = relcnt+1; - } - rep->maxerror = ae_maxreal(rep->maxerror, ae_fabs(v-yoriginal.ptr.p_double[i], _state), _state); - } - rep->rmserror = ae_sqrt(rep->rmserror/n, _state); - rep->avgerror = rep->avgerror/n; - if( ae_fp_neq(relcnt,0) ) - { - rep->avgrelerror = rep->avgrelerror/relcnt; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Weighted fitting by cubic spline, with constraints on function values or -derivatives. - -Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with continuous second -derivatives and non-fixed first derivatives at interval ends. Small -regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitHermiteWC() - fitting by Hermite splines (more flexible, - less smooth) - Spline1DFitCubic() - "lightweight" fitting by cubic splines, - without invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - S - spline interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function values AND/OR its - derivatives at the interval boundaries. -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubicwc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state) -{ - ae_int_t i; - - *info = 0; - _spline1dinterpolant_clear(s); - _spline1dfitreport_clear(rep); - - ae_assert(n>=1, "Spline1DFitCubicWC: N<1!", _state); - ae_assert(m>=4, "Spline1DFitCubicWC: M<4!", _state); - ae_assert(k>=0, "Spline1DFitCubicWC: K<0!", _state); - ae_assert(k=M!", _state); - ae_assert(x->cnt>=n, "Spline1DFitCubicWC: Length(X)cnt>=n, "Spline1DFitCubicWC: Length(Y)cnt>=n, "Spline1DFitCubicWC: Length(W)cnt>=k, "Spline1DFitCubicWC: Length(XC)cnt>=k, "Spline1DFitCubicWC: Length(YC)cnt>=k, "Spline1DFitCubicWC: Length(DC)ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "Spline1DFitCubicWC: DC[i] is neither 0 or 1!", _state); - } - lsfit_spline1dfitinternal(0, x, y, w, n, xc, yc, dc, k, m, info, s, rep, _state); -} - - -/************************************************************************* -Weighted fitting by Hermite spline, with constraints on function values -or first derivatives. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are Hermite splines. Small regularizing -term is used when solving constrained tasks (to improve stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitCubicWC() - fitting by Cubic splines (less flexible, - more smooth) - Spline1DFitHermite() - "lightweight" Hermite fitting, without - invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4, - M IS EVEN! - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -2 means odd M was passed (which is not supported) - -1 means another errors in parameters passed - (N<=0, for example) - S - spline interpolant. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -IMPORTANT: - this subroitine supports only even M's - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the several special cases, however, we can guarantee consistency. -* one of this cases is M>=4 and constraints on the function value - (AND/OR its derivative) at the interval boundaries. -* another special case is M>=4 and ONE constraint on the function value - (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)] - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermitewc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state) -{ - ae_int_t i; - - *info = 0; - _spline1dinterpolant_clear(s); - _spline1dfitreport_clear(rep); - - ae_assert(n>=1, "Spline1DFitHermiteWC: N<1!", _state); - ae_assert(m>=4, "Spline1DFitHermiteWC: M<4!", _state); - ae_assert(m%2==0, "Spline1DFitHermiteWC: M is odd!", _state); - ae_assert(k>=0, "Spline1DFitHermiteWC: K<0!", _state); - ae_assert(k=M!", _state); - ae_assert(x->cnt>=n, "Spline1DFitHermiteWC: Length(X)cnt>=n, "Spline1DFitHermiteWC: Length(Y)cnt>=n, "Spline1DFitHermiteWC: Length(W)cnt>=k, "Spline1DFitHermiteWC: Length(XC)cnt>=k, "Spline1DFitHermiteWC: Length(YC)cnt>=k, "Spline1DFitHermiteWC: Length(DC)ptr.p_int[i]==0||dc->ptr.p_int[i]==1, "Spline1DFitHermiteWC: DC[i] is neither 0 or 1!", _state); - } - lsfit_spline1dfitinternal(1, x, y, w, n, xc, yc, dc, k, m, info, s, rep, _state); -} - - -/************************************************************************* -Least squares fitting by cubic spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitCubicWC(). See Spline1DFitCubicWC() for more information -about subroutine parameters (we don't duplicate it here because of length) - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector w; - ae_vector xc; - ae_vector yc; - ae_vector dc; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _spline1dinterpolant_clear(s); - _spline1dfitreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&dc, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "Spline1DFitCubic: N<1!", _state); - ae_assert(m>=4, "Spline1DFitCubic: M<4!", _state); - ae_assert(x->cnt>=n, "Spline1DFitCubic: Length(X)cnt>=n, "Spline1DFitCubic: Length(Y)=1, "Spline1DFitHermite: N<1!", _state); - ae_assert(m>=4, "Spline1DFitHermite: M<4!", _state); - ae_assert(m%2==0, "Spline1DFitHermite: M is odd!", _state); - ae_assert(x->cnt>=n, "Spline1DFitHermite: Length(X)cnt>=n, "Spline1DFitHermite: Length(Y)=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -1 incorrect N/M were specified - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearw(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - - ae_assert(n>=1, "LSFitLinearW: N<1!", _state); - ae_assert(m>=1, "LSFitLinearW: M<1!", _state); - ae_assert(y->cnt>=n, "LSFitLinearW: length(Y)cnt>=n, "LSFitLinearW: length(W)rows>=n, "LSFitLinearW: rows(FMatrix)cols>=m, "LSFitLinearW: cols(FMatrix)=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearwc(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - /* Real */ ae_matrix* cmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _y; - ae_matrix _cmatrix; - ae_int_t i; - ae_int_t j; - ae_vector tau; - ae_matrix q; - ae_matrix f2; - ae_vector tmp; - ae_vector c0; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_matrix_init_copy(&_cmatrix, cmatrix, _state, ae_true); - cmatrix = &_cmatrix; - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&q, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&f2, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c0, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "LSFitLinearWC: N<1!", _state); - ae_assert(m>=1, "LSFitLinearWC: M<1!", _state); - ae_assert(k>=0, "LSFitLinearWC: K<0!", _state); - ae_assert(y->cnt>=n, "LSFitLinearWC: length(Y)cnt>=n, "LSFitLinearWC: length(W)rows>=n, "LSFitLinearWC: rows(FMatrix)cols>=m, "LSFitLinearWC: cols(FMatrix)rows>=k, "LSFitLinearWC: rows(CMatrix)cols>=m+1||k==0, "LSFitLinearWC: cols(CMatrix)=m ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Solve - */ - if( k==0 ) - { - - /* - * no constraints - */ - lsfit_lsfitlinearinternal(y, w, fmatrix, n, m, info, c, rep, _state); - } - else - { - - /* - * First, find general form solution of constraints system: - * * factorize C = L*Q - * * unpack Q - * * fill upper part of C with zeros (for RCond) - * - * We got C=C0+Q2'*y where Q2 is lower M-K rows of Q. - */ - rmatrixlq(cmatrix, k, m, &tau, _state); - rmatrixlqunpackq(cmatrix, k, m, &tau, m, &q, _state); - for(i=0; i<=k-1; i++) - { - for(j=i+1; j<=m-1; j++) - { - cmatrix->ptr.pp_double[i][j] = 0.0; - } - } - if( ae_fp_less(rmatrixlurcondinf(cmatrix, k, _state),1000*ae_machineepsilon) ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&tmp, k, _state); - for(i=0; i<=k-1; i++) - { - if( i>0 ) - { - v = ae_v_dotproduct(&cmatrix->ptr.pp_double[i][0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,i-1)); - } - else - { - v = 0; - } - tmp.ptr.p_double[i] = (cmatrix->ptr.pp_double[i][m]-v)/cmatrix->ptr.pp_double[i][i]; - } - ae_vector_set_length(&c0, m, _state); - for(i=0; i<=m-1; i++) - { - c0.ptr.p_double[i] = 0; - } - for(i=0; i<=k-1; i++) - { - v = tmp.ptr.p_double[i]; - ae_v_addd(&c0.ptr.p_double[0], 1, &q.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - - /* - * Second, prepare modified matrix F2 = F*Q2' and solve modified task - */ - ae_vector_set_length(&tmp, ae_maxint(n, m, _state)+1, _state); - ae_matrix_set_length(&f2, n, m-k, _state); - matrixvectormultiply(fmatrix, 0, n-1, 0, m-1, ae_false, &c0, 0, m-1, -1.0, y, 0, n-1, 1.0, _state); - matrixmatrixmultiply(fmatrix, 0, n-1, 0, m-1, ae_false, &q, k, m-1, 0, m-1, ae_true, 1.0, &f2, 0, n-1, 0, m-k-1, 0.0, &tmp, _state); - lsfit_lsfitlinearinternal(y, w, &f2, n, m-k, info, &tmp, rep, _state); - rep->taskrcond = -1; - if( *info<=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * then, convert back to original answer: C = C0 + Q2'*Y0 - */ - ae_vector_set_length(c, m, _state); - ae_v_move(&c->ptr.p_double[0], 1, &c0.ptr.p_double[0], 1, ae_v_len(0,m-1)); - matrixvectormultiply(&q, k, m-1, 0, m-1, ae_true, &tmp, 0, m-k-1, 1.0, c, 0, m-1, 1.0, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinear(/* Real */ ae_vector* y, - /* Real */ ae_matrix* fmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector w; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "LSFitLinear: N<1!", _state); - ae_assert(m>=1, "LSFitLinear: M<1!", _state); - ae_assert(y->cnt>=n, "LSFitLinear: length(Y)rows>=n, "LSFitLinear: rows(FMatrix)cols>=m, "LSFitLinear: cols(FMatrix)=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearc(/* Real */ ae_vector* y, - /* Real */ ae_matrix* fmatrix, - /* Real */ ae_matrix* cmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _y; - ae_vector w; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "LSFitLinearC: N<1!", _state); - ae_assert(m>=1, "LSFitLinearC: M<1!", _state); - ae_assert(k>=0, "LSFitLinearC: K<0!", _state); - ae_assert(y->cnt>=n, "LSFitLinearC: length(Y)rows>=n, "LSFitLinearC: rows(FMatrix)cols>=m, "LSFitLinearC: cols(FMatrix)rows>=k, "LSFitLinearC: rows(CMatrix)cols>=m+1||k==0, "LSFitLinearC: cols(CMatrix)1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewf(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - double diffstep, - lsfitstate* state, - ae_state *_state) -{ - ae_int_t i; - - _lsfitstate_clear(state); - - ae_assert(n>=1, "LSFitCreateWF: N<1!", _state); - ae_assert(m>=1, "LSFitCreateWF: M<1!", _state); - ae_assert(k>=1, "LSFitCreateWF: K<1!", _state); - ae_assert(c->cnt>=k, "LSFitCreateWF: length(C)cnt>=n, "LSFitCreateWF: length(Y)cnt>=n, "LSFitCreateWF: length(W)rows>=n, "LSFitCreateWF: rows(X)cols>=m, "LSFitCreateWF: cols(X)teststep = 0; - state->diffstep = diffstep; - state->npoints = n; - state->nweights = n; - state->wkind = 1; - state->m = m; - state->k = k; - lsfitsetcond(state, 0.0, 0.0, 0, _state); - lsfitsetstpmax(state, 0.0, _state); - lsfitsetxrep(state, ae_false, _state); - ae_matrix_set_length(&state->taskx, n, m, _state); - ae_vector_set_length(&state->tasky, n, _state); - ae_vector_set_length(&state->taskw, n, _state); - ae_vector_set_length(&state->c, k, _state); - ae_vector_set_length(&state->x, m, _state); - ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->taskw.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->tasky.ptr.p_double[i] = y->ptr.p_double[i]; - } - ae_vector_set_length(&state->s, k, _state); - ae_vector_set_length(&state->bndl, k, _state); - ae_vector_set_length(&state->bndu, k, _state); - for(i=0; i<=k-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - } - state->optalgo = 0; - state->prevnpt = -1; - state->prevalgo = -1; - minlmcreatev(k, n, &state->c, diffstep, &state->optstate, _state); - lsfit_lsfitclearrequestfields(state, _state); - ae_vector_set_length(&state->rstate.ia, 6+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatef(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - double diffstep, - lsfitstate* state, - ae_state *_state) -{ - ae_int_t i; - - _lsfitstate_clear(state); - - ae_assert(n>=1, "LSFitCreateF: N<1!", _state); - ae_assert(m>=1, "LSFitCreateF: M<1!", _state); - ae_assert(k>=1, "LSFitCreateF: K<1!", _state); - ae_assert(c->cnt>=k, "LSFitCreateF: length(C)cnt>=n, "LSFitCreateF: length(Y)rows>=n, "LSFitCreateF: rows(X)cols>=m, "LSFitCreateF: cols(X)rows>=n, "LSFitCreateF: rows(X)cols>=m, "LSFitCreateF: cols(X)teststep = 0; - state->diffstep = diffstep; - state->npoints = n; - state->wkind = 0; - state->m = m; - state->k = k; - lsfitsetcond(state, 0.0, 0.0, 0, _state); - lsfitsetstpmax(state, 0.0, _state); - lsfitsetxrep(state, ae_false, _state); - ae_matrix_set_length(&state->taskx, n, m, _state); - ae_vector_set_length(&state->tasky, n, _state); - ae_vector_set_length(&state->c, k, _state); - ae_vector_set_length(&state->x, m, _state); - ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1)); - for(i=0; i<=n-1; i++) - { - ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->tasky.ptr.p_double[i] = y->ptr.p_double[i]; - } - ae_vector_set_length(&state->s, k, _state); - ae_vector_set_length(&state->bndl, k, _state); - ae_vector_set_length(&state->bndu, k, _state); - for(i=0; i<=k-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - } - state->optalgo = 0; - state->prevnpt = -1; - state->prevalgo = -1; - minlmcreatev(k, n, &state->c, diffstep, &state->optstate, _state); - lsfit_lsfitclearrequestfields(state, _state); - ae_vector_set_length(&state->rstate.ia, 6+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient only. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also: - LSFitResults - LSFitCreateFG (fitting without weights) - LSFitCreateWFGH (fitting using Hessian) - LSFitCreateFGH (fitting using Hessian, without weights) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfg(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_bool cheapfg, - lsfitstate* state, - ae_state *_state) -{ - ae_int_t i; - - _lsfitstate_clear(state); - - ae_assert(n>=1, "LSFitCreateWFG: N<1!", _state); - ae_assert(m>=1, "LSFitCreateWFG: M<1!", _state); - ae_assert(k>=1, "LSFitCreateWFG: K<1!", _state); - ae_assert(c->cnt>=k, "LSFitCreateWFG: length(C)cnt>=n, "LSFitCreateWFG: length(Y)cnt>=n, "LSFitCreateWFG: length(W)rows>=n, "LSFitCreateWFG: rows(X)cols>=m, "LSFitCreateWFG: cols(X)teststep = 0; - state->diffstep = 0; - state->npoints = n; - state->nweights = n; - state->wkind = 1; - state->m = m; - state->k = k; - lsfitsetcond(state, 0.0, 0.0, 0, _state); - lsfitsetstpmax(state, 0.0, _state); - lsfitsetxrep(state, ae_false, _state); - ae_matrix_set_length(&state->taskx, n, m, _state); - ae_vector_set_length(&state->tasky, n, _state); - ae_vector_set_length(&state->taskw, n, _state); - ae_vector_set_length(&state->c, k, _state); - ae_vector_set_length(&state->x, m, _state); - ae_vector_set_length(&state->g, k, _state); - ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->taskw.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->tasky.ptr.p_double[i] = y->ptr.p_double[i]; - } - ae_vector_set_length(&state->s, k, _state); - ae_vector_set_length(&state->bndl, k, _state); - ae_vector_set_length(&state->bndu, k, _state); - for(i=0; i<=k-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - } - state->optalgo = 1; - state->prevnpt = -1; - state->prevalgo = -1; - if( cheapfg ) - { - minlmcreatevgj(k, n, &state->c, &state->optstate, _state); - } - else - { - minlmcreatevj(k, n, &state->c, &state->optstate, _state); - } - lsfit_lsfitclearrequestfields(state, _state); - ae_vector_set_length(&state->rstate.ia, 6+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Nonlinear least squares fitting using gradient only, without individual -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefg(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_bool cheapfg, - lsfitstate* state, - ae_state *_state) -{ - ae_int_t i; - - _lsfitstate_clear(state); - - ae_assert(n>=1, "LSFitCreateFG: N<1!", _state); - ae_assert(m>=1, "LSFitCreateFG: M<1!", _state); - ae_assert(k>=1, "LSFitCreateFG: K<1!", _state); - ae_assert(c->cnt>=k, "LSFitCreateFG: length(C)cnt>=n, "LSFitCreateFG: length(Y)rows>=n, "LSFitCreateFG: rows(X)cols>=m, "LSFitCreateFG: cols(X)rows>=n, "LSFitCreateFG: rows(X)cols>=m, "LSFitCreateFG: cols(X)teststep = 0; - state->diffstep = 0; - state->npoints = n; - state->wkind = 0; - state->m = m; - state->k = k; - lsfitsetcond(state, 0.0, 0.0, 0, _state); - lsfitsetstpmax(state, 0.0, _state); - lsfitsetxrep(state, ae_false, _state); - ae_matrix_set_length(&state->taskx, n, m, _state); - ae_vector_set_length(&state->tasky, n, _state); - ae_vector_set_length(&state->c, k, _state); - ae_vector_set_length(&state->x, m, _state); - ae_vector_set_length(&state->g, k, _state); - ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1)); - for(i=0; i<=n-1; i++) - { - ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->tasky.ptr.p_double[i] = y->ptr.p_double[i]; - } - ae_vector_set_length(&state->s, k, _state); - ae_vector_set_length(&state->bndl, k, _state); - ae_vector_set_length(&state->bndu, k, _state); - for(i=0; i<=k-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - } - state->optalgo = 1; - state->prevnpt = -1; - state->prevalgo = -1; - if( cheapfg ) - { - minlmcreatevgj(k, n, &state->c, &state->optstate, _state); - } - else - { - minlmcreatevj(k, n, &state->c, &state->optstate, _state); - } - lsfit_lsfitclearrequestfields(state, _state); - ae_vector_set_length(&state->rstate.ia, 6+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient/Hessian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfgh(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - lsfitstate* state, - ae_state *_state) -{ - ae_int_t i; - - _lsfitstate_clear(state); - - ae_assert(n>=1, "LSFitCreateWFGH: N<1!", _state); - ae_assert(m>=1, "LSFitCreateWFGH: M<1!", _state); - ae_assert(k>=1, "LSFitCreateWFGH: K<1!", _state); - ae_assert(c->cnt>=k, "LSFitCreateWFGH: length(C)cnt>=n, "LSFitCreateWFGH: length(Y)cnt>=n, "LSFitCreateWFGH: length(W)rows>=n, "LSFitCreateWFGH: rows(X)cols>=m, "LSFitCreateWFGH: cols(X)teststep = 0; - state->diffstep = 0; - state->npoints = n; - state->nweights = n; - state->wkind = 1; - state->m = m; - state->k = k; - lsfitsetcond(state, 0.0, 0.0, 0, _state); - lsfitsetstpmax(state, 0.0, _state); - lsfitsetxrep(state, ae_false, _state); - ae_matrix_set_length(&state->taskx, n, m, _state); - ae_vector_set_length(&state->tasky, n, _state); - ae_vector_set_length(&state->taskw, n, _state); - ae_vector_set_length(&state->c, k, _state); - ae_matrix_set_length(&state->h, k, k, _state); - ae_vector_set_length(&state->x, m, _state); - ae_vector_set_length(&state->g, k, _state); - ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->taskw.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->tasky.ptr.p_double[i] = y->ptr.p_double[i]; - } - ae_vector_set_length(&state->s, k, _state); - ae_vector_set_length(&state->bndl, k, _state); - ae_vector_set_length(&state->bndu, k, _state); - for(i=0; i<=k-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - } - state->optalgo = 2; - state->prevnpt = -1; - state->prevalgo = -1; - minlmcreatefgh(k, &state->c, &state->optstate, _state); - lsfit_lsfitclearrequestfields(state, _state); - ae_vector_set_length(&state->rstate.ia, 6+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Nonlinear least squares fitting using gradient/Hessian, without individial -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefgh(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - lsfitstate* state, - ae_state *_state) -{ - ae_int_t i; - - _lsfitstate_clear(state); - - ae_assert(n>=1, "LSFitCreateFGH: N<1!", _state); - ae_assert(m>=1, "LSFitCreateFGH: M<1!", _state); - ae_assert(k>=1, "LSFitCreateFGH: K<1!", _state); - ae_assert(c->cnt>=k, "LSFitCreateFGH: length(C)cnt>=n, "LSFitCreateFGH: length(Y)rows>=n, "LSFitCreateFGH: rows(X)cols>=m, "LSFitCreateFGH: cols(X)teststep = 0; - state->diffstep = 0; - state->npoints = n; - state->wkind = 0; - state->m = m; - state->k = k; - lsfitsetcond(state, 0.0, 0.0, 0, _state); - lsfitsetstpmax(state, 0.0, _state); - lsfitsetxrep(state, ae_false, _state); - ae_matrix_set_length(&state->taskx, n, m, _state); - ae_vector_set_length(&state->tasky, n, _state); - ae_vector_set_length(&state->c, k, _state); - ae_matrix_set_length(&state->h, k, k, _state); - ae_vector_set_length(&state->x, m, _state); - ae_vector_set_length(&state->g, k, _state); - ae_v_move(&state->c.ptr.p_double[0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,k-1)); - for(i=0; i<=n-1; i++) - { - ae_v_move(&state->taskx.ptr.pp_double[i][0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->tasky.ptr.p_double[i] = y->ptr.p_double[i]; - } - ae_vector_set_length(&state->s, k, _state); - ae_vector_set_length(&state->bndl, k, _state); - ae_vector_set_length(&state->bndu, k, _state); - for(i=0; i<=k-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - } - state->optalgo = 2; - state->prevnpt = -1; - state->prevalgo = -1; - minlmcreatefgh(k, &state->c, &state->optstate, _state); - lsfit_lsfitclearrequestfields(state, _state); - ae_vector_set_length(&state->rstate.ia, 6+1, _state); - ae_vector_set_length(&state->rstate.ra, 8+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Stopping conditions for nonlinear least squares fitting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsF - stopping criterion. Algorithm stops if - |F(k+1)-F(k)| <= EpsF*max{|F(k)|, |F(k+1)|, 1} - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by LSFitSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. Only Levenberg-Marquardt - iterations are counted (L-BFGS/CG iterations are NOT - counted because their cost is very low compared to that of - LM). - -NOTE - -Passing EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to automatic -stopping criterion selection (according to the scheme used by MINLM unit). - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetcond(lsfitstate* state, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsf, _state), "LSFitSetCond: EpsF is not finite!", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "LSFitSetCond: negative EpsF!", _state); - ae_assert(ae_isfinite(epsx, _state), "LSFitSetCond: EpsX is not finite!", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "LSFitSetCond: negative EpsX!", _state); - ae_assert(maxits>=0, "LSFitSetCond: negative MaxIts!", _state); - state->epsf = epsf; - state->epsx = epsx; - state->maxits = maxits; -} - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - -NOTE: non-zero StpMax leads to moderate performance degradation because -intermediate step of preconditioned L-BFGS optimization is incompatible -with limits on step size. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetstpmax(lsfitstate* state, double stpmax, ae_state *_state) -{ - - - ae_assert(ae_fp_greater_eq(stpmax,0), "LSFitSetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -When reports are needed, State.C (current parameters) and State.F (current -value of fitting function) are reported. - - - -- ALGLIB -- - Copyright 15.08.2010 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetxrep(lsfitstate* state, ae_bool needxrep, ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function sets scaling coefficients for underlying optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Generally, scale is NOT considered to be a form of preconditioner. But LM -optimizer is unique in that it uses scaling matrix both in the stopping -condition tests and as Marquardt damping factor. - -Proper scaling is very important for the algorithm performance. It is less -important for the quality of results, but still has some influence (it is -easier to converge when variables are properly scaled, so premature -stopping is possible when very badly scalled variables are combined with -relaxed stopping conditions). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetscale(lsfitstate* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(s->cnt>=state->k, "LSFitSetScale: Length(S)k-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "LSFitSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "LSFitSetScale: S contains infinite or NAN elements", _state); - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -This function sets boundary constraints for underlying optimizer - -Boundary constraints are inactive by default (after initial creation). -They are preserved until explicitly turned off with another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[K]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[K]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: unlike other constrained optimization algorithms, this solver has -following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetbc(lsfitstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - - k = state->k; - ae_assert(bndl->cnt>=k, "LSFitSetBC: Length(BndL)cnt>=k, "LSFitSetBC: Length(BndU)ptr.p_double[i], _state)||ae_isneginf(bndl->ptr.p_double[i], _state), "LSFitSetBC: BndL contains NAN or +INF", _state); - ae_assert(ae_isfinite(bndu->ptr.p_double[i], _state)||ae_isposinf(bndu->ptr.p_double[i], _state), "LSFitSetBC: BndU contains NAN or -INF", _state); - if( ae_isfinite(bndl->ptr.p_double[i], _state)&&ae_isfinite(bndu->ptr.p_double[i], _state) ) - { - ae_assert(ae_fp_less_eq(bndl->ptr.p_double[i],bndu->ptr.p_double[i]), "LSFitSetBC: BndL[i]>BndU[i]", _state); - } - state->bndl.ptr.p_double[i] = bndl->ptr.p_double[i]; - state->bndu.ptr.p_double[i] = bndu->ptr.p_double[i]; - } -} - - -/************************************************************************* -NOTES: - -1. this algorithm is somewhat unusual because it works with parameterized - function f(C,X), where X is a function argument (we have many points - which are characterized by different argument values), and C is a - parameter to fit. - - For example, if we want to do linear fit by f(c0,c1,x) = c0*x+c1, then - x will be argument, and {c0,c1} will be parameters. - - It is important to understand that this algorithm finds minimum in the - space of function PARAMETERS (not arguments), so it needs derivatives - of f() with respect to C, not X. - - In the example above it will need f=c0*x+c1 and {df/dc0,df/dc1} = {x,1} - instead of {df/dx} = {c0}. - -2. Callback functions accept C as the first parameter, and X as the second - -3. If state was created with LSFitCreateFG(), algorithm needs just - function and its gradient, but if state was created with - LSFitCreateFGH(), algorithm will need function, gradient and Hessian. - - According to the said above, there ase several versions of this - function, which accept different sets of callbacks. - - This flexibility opens way to subtle errors - you may create state with - LSFitCreateFGH() (optimization using Hessian), but call function which - does not accept Hessian. So when algorithm will request Hessian, there - will be no callback to call. In this case exception will be thrown. - - Be careful to avoid such errors because there is no way to find them at - compile time - you can see them at runtime only. - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool lsfititeration(lsfitstate* state, ae_state *_state) -{ - double lx; - double lf; - double ld; - double rx; - double rf; - double rd; - ae_int_t n; - ae_int_t m; - ae_int_t k; - double v; - double vv; - double relcnt; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t info; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - k = state->rstate.ia.ptr.p_int[2]; - i = state->rstate.ia.ptr.p_int[3]; - j = state->rstate.ia.ptr.p_int[4]; - j1 = state->rstate.ia.ptr.p_int[5]; - info = state->rstate.ia.ptr.p_int[6]; - lx = state->rstate.ra.ptr.p_double[0]; - lf = state->rstate.ra.ptr.p_double[1]; - ld = state->rstate.ra.ptr.p_double[2]; - rx = state->rstate.ra.ptr.p_double[3]; - rf = state->rstate.ra.ptr.p_double[4]; - rd = state->rstate.ra.ptr.p_double[5]; - v = state->rstate.ra.ptr.p_double[6]; - vv = state->rstate.ra.ptr.p_double[7]; - relcnt = state->rstate.ra.ptr.p_double[8]; - } - else - { - n = -983; - m = -989; - k = -834; - i = 900; - j = -287; - j1 = 364; - info = 214; - lx = -338; - lf = -686; - ld = 912; - rx = 585; - rf = 497; - rd = -271; - v = -581; - vv = 745; - relcnt = -533; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - if( state->rstate.stage==8 ) - { - goto lbl_8; - } - if( state->rstate.stage==9 ) - { - goto lbl_9; - } - if( state->rstate.stage==10 ) - { - goto lbl_10; - } - if( state->rstate.stage==11 ) - { - goto lbl_11; - } - if( state->rstate.stage==12 ) - { - goto lbl_12; - } - if( state->rstate.stage==13 ) - { - goto lbl_13; - } - - /* - * Routine body - */ - - /* - * Init - */ - if( state->wkind==1 ) - { - ae_assert(state->npoints==state->nweights, "LSFitFit: number of points is not equal to the number of weights", _state); - } - state->repvaridx = -1; - n = state->npoints; - m = state->m; - k = state->k; - minlmsetcond(&state->optstate, 0.0, state->epsf, state->epsx, state->maxits, _state); - minlmsetstpmax(&state->optstate, state->stpmax, _state); - minlmsetxrep(&state->optstate, state->xrep, _state); - minlmsetscale(&state->optstate, &state->s, _state); - minlmsetbc(&state->optstate, &state->bndl, &state->bndu, _state); - - /* - * Check that user-supplied gradient is correct - */ - lsfit_lsfitclearrequestfields(state, _state); - if( !(ae_fp_greater(state->teststep,0)&&state->optalgo==1) ) - { - goto lbl_14; - } - for(i=0; i<=k-1; i++) - { - if( ae_isfinite(state->bndl.ptr.p_double[i], _state) ) - { - state->c.ptr.p_double[i] = ae_maxreal(state->c.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - if( ae_isfinite(state->bndu.ptr.p_double[i], _state) ) - { - state->c.ptr.p_double[i] = ae_minreal(state->c.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - } - state->needfg = ae_true; - i = 0; -lbl_16: - if( i>k-1 ) - { - goto lbl_18; - } - ae_assert(ae_fp_less_eq(state->bndl.ptr.p_double[i],state->c.ptr.p_double[i])&&ae_fp_less_eq(state->c.ptr.p_double[i],state->bndu.ptr.p_double[i]), "LSFitIteration: internal error(State.C is out of bounds)", _state); - v = state->c.ptr.p_double[i]; - j = 0; -lbl_19: - if( j>n-1 ) - { - goto lbl_21; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[j][0], 1, ae_v_len(0,m-1)); - state->c.ptr.p_double[i] = v-state->teststep*state->s.ptr.p_double[i]; - if( ae_isfinite(state->bndl.ptr.p_double[i], _state) ) - { - state->c.ptr.p_double[i] = ae_maxreal(state->c.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - lx = state->c.ptr.p_double[i]; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - lf = state->f; - ld = state->g.ptr.p_double[i]; - state->c.ptr.p_double[i] = v+state->teststep*state->s.ptr.p_double[i]; - if( ae_isfinite(state->bndu.ptr.p_double[i], _state) ) - { - state->c.ptr.p_double[i] = ae_minreal(state->c.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - rx = state->c.ptr.p_double[i]; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - rf = state->f; - rd = state->g.ptr.p_double[i]; - state->c.ptr.p_double[i] = (lx+rx)/2; - if( ae_isfinite(state->bndl.ptr.p_double[i], _state) ) - { - state->c.ptr.p_double[i] = ae_maxreal(state->c.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - if( ae_isfinite(state->bndu.ptr.p_double[i], _state) ) - { - state->c.ptr.p_double[i] = ae_minreal(state->c.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->c.ptr.p_double[i] = v; - if( !derivativecheck(lf, ld, rf, rd, state->f, state->g.ptr.p_double[i], rx-lx, _state) ) - { - state->repvaridx = i; - state->repterminationtype = -7; - result = ae_false; - return result; - } - j = j+1; - goto lbl_19; -lbl_21: - i = i+1; - goto lbl_16; -lbl_18: - state->needfg = ae_false; -lbl_14: - - /* - * Fill WCur by weights: - * * for WKind=0 unit weights are chosen - * * for WKind=1 we use user-supplied weights stored in State.TaskW - */ - rvectorsetlengthatleast(&state->wcur, n, _state); - for(i=0; i<=n-1; i++) - { - state->wcur.ptr.p_double[i] = 1.0; - if( state->wkind==1 ) - { - state->wcur.ptr.p_double[i] = state->taskw.ptr.p_double[i]; - } - } - - /* - * Optimize - */ -lbl_22: - if( !minlmiteration(&state->optstate, _state) ) - { - goto lbl_23; - } - if( !state->optstate.needfi ) - { - goto lbl_24; - } - - /* - * calculate f[] = wi*(f(xi,c)-yi) - */ - i = 0; -lbl_26: - if( i>n-1 ) - { - goto lbl_28; - } - ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - lsfit_lsfitclearrequestfields(state, _state); - state->needf = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needf = ae_false; - vv = state->wcur.ptr.p_double[i]; - state->optstate.fi.ptr.p_double[i] = vv*(state->f-state->tasky.ptr.p_double[i]); - i = i+1; - goto lbl_26; -lbl_28: - goto lbl_22; -lbl_24: - if( !state->optstate.needf ) - { - goto lbl_29; - } - - /* - * calculate F = sum (wi*(f(xi,c)-yi))^2 - */ - state->optstate.f = 0; - i = 0; -lbl_31: - if( i>n-1 ) - { - goto lbl_33; - } - ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - lsfit_lsfitclearrequestfields(state, _state); - state->needf = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->needf = ae_false; - vv = state->wcur.ptr.p_double[i]; - state->optstate.f = state->optstate.f+ae_sqr(vv*(state->f-state->tasky.ptr.p_double[i]), _state); - i = i+1; - goto lbl_31; -lbl_33: - goto lbl_22; -lbl_29: - if( !state->optstate.needfg ) - { - goto lbl_34; - } - - /* - * calculate F/gradF - */ - state->optstate.f = 0; - for(i=0; i<=k-1; i++) - { - state->optstate.g.ptr.p_double[i] = 0; - } - i = 0; -lbl_36: - if( i>n-1 ) - { - goto lbl_38; - } - ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - lsfit_lsfitclearrequestfields(state, _state); - state->needfg = ae_true; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->needfg = ae_false; - vv = state->wcur.ptr.p_double[i]; - state->optstate.f = state->optstate.f+ae_sqr(vv*(state->f-state->tasky.ptr.p_double[i]), _state); - v = ae_sqr(vv, _state)*2*(state->f-state->tasky.ptr.p_double[i]); - ae_v_addd(&state->optstate.g.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), v); - i = i+1; - goto lbl_36; -lbl_38: - goto lbl_22; -lbl_34: - if( !state->optstate.needfij ) - { - goto lbl_39; - } - - /* - * calculate Fi/jac(Fi) - */ - i = 0; -lbl_41: - if( i>n-1 ) - { - goto lbl_43; - } - ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - lsfit_lsfitclearrequestfields(state, _state); - state->needfg = ae_true; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->needfg = ae_false; - vv = state->wcur.ptr.p_double[i]; - state->optstate.fi.ptr.p_double[i] = vv*(state->f-state->tasky.ptr.p_double[i]); - ae_v_moved(&state->optstate.j.ptr.pp_double[i][0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), vv); - i = i+1; - goto lbl_41; -lbl_43: - goto lbl_22; -lbl_39: - if( !state->optstate.needfgh ) - { - goto lbl_44; - } - - /* - * calculate F/grad(F)/hess(F) - */ - state->optstate.f = 0; - for(i=0; i<=k-1; i++) - { - state->optstate.g.ptr.p_double[i] = 0; - } - for(i=0; i<=k-1; i++) - { - for(j=0; j<=k-1; j++) - { - state->optstate.h.ptr.pp_double[i][j] = 0; - } - } - i = 0; -lbl_46: - if( i>n-1 ) - { - goto lbl_48; - } - ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - lsfit_lsfitclearrequestfields(state, _state); - state->needfgh = ae_true; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->needfgh = ae_false; - vv = state->wcur.ptr.p_double[i]; - state->optstate.f = state->optstate.f+ae_sqr(vv*(state->f-state->tasky.ptr.p_double[i]), _state); - v = ae_sqr(vv, _state)*2*(state->f-state->tasky.ptr.p_double[i]); - ae_v_addd(&state->optstate.g.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), v); - for(j=0; j<=k-1; j++) - { - v = 2*ae_sqr(vv, _state)*state->g.ptr.p_double[j]; - ae_v_addd(&state->optstate.h.ptr.pp_double[j][0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,k-1), v); - v = 2*ae_sqr(vv, _state)*(state->f-state->tasky.ptr.p_double[i]); - ae_v_addd(&state->optstate.h.ptr.pp_double[j][0], 1, &state->h.ptr.pp_double[j][0], 1, ae_v_len(0,k-1), v); - } - i = i+1; - goto lbl_46; -lbl_48: - goto lbl_22; -lbl_44: - if( !state->optstate.xupdated ) - { - goto lbl_49; - } - - /* - * Report new iteration - */ - ae_v_move(&state->c.ptr.p_double[0], 1, &state->optstate.x.ptr.p_double[0], 1, ae_v_len(0,k-1)); - state->f = state->optstate.f; - lsfit_lsfitclearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 8; - goto lbl_rcomm; -lbl_8: - state->xupdated = ae_false; - goto lbl_22; -lbl_49: - goto lbl_22; -lbl_23: - minlmresults(&state->optstate, &state->c, &state->optrep, _state); - state->repterminationtype = state->optrep.terminationtype; - state->repiterationscount = state->optrep.iterationscount; - - /* - * calculate errors - */ - if( state->repterminationtype<=0 ) - { - goto lbl_51; - } - - /* - * Calculate RMS/Avg/Max/... errors - */ - state->reprmserror = 0; - state->repwrmserror = 0; - state->repavgerror = 0; - state->repavgrelerror = 0; - state->repmaxerror = 0; - relcnt = 0; - i = 0; -lbl_53: - if( i>n-1 ) - { - goto lbl_55; - } - ae_v_move(&state->c.ptr.p_double[0], 1, &state->c.ptr.p_double[0], 1, ae_v_len(0,k-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - lsfit_lsfitclearrequestfields(state, _state); - state->needf = ae_true; - state->rstate.stage = 9; - goto lbl_rcomm; -lbl_9: - state->needf = ae_false; - v = state->f; - vv = state->wcur.ptr.p_double[i]; - state->reprmserror = state->reprmserror+ae_sqr(v-state->tasky.ptr.p_double[i], _state); - state->repwrmserror = state->repwrmserror+ae_sqr(vv*(v-state->tasky.ptr.p_double[i]), _state); - state->repavgerror = state->repavgerror+ae_fabs(v-state->tasky.ptr.p_double[i], _state); - if( ae_fp_neq(state->tasky.ptr.p_double[i],0) ) - { - state->repavgrelerror = state->repavgrelerror+ae_fabs(v-state->tasky.ptr.p_double[i], _state)/ae_fabs(state->tasky.ptr.p_double[i], _state); - relcnt = relcnt+1; - } - state->repmaxerror = ae_maxreal(state->repmaxerror, ae_fabs(v-state->tasky.ptr.p_double[i], _state), _state); - i = i+1; - goto lbl_53; -lbl_55: - state->reprmserror = ae_sqrt(state->reprmserror/n, _state); - state->repwrmserror = ae_sqrt(state->repwrmserror/n, _state); - state->repavgerror = state->repavgerror/n; - if( ae_fp_neq(relcnt,0) ) - { - state->repavgrelerror = state->repavgrelerror/relcnt; - } - - /* - * Calculate covariance matrix - */ - rmatrixsetlengthatleast(&state->tmpjac, n, k, _state); - rvectorsetlengthatleast(&state->tmpf, n, _state); - rvectorsetlengthatleast(&state->tmp, k, _state); - if( ae_fp_less_eq(state->diffstep,0) ) - { - goto lbl_56; - } - - /* - * Compute Jacobian by means of numerical differentiation - */ - lsfit_lsfitclearrequestfields(state, _state); - state->needf = ae_true; - i = 0; -lbl_58: - if( i>n-1 ) - { - goto lbl_60; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - state->rstate.stage = 10; - goto lbl_rcomm; -lbl_10: - state->tmpf.ptr.p_double[i] = state->f; - j = 0; -lbl_61: - if( j>k-1 ) - { - goto lbl_63; - } - v = state->c.ptr.p_double[j]; - lx = v-state->diffstep*state->s.ptr.p_double[j]; - state->c.ptr.p_double[j] = lx; - if( ae_isfinite(state->bndl.ptr.p_double[j], _state) ) - { - state->c.ptr.p_double[j] = ae_maxreal(state->c.ptr.p_double[j], state->bndl.ptr.p_double[j], _state); - } - state->rstate.stage = 11; - goto lbl_rcomm; -lbl_11: - lf = state->f; - rx = v+state->diffstep*state->s.ptr.p_double[j]; - state->c.ptr.p_double[j] = rx; - if( ae_isfinite(state->bndu.ptr.p_double[j], _state) ) - { - state->c.ptr.p_double[j] = ae_minreal(state->c.ptr.p_double[j], state->bndu.ptr.p_double[j], _state); - } - state->rstate.stage = 12; - goto lbl_rcomm; -lbl_12: - rf = state->f; - state->c.ptr.p_double[j] = v; - if( ae_fp_neq(rx,lx) ) - { - state->tmpjac.ptr.pp_double[i][j] = (rf-lf)/(rx-lx); - } - else - { - state->tmpjac.ptr.pp_double[i][j] = 0; - } - j = j+1; - goto lbl_61; -lbl_63: - i = i+1; - goto lbl_58; -lbl_60: - state->needf = ae_false; - goto lbl_57; -lbl_56: - - /* - * Jacobian is calculated with user-provided analytic gradient - */ - lsfit_lsfitclearrequestfields(state, _state); - state->needfg = ae_true; - i = 0; -lbl_64: - if( i>n-1 ) - { - goto lbl_66; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->taskx.ptr.pp_double[i][0], 1, ae_v_len(0,m-1)); - state->pointindex = i; - state->rstate.stage = 13; - goto lbl_rcomm; -lbl_13: - state->tmpf.ptr.p_double[i] = state->f; - for(j=0; j<=k-1; j++) - { - state->tmpjac.ptr.pp_double[i][j] = state->g.ptr.p_double[j]; - } - i = i+1; - goto lbl_64; -lbl_66: - state->needfg = ae_false; -lbl_57: - for(i=0; i<=k-1; i++) - { - state->tmp.ptr.p_double[i] = 0.0; - } - lsfit_estimateerrors(&state->tmpjac, &state->tmpf, &state->tasky, &state->wcur, &state->tmp, &state->s, n, k, &state->rep, &state->tmpjacw, 0, _state); -lbl_51: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = k; - state->rstate.ia.ptr.p_int[3] = i; - state->rstate.ia.ptr.p_int[4] = j; - state->rstate.ia.ptr.p_int[5] = j1; - state->rstate.ia.ptr.p_int[6] = info; - state->rstate.ra.ptr.p_double[0] = lx; - state->rstate.ra.ptr.p_double[1] = lf; - state->rstate.ra.ptr.p_double[2] = ld; - state->rstate.ra.ptr.p_double[3] = rx; - state->rstate.ra.ptr.p_double[4] = rf; - state->rstate.ra.ptr.p_double[5] = rd; - state->rstate.ra.ptr.p_double[6] = v; - state->rstate.ra.ptr.p_double[7] = vv; - state->rstate.ra.ptr.p_double[8] = relcnt; - return result; -} - - -/************************************************************************* -Nonlinear least squares fitting results. - -Called after return from LSFitFit(). - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - Info - completion code: - * -7 gradient verification failed. - See LSFitSetGradientCheck() for more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - C - array[0..K-1], solution - Rep - optimization report. On success following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - * WRMSError weighted rms error on the (X,Y). - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(J*CovPar*J')), - where J is Jacobian matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitresults(lsfitstate* state, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - - lsfit_clearreport(rep, _state); - *info = state->repterminationtype; - rep->varidx = state->repvaridx; - if( *info>0 ) - { - ae_vector_set_length(c, state->k, _state); - ae_v_move(&c->ptr.p_double[0], 1, &state->c.ptr.p_double[0], 1, ae_v_len(0,state->k-1)); - rep->rmserror = state->reprmserror; - rep->wrmserror = state->repwrmserror; - rep->avgerror = state->repavgerror; - rep->avgrelerror = state->repavgrelerror; - rep->maxerror = state->repmaxerror; - rep->iterationscount = state->repiterationscount; - ae_matrix_set_length(&rep->covpar, state->k, state->k, _state); - ae_vector_set_length(&rep->errpar, state->k, _state); - ae_vector_set_length(&rep->errcurve, state->npoints, _state); - ae_vector_set_length(&rep->noise, state->npoints, _state); - rep->r2 = state->rep.r2; - for(i=0; i<=state->k-1; i++) - { - for(j=0; j<=state->k-1; j++) - { - rep->covpar.ptr.pp_double[i][j] = state->rep.covpar.ptr.pp_double[i][j]; - } - rep->errpar.ptr.p_double[i] = state->rep.errpar.ptr.p_double[i]; - } - for(i=0; i<=state->npoints-1; i++) - { - rep->errcurve.ptr.p_double[i] = state->rep.errcurve.ptr.p_double[i]; - rep->noise.ptr.p_double[i] = state->rep.noise.ptr.p_double[i]; - } - } -} - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before fitting begins -* LSFitFit() is called -* prior to actual fitting, for each point in data set X_i and each - component of parameters being fited C_j algorithm performs following - steps: - * two trial steps are made to C_j-TestStep*S[j] and C_j+TestStep*S[j], - where C_j is j-th parameter and S[j] is a scale of j-th parameter - * if needed, steps are bounded with respect to constraints on C[] - * F(X_i|C) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N*K (points count * parameters count) gradient - evaluations. It is very costly and you should use it only for low - dimensional problems, when you want to be sure that you've - correctly calculated analytic derivatives. You should not use it - in the production code (unless you want to check derivatives - provided by some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with LSFitSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -NOTE 4: this function works only for optimizers created with LSFitCreateWFG() - or LSFitCreateFG() constructors. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetgradientcheck(lsfitstate* state, - double teststep, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(teststep, _state), "LSFitSetGradientCheck: TestStep contains NaN or Infinite", _state); - ae_assert(ae_fp_greater_eq(teststep,0), "LSFitSetGradientCheck: invalid argument TestStep(TestStep<0)", _state); - state->teststep = teststep; -} - - -/************************************************************************* -Internal subroutine: automatic scaling for LLS tasks. -NEVER CALL IT DIRECTLY! - -Maps abscissas to [-1,1], standartizes ordinates and correspondingly scales -constraints. It also scales weights so that max(W[i])=1 - -Transformations performed: -* X, XC [XA,XB] => [-1,+1] - transformation makes min(X)=-1, max(X)=+1 - -* Y [SA,SB] => [0,1] - transformation makes mean(Y)=0, stddev(Y)=1 - -* YC transformed accordingly to SA, SB, DC[I] - - -- ALGLIB PROJECT -- - Copyright 08.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitscalexy(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - double* xa, - double* xb, - double* sa, - double* sb, - /* Real */ ae_vector* xoriginal, - /* Real */ ae_vector* yoriginal, - ae_state *_state) -{ - double xmin; - double xmax; - ae_int_t i; - double mx; - - *xa = 0; - *xb = 0; - *sa = 0; - *sb = 0; - ae_vector_clear(xoriginal); - ae_vector_clear(yoriginal); - - ae_assert(n>=1, "LSFitScaleXY: incorrect N", _state); - ae_assert(k>=0, "LSFitScaleXY: incorrect K", _state); - - /* - * Calculate xmin/xmax. - * Force xmin<>xmax. - */ - xmin = x->ptr.p_double[0]; - xmax = x->ptr.p_double[0]; - for(i=1; i<=n-1; i++) - { - xmin = ae_minreal(xmin, x->ptr.p_double[i], _state); - xmax = ae_maxreal(xmax, x->ptr.p_double[i], _state); - } - for(i=0; i<=k-1; i++) - { - xmin = ae_minreal(xmin, xc->ptr.p_double[i], _state); - xmax = ae_maxreal(xmax, xc->ptr.p_double[i], _state); - } - if( ae_fp_eq(xmin,xmax) ) - { - if( ae_fp_eq(xmin,0) ) - { - xmin = -1; - xmax = 1; - } - else - { - if( ae_fp_greater(xmin,0) ) - { - xmin = 0.5*xmin; - } - else - { - xmax = 0.5*xmax; - } - } - } - - /* - * Transform abscissas: map [XA,XB] to [0,1] - * - * Store old X[] in XOriginal[] (it will be used - * to calculate relative error). - */ - ae_vector_set_length(xoriginal, n, _state); - ae_v_move(&xoriginal->ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - *xa = xmin; - *xb = xmax; - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = 2*(x->ptr.p_double[i]-0.5*(*xa+(*xb)))/(*xb-(*xa)); - } - for(i=0; i<=k-1; i++) - { - ae_assert(dc->ptr.p_int[i]>=0, "LSFitScaleXY: internal error!", _state); - xc->ptr.p_double[i] = 2*(xc->ptr.p_double[i]-0.5*(*xa+(*xb)))/(*xb-(*xa)); - yc->ptr.p_double[i] = yc->ptr.p_double[i]*ae_pow(0.5*(*xb-(*xa)), dc->ptr.p_int[i], _state); - } - - /* - * Transform function values: map [SA,SB] to [0,1] - * SA = mean(Y), - * SB = SA+stddev(Y). - * - * Store old Y[] in YOriginal[] (it will be used - * to calculate relative error). - */ - ae_vector_set_length(yoriginal, n, _state); - ae_v_move(&yoriginal->ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - *sa = 0; - for(i=0; i<=n-1; i++) - { - *sa = *sa+y->ptr.p_double[i]; - } - *sa = *sa/n; - *sb = 0; - for(i=0; i<=n-1; i++) - { - *sb = *sb+ae_sqr(y->ptr.p_double[i]-(*sa), _state); - } - *sb = ae_sqrt(*sb/n, _state)+(*sa); - if( ae_fp_eq(*sb,*sa) ) - { - *sb = 2*(*sa); - } - if( ae_fp_eq(*sb,*sa) ) - { - *sb = *sa+1; - } - for(i=0; i<=n-1; i++) - { - y->ptr.p_double[i] = (y->ptr.p_double[i]-(*sa))/(*sb-(*sa)); - } - for(i=0; i<=k-1; i++) - { - if( dc->ptr.p_int[i]==0 ) - { - yc->ptr.p_double[i] = (yc->ptr.p_double[i]-(*sa))/(*sb-(*sa)); - } - else - { - yc->ptr.p_double[i] = yc->ptr.p_double[i]/(*sb-(*sa)); - } - } - - /* - * Scale weights - */ - mx = 0; - for(i=0; i<=n-1; i++) - { - mx = ae_maxreal(mx, ae_fabs(w->ptr.p_double[i], _state), _state); - } - if( ae_fp_neq(mx,0) ) - { - for(i=0; i<=n-1; i++) - { - w->ptr.p_double[i] = w->ptr.p_double[i]/mx; - } - } -} - - -/************************************************************************* -Internal spline fitting subroutine - - -- ALGLIB PROJECT -- - Copyright 08.09.2009 by Bochkanov Sergey -*************************************************************************/ -static void lsfit_spline1dfitinternal(ae_int_t st, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _w; - ae_vector _xc; - ae_vector _yc; - ae_matrix fmatrix; - ae_matrix cmatrix; - ae_vector y2; - ae_vector w2; - ae_vector sx; - ae_vector sy; - ae_vector sd; - ae_vector tmp; - ae_vector xoriginal; - ae_vector yoriginal; - lsfitreport lrep; - double v0; - double v1; - double v2; - double mx; - spline1dinterpolant s2; - ae_int_t i; - ae_int_t j; - ae_int_t relcnt; - double xa; - double xb; - double sa; - double sb; - double bl; - double br; - double decay; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_w, w, _state, ae_true); - w = &_w; - ae_vector_init_copy(&_xc, xc, _state, ae_true); - xc = &_xc; - ae_vector_init_copy(&_yc, yc, _state, ae_true); - yc = &_yc; - *info = 0; - _spline1dinterpolant_clear(s); - _spline1dfitreport_clear(rep); - ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&cmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sy, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sd, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xoriginal, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yoriginal, 0, DT_REAL, _state, ae_true); - _lsfitreport_init(&lrep, _state, ae_true); - _spline1dinterpolant_init(&s2, _state, ae_true); - - ae_assert(st==0||st==1, "Spline1DFit: internal error!", _state); - if( st==0&&m<4 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( st==1&&m<4 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( (n<1||k<0)||k>=m ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=k-1; i++) - { - *info = 0; - if( dc->ptr.p_int[i]<0 ) - { - *info = -1; - } - if( dc->ptr.p_int[i]>1 ) - { - *info = -1; - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - } - if( st==1&&m%2!=0 ) - { - - /* - * Hermite fitter must have even number of basis functions - */ - *info = -2; - ae_frame_leave(_state); - return; - } - - /* - * weight decay for correct handling of task which becomes - * degenerate after constraints are applied - */ - decay = 10000*ae_machineepsilon; - - /* - * Scale X, Y, XC, YC - */ - lsfitscalexy(x, y, w, n, xc, yc, dc, k, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state); - - /* - * allocate space, initialize: - * * SX - grid for basis functions - * * SY - values of basis functions at grid points - * * FMatrix- values of basis functions at X[] - * * CMatrix- values (derivatives) of basis functions at XC[] - */ - ae_vector_set_length(&y2, n+m, _state); - ae_vector_set_length(&w2, n+m, _state); - ae_matrix_set_length(&fmatrix, n+m, m, _state); - if( k>0 ) - { - ae_matrix_set_length(&cmatrix, k, m+1, _state); - } - if( st==0 ) - { - - /* - * allocate space for cubic spline - */ - ae_vector_set_length(&sx, m-2, _state); - ae_vector_set_length(&sy, m-2, _state); - for(j=0; j<=m-2-1; j++) - { - sx.ptr.p_double[j] = (double)(2*j)/(double)(m-2-1)-1; - } - } - if( st==1 ) - { - - /* - * allocate space for Hermite spline - */ - ae_vector_set_length(&sx, m/2, _state); - ae_vector_set_length(&sy, m/2, _state); - ae_vector_set_length(&sd, m/2, _state); - for(j=0; j<=m/2-1; j++) - { - sx.ptr.p_double[j] = (double)(2*j)/(double)(m/2-1)-1; - } - } - - /* - * Prepare design and constraints matrices: - * * fill constraints matrix - * * fill first N rows of design matrix with values - * * fill next M rows of design matrix with regularizing term - * * append M zeros to Y - * * append M elements, mean(abs(W)) each, to W - */ - for(j=0; j<=m-1; j++) - { - - /* - * prepare Jth basis function - */ - if( st==0 ) - { - - /* - * cubic spline basis - */ - for(i=0; i<=m-2-1; i++) - { - sy.ptr.p_double[i] = 0; - } - bl = 0; - br = 0; - if( jptr.p_double[i], _state); - } - for(i=0; i<=k-1; i++) - { - ae_assert(dc->ptr.p_int[i]>=0&&dc->ptr.p_int[i]<=2, "Spline1DFit: internal error!", _state); - spline1ddiff(&s2, xc->ptr.p_double[i], &v0, &v1, &v2, _state); - if( dc->ptr.p_int[i]==0 ) - { - cmatrix.ptr.pp_double[i][j] = v0; - } - if( dc->ptr.p_int[i]==1 ) - { - cmatrix.ptr.pp_double[i][j] = v1; - } - if( dc->ptr.p_int[i]==2 ) - { - cmatrix.ptr.pp_double[i][j] = v2; - } - } - } - for(i=0; i<=k-1; i++) - { - cmatrix.ptr.pp_double[i][m] = yc->ptr.p_double[i]; - } - for(i=0; i<=m-1; i++) - { - for(j=0; j<=m-1; j++) - { - if( i==j ) - { - fmatrix.ptr.pp_double[n+i][j] = decay; - } - else - { - fmatrix.ptr.pp_double[n+i][j] = 0; - } - } - } - ae_vector_set_length(&y2, n+m, _state); - ae_vector_set_length(&w2, n+m, _state); - ae_v_move(&y2.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&w2.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1)); - mx = 0; - for(i=0; i<=n-1; i++) - { - mx = mx+ae_fabs(w->ptr.p_double[i], _state); - } - mx = mx/n; - for(i=0; i<=m-1; i++) - { - y2.ptr.p_double[n+i] = 0; - w2.ptr.p_double[n+i] = mx; - } - - /* - * Solve constrained task - */ - if( k>0 ) - { - - /* - * solve using regularization - */ - lsfitlinearwc(&y2, &w2, &fmatrix, &cmatrix, n+m, m, k, info, &tmp, &lrep, _state); - } - else - { - - /* - * no constraints, no regularization needed - */ - lsfitlinearwc(y, w, &fmatrix, &cmatrix, n, m, k, info, &tmp, &lrep, _state); - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Generate spline and scale it - */ - if( st==0 ) - { - - /* - * cubic spline basis - */ - ae_v_move(&sy.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-2-1)); - spline1dbuildcubic(&sx, &sy, m-2, 1, tmp.ptr.p_double[m-2], 1, tmp.ptr.p_double[m-1], s, _state); - } - if( st==1 ) - { - - /* - * Hermite basis - */ - for(i=0; i<=m/2-1; i++) - { - sy.ptr.p_double[i] = tmp.ptr.p_double[2*i]; - sd.ptr.p_double[i] = tmp.ptr.p_double[2*i+1]; - } - spline1dbuildhermite(&sx, &sy, &sd, m/2, s, _state); - } - spline1dlintransx(s, 2/(xb-xa), -(xa+xb)/(xb-xa), _state); - spline1dlintransy(s, sb-sa, sa, _state); - - /* - * Scale absolute errors obtained from LSFitLinearW. - * Relative error should be calculated separately - * (because of shifting/scaling of the task) - */ - rep->taskrcond = lrep.taskrcond; - rep->rmserror = lrep.rmserror*(sb-sa); - rep->avgerror = lrep.avgerror*(sb-sa); - rep->maxerror = lrep.maxerror*(sb-sa); - rep->avgrelerror = 0; - relcnt = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(yoriginal.ptr.p_double[i],0) ) - { - rep->avgrelerror = rep->avgrelerror+ae_fabs(spline1dcalc(s, xoriginal.ptr.p_double[i], _state)-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state); - relcnt = relcnt+1; - } - } - if( relcnt!=0 ) - { - rep->avgrelerror = rep->avgrelerror/relcnt; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal fitting subroutine -*************************************************************************/ -static void lsfit_lsfitlinearinternal(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - double threshold; - ae_matrix ft; - ae_matrix q; - ae_matrix l; - ae_matrix r; - ae_vector b; - ae_vector wmod; - ae_vector tau; - ae_vector nzeros; - ae_vector s; - ae_int_t i; - ae_int_t j; - double v; - ae_vector sv; - ae_matrix u; - ae_matrix vt; - ae_vector tmp; - ae_vector utb; - ae_vector sutb; - ae_int_t relcnt; - - ae_frame_make(_state, &_frame_block); - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - ae_matrix_init(&ft, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&q, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&l, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&r, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&b, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wmod, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&nzeros, 0, DT_REAL, _state, ae_true); - ae_vector_init(&s, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sv, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&u, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vt, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&utb, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sutb, 0, DT_REAL, _state, ae_true); - - lsfit_clearreport(rep, _state); - if( n<1||m<1 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - *info = 1; - threshold = ae_sqrt(ae_machineepsilon, _state); - - /* - * Degenerate case, needs special handling - */ - if( nptr.p_double[j]; - ae_v_moved(&ft.ptr.pp_double[j][0], 1, &fmatrix->ptr.pp_double[j][0], 1, ae_v_len(0,m-1), v); - b.ptr.p_double[j] = w->ptr.p_double[j]*y->ptr.p_double[j]; - wmod.ptr.p_double[j] = 1; - } - - /* - * LQ decomposition and reduction to M=N - */ - ae_vector_set_length(c, m, _state); - for(i=0; i<=m-1; i++) - { - c->ptr.p_double[i] = 0; - } - rep->taskrcond = 0; - rmatrixlq(&ft, n, m, &tau, _state); - rmatrixlqunpackq(&ft, n, m, &tau, n, &q, _state); - rmatrixlqunpackl(&ft, n, m, &l, _state); - lsfit_lsfitlinearinternal(&b, &wmod, &l, n, n, info, &tmp, rep, _state); - if( *info<=0 ) - { - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - v = tmp.ptr.p_double[i]; - ae_v_addd(&c->ptr.p_double[0], 1, &q.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - ae_frame_leave(_state); - return; - } - - /* - * N>=M. Generate design matrix and reduce to N=M using - * QR decomposition. - */ - ae_matrix_set_length(&ft, n, m, _state); - ae_vector_set_length(&b, n, _state); - for(j=0; j<=n-1; j++) - { - v = w->ptr.p_double[j]; - ae_v_moved(&ft.ptr.pp_double[j][0], 1, &fmatrix->ptr.pp_double[j][0], 1, ae_v_len(0,m-1), v); - b.ptr.p_double[j] = w->ptr.p_double[j]*y->ptr.p_double[j]; - } - rmatrixqr(&ft, n, m, &tau, _state); - rmatrixqrunpackq(&ft, n, m, &tau, m, &q, _state); - rmatrixqrunpackr(&ft, n, m, &r, _state); - ae_vector_set_length(&tmp, m, _state); - for(i=0; i<=m-1; i++) - { - tmp.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = b.ptr.p_double[i]; - ae_v_addd(&tmp.ptr.p_double[0], 1, &q.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - ae_vector_set_length(&b, m, _state); - ae_v_move(&b.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1)); - - /* - * R contains reduced MxM design upper triangular matrix, - * B contains reduced Mx1 right part. - * - * Determine system condition number and decide - * should we use triangular solver (faster) or - * SVD-based solver (more stable). - * - * We can use LU-based RCond estimator for this task. - */ - rep->taskrcond = rmatrixlurcondinf(&r, m, _state); - if( ae_fp_greater(rep->taskrcond,threshold) ) - { - - /* - * use QR-based solver - */ - ae_vector_set_length(c, m, _state); - c->ptr.p_double[m-1] = b.ptr.p_double[m-1]/r.ptr.pp_double[m-1][m-1]; - for(i=m-2; i>=0; i--) - { - v = ae_v_dotproduct(&r.ptr.pp_double[i][i+1], 1, &c->ptr.p_double[i+1], 1, ae_v_len(i+1,m-1)); - c->ptr.p_double[i] = (b.ptr.p_double[i]-v)/r.ptr.pp_double[i][i]; - } - } - else - { - - /* - * use SVD-based solver - */ - if( !rmatrixsvd(&r, m, m, 1, 1, 2, &sv, &u, &vt, _state) ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&utb, m, _state); - ae_vector_set_length(&sutb, m, _state); - for(i=0; i<=m-1; i++) - { - utb.ptr.p_double[i] = 0; - } - for(i=0; i<=m-1; i++) - { - v = b.ptr.p_double[i]; - ae_v_addd(&utb.ptr.p_double[0], 1, &u.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - if( ae_fp_greater(sv.ptr.p_double[0],0) ) - { - rep->taskrcond = sv.ptr.p_double[m-1]/sv.ptr.p_double[0]; - for(i=0; i<=m-1; i++) - { - if( ae_fp_greater(sv.ptr.p_double[i],threshold*sv.ptr.p_double[0]) ) - { - sutb.ptr.p_double[i] = utb.ptr.p_double[i]/sv.ptr.p_double[i]; - } - else - { - sutb.ptr.p_double[i] = 0; - } - } - } - else - { - rep->taskrcond = 0; - for(i=0; i<=m-1; i++) - { - sutb.ptr.p_double[i] = 0; - } - } - ae_vector_set_length(c, m, _state); - for(i=0; i<=m-1; i++) - { - c->ptr.p_double[i] = 0; - } - for(i=0; i<=m-1; i++) - { - v = sutb.ptr.p_double[i]; - ae_v_addd(&c->ptr.p_double[0], 1, &vt.ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - } - } - - /* - * calculate errors - */ - rep->rmserror = 0; - rep->avgerror = 0; - rep->avgrelerror = 0; - rep->maxerror = 0; - relcnt = 0; - for(i=0; i<=n-1; i++) - { - v = ae_v_dotproduct(&fmatrix->ptr.pp_double[i][0], 1, &c->ptr.p_double[0], 1, ae_v_len(0,m-1)); - rep->rmserror = rep->rmserror+ae_sqr(v-y->ptr.p_double[i], _state); - rep->avgerror = rep->avgerror+ae_fabs(v-y->ptr.p_double[i], _state); - if( ae_fp_neq(y->ptr.p_double[i],0) ) - { - rep->avgrelerror = rep->avgrelerror+ae_fabs(v-y->ptr.p_double[i], _state)/ae_fabs(y->ptr.p_double[i], _state); - relcnt = relcnt+1; - } - rep->maxerror = ae_maxreal(rep->maxerror, ae_fabs(v-y->ptr.p_double[i], _state), _state); - } - rep->rmserror = ae_sqrt(rep->rmserror/n, _state); - rep->avgerror = rep->avgerror/n; - if( relcnt!=0 ) - { - rep->avgrelerror = rep->avgrelerror/relcnt; - } - ae_vector_set_length(&nzeros, n, _state); - ae_vector_set_length(&s, m, _state); - for(i=0; i<=m-1; i++) - { - s.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - s.ptr.p_double[j] = s.ptr.p_double[j]+ae_sqr(fmatrix->ptr.pp_double[i][j], _state); - } - nzeros.ptr.p_double[i] = 0; - } - for(i=0; i<=m-1; i++) - { - if( ae_fp_neq(s.ptr.p_double[i],0) ) - { - s.ptr.p_double[i] = ae_sqrt(1/s.ptr.p_double[i], _state); - } - else - { - s.ptr.p_double[i] = 1; - } - } - lsfit_estimateerrors(fmatrix, &nzeros, y, w, c, &s, n, m, rep, &r, 1, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine -*************************************************************************/ -static void lsfit_lsfitclearrequestfields(lsfitstate* state, - ae_state *_state) -{ - - - state->needf = ae_false; - state->needfg = ae_false; - state->needfgh = ae_false; - state->xupdated = ae_false; -} - - -/************************************************************************* -Internal subroutine, calculates barycentric basis functions. -Used for efficient simultaneous calculation of N basis functions. - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -static void lsfit_barycentriccalcbasis(barycentricinterpolant* b, - double t, - /* Real */ ae_vector* y, - ae_state *_state) -{ - double s2; - double s; - double v; - ae_int_t i; - ae_int_t j; - - - - /* - * special case: N=1 - */ - if( b->n==1 ) - { - y->ptr.p_double[0] = 1; - return; - } - - /* - * Here we assume that task is normalized, i.e.: - * 1. abs(Y[i])<=1 - * 2. abs(W[i])<=1 - * 3. X[] is ordered - * - * First, we decide: should we use "safe" formula (guarded - * against overflow) or fast one? - */ - s = ae_fabs(t-b->x.ptr.p_double[0], _state); - for(i=0; i<=b->n-1; i++) - { - v = b->x.ptr.p_double[i]; - if( ae_fp_eq(v,t) ) - { - for(j=0; j<=b->n-1; j++) - { - y->ptr.p_double[j] = 0; - } - y->ptr.p_double[i] = 1; - return; - } - v = ae_fabs(t-v, _state); - if( ae_fp_less(v,s) ) - { - s = v; - } - } - s2 = 0; - for(i=0; i<=b->n-1; i++) - { - v = s/(t-b->x.ptr.p_double[i]); - v = v*b->w.ptr.p_double[i]; - y->ptr.p_double[i] = v; - s2 = s2+v; - } - v = 1/s2; - ae_v_muld(&y->ptr.p_double[0], 1, ae_v_len(0,b->n-1), v); -} - - -/************************************************************************* -This is internal function for Chebyshev fitting. - -It assumes that input data are normalized: -* X/XC belong to [-1,+1], -* mean(Y)=0, stddev(Y)=1. - -It does not checks inputs for errors. - -This function is used to fit general (shifted) Chebyshev models, power -basis models or barycentric models. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - N - number of points, N>0. - XC - points where polynomial values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that P(XC[i])=YC[i] - * DC[i]=1 means that P'(XC[i])=YC[i] - K - number of constraints, 0<=K=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - C - interpolant in Chebyshev form; [-1,+1] is used as base interval - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -static void lsfit_internalchebyshevfit(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _xc; - ae_vector _yc; - ae_vector y2; - ae_vector w2; - ae_vector tmp; - ae_vector tmp2; - ae_vector tmpdiff; - ae_vector bx; - ae_vector by; - ae_vector bw; - ae_matrix fmatrix; - ae_matrix cmatrix; - ae_int_t i; - ae_int_t j; - double mx; - double decay; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_xc, xc, _state, ae_true); - xc = &_xc; - ae_vector_init_copy(&_yc, yc, _state, ae_true); - yc = &_yc; - *info = 0; - ae_vector_clear(c); - _lsfitreport_clear(rep); - ae_vector_init(&y2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpdiff, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&by, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bw, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&cmatrix, 0, 0, DT_REAL, _state, ae_true); - - lsfit_clearreport(rep, _state); - - /* - * weight decay for correct handling of task which becomes - * degenerate after constraints are applied - */ - decay = 10000*ae_machineepsilon; - - /* - * allocate space, initialize/fill: - * * FMatrix- values of basis functions at X[] - * * CMatrix- values (derivatives) of basis functions at XC[] - * * fill constraints matrix - * * fill first N rows of design matrix with values - * * fill next M rows of design matrix with regularizing term - * * append M zeros to Y - * * append M elements, mean(abs(W)) each, to W - */ - ae_vector_set_length(&y2, n+m, _state); - ae_vector_set_length(&w2, n+m, _state); - ae_vector_set_length(&tmp, m, _state); - ae_vector_set_length(&tmpdiff, m, _state); - ae_matrix_set_length(&fmatrix, n+m, m, _state); - if( k>0 ) - { - ae_matrix_set_length(&cmatrix, k, m+1, _state); - } - - /* - * Fill design matrix, Y2, W2: - * * first N rows with basis functions for original points - * * next M rows with decay terms - */ - for(i=0; i<=n-1; i++) - { - - /* - * prepare Ith row - * use Tmp for calculations to avoid multidimensional arrays overhead - */ - for(j=0; j<=m-1; j++) - { - if( j==0 ) - { - tmp.ptr.p_double[j] = 1; - } - else - { - if( j==1 ) - { - tmp.ptr.p_double[j] = x->ptr.p_double[i]; - } - else - { - tmp.ptr.p_double[j] = 2*x->ptr.p_double[i]*tmp.ptr.p_double[j-1]-tmp.ptr.p_double[j-2]; - } - } - } - ae_v_move(&fmatrix.ptr.pp_double[i][0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1)); - } - for(i=0; i<=m-1; i++) - { - for(j=0; j<=m-1; j++) - { - if( i==j ) - { - fmatrix.ptr.pp_double[n+i][j] = decay; - } - else - { - fmatrix.ptr.pp_double[n+i][j] = 0; - } - } - } - ae_v_move(&y2.ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&w2.ptr.p_double[0], 1, &w->ptr.p_double[0], 1, ae_v_len(0,n-1)); - mx = 0; - for(i=0; i<=n-1; i++) - { - mx = mx+ae_fabs(w->ptr.p_double[i], _state); - } - mx = mx/n; - for(i=0; i<=m-1; i++) - { - y2.ptr.p_double[n+i] = 0; - w2.ptr.p_double[n+i] = mx; - } - - /* - * fill constraints matrix - */ - for(i=0; i<=k-1; i++) - { - - /* - * prepare Ith row - * use Tmp for basis function values, - * TmpDiff for basos function derivatives - */ - for(j=0; j<=m-1; j++) - { - if( j==0 ) - { - tmp.ptr.p_double[j] = 1; - tmpdiff.ptr.p_double[j] = 0; - } - else - { - if( j==1 ) - { - tmp.ptr.p_double[j] = xc->ptr.p_double[i]; - tmpdiff.ptr.p_double[j] = 1; - } - else - { - tmp.ptr.p_double[j] = 2*xc->ptr.p_double[i]*tmp.ptr.p_double[j-1]-tmp.ptr.p_double[j-2]; - tmpdiff.ptr.p_double[j] = 2*(tmp.ptr.p_double[j-1]+xc->ptr.p_double[i]*tmpdiff.ptr.p_double[j-1])-tmpdiff.ptr.p_double[j-2]; - } - } - } - if( dc->ptr.p_int[i]==0 ) - { - ae_v_move(&cmatrix.ptr.pp_double[i][0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1)); - } - if( dc->ptr.p_int[i]==1 ) - { - ae_v_move(&cmatrix.ptr.pp_double[i][0], 1, &tmpdiff.ptr.p_double[0], 1, ae_v_len(0,m-1)); - } - cmatrix.ptr.pp_double[i][m] = yc->ptr.p_double[i]; - } - - /* - * Solve constrained task - */ - if( k>0 ) - { - - /* - * solve using regularization - */ - lsfitlinearwc(&y2, &w2, &fmatrix, &cmatrix, n+m, m, k, info, c, rep, _state); - } - else - { - - /* - * no constraints, no regularization needed - */ - lsfitlinearwc(y, w, &fmatrix, &cmatrix, n, m, 0, info, c, rep, _state); - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal Floater-Hormann fitting subroutine for fixed D -*************************************************************************/ -static void lsfit_barycentricfitwcfixedd(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t d, - ae_int_t* info, - barycentricinterpolant* b, - barycentricfitreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - ae_vector _w; - ae_vector _xc; - ae_vector _yc; - ae_matrix fmatrix; - ae_matrix cmatrix; - ae_vector y2; - ae_vector w2; - ae_vector sx; - ae_vector sy; - ae_vector sbf; - ae_vector xoriginal; - ae_vector yoriginal; - ae_vector tmp; - lsfitreport lrep; - double v0; - double v1; - double mx; - barycentricinterpolant b2; - ae_int_t i; - ae_int_t j; - ae_int_t relcnt; - double xa; - double xb; - double sa; - double sb; - double decay; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_vector_init_copy(&_w, w, _state, ae_true); - w = &_w; - ae_vector_init_copy(&_xc, xc, _state, ae_true); - xc = &_xc; - ae_vector_init_copy(&_yc, yc, _state, ae_true); - yc = &_yc; - *info = 0; - _barycentricinterpolant_clear(b); - _barycentricfitreport_clear(rep); - ae_matrix_init(&fmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&cmatrix, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sy, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sbf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xoriginal, 0, DT_REAL, _state, ae_true); - ae_vector_init(&yoriginal, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - _lsfitreport_init(&lrep, _state, ae_true); - _barycentricinterpolant_init(&b2, _state, ae_true); - - if( ((n<1||m<2)||k<0)||k>=m ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=k-1; i++) - { - *info = 0; - if( dc->ptr.p_int[i]<0 ) - { - *info = -1; - } - if( dc->ptr.p_int[i]>1 ) - { - *info = -1; - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - } - - /* - * weight decay for correct handling of task which becomes - * degenerate after constraints are applied - */ - decay = 10000*ae_machineepsilon; - - /* - * Scale X, Y, XC, YC - */ - lsfitscalexy(x, y, w, n, xc, yc, dc, k, &xa, &xb, &sa, &sb, &xoriginal, &yoriginal, _state); - - /* - * allocate space, initialize: - * * FMatrix- values of basis functions at X[] - * * CMatrix- values (derivatives) of basis functions at XC[] - */ - ae_vector_set_length(&y2, n+m, _state); - ae_vector_set_length(&w2, n+m, _state); - ae_matrix_set_length(&fmatrix, n+m, m, _state); - if( k>0 ) - { - ae_matrix_set_length(&cmatrix, k, m+1, _state); - } - ae_vector_set_length(&y2, n+m, _state); - ae_vector_set_length(&w2, n+m, _state); - - /* - * Prepare design and constraints matrices: - * * fill constraints matrix - * * fill first N rows of design matrix with values - * * fill next M rows of design matrix with regularizing term - * * append M zeros to Y - * * append M elements, mean(abs(W)) each, to W - */ - ae_vector_set_length(&sx, m, _state); - ae_vector_set_length(&sy, m, _state); - ae_vector_set_length(&sbf, m, _state); - for(j=0; j<=m-1; j++) - { - sx.ptr.p_double[j] = (double)(2*j)/(double)(m-1)-1; - } - for(i=0; i<=m-1; i++) - { - sy.ptr.p_double[i] = 1; - } - barycentricbuildfloaterhormann(&sx, &sy, m, d, &b2, _state); - mx = 0; - for(i=0; i<=n-1; i++) - { - lsfit_barycentriccalcbasis(&b2, x->ptr.p_double[i], &sbf, _state); - ae_v_move(&fmatrix.ptr.pp_double[i][0], 1, &sbf.ptr.p_double[0], 1, ae_v_len(0,m-1)); - y2.ptr.p_double[i] = y->ptr.p_double[i]; - w2.ptr.p_double[i] = w->ptr.p_double[i]; - mx = mx+ae_fabs(w->ptr.p_double[i], _state)/n; - } - for(i=0; i<=m-1; i++) - { - for(j=0; j<=m-1; j++) - { - if( i==j ) - { - fmatrix.ptr.pp_double[n+i][j] = decay; - } - else - { - fmatrix.ptr.pp_double[n+i][j] = 0; - } - } - y2.ptr.p_double[n+i] = 0; - w2.ptr.p_double[n+i] = mx; - } - if( k>0 ) - { - for(j=0; j<=m-1; j++) - { - for(i=0; i<=m-1; i++) - { - sy.ptr.p_double[i] = 0; - } - sy.ptr.p_double[j] = 1; - barycentricbuildfloaterhormann(&sx, &sy, m, d, &b2, _state); - for(i=0; i<=k-1; i++) - { - ae_assert(dc->ptr.p_int[i]>=0&&dc->ptr.p_int[i]<=1, "BarycentricFit: internal error!", _state); - barycentricdiff1(&b2, xc->ptr.p_double[i], &v0, &v1, _state); - if( dc->ptr.p_int[i]==0 ) - { - cmatrix.ptr.pp_double[i][j] = v0; - } - if( dc->ptr.p_int[i]==1 ) - { - cmatrix.ptr.pp_double[i][j] = v1; - } - } - } - for(i=0; i<=k-1; i++) - { - cmatrix.ptr.pp_double[i][m] = yc->ptr.p_double[i]; - } - } - - /* - * Solve constrained task - */ - if( k>0 ) - { - - /* - * solve using regularization - */ - lsfitlinearwc(&y2, &w2, &fmatrix, &cmatrix, n+m, m, k, info, &tmp, &lrep, _state); - } - else - { - - /* - * no constraints, no regularization needed - */ - lsfitlinearwc(y, w, &fmatrix, &cmatrix, n, m, k, info, &tmp, &lrep, _state); - } - if( *info<0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Generate interpolant and scale it - */ - ae_v_move(&sy.ptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,m-1)); - barycentricbuildfloaterhormann(&sx, &sy, m, d, b, _state); - barycentriclintransx(b, 2/(xb-xa), -(xa+xb)/(xb-xa), _state); - barycentriclintransy(b, sb-sa, sa, _state); - - /* - * Scale absolute errors obtained from LSFitLinearW. - * Relative error should be calculated separately - * (because of shifting/scaling of the task) - */ - rep->taskrcond = lrep.taskrcond; - rep->rmserror = lrep.rmserror*(sb-sa); - rep->avgerror = lrep.avgerror*(sb-sa); - rep->maxerror = lrep.maxerror*(sb-sa); - rep->avgrelerror = 0; - relcnt = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(yoriginal.ptr.p_double[i],0) ) - { - rep->avgrelerror = rep->avgrelerror+ae_fabs(barycentriccalc(b, xoriginal.ptr.p_double[i], _state)-yoriginal.ptr.p_double[i], _state)/ae_fabs(yoriginal.ptr.p_double[i], _state); - relcnt = relcnt+1; - } - } - if( relcnt!=0 ) - { - rep->avgrelerror = rep->avgrelerror/relcnt; - } - ae_frame_leave(_state); -} - - -static void lsfit_clearreport(lsfitreport* rep, ae_state *_state) -{ - - - rep->taskrcond = 0; - rep->iterationscount = 0; - rep->varidx = -1; - rep->rmserror = 0; - rep->avgerror = 0; - rep->avgrelerror = 0; - rep->maxerror = 0; - rep->wrmserror = 0; - rep->r2 = 0; - ae_matrix_set_length(&rep->covpar, 0, 0, _state); - ae_vector_set_length(&rep->errpar, 0, _state); - ae_vector_set_length(&rep->errcurve, 0, _state); - ae_vector_set_length(&rep->noise, 0, _state); -} - - -/************************************************************************* -This internal function estimates covariance matrix and other error-related -information for linear/nonlinear least squares model. - -It has a bit awkward interface, but it can be used for both linear and -nonlinear problems. - -INPUT PARAMETERS: - F1 - array[0..N-1,0..K-1]: - * for linear problems - matrix of function values - * for nonlinear problems - Jacobian matrix - F0 - array[0..N-1]: - * for linear problems - must be filled with zeros - * for nonlinear problems - must store values of function being - fitted - Y - array[0..N-1]: - * for linear and nonlinear problems - must store target values - W - weights, array[0..N-1]: - * for linear and nonlinear problems - weights - X - array[0..K-1]: - * for linear and nonlinear problems - current solution - S - array[0..K-1]: - * its components should be strictly positive - * squared inverse of this diagonal matrix is used as damping - factor for covariance matrix (linear and nonlinear problems) - * for nonlinear problems, when scale of the variables is usually - explicitly given by user, you may use scale vector for this - parameter - * for linear problems you may set this parameter to - S=sqrt(1/diag(F'*F)) - * this parameter is automatically rescaled by this function, - only relative magnitudes of its components (with respect to - each other) matter. - N - number of points, N>0. - K - number of dimensions - Rep - structure which is used to store results - Z - additional matrix which, depending on ZKind, may contain some - information used to accelerate calculations - or just can be - temporary buffer: - * for ZKind=0 Z contains no information, just temporary - buffer which can be resized and used as needed - * for ZKind=1 Z contains triangular matrix from QR - decomposition of W*F1. This matrix can be used - to speedup calculation of covariance matrix. - It should not be changed by algorithm. - ZKind- contents of Z - -OUTPUT PARAMETERS: - -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(J*CovPar*J')), - where J is Jacobian matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] -* Rep.R2 coefficient of determination (non-weighted) - -Other fields of Rep are not changed. - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -static void lsfit_estimateerrors(/* Real */ ae_matrix* f1, - /* Real */ ae_vector* f0, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* x, - /* Real */ ae_vector* s, - ae_int_t n, - ae_int_t k, - lsfitreport* rep, - /* Real */ ae_matrix* z, - ae_int_t zkind, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _s; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - double v; - double noisec; - ae_int_t info; - matinvreport invrep; - ae_int_t nzcnt; - double avg; - double rss; - double tss; - double sz; - double ss; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_s, s, _state, ae_true); - s = &_s; - _matinvreport_init(&invrep, _state, ae_true); - - - /* - * Compute NZCnt - count of non-zero weights - */ - nzcnt = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(w->ptr.p_double[i],0) ) - { - nzcnt = nzcnt+1; - } - } - - /* - * Compute R2 - */ - if( nzcnt>0 ) - { - avg = 0.0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(w->ptr.p_double[i],0) ) - { - avg = avg+y->ptr.p_double[i]; - } - } - avg = avg/nzcnt; - rss = 0.0; - tss = 0.0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(w->ptr.p_double[i],0) ) - { - v = ae_v_dotproduct(&f1->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,k-1)); - v = v+f0->ptr.p_double[i]; - rss = rss+ae_sqr(v-y->ptr.p_double[i], _state); - tss = tss+ae_sqr(y->ptr.p_double[i]-avg, _state); - } - } - if( ae_fp_neq(tss,0) ) - { - rep->r2 = ae_maxreal(1.0-rss/tss, 0.0, _state); - } - else - { - rep->r2 = 1.0; - } - } - else - { - rep->r2 = 0; - } - - /* - * Compute estimate of proportionality between noise in the data and weights: - * NoiseC = mean(per-point-noise*per-point-weight) - * Noise level (standard deviation) at each point is equal to NoiseC/W[I]. - */ - if( nzcnt>k ) - { - noisec = 0.0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(w->ptr.p_double[i],0) ) - { - v = ae_v_dotproduct(&f1->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,k-1)); - v = v+f0->ptr.p_double[i]; - noisec = noisec+ae_sqr((v-y->ptr.p_double[i])*w->ptr.p_double[i], _state); - } - } - noisec = ae_sqrt(noisec/(nzcnt-k), _state); - } - else - { - noisec = 0.0; - } - - /* - * Two branches on noise level: - * * NoiseC>0 normal situation - * * NoiseC=0 degenerate case CovPar is filled by zeros - */ - rmatrixsetlengthatleast(&rep->covpar, k, k, _state); - if( ae_fp_greater(noisec,0) ) - { - - /* - * Normal situation: non-zero noise level - */ - ae_assert(zkind==0||zkind==1, "LSFit: internal error in EstimateErrors() function", _state); - if( zkind==0 ) - { - - /* - * Z contains no additional information which can be used to speed up - * calculations. We have to calculate covariance matrix on our own: - * * Compute scaled Jacobian N*J, where N[i,i]=WCur[I]/NoiseC, store in Z - * * Compute Z'*Z, store in CovPar - * * Apply moderate regularization to CovPar and compute matrix inverse. - * In case inverse failed, increase regularization parameter and try - * again. - */ - rmatrixsetlengthatleast(z, n, k, _state); - for(i=0; i<=n-1; i++) - { - v = w->ptr.p_double[i]/noisec; - ae_v_moved(&z->ptr.pp_double[i][0], 1, &f1->ptr.pp_double[i][0], 1, ae_v_len(0,k-1), v); - } - - /* - * Convert S to automatically scaled damped matrix: - * * calculate SZ - sum of diagonal elements of Z'*Z - * * calculate SS - sum of diagonal elements of S^(-2) - * * overwrite S by (SZ/SS)*S^(-2) - * * now S has approximately same magnitude as giagonal of Z'*Z - */ - sz = 0; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=k-1; j++) - { - sz = sz+z->ptr.pp_double[i][j]*z->ptr.pp_double[i][j]; - } - } - if( ae_fp_eq(sz,0) ) - { - sz = 1; - } - ss = 0; - for(j=0; j<=k-1; j++) - { - ss = ss+1/ae_sqr(s->ptr.p_double[j], _state); - } - for(j=0; j<=k-1; j++) - { - s->ptr.p_double[j] = sz/ss/ae_sqr(s->ptr.p_double[j], _state); - } - - /* - * Calculate damped inverse inv(Z'*Z+S). - * We increase damping factor V until Z'*Z become well-conditioned. - */ - v = 1.0E3*ae_machineepsilon; - do - { - rmatrixsyrk(k, n, 1.0, z, 0, 0, 2, 0.0, &rep->covpar, 0, 0, ae_true, _state); - for(i=0; i<=k-1; i++) - { - rep->covpar.ptr.pp_double[i][i] = rep->covpar.ptr.pp_double[i][i]+v*s->ptr.p_double[i]; - } - spdmatrixinverse(&rep->covpar, k, ae_true, &info, &invrep, _state); - v = 10*v; - } - while(info<=0); - for(i=0; i<=k-1; i++) - { - for(j=i+1; j<=k-1; j++) - { - rep->covpar.ptr.pp_double[j][i] = rep->covpar.ptr.pp_double[i][j]; - } - } - } - if( zkind==1 ) - { - - /* - * We can reuse additional information: - * * Z contains R matrix from QR decomposition of W*F1 - * * After multiplication by 1/NoiseC we get Z_mod = N*F1, where diag(N)=w[i]/NoiseC - * * Such triangular Z_mod is a Cholesky factor from decomposition of J'*N'*N*J. - * Thus, we can calculate covariance matrix as inverse of the matrix given by - * its Cholesky decomposition. It allow us to avoid time-consuming calculation - * of J'*N'*N*J in CovPar - complexity is reduced from O(N*K^2) to O(K^3), which - * is quite good because K is usually orders of magnitude smaller than N. - * - * First, convert S to automatically scaled damped matrix: - * * calculate SZ - sum of magnitudes of diagonal elements of Z/NoiseC - * * calculate SS - sum of diagonal elements of S^(-1) - * * overwrite S by (SZ/SS)*S^(-1) - * * now S has approximately same magnitude as giagonal of Z'*Z - */ - sz = 0; - for(j=0; j<=k-1; j++) - { - sz = sz+ae_fabs(z->ptr.pp_double[j][j]/noisec, _state); - } - if( ae_fp_eq(sz,0) ) - { - sz = 1; - } - ss = 0; - for(j=0; j<=k-1; j++) - { - ss = ss+1/s->ptr.p_double[j]; - } - for(j=0; j<=k-1; j++) - { - s->ptr.p_double[j] = sz/ss/s->ptr.p_double[j]; - } - - /* - * Calculate damped inverse of inv((Z+v*S)'*(Z+v*S)) - * We increase damping factor V until matrix become well-conditioned. - */ - v = 1.0E3*ae_machineepsilon; - do - { - for(i=0; i<=k-1; i++) - { - for(j=i; j<=k-1; j++) - { - rep->covpar.ptr.pp_double[i][j] = z->ptr.pp_double[i][j]/noisec; - } - rep->covpar.ptr.pp_double[i][i] = rep->covpar.ptr.pp_double[i][i]+v*s->ptr.p_double[i]; - } - spdmatrixcholeskyinverse(&rep->covpar, k, ae_true, &info, &invrep, _state); - v = 10*v; - } - while(info<=0); - for(i=0; i<=k-1; i++) - { - for(j=i+1; j<=k-1; j++) - { - rep->covpar.ptr.pp_double[j][i] = rep->covpar.ptr.pp_double[i][j]; - } - } - } - } - else - { - - /* - * Degenerate situation: zero noise level, covariance matrix is zero. - */ - for(i=0; i<=k-1; i++) - { - for(j=0; j<=k-1; j++) - { - rep->covpar.ptr.pp_double[j][i] = 0; - } - } - } - - /* - * Estimate erorrs in parameters, curve and per-point noise - */ - rvectorsetlengthatleast(&rep->errpar, k, _state); - rvectorsetlengthatleast(&rep->errcurve, n, _state); - rvectorsetlengthatleast(&rep->noise, n, _state); - for(i=0; i<=k-1; i++) - { - rep->errpar.ptr.p_double[i] = ae_sqrt(rep->covpar.ptr.pp_double[i][i], _state); - } - for(i=0; i<=n-1; i++) - { - - /* - * ErrCurve[I] is sqrt(P[i,i]) where P=J*CovPar*J' - */ - v = 0.0; - for(j=0; j<=k-1; j++) - { - for(j1=0; j1<=k-1; j1++) - { - v = v+f1->ptr.pp_double[i][j]*rep->covpar.ptr.pp_double[j][j1]*f1->ptr.pp_double[i][j1]; - } - } - rep->errcurve.ptr.p_double[i] = ae_sqrt(v, _state); - - /* - * Noise[i] is filled using weights and current estimate of noise level - */ - if( ae_fp_neq(w->ptr.p_double[i],0) ) - { - rep->noise.ptr.p_double[i] = noisec/w->ptr.p_double[i]; - } - else - { - rep->noise.ptr.p_double[i] = 0; - } - } - ae_frame_leave(_state); -} - - -ae_bool _polynomialfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - polynomialfitreport *p = (polynomialfitreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _polynomialfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - polynomialfitreport *dst = (polynomialfitreport*)_dst; - polynomialfitreport *src = (polynomialfitreport*)_src; - dst->taskrcond = src->taskrcond; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->maxerror = src->maxerror; - return ae_true; -} - - -void _polynomialfitreport_clear(void* _p) -{ - polynomialfitreport *p = (polynomialfitreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _polynomialfitreport_destroy(void* _p) -{ - polynomialfitreport *p = (polynomialfitreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _barycentricfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - barycentricfitreport *p = (barycentricfitreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _barycentricfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - barycentricfitreport *dst = (barycentricfitreport*)_dst; - barycentricfitreport *src = (barycentricfitreport*)_src; - dst->taskrcond = src->taskrcond; - dst->dbest = src->dbest; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->maxerror = src->maxerror; - return ae_true; -} - - -void _barycentricfitreport_clear(void* _p) -{ - barycentricfitreport *p = (barycentricfitreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _barycentricfitreport_destroy(void* _p) -{ - barycentricfitreport *p = (barycentricfitreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _spline1dfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - spline1dfitreport *p = (spline1dfitreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _spline1dfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - spline1dfitreport *dst = (spline1dfitreport*)_dst; - spline1dfitreport *src = (spline1dfitreport*)_src; - dst->taskrcond = src->taskrcond; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->maxerror = src->maxerror; - return ae_true; -} - - -void _spline1dfitreport_clear(void* _p) -{ - spline1dfitreport *p = (spline1dfitreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _spline1dfitreport_destroy(void* _p) -{ - spline1dfitreport *p = (spline1dfitreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _lsfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - lsfitreport *p = (lsfitreport*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->covpar, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->errpar, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->errcurve, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->noise, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _lsfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - lsfitreport *dst = (lsfitreport*)_dst; - lsfitreport *src = (lsfitreport*)_src; - dst->taskrcond = src->taskrcond; - dst->iterationscount = src->iterationscount; - dst->varidx = src->varidx; - dst->rmserror = src->rmserror; - dst->avgerror = src->avgerror; - dst->avgrelerror = src->avgrelerror; - dst->maxerror = src->maxerror; - dst->wrmserror = src->wrmserror; - if( !ae_matrix_init_copy(&dst->covpar, &src->covpar, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->errpar, &src->errpar, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->errcurve, &src->errcurve, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->noise, &src->noise, _state, make_automatic) ) - return ae_false; - dst->r2 = src->r2; - return ae_true; -} - - -void _lsfitreport_clear(void* _p) -{ - lsfitreport *p = (lsfitreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->covpar); - ae_vector_clear(&p->errpar); - ae_vector_clear(&p->errcurve); - ae_vector_clear(&p->noise); -} - - -void _lsfitreport_destroy(void* _p) -{ - lsfitreport *p = (lsfitreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->covpar); - ae_vector_destroy(&p->errpar); - ae_vector_destroy(&p->errcurve); - ae_vector_destroy(&p->noise); -} - - -ae_bool _lsfitstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - lsfitstate *p = (lsfitstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->taskx, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tasky, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->taskw, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->c, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->h, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->wcur, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmpjac, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmpjacw, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_matinvreport_init(&p->invrep, _state, make_automatic) ) - return ae_false; - if( !_lsfitreport_init(&p->rep, _state, make_automatic) ) - return ae_false; - if( !_minlmstate_init(&p->optstate, _state, make_automatic) ) - return ae_false; - if( !_minlmreport_init(&p->optrep, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _lsfitstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - lsfitstate *dst = (lsfitstate*)_dst; - lsfitstate *src = (lsfitstate*)_src; - dst->optalgo = src->optalgo; - dst->m = src->m; - dst->k = src->k; - dst->epsf = src->epsf; - dst->epsx = src->epsx; - dst->maxits = src->maxits; - dst->stpmax = src->stpmax; - dst->xrep = src->xrep; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->taskx, &src->taskx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tasky, &src->tasky, _state, make_automatic) ) - return ae_false; - dst->npoints = src->npoints; - if( !ae_vector_init_copy(&dst->taskw, &src->taskw, _state, make_automatic) ) - return ae_false; - dst->nweights = src->nweights; - dst->wkind = src->wkind; - dst->wits = src->wits; - dst->diffstep = src->diffstep; - dst->teststep = src->teststep; - dst->xupdated = src->xupdated; - dst->needf = src->needf; - dst->needfg = src->needfg; - dst->needfgh = src->needfgh; - dst->pointindex = src->pointindex; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->c, &src->c, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->h, &src->h, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->wcur, &src->wcur, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp, &src->tmp, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpf, &src->tmpf, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmpjac, &src->tmpjac, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmpjacw, &src->tmpjacw, _state, make_automatic) ) - return ae_false; - dst->tmpnoise = src->tmpnoise; - if( !_matinvreport_init_copy(&dst->invrep, &src->invrep, _state, make_automatic) ) - return ae_false; - dst->repiterationscount = src->repiterationscount; - dst->repterminationtype = src->repterminationtype; - dst->repvaridx = src->repvaridx; - dst->reprmserror = src->reprmserror; - dst->repavgerror = src->repavgerror; - dst->repavgrelerror = src->repavgrelerror; - dst->repmaxerror = src->repmaxerror; - dst->repwrmserror = src->repwrmserror; - if( !_lsfitreport_init_copy(&dst->rep, &src->rep, _state, make_automatic) ) - return ae_false; - if( !_minlmstate_init_copy(&dst->optstate, &src->optstate, _state, make_automatic) ) - return ae_false; - if( !_minlmreport_init_copy(&dst->optrep, &src->optrep, _state, make_automatic) ) - return ae_false; - dst->prevnpt = src->prevnpt; - dst->prevalgo = src->prevalgo; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _lsfitstate_clear(void* _p) -{ - lsfitstate *p = (lsfitstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->s); - ae_vector_clear(&p->bndl); - ae_vector_clear(&p->bndu); - ae_matrix_clear(&p->taskx); - ae_vector_clear(&p->tasky); - ae_vector_clear(&p->taskw); - ae_vector_clear(&p->x); - ae_vector_clear(&p->c); - ae_vector_clear(&p->g); - ae_matrix_clear(&p->h); - ae_vector_clear(&p->wcur); - ae_vector_clear(&p->tmp); - ae_vector_clear(&p->tmpf); - ae_matrix_clear(&p->tmpjac); - ae_matrix_clear(&p->tmpjacw); - _matinvreport_clear(&p->invrep); - _lsfitreport_clear(&p->rep); - _minlmstate_clear(&p->optstate); - _minlmreport_clear(&p->optrep); - _rcommstate_clear(&p->rstate); -} - - -void _lsfitstate_destroy(void* _p) -{ - lsfitstate *p = (lsfitstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->bndl); - ae_vector_destroy(&p->bndu); - ae_matrix_destroy(&p->taskx); - ae_vector_destroy(&p->tasky); - ae_vector_destroy(&p->taskw); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->c); - ae_vector_destroy(&p->g); - ae_matrix_destroy(&p->h); - ae_vector_destroy(&p->wcur); - ae_vector_destroy(&p->tmp); - ae_vector_destroy(&p->tmpf); - ae_matrix_destroy(&p->tmpjac); - ae_matrix_destroy(&p->tmpjacw); - _matinvreport_destroy(&p->invrep); - _lsfitreport_destroy(&p->rep); - _minlmstate_destroy(&p->optstate); - _minlmreport_destroy(&p->optrep); - _rcommstate_destroy(&p->rstate); -} - - - - -/************************************************************************* -This function builds non-periodic 2-dimensional parametric spline which -starts at (X[0],Y[0]) and ends at (X[N-1],Y[N-1]). - -INPUT PARAMETERS: - XY - points, array[0..N-1,0..1]. - XY[I,0:1] corresponds to the Ith point. - Order of points is important! - N - points count, N>=5 for Akima splines, N>=2 for other types of - splines. - ST - spline type: - * 0 Akima spline - * 1 parabolically terminated Catmull-Rom spline (Tension=0) - * 2 parabolically terminated cubic spline - PT - parameterization type: - * 0 uniform - * 1 chord length - * 2 centripetal - -OUTPUT PARAMETERS: - P - parametric spline interpolant - - -NOTES: -* this function assumes that there all consequent points are distinct. - I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2), (x2,y2)<>(x3,y3) and so on. - However, non-consequent points may coincide, i.e. we can have (x0,y0)= - =(x2,y2). - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2build(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline2interpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _xy; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_xy, xy, _state, ae_true); - xy = &_xy; - _pspline2interpolant_clear(p); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - ae_assert(st>=0&&st<=2, "PSpline2Build: incorrect spline type!", _state); - ae_assert(pt>=0&&pt<=2, "PSpline2Build: incorrect parameterization type!", _state); - if( st==0 ) - { - ae_assert(n>=5, "PSpline2Build: N<5 (minimum value for Akima splines)!", _state); - } - else - { - ae_assert(n>=2, "PSpline2Build: N<2!", _state); - } - - /* - * Prepare - */ - p->n = n; - p->periodic = ae_false; - ae_vector_set_length(&tmp, n, _state); - - /* - * Build parameterization, check that all parameters are distinct - */ - pspline_pspline2par(xy, n, pt, &p->p, _state); - ae_assert(aredistinct(&p->p, n, _state), "PSpline2Build: consequent points are too close!", _state); - - /* - * Build splines - */ - if( st==0 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - spline1dbuildakima(&p->p, &tmp, n, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - spline1dbuildakima(&p->p, &tmp, n, &p->y, _state); - } - if( st==1 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->y, _state); - } - if( st==2 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->y, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function builds non-periodic 3-dimensional parametric spline which -starts at (X[0],Y[0],Z[0]) and ends at (X[N-1],Y[N-1],Z[N-1]). - -Same as PSpline2Build() function, but for 3D, so we won't duplicate its -description here. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3build(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline3interpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _xy; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_xy, xy, _state, ae_true); - xy = &_xy; - _pspline3interpolant_clear(p); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - ae_assert(st>=0&&st<=2, "PSpline3Build: incorrect spline type!", _state); - ae_assert(pt>=0&&pt<=2, "PSpline3Build: incorrect parameterization type!", _state); - if( st==0 ) - { - ae_assert(n>=5, "PSpline3Build: N<5 (minimum value for Akima splines)!", _state); - } - else - { - ae_assert(n>=2, "PSpline3Build: N<2!", _state); - } - - /* - * Prepare - */ - p->n = n; - p->periodic = ae_false; - ae_vector_set_length(&tmp, n, _state); - - /* - * Build parameterization, check that all parameters are distinct - */ - pspline_pspline3par(xy, n, pt, &p->p, _state); - ae_assert(aredistinct(&p->p, n, _state), "PSpline3Build: consequent points are too close!", _state); - - /* - * Build splines - */ - if( st==0 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - spline1dbuildakima(&p->p, &tmp, n, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - spline1dbuildakima(&p->p, &tmp, n, &p->y, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1)); - spline1dbuildakima(&p->p, &tmp, n, &p->z, _state); - } - if( st==1 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->y, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcatmullrom(&p->p, &tmp, n, 0, 0.0, &p->z, _state); - } - if( st==2 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->y, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1)); - spline1dbuildcubic(&p->p, &tmp, n, 0, 0.0, 0, 0.0, &p->z, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function builds periodic 2-dimensional parametric spline which -starts at (X[0],Y[0]), goes through all points to (X[N-1],Y[N-1]) and then -back to (X[0],Y[0]). - -INPUT PARAMETERS: - XY - points, array[0..N-1,0..1]. - XY[I,0:1] corresponds to the Ith point. - XY[N-1,0:1] must be different from XY[0,0:1]. - Order of points is important! - N - points count, N>=3 for other types of splines. - ST - spline type: - * 1 Catmull-Rom spline (Tension=0) with cyclic boundary conditions - * 2 cubic spline with cyclic boundary conditions - PT - parameterization type: - * 0 uniform - * 1 chord length - * 2 centripetal - -OUTPUT PARAMETERS: - P - parametric spline interpolant - - -NOTES: -* this function assumes that there all consequent points are distinct. - I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2), (x2,y2)<>(x3,y3) and so on. - However, non-consequent points may coincide, i.e. we can have (x0,y0)= - =(x2,y2). -* last point of sequence is NOT equal to the first point. You shouldn't - make curve "explicitly periodic" by making them equal. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2buildperiodic(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline2interpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _xy; - ae_matrix xyp; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_xy, xy, _state, ae_true); - xy = &_xy; - _pspline2interpolant_clear(p); - ae_matrix_init(&xyp, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - ae_assert(st>=1&&st<=2, "PSpline2BuildPeriodic: incorrect spline type!", _state); - ae_assert(pt>=0&&pt<=2, "PSpline2BuildPeriodic: incorrect parameterization type!", _state); - ae_assert(n>=3, "PSpline2BuildPeriodic: N<3!", _state); - - /* - * Prepare - */ - p->n = n; - p->periodic = ae_true; - ae_vector_set_length(&tmp, n+1, _state); - ae_matrix_set_length(&xyp, n+1, 2, _state); - ae_v_move(&xyp.ptr.pp_double[0][0], xyp.stride, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - ae_v_move(&xyp.ptr.pp_double[0][1], xyp.stride, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - ae_v_move(&xyp.ptr.pp_double[n][0], 1, &xy->ptr.pp_double[0][0], 1, ae_v_len(0,1)); - - /* - * Build parameterization, check that all parameters are distinct - */ - pspline_pspline2par(&xyp, n+1, pt, &p->p, _state); - ae_assert(aredistinct(&p->p, n+1, _state), "PSpline2BuildPeriodic: consequent (or first and last) points are too close!", _state); - - /* - * Build splines - */ - if( st==1 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n)); - spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n)); - spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->y, _state); - } - if( st==2 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n)); - spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n)); - spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->y, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function builds periodic 3-dimensional parametric spline which -starts at (X[0],Y[0],Z[0]), goes through all points to (X[N-1],Y[N-1],Z[N-1]) -and then back to (X[0],Y[0],Z[0]). - -Same as PSpline2Build() function, but for 3D, so we won't duplicate its -description here. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3buildperiodic(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline3interpolant* p, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _xy; - ae_matrix xyp; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_xy, xy, _state, ae_true); - xy = &_xy; - _pspline3interpolant_clear(p); - ae_matrix_init(&xyp, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - ae_assert(st>=1&&st<=2, "PSpline3BuildPeriodic: incorrect spline type!", _state); - ae_assert(pt>=0&&pt<=2, "PSpline3BuildPeriodic: incorrect parameterization type!", _state); - ae_assert(n>=3, "PSpline3BuildPeriodic: N<3!", _state); - - /* - * Prepare - */ - p->n = n; - p->periodic = ae_true; - ae_vector_set_length(&tmp, n+1, _state); - ae_matrix_set_length(&xyp, n+1, 3, _state); - ae_v_move(&xyp.ptr.pp_double[0][0], xyp.stride, &xy->ptr.pp_double[0][0], xy->stride, ae_v_len(0,n-1)); - ae_v_move(&xyp.ptr.pp_double[0][1], xyp.stride, &xy->ptr.pp_double[0][1], xy->stride, ae_v_len(0,n-1)); - ae_v_move(&xyp.ptr.pp_double[0][2], xyp.stride, &xy->ptr.pp_double[0][2], xy->stride, ae_v_len(0,n-1)); - ae_v_move(&xyp.ptr.pp_double[n][0], 1, &xy->ptr.pp_double[0][0], 1, ae_v_len(0,2)); - - /* - * Build parameterization, check that all parameters are distinct - */ - pspline_pspline3par(&xyp, n+1, pt, &p->p, _state); - ae_assert(aredistinct(&p->p, n+1, _state), "PSplineBuild2Periodic: consequent (or first and last) points are too close!", _state); - - /* - * Build splines - */ - if( st==1 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n)); - spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n)); - spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->y, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][2], xyp.stride, ae_v_len(0,n)); - spline1dbuildcatmullrom(&p->p, &tmp, n+1, -1, 0.0, &p->z, _state); - } - if( st==2 ) - { - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][0], xyp.stride, ae_v_len(0,n)); - spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->x, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][1], xyp.stride, ae_v_len(0,n)); - spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->y, _state); - ae_v_move(&tmp.ptr.p_double[0], 1, &xyp.ptr.pp_double[0][2], xyp.stride, ae_v_len(0,n)); - spline1dbuildcubic(&p->p, &tmp, n+1, -1, 0.0, -1, 0.0, &p->z, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function returns vector of parameter values correspoding to points. - -I.e. for P created from (X[0],Y[0])...(X[N-1],Y[N-1]) and U=TValues(P) we -have - (X[0],Y[0]) = PSpline2Calc(P,U[0]), - (X[1],Y[1]) = PSpline2Calc(P,U[1]), - (X[2],Y[2]) = PSpline2Calc(P,U[2]), - ... - -INPUT PARAMETERS: - P - parametric spline interpolant - -OUTPUT PARAMETERS: - N - array size - T - array[0..N-1] - - -NOTES: -* for non-periodic splines U[0]=0, U[0]n>=2, "PSpline2ParameterValues: internal error!", _state); - *n = p->n; - ae_vector_set_length(t, *n, _state); - ae_v_move(&t->ptr.p_double[0], 1, &p->p.ptr.p_double[0], 1, ae_v_len(0,*n-1)); - t->ptr.p_double[0] = 0; - if( !p->periodic ) - { - t->ptr.p_double[*n-1] = 1; - } -} - - -/************************************************************************* -This function returns vector of parameter values correspoding to points. - -Same as PSpline2ParameterValues(), but for 3D. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3parametervalues(pspline3interpolant* p, - ae_int_t* n, - /* Real */ ae_vector* t, - ae_state *_state) -{ - - *n = 0; - ae_vector_clear(t); - - ae_assert(p->n>=2, "PSpline3ParameterValues: internal error!", _state); - *n = p->n; - ae_vector_set_length(t, *n, _state); - ae_v_move(&t->ptr.p_double[0], 1, &p->p.ptr.p_double[0], 1, ae_v_len(0,*n-1)); - t->ptr.p_double[0] = 0; - if( !p->periodic ) - { - t->ptr.p_double[*n-1] = 1; - } -} - - -/************************************************************************* -This function calculates the value of the parametric spline for a given -value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-position - Y - Y-position - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2calc(pspline2interpolant* p, - double t, - double* x, - double* y, - ae_state *_state) -{ - - *x = 0; - *y = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - *x = spline1dcalc(&p->x, t, _state); - *y = spline1dcalc(&p->y, t, _state); -} - - -/************************************************************************* -This function calculates the value of the parametric spline for a given -value of parameter T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-position - Y - Y-position - Z - Z-position - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3calc(pspline3interpolant* p, - double t, - double* x, - double* y, - double* z, - ae_state *_state) -{ - - *x = 0; - *y = 0; - *z = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - *x = spline1dcalc(&p->x, t, _state); - *y = spline1dcalc(&p->y, t, _state); - *z = spline1dcalc(&p->z, t, _state); -} - - -/************************************************************************* -This function calculates tangent vector for a given value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-component of tangent vector (normalized) - Y - Y-component of tangent vector (normalized) - -NOTE: - X^2+Y^2 is either 1 (for non-zero tangent vector) or 0. - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2tangent(pspline2interpolant* p, - double t, - double* x, - double* y, - ae_state *_state) -{ - double v; - double v0; - double v1; - - *x = 0; - *y = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - pspline2diff(p, t, &v0, x, &v1, y, _state); - if( ae_fp_neq(*x,0)||ae_fp_neq(*y,0) ) - { - - /* - * this code is a bit more complex than X^2+Y^2 to avoid - * overflow for large values of X and Y. - */ - v = safepythag2(*x, *y, _state); - *x = *x/v; - *y = *y/v; - } -} - - -/************************************************************************* -This function calculates tangent vector for a given value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-component of tangent vector (normalized) - Y - Y-component of tangent vector (normalized) - Z - Z-component of tangent vector (normalized) - -NOTE: - X^2+Y^2+Z^2 is either 1 (for non-zero tangent vector) or 0. - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3tangent(pspline3interpolant* p, - double t, - double* x, - double* y, - double* z, - ae_state *_state) -{ - double v; - double v0; - double v1; - double v2; - - *x = 0; - *y = 0; - *z = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - pspline3diff(p, t, &v0, x, &v1, y, &v2, z, _state); - if( (ae_fp_neq(*x,0)||ae_fp_neq(*y,0))||ae_fp_neq(*z,0) ) - { - v = safepythag3(*x, *y, *z, _state); - *x = *x/v; - *y = *y/v; - *z = *z/v; - } -} - - -/************************************************************************* -This function calculates derivative, i.e. it returns (dX/dT,dY/dT). - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - X-derivative - Y - Y-value - DY - Y-derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2diff(pspline2interpolant* p, - double t, - double* x, - double* dx, - double* y, - double* dy, - ae_state *_state) -{ - double d2s; - - *x = 0; - *dx = 0; - *y = 0; - *dy = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - spline1ddiff(&p->x, t, x, dx, &d2s, _state); - spline1ddiff(&p->y, t, y, dy, &d2s, _state); -} - - -/************************************************************************* -This function calculates derivative, i.e. it returns (dX/dT,dY/dT,dZ/dT). - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - X-derivative - Y - Y-value - DY - Y-derivative - Z - Z-value - DZ - Z-derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3diff(pspline3interpolant* p, - double t, - double* x, - double* dx, - double* y, - double* dy, - double* z, - double* dz, - ae_state *_state) -{ - double d2s; - - *x = 0; - *dx = 0; - *y = 0; - *dy = 0; - *z = 0; - *dz = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - spline1ddiff(&p->x, t, x, dx, &d2s, _state); - spline1ddiff(&p->y, t, y, dy, &d2s, _state); - spline1ddiff(&p->z, t, z, dz, &d2s, _state); -} - - -/************************************************************************* -This function calculates first and second derivative with respect to T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - derivative - D2X - second derivative - Y - Y-value - DY - derivative - D2Y - second derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2diff2(pspline2interpolant* p, - double t, - double* x, - double* dx, - double* d2x, - double* y, - double* dy, - double* d2y, - ae_state *_state) -{ - - *x = 0; - *dx = 0; - *d2x = 0; - *y = 0; - *dy = 0; - *d2y = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - spline1ddiff(&p->x, t, x, dx, d2x, _state); - spline1ddiff(&p->y, t, y, dy, d2y, _state); -} - - -/************************************************************************* -This function calculates first and second derivative with respect to T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - derivative - D2X - second derivative - Y - Y-value - DY - derivative - D2Y - second derivative - Z - Z-value - DZ - derivative - D2Z - second derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3diff2(pspline3interpolant* p, - double t, - double* x, - double* dx, - double* d2x, - double* y, - double* dy, - double* d2y, - double* z, - double* dz, - double* d2z, - ae_state *_state) -{ - - *x = 0; - *dx = 0; - *d2x = 0; - *y = 0; - *dy = 0; - *d2y = 0; - *z = 0; - *dz = 0; - *d2z = 0; - - if( p->periodic ) - { - t = t-ae_ifloor(t, _state); - } - spline1ddiff(&p->x, t, x, dx, d2x, _state); - spline1ddiff(&p->y, t, y, dy, d2y, _state); - spline1ddiff(&p->z, t, z, dz, d2z, _state); -} - - -/************************************************************************* -This function calculates arc length, i.e. length of curve between t=a -and t=b. - -INPUT PARAMETERS: - P - parametric spline interpolant - A,B - parameter values corresponding to arc ends: - * B>A will result in positive length returned - * Bx, state.x, &sx, &dsx, &d2sx, _state); - spline1ddiff(&p->y, state.x, &sy, &dsy, &d2sy, _state); - state.f = safepythag2(dsx, dsy, _state); - } - autogkresults(&state, &result, &rep, _state); - ae_assert(rep.terminationtype>0, "PSpline2ArcLength: internal error!", _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -This function calculates arc length, i.e. length of curve between t=a -and t=b. - -INPUT PARAMETERS: - P - parametric spline interpolant - A,B - parameter values corresponding to arc ends: - * B>A will result in positive length returned - * Bx, state.x, &sx, &dsx, &d2sx, _state); - spline1ddiff(&p->y, state.x, &sy, &dsy, &d2sy, _state); - spline1ddiff(&p->z, state.x, &sz, &dsz, &d2sz, _state); - state.f = safepythag3(dsx, dsy, dsz, _state); - } - autogkresults(&state, &result, &rep, _state); - ae_assert(rep.terminationtype>0, "PSpline3ArcLength: internal error!", _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Builds non-periodic parameterization for 2-dimensional spline -*************************************************************************/ -static void pspline_pspline2par(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t pt, - /* Real */ ae_vector* p, - ae_state *_state) -{ - double v; - ae_int_t i; - - ae_vector_clear(p); - - ae_assert(pt>=0&&pt<=2, "PSpline2Par: internal error!", _state); - - /* - * Build parameterization: - * * fill by non-normalized values - * * normalize them so we have P[0]=0, P[N-1]=1. - */ - ae_vector_set_length(p, n, _state); - if( pt==0 ) - { - for(i=0; i<=n-1; i++) - { - p->ptr.p_double[i] = i; - } - } - if( pt==1 ) - { - p->ptr.p_double[0] = 0; - for(i=1; i<=n-1; i++) - { - p->ptr.p_double[i] = p->ptr.p_double[i-1]+safepythag2(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], _state); - } - } - if( pt==2 ) - { - p->ptr.p_double[0] = 0; - for(i=1; i<=n-1; i++) - { - p->ptr.p_double[i] = p->ptr.p_double[i-1]+ae_sqrt(safepythag2(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], _state), _state); - } - } - v = 1/p->ptr.p_double[n-1]; - ae_v_muld(&p->ptr.p_double[0], 1, ae_v_len(0,n-1), v); -} - - -/************************************************************************* -Builds non-periodic parameterization for 3-dimensional spline -*************************************************************************/ -static void pspline_pspline3par(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t pt, - /* Real */ ae_vector* p, - ae_state *_state) -{ - double v; - ae_int_t i; - - ae_vector_clear(p); - - ae_assert(pt>=0&&pt<=2, "PSpline3Par: internal error!", _state); - - /* - * Build parameterization: - * * fill by non-normalized values - * * normalize them so we have P[0]=0, P[N-1]=1. - */ - ae_vector_set_length(p, n, _state); - if( pt==0 ) - { - for(i=0; i<=n-1; i++) - { - p->ptr.p_double[i] = i; - } - } - if( pt==1 ) - { - p->ptr.p_double[0] = 0; - for(i=1; i<=n-1; i++) - { - p->ptr.p_double[i] = p->ptr.p_double[i-1]+safepythag3(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], xy->ptr.pp_double[i][2]-xy->ptr.pp_double[i-1][2], _state); - } - } - if( pt==2 ) - { - p->ptr.p_double[0] = 0; - for(i=1; i<=n-1; i++) - { - p->ptr.p_double[i] = p->ptr.p_double[i-1]+ae_sqrt(safepythag3(xy->ptr.pp_double[i][0]-xy->ptr.pp_double[i-1][0], xy->ptr.pp_double[i][1]-xy->ptr.pp_double[i-1][1], xy->ptr.pp_double[i][2]-xy->ptr.pp_double[i-1][2], _state), _state); - } - } - v = 1/p->ptr.p_double[n-1]; - ae_v_muld(&p->ptr.p_double[0], 1, ae_v_len(0,n-1), v); -} - - -ae_bool _pspline2interpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - pspline2interpolant *p = (pspline2interpolant*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->p, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init(&p->x, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init(&p->y, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _pspline2interpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - pspline2interpolant *dst = (pspline2interpolant*)_dst; - pspline2interpolant *src = (pspline2interpolant*)_src; - dst->n = src->n; - dst->periodic = src->periodic; - if( !ae_vector_init_copy(&dst->p, &src->p, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _pspline2interpolant_clear(void* _p) -{ - pspline2interpolant *p = (pspline2interpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->p); - _spline1dinterpolant_clear(&p->x); - _spline1dinterpolant_clear(&p->y); -} - - -void _pspline2interpolant_destroy(void* _p) -{ - pspline2interpolant *p = (pspline2interpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->p); - _spline1dinterpolant_destroy(&p->x); - _spline1dinterpolant_destroy(&p->y); -} - - -ae_bool _pspline3interpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - pspline3interpolant *p = (pspline3interpolant*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->p, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init(&p->x, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init(&p->y, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init(&p->z, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _pspline3interpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - pspline3interpolant *dst = (pspline3interpolant*)_dst; - pspline3interpolant *src = (pspline3interpolant*)_src; - dst->n = src->n; - dst->periodic = src->periodic; - if( !ae_vector_init_copy(&dst->p, &src->p, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !_spline1dinterpolant_init_copy(&dst->z, &src->z, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _pspline3interpolant_clear(void* _p) -{ - pspline3interpolant *p = (pspline3interpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->p); - _spline1dinterpolant_clear(&p->x); - _spline1dinterpolant_clear(&p->y); - _spline1dinterpolant_clear(&p->z); -} - - -void _pspline3interpolant_destroy(void* _p) -{ - pspline3interpolant *p = (pspline3interpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->p); - _spline1dinterpolant_destroy(&p->x); - _spline1dinterpolant_destroy(&p->y); - _spline1dinterpolant_destroy(&p->z); -} - - - - -/************************************************************************* -This function creates RBF model for a scalar (NY=1) or vector (NY>1) -function in a NX-dimensional space (NX=2 or NX=3). - -Newly created model is empty. It can be used for interpolation right after -creation, but it just returns zeros. You have to add points to the model, -tune interpolation settings, and then call model construction function -RBFBuildModel() which will update model according to your specification. - -USAGE: -1. User creates model with RBFCreate() -2. User adds dataset with RBFSetPoints() (points do NOT have to be on a - regular grid) -3. (OPTIONAL) User chooses polynomial term by calling: - * RBFLinTerm() to set linear term - * RBFConstTerm() to set constant term - * RBFZeroTerm() to set zero term - By default, linear term is used. -4. User chooses specific RBF algorithm to use: either QNN (RBFSetAlgoQNN) - or ML (RBFSetAlgoMultiLayer). -5. User calls RBFBuildModel() function which rebuilds model according to - the specification -6. User may call RBFCalc() to calculate model value at the specified point, - RBFGridCalc() to calculate model values at the points of the regular - grid. User may extract model coefficients with RBFUnpack() call. - -INPUT PARAMETERS: - NX - dimension of the space, NX=2 or NX=3 - NY - function dimension, NY>=1 - -OUTPUT PARAMETERS: - S - RBF model (initially equals to zero) - -NOTE 1: memory requirements. RBF models require amount of memory which is - proportional to the number of data points. Memory is allocated - during model construction, but most of this memory is freed after - model coefficients are calculated. - - Some approximate estimates for N centers with default settings are - given below: - * about 250*N*(sizeof(double)+2*sizeof(int)) bytes of memory is - needed during model construction stage. - * about 15*N*sizeof(double) bytes is needed after model is built. - For example, for N=100000 we may need 0.6 GB of memory to build - model, but just about 0.012 GB to store it. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcreate(ae_int_t nx, ae_int_t ny, rbfmodel* s, ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - _rbfmodel_clear(s); - - ae_assert(nx==2||nx==3, "RBFCreate: NX<>2 and NX<>3", _state); - ae_assert(ny>=1, "RBFCreate: NY<1", _state); - s->nx = nx; - s->ny = ny; - s->nl = 0; - s->nc = 0; - ae_matrix_set_length(&s->v, ny, rbf_mxnx+1, _state); - for(i=0; i<=ny-1; i++) - { - for(j=0; j<=rbf_mxnx; j++) - { - s->v.ptr.pp_double[i][j] = 0; - } - } - s->n = 0; - s->rmax = 0; - s->gridtype = 2; - s->fixrad = ae_false; - s->radvalue = 1; - s->radzvalue = 5; - s->aterm = 1; - s->algorithmtype = 1; - - /* - * stopping criteria - */ - s->epsort = rbf_eps; - s->epserr = rbf_eps; - s->maxits = 0; -} - - -/************************************************************************* -This function adds dataset. - -This function overrides results of the previous calls, i.e. multiple calls -of this function will result in only the last set being added. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call. - XY - points, array[N,NX+NY]. One row corresponds to one point - in the dataset. First NX elements are coordinates, next - NY elements are function values. Array may be larger than - specific, in this case only leading [N,NX+NY] elements - will be used. - N - number of points in the dataset - -After you've added dataset and (optionally) tuned algorithm settings you -should call RBFBuildModel() in order to build a model for you. - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetpoints(rbfmodel* s, - /* Real */ ae_matrix* xy, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - ae_assert(n>0, "RBFSetPoints: N<0", _state); - ae_assert(xy->rows>=n, "RBFSetPoints: Rows(XY)cols>=s->nx+s->ny, "RBFSetPoints: Cols(XY)n = n; - ae_matrix_set_length(&s->x, s->n, rbf_mxnx, _state); - ae_matrix_set_length(&s->y, s->n, s->ny, _state); - for(i=0; i<=s->n-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - s->x.ptr.pp_double[i][j] = 0; - } - for(j=0; j<=s->nx-1; j++) - { - s->x.ptr.pp_double[i][j] = xy->ptr.pp_double[i][j]; - } - for(j=0; j<=s->ny-1; j++) - { - s->y.ptr.pp_double[i][j] = xy->ptr.pp_double[i][j+s->nx]; - } - } -} - - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-QNN and it is good for point sets with -following properties: -a) all points are distinct -b) all points are well separated. -c) points distribution is approximately uniform. There is no "contour - lines", clusters of points, or other small-scale structures. - -Algorithm description: -1) interpolation centers are allocated to data points -2) interpolation radii are calculated as distances to the nearest centers - times Q coefficient (where Q is a value from [0.75,1.50]). -3) after performing (2) radii are transformed in order to avoid situation - when single outlier has very large radius and influences many points - across all dataset. Transformation has following form: - new_r[i] = min(r[i],Z*median(r[])) - where r[i] is I-th radius, median() is a median radius across entire - dataset, Z is user-specified value which controls amount of deviation - from median radius. - -When (a) is violated, we will be unable to build RBF model. When (b) or -(c) are violated, model will be built, but interpolation quality will be -low. See http://www.alglib.net/interpolation/ for more information on this -subject. - -This algorithm is used by default. - -Additional Q parameter controls smoothness properties of the RBF basis: -* Q<0.75 will give perfectly conditioned basis, but terrible smoothness - properties (RBF interpolant will have sharp peaks around function values) -* Q around 1.0 gives good balance between smoothness and condition number -* Q>1.5 will lead to badly conditioned systems and slow convergence of the - underlying linear solver (although smoothness will be very good) -* Q>2.0 will effectively make optimizer useless because it won't converge - within reasonable amount of iterations. It is possible to set such large - Q, but it is advised not to do so. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Q - Q parameter, Q>0, recommended value - 1.0 - Z - Z parameter, Z>0, recommended value - 5.0 - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgoqnn(rbfmodel* s, double q, double z, ae_state *_state) -{ - - - ae_assert(ae_isfinite(q, _state), "RBFSetAlgoQNN: Q is infinite or NAN", _state); - ae_assert(ae_fp_greater(q,0), "RBFSetAlgoQNN: Q<=0", _state); - rbf_rbfgridpoints(s, _state); - rbf_rbfradnn(s, q, z, _state); - s->algorithmtype = 1; -} - - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-ML. It builds multilayer RBF model, i.e. -model with subsequently decreasing radii, which allows us to combine -smoothness (due to large radii of the first layers) with exactness (due -to small radii of the last layers) and fast convergence. - -Internally RBF-ML uses many different means of acceleration, from sparse -matrices to KD-trees, which results in algorithm whose working time is -roughly proportional to N*log(N)*Density*RBase^2*NLayers, where N is a -number of points, Density is an average density if points per unit of the -interpolation space, RBase is an initial radius, NLayers is a number of -layers. - -RBF-ML is good for following kinds of interpolation problems: -1. "exact" problems (perfect fit) with well separated points -2. least squares problems with arbitrary distribution of points (algorithm - gives perfect fit where it is possible, and resorts to least squares - fit in the hard areas). -3. noisy problems where we want to apply some controlled amount of - smoothing. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - RBase - RBase parameter, RBase>0 - NLayers - NLayers parameter, NLayers>0, recommended value to start - with - about 5. - LambdaV - regularization value, can be useful when solving problem - in the least squares sense. Optimal lambda is problem- - dependent and require trial and error. In our experience, - good lambda can be as large as 0.1, and you can use 0.001 - as initial guess. - Default value - 0.01, which is used when LambdaV is not - given. You can specify zero value, but it is not - recommended to do so. - -TUNING ALGORITHM - -In order to use this algorithm you have to choose three parameters: -* initial radius RBase -* number of layers in the model NLayers -* regularization coefficient LambdaV - -Initial radius is easy to choose - you can pick any number several times -larger than the average distance between points. Algorithm won't break -down if you choose radius which is too large (model construction time will -increase, but model will be built correctly). - -Choose such number of layers that RLast=RBase/2^(NLayers-1) (radius used -by the last layer) will be smaller than the typical distance between -points. In case model error is too large, you can increase number of -layers. Having more layers will make model construction and evaluation -proportionally slower, but it will allow you to have model which precisely -fits your data. From the other side, if you want to suppress noise, you -can DECREASE number of layers to make your model less flexible. - -Regularization coefficient LambdaV controls smoothness of the individual -models built for each layer. We recommend you to use default value in case -you don't want to tune this parameter, because having non-zero LambdaV -accelerates and stabilizes internal iterative algorithm. In case you want -to suppress noise you can use LambdaV as additional parameter (larger -value = more smoothness) to tune. - -TYPICAL ERRORS - -1. Using initial radius which is too large. Memory requirements of the - RBF-ML are roughly proportional to N*Density*RBase^2 (where Density is - an average density of points per unit of the interpolation space). In - the extreme case of the very large RBase we will need O(N^2) units of - memory - and many layers in order to decrease radius to some reasonably - small value. - -2. Using too small number of layers - RBF models with large radius are not - flexible enough to reproduce small variations in the target function. - You need many layers with different radii, from large to small, in - order to have good model. - -3. Using initial radius which is too small. You will get model with - "holes" in the areas which are too far away from interpolation centers. - However, algorithm will work correctly (and quickly) in this case. - -4. Using too many layers - you will get too large and too slow model. This - model will perfectly reproduce your function, but maybe you will be - able to achieve similar results with less layers (and less memory). - - -- ALGLIB -- - Copyright 02.03.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgomultilayer(rbfmodel* s, - double rbase, - ae_int_t nlayers, - double lambdav, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(rbase, _state), "RBFSetAlgoMultiLayer: RBase is infinite or NaN", _state); - ae_assert(ae_fp_greater(rbase,0), "RBFSetAlgoMultiLayer: RBase<=0", _state); - ae_assert(nlayers>=0, "RBFSetAlgoMultiLayer: NLayers<0", _state); - ae_assert(ae_isfinite(lambdav, _state), "RBFSetAlgoMultiLayer: LambdaV is infinite or NAN", _state); - ae_assert(ae_fp_greater_eq(lambdav,0), "RBFSetAlgoMultiLayer: LambdaV<0", _state); - s->radvalue = rbase; - s->nlayers = nlayers; - s->algorithmtype = 2; - s->lambdav = lambdav; -} - - -/************************************************************************* -This function sets linear term (model is a sum of radial basis functions -plus linear polynomial). This function won't have effect until next call -to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetlinterm(rbfmodel* s, ae_state *_state) -{ - - - s->aterm = 1; -} - - -/************************************************************************* -This function sets constant term (model is a sum of radial basis functions -plus constant). This function won't have effect until next call to -RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetconstterm(rbfmodel* s, ae_state *_state) -{ - - - s->aterm = 2; -} - - -/************************************************************************* -This function sets zero term (model is a sum of radial basis functions -without polynomial term). This function won't have effect until next call -to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetzeroterm(rbfmodel* s, ae_state *_state) -{ - - - s->aterm = 3; -} - - -/************************************************************************* -This function sets stopping criteria of the underlying linear solver. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - EpsOrt - orthogonality stopping criterion, EpsOrt>=0. Algorithm will - stop when ||A'*r||<=EpsOrt where A' is a transpose of the - system matrix, r is a residual vector. - Recommended value of EpsOrt is equal to 1E-6. - This criterion will stop algorithm when we have "bad fit" - situation, i.e. when we should stop in a point with large, - nonzero residual. - EpsErr - residual stopping criterion. Algorithm will stop when - ||r||<=EpsErr*||b||, where r is a residual vector, b is a - right part of the system (function values). - Recommended value of EpsErr is equal to 1E-3 or 1E-6. - This criterion will stop algorithm in a "good fit" - situation when we have near-zero residual near the desired - solution. - MaxIts - this criterion will stop algorithm after MaxIts iterations. - It should be used for debugging purposes only! - Zero MaxIts means that no limit is placed on the number of - iterations. - -We recommend to set moderate non-zero values EpsOrt and EpsErr -simultaneously. Values equal to 10E-6 are good to start with. In case you -need high performance and do not need high precision , you may decrease -EpsErr down to 0.001. However, we do not recommend decreasing EpsOrt. - -As for MaxIts, we recommend to leave it zero unless you know what you do. - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetcond(rbfmodel* s, - double epsort, - double epserr, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsort, _state)&&ae_fp_greater_eq(epsort,0), "RBFSetCond: EpsOrt is negative, INF or NAN", _state); - ae_assert(ae_isfinite(epserr, _state)&&ae_fp_greater_eq(epserr,0), "RBFSetCond: EpsB is negative, INF or NAN", _state); - ae_assert(maxits>=0, "RBFSetCond: MaxIts is negative", _state); - if( (ae_fp_eq(epsort,0)&&ae_fp_eq(epserr,0))&&maxits==0 ) - { - s->epsort = rbf_eps; - s->epserr = rbf_eps; - s->maxits = 0; - } - else - { - s->epsort = epsort; - s->epserr = epserr; - s->maxits = maxits; - } -} - - -/************************************************************************* -This function builds RBF model and returns report (contains some -information which can be used for evaluation of the algorithm properties). - -Call to this function modifies RBF model by calculating its centers/radii/ -weights and saving them into RBFModel structure. Initially RBFModel -contain zero coefficients, but after call to this function we will have -coefficients which were calculated in order to fit our dataset. - -After you called this function you can call RBFCalc(), RBFGridCalc() and -other model calculation functions. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Rep - report: - * Rep.TerminationType: - * -5 - non-distinct basis function centers were detected, - interpolation aborted - * -4 - nonconvergence of the internal SVD solver - * 1 - successful termination - Fields are used for debugging purposes: - * Rep.IterationsCount - iterations count of the LSQR solver - * Rep.NMV - number of matrix-vector products - * Rep.ARows - rows count for the system matrix - * Rep.ACols - columns count for the system matrix - * Rep.ANNZ - number of significantly non-zero elements - (elements above some algorithm-determined threshold) - -NOTE: failure to build model will leave current state of the structure -unchanged. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfbuildmodel(rbfmodel* s, rbfreport* rep, ae_state *_state) -{ - ae_frame _frame_block; - kdtree tree; - kdtree ctree; - ae_vector dist; - ae_vector xcx; - ae_matrix a; - ae_matrix v; - ae_matrix omega; - ae_vector y; - ae_matrix residualy; - ae_vector radius; - ae_matrix xc; - ae_vector mnx; - ae_vector mxx; - ae_vector edge; - ae_vector mxsteps; - ae_int_t nc; - double rmax; - ae_vector tags; - ae_vector ctags; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t k2; - ae_int_t snnz; - ae_vector tmp0; - ae_vector tmp1; - ae_int_t layerscnt; - - ae_frame_make(_state, &_frame_block); - _rbfreport_clear(rep); - _kdtree_init(&tree, _state, ae_true); - _kdtree_init(&ctree, _state, ae_true); - ae_vector_init(&dist, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xcx, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&v, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&omega, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&residualy, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&radius, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xc, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&mnx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&mxx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&edge, 0, DT_REAL, _state, ae_true); - ae_vector_init(&mxsteps, 0, DT_INT, _state, ae_true); - ae_vector_init(&tags, 0, DT_INT, _state, ae_true); - ae_vector_init(&ctags, 0, DT_INT, _state, ae_true); - ae_vector_init(&tmp0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp1, 0, DT_REAL, _state, ae_true); - - ae_assert(s->nx==2||s->nx==3, "RBFBuildModel: S.NX<>2 or S.NX<>3!", _state); - - /* - * Quick exit when we have no points - */ - if( s->n==0 ) - { - rep->terminationtype = 1; - rep->iterationscount = 0; - rep->nmv = 0; - rep->arows = 0; - rep->acols = 0; - kdtreebuildtagged(&s->xc, &tags, 0, rbf_mxnx, 0, 2, &s->tree, _state); - ae_matrix_set_length(&s->xc, 0, 0, _state); - ae_matrix_set_length(&s->wr, 0, 0, _state); - s->nc = 0; - s->rmax = 0; - ae_matrix_set_length(&s->v, s->ny, rbf_mxnx+1, _state); - for(i=0; i<=s->ny-1; i++) - { - for(j=0; j<=rbf_mxnx; j++) - { - s->v.ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * General case, N>0 - */ - rep->annz = 0; - rep->iterationscount = 0; - rep->nmv = 0; - ae_vector_set_length(&xcx, rbf_mxnx, _state); - - /* - * First model in a sequence - linear model. - * Residuals from linear regression are stored in the ResidualY variable - * (used later to build RBF models). - */ - ae_matrix_set_length(&residualy, s->n, s->ny, _state); - for(i=0; i<=s->n-1; i++) - { - for(j=0; j<=s->ny-1; j++) - { - residualy.ptr.pp_double[i][j] = s->y.ptr.pp_double[i][j]; - } - } - if( !rbf_buildlinearmodel(&s->x, &residualy, s->n, s->ny, s->aterm, &v, _state) ) - { - rep->terminationtype = -5; - ae_frame_leave(_state); - return; - } - - /* - * Handle special case: multilayer model with NLayers=0. - * Quick exit. - */ - if( s->algorithmtype==2&&s->nlayers==0 ) - { - rep->terminationtype = 1; - rep->iterationscount = 0; - rep->nmv = 0; - rep->arows = 0; - rep->acols = 0; - kdtreebuildtagged(&s->xc, &tags, 0, rbf_mxnx, 0, 2, &s->tree, _state); - ae_matrix_set_length(&s->xc, 0, 0, _state); - ae_matrix_set_length(&s->wr, 0, 0, _state); - s->nc = 0; - s->rmax = 0; - ae_matrix_set_length(&s->v, s->ny, rbf_mxnx+1, _state); - for(i=0; i<=s->ny-1; i++) - { - for(j=0; j<=rbf_mxnx; j++) - { - s->v.ptr.pp_double[i][j] = v.ptr.pp_double[i][j]; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Second model in a sequence - RBF term. - * - * NOTE: assignments below are not necessary, but without them - * MSVC complains about unitialized variables. - */ - nc = 0; - rmax = 0; - layerscnt = 0; - if( s->algorithmtype==1 ) - { - - /* - * Add RBF model. - * This model uses local KD-trees to speed-up nearest neighbor searches. - */ - if( s->gridtype==1 ) - { - ae_vector_set_length(&mxx, s->nx, _state); - ae_vector_set_length(&mnx, s->nx, _state); - ae_vector_set_length(&mxsteps, s->nx, _state); - ae_vector_set_length(&edge, s->nx, _state); - for(i=0; i<=s->nx-1; i++) - { - mxx.ptr.p_double[i] = s->x.ptr.pp_double[0][i]; - mnx.ptr.p_double[i] = s->x.ptr.pp_double[0][i]; - } - for(i=0; i<=s->n-1; i++) - { - for(j=0; j<=s->nx-1; j++) - { - if( ae_fp_less(mxx.ptr.p_double[j],s->x.ptr.pp_double[i][j]) ) - { - mxx.ptr.p_double[j] = s->x.ptr.pp_double[i][j]; - } - if( ae_fp_greater(mnx.ptr.p_double[j],s->x.ptr.pp_double[i][j]) ) - { - mnx.ptr.p_double[j] = s->x.ptr.pp_double[i][j]; - } - } - } - for(i=0; i<=s->nx-1; i++) - { - mxsteps.ptr.p_int[i] = ae_trunc((mxx.ptr.p_double[i]-mnx.ptr.p_double[i])/(2*s->h), _state)+1; - edge.ptr.p_double[i] = (mxx.ptr.p_double[i]+mnx.ptr.p_double[i])/2-s->h*mxsteps.ptr.p_int[i]; - } - nc = 1; - for(i=0; i<=s->nx-1; i++) - { - mxsteps.ptr.p_int[i] = 2*mxsteps.ptr.p_int[i]+1; - nc = nc*mxsteps.ptr.p_int[i]; - } - ae_matrix_set_length(&xc, nc, rbf_mxnx, _state); - if( s->nx==2 ) - { - for(i=0; i<=mxsteps.ptr.p_int[0]-1; i++) - { - for(j=0; j<=mxsteps.ptr.p_int[1]-1; j++) - { - for(k2=0; k2<=rbf_mxnx-1; k2++) - { - xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][k2] = 0; - } - xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][0] = edge.ptr.p_double[0]+s->h*i; - xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][1] = edge.ptr.p_double[1]+s->h*j; - } - } - } - if( s->nx==3 ) - { - for(i=0; i<=mxsteps.ptr.p_int[0]-1; i++) - { - for(j=0; j<=mxsteps.ptr.p_int[1]-1; j++) - { - for(k=0; k<=mxsteps.ptr.p_int[2]-1; k++) - { - for(k2=0; k2<=rbf_mxnx-1; k2++) - { - xc.ptr.pp_double[i*mxsteps.ptr.p_int[1]+j][k2] = 0; - } - xc.ptr.pp_double[(i*mxsteps.ptr.p_int[1]+j)*mxsteps.ptr.p_int[2]+k][0] = edge.ptr.p_double[0]+s->h*i; - xc.ptr.pp_double[(i*mxsteps.ptr.p_int[1]+j)*mxsteps.ptr.p_int[2]+k][1] = edge.ptr.p_double[1]+s->h*j; - xc.ptr.pp_double[(i*mxsteps.ptr.p_int[1]+j)*mxsteps.ptr.p_int[2]+k][2] = edge.ptr.p_double[2]+s->h*k; - } - } - } - } - } - else - { - if( s->gridtype==2 ) - { - nc = s->n; - ae_matrix_set_length(&xc, nc, rbf_mxnx, _state); - for(i=0; i<=nc-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xc.ptr.pp_double[i][j] = s->x.ptr.pp_double[i][j]; - } - } - } - else - { - if( s->gridtype==3 ) - { - nc = s->nc; - ae_matrix_set_length(&xc, nc, rbf_mxnx, _state); - for(i=0; i<=nc-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xc.ptr.pp_double[i][j] = s->xc.ptr.pp_double[i][j]; - } - } - } - else - { - ae_assert(ae_false, "RBFBuildModel: either S.GridType<1 or S.GridType>3!", _state); - } - } - } - rmax = 0; - ae_vector_set_length(&radius, nc, _state); - ae_vector_set_length(&ctags, nc, _state); - for(i=0; i<=nc-1; i++) - { - ctags.ptr.p_int[i] = i; - } - kdtreebuildtagged(&xc, &ctags, nc, rbf_mxnx, 0, 2, &ctree, _state); - if( s->fixrad ) - { - - /* - * Fixed radius - */ - for(i=0; i<=nc-1; i++) - { - radius.ptr.p_double[i] = s->radvalue; - } - rmax = radius.ptr.p_double[0]; - } - else - { - - /* - * Dynamic radius - */ - if( nc==0 ) - { - rmax = 1; - } - else - { - if( nc==1 ) - { - radius.ptr.p_double[0] = s->radvalue; - rmax = radius.ptr.p_double[0]; - } - else - { - - /* - * NC>1, calculate radii using distances to nearest neigbors - */ - for(i=0; i<=nc-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xcx.ptr.p_double[j] = xc.ptr.pp_double[i][j]; - } - if( kdtreequeryknn(&ctree, &xcx, 1, ae_false, _state)>0 ) - { - kdtreequeryresultsdistances(&ctree, &dist, _state); - radius.ptr.p_double[i] = s->radvalue*dist.ptr.p_double[0]; - } - else - { - - /* - * No neighbors found (it will happen when we have only one center). - * Initialize radius with default value. - */ - radius.ptr.p_double[i] = 1.0; - } - } - - /* - * Apply filtering - */ - rvectorsetlengthatleast(&tmp0, nc, _state); - for(i=0; i<=nc-1; i++) - { - tmp0.ptr.p_double[i] = radius.ptr.p_double[i]; - } - tagsortfast(&tmp0, &tmp1, nc, _state); - for(i=0; i<=nc-1; i++) - { - radius.ptr.p_double[i] = ae_minreal(radius.ptr.p_double[i], s->radzvalue*tmp0.ptr.p_double[nc/2], _state); - } - - /* - * Calculate RMax, check that all radii are non-zero - */ - for(i=0; i<=nc-1; i++) - { - rmax = ae_maxreal(rmax, radius.ptr.p_double[i], _state); - } - for(i=0; i<=nc-1; i++) - { - if( ae_fp_eq(radius.ptr.p_double[i],0) ) - { - rep->terminationtype = -5; - ae_frame_leave(_state); - return; - } - } - } - } - } - ivectorsetlengthatleast(&tags, s->n, _state); - for(i=0; i<=s->n-1; i++) - { - tags.ptr.p_int[i] = i; - } - kdtreebuildtagged(&s->x, &tags, s->n, rbf_mxnx, 0, 2, &tree, _state); - rbf_buildrbfmodellsqr(&s->x, &residualy, &xc, &radius, s->n, nc, s->ny, &tree, &ctree, s->epsort, s->epserr, s->maxits, &rep->annz, &snnz, &omega, &rep->terminationtype, &rep->iterationscount, &rep->nmv, _state); - layerscnt = 1; - } - else - { - if( s->algorithmtype==2 ) - { - rmax = s->radvalue; - rbf_buildrbfmlayersmodellsqr(&s->x, &residualy, &xc, s->radvalue, &radius, s->n, &nc, s->ny, s->nlayers, &ctree, 1.0E-6, 1.0E-6, 50, s->lambdav, &rep->annz, &omega, &rep->terminationtype, &rep->iterationscount, &rep->nmv, _state); - layerscnt = s->nlayers; - } - else - { - ae_assert(ae_false, "RBFBuildModel: internal error(AlgorithmType neither 1 nor 2)", _state); - } - } - if( rep->terminationtype<=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Model is built - */ - s->nc = nc/layerscnt; - s->rmax = rmax; - s->nl = layerscnt; - ae_matrix_set_length(&s->xc, s->nc, rbf_mxnx, _state); - ae_matrix_set_length(&s->wr, s->nc, 1+s->nl*s->ny, _state); - ae_matrix_set_length(&s->v, s->ny, rbf_mxnx+1, _state); - for(i=0; i<=s->nc-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - s->xc.ptr.pp_double[i][j] = xc.ptr.pp_double[i][j]; - } - } - ivectorsetlengthatleast(&tags, s->nc, _state); - for(i=0; i<=s->nc-1; i++) - { - tags.ptr.p_int[i] = i; - } - kdtreebuildtagged(&s->xc, &tags, s->nc, rbf_mxnx, 0, 2, &s->tree, _state); - for(i=0; i<=s->nc-1; i++) - { - s->wr.ptr.pp_double[i][0] = radius.ptr.p_double[i]; - for(k=0; k<=layerscnt-1; k++) - { - for(j=0; j<=s->ny-1; j++) - { - s->wr.ptr.pp_double[i][1+k*s->ny+j] = omega.ptr.pp_double[k*s->nc+i][j]; - } - } - } - for(i=0; i<=s->ny-1; i++) - { - for(j=0; j<=rbf_mxnx; j++) - { - s->v.ptr.pp_double[i][j] = v.ptr.pp_double[i][j]; - } - } - rep->terminationtype = 1; - rep->arows = s->n; - rep->acols = s->nc; - ae_frame_leave(_state); -} - - -/************************************************************************* -This function calculates values of the RBF model in the given point. - -This function should be used when we have NY=1 (scalar function) and NX=2 -(2-dimensional space). If you have 3-dimensional space, use RBFCalc3(). If -you have general situation (NX-dimensional space, NY-dimensional function) -you should use general, less efficient implementation RBFCalc(). - -If you want to calculate function values many times, consider using -RBFGridCalc2(), which is far more efficient than many subsequent calls to -RBFCalc2(). - -This function returns 0.0 when: -* model is not initialized -* NX<>2 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - first coordinate, finite number - X1 - second coordinate, finite number - -RESULT: - value of the model or 0.0 (as defined above) - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -double rbfcalc2(rbfmodel* s, double x0, double x1, ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t lx; - ae_int_t tg; - double d2; - double t; - double bfcur; - double rcur; - double result; - - - ae_assert(ae_isfinite(x0, _state), "RBFCalc2: invalid value for X0 (X0 is Inf)!", _state); - ae_assert(ae_isfinite(x1, _state), "RBFCalc2: invalid value for X1 (X1 is Inf)!", _state); - if( s->ny!=1||s->nx!=2 ) - { - result = 0; - return result; - } - result = s->v.ptr.pp_double[0][0]*x0+s->v.ptr.pp_double[0][1]*x1+s->v.ptr.pp_double[0][rbf_mxnx]; - if( s->nc==0 ) - { - return result; - } - rvectorsetlengthatleast(&s->calcbufxcx, rbf_mxnx, _state); - for(i=0; i<=rbf_mxnx-1; i++) - { - s->calcbufxcx.ptr.p_double[i] = 0.0; - } - s->calcbufxcx.ptr.p_double[0] = x0; - s->calcbufxcx.ptr.p_double[1] = x1; - lx = kdtreequeryrnn(&s->tree, &s->calcbufxcx, s->rmax*rbf_rbffarradius, ae_true, _state); - kdtreequeryresultsx(&s->tree, &s->calcbufx, _state); - kdtreequeryresultstags(&s->tree, &s->calcbuftags, _state); - for(i=0; i<=lx-1; i++) - { - tg = s->calcbuftags.ptr.p_int[i]; - d2 = ae_sqr(x0-s->calcbufx.ptr.pp_double[i][0], _state)+ae_sqr(x1-s->calcbufx.ptr.pp_double[i][1], _state); - rcur = s->wr.ptr.pp_double[tg][0]; - bfcur = ae_exp(-d2/(rcur*rcur), _state); - for(j=0; j<=s->nl-1; j++) - { - result = result+bfcur*s->wr.ptr.pp_double[tg][1+j]; - rcur = 0.5*rcur; - t = bfcur*bfcur; - bfcur = t*t; - } - } - return result; -} - - -/************************************************************************* -This function calculates values of the RBF model in the given point. - -This function should be used when we have NY=1 (scalar function) and NX=3 -(3-dimensional space). If you have 2-dimensional space, use RBFCalc2(). If -you have general situation (NX-dimensional space, NY-dimensional function) -you should use general, less efficient implementation RBFCalc(). - -This function returns 0.0 when: -* model is not initialized -* NX<>3 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - first coordinate, finite number - X1 - second coordinate, finite number - X2 - third coordinate, finite number - -RESULT: - value of the model or 0.0 (as defined above) - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -double rbfcalc3(rbfmodel* s, - double x0, - double x1, - double x2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t lx; - ae_int_t tg; - double t; - double rcur; - double bf; - double result; - - - ae_assert(ae_isfinite(x0, _state), "RBFCalc3: invalid value for X0 (X0 is Inf or NaN)!", _state); - ae_assert(ae_isfinite(x1, _state), "RBFCalc3: invalid value for X1 (X1 is Inf or NaN)!", _state); - ae_assert(ae_isfinite(x2, _state), "RBFCalc3: invalid value for X2 (X2 is Inf or NaN)!", _state); - if( s->ny!=1||s->nx!=3 ) - { - result = 0; - return result; - } - result = s->v.ptr.pp_double[0][0]*x0+s->v.ptr.pp_double[0][1]*x1+s->v.ptr.pp_double[0][2]*x2+s->v.ptr.pp_double[0][rbf_mxnx]; - if( s->nc==0 ) - { - return result; - } - - /* - * calculating value for F(X) - */ - rvectorsetlengthatleast(&s->calcbufxcx, rbf_mxnx, _state); - for(i=0; i<=rbf_mxnx-1; i++) - { - s->calcbufxcx.ptr.p_double[i] = 0.0; - } - s->calcbufxcx.ptr.p_double[0] = x0; - s->calcbufxcx.ptr.p_double[1] = x1; - s->calcbufxcx.ptr.p_double[2] = x2; - lx = kdtreequeryrnn(&s->tree, &s->calcbufxcx, s->rmax*rbf_rbffarradius, ae_true, _state); - kdtreequeryresultsx(&s->tree, &s->calcbufx, _state); - kdtreequeryresultstags(&s->tree, &s->calcbuftags, _state); - for(i=0; i<=lx-1; i++) - { - tg = s->calcbuftags.ptr.p_int[i]; - rcur = s->wr.ptr.pp_double[tg][0]; - bf = ae_exp(-(ae_sqr(x0-s->calcbufx.ptr.pp_double[i][0], _state)+ae_sqr(x1-s->calcbufx.ptr.pp_double[i][1], _state)+ae_sqr(x2-s->calcbufx.ptr.pp_double[i][2], _state))/ae_sqr(rcur, _state), _state); - for(j=0; j<=s->nl-1; j++) - { - result = result+bf*s->wr.ptr.pp_double[tg][1+j]; - t = bf*bf; - bf = t*t; - } - } - return result; -} - - -/************************************************************************* -This function calculates values of the RBF model at the given point. - -This is general function which can be used for arbitrary NX (dimension of -the space of arguments) and NY (dimension of the function itself). However -when you have NY=1 you may find more convenient to use RBFCalc2() or -RBFCalc3(). - -This function returns 0.0 when model is not initialized. - -INPUT PARAMETERS: - S - RBF model - X - coordinates, array[NX]. - X may have more than NX elements, in this case only - leading NX will be used. - -OUTPUT PARAMETERS: - Y - function value, array[NY]. Y is out-parameter and - reallocated after call to this function. In case you want - to reuse previously allocated Y, you may use RBFCalcBuf(), - which reallocates Y only when it is too small. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcalc(rbfmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - - ae_vector_clear(y); - - ae_assert(x->cnt>=s->nx, "RBFCalc: Length(X)nx, _state), "RBFCalc: X contains infinite or NaN values", _state); - rbfcalcbuf(s, x, y, _state); -} - - -/************************************************************************* -This function calculates values of the RBF model at the given point. - -Same as RBFCalc(), but does not reallocate Y when in is large enough to -store function values. - -INPUT PARAMETERS: - S - RBF model - X - coordinates, array[NX]. - X may have more than NX elements, in this case only - leading NX will be used. - Y - possibly preallocated array - -OUTPUT PARAMETERS: - Y - function value, array[NY]. Y is not reallocated when it - is larger than NY. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcalcbuf(rbfmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t lx; - ae_int_t tg; - double t; - double rcur; - double bf; - - - ae_assert(x->cnt>=s->nx, "RBFCalcBuf: Length(X)nx, _state), "RBFCalcBuf: X contains infinite or NaN values", _state); - if( y->cntny ) - { - ae_vector_set_length(y, s->ny, _state); - } - for(i=0; i<=s->ny-1; i++) - { - y->ptr.p_double[i] = s->v.ptr.pp_double[i][rbf_mxnx]; - for(j=0; j<=s->nx-1; j++) - { - y->ptr.p_double[i] = y->ptr.p_double[i]+s->v.ptr.pp_double[i][j]*x->ptr.p_double[j]; - } - } - if( s->nc==0 ) - { - return; - } - rvectorsetlengthatleast(&s->calcbufxcx, rbf_mxnx, _state); - for(i=0; i<=rbf_mxnx-1; i++) - { - s->calcbufxcx.ptr.p_double[i] = 0.0; - } - for(i=0; i<=s->nx-1; i++) - { - s->calcbufxcx.ptr.p_double[i] = x->ptr.p_double[i]; - } - lx = kdtreequeryrnn(&s->tree, &s->calcbufxcx, s->rmax*rbf_rbffarradius, ae_true, _state); - kdtreequeryresultsx(&s->tree, &s->calcbufx, _state); - kdtreequeryresultstags(&s->tree, &s->calcbuftags, _state); - for(i=0; i<=s->ny-1; i++) - { - for(j=0; j<=lx-1; j++) - { - tg = s->calcbuftags.ptr.p_int[j]; - rcur = s->wr.ptr.pp_double[tg][0]; - bf = ae_exp(-(ae_sqr(s->calcbufxcx.ptr.p_double[0]-s->calcbufx.ptr.pp_double[j][0], _state)+ae_sqr(s->calcbufxcx.ptr.p_double[1]-s->calcbufx.ptr.pp_double[j][1], _state)+ae_sqr(s->calcbufxcx.ptr.p_double[2]-s->calcbufx.ptr.pp_double[j][2], _state))/ae_sqr(rcur, _state), _state); - for(k=0; k<=s->nl-1; k++) - { - y->ptr.p_double[i] = y->ptr.p_double[i]+bf*s->wr.ptr.pp_double[tg][1+k*s->ny+i]; - t = bf*bf; - bf = t*t; - } - } - } -} - - -/************************************************************************* -This function calculates values of the RBF model at the regular grid. - -Grid have N0*N1 points, with Point[I,J] = (X0[I], X1[J]) - -This function returns 0.0 when: -* model is not initialized -* NX<>2 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - array of grid nodes, first coordinates, array[N0] - N0 - grid size (number of nodes) in the first dimension - X1 - array of grid nodes, second coordinates, array[N1] - N1 - grid size (number of nodes) in the second dimension - -OUTPUT PARAMETERS: - Y - function values, array[N0,N1]. Y is out-variable and - is reallocated by this function. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfgridcalc2(rbfmodel* s, - /* Real */ ae_vector* x0, - ae_int_t n0, - /* Real */ ae_vector* x1, - ae_int_t n1, - /* Real */ ae_matrix* y, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector cpx0; - ae_vector cpx1; - ae_vector p01; - ae_vector p11; - ae_vector p2; - double rlimit; - double xcnorm2; - ae_int_t hp01; - double hcpx0; - double xc0; - double xc1; - double omega; - double radius; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t d; - ae_int_t i00; - ae_int_t i01; - ae_int_t i10; - ae_int_t i11; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(y); - ae_vector_init(&cpx0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&cpx1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p01, 0, DT_INT, _state, ae_true); - ae_vector_init(&p11, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - - ae_assert(n0>0, "RBFGridCalc2: invalid value for N0 (N0<=0)!", _state); - ae_assert(n1>0, "RBFGridCalc2: invalid value for N1 (N1<=0)!", _state); - ae_assert(x0->cnt>=n0, "RBFGridCalc2: Length(X0)cnt>=n1, "RBFGridCalc2: Length(X1)ptr.pp_double[i][j] = 0; - } - } - if( (s->ny!=1||s->nx!=2)||s->nc==0 ) - { - ae_frame_leave(_state); - return; - } - - /* - *create and sort arrays - */ - ae_vector_set_length(&cpx0, n0, _state); - for(i=0; i<=n0-1; i++) - { - cpx0.ptr.p_double[i] = x0->ptr.p_double[i]; - } - tagsort(&cpx0, n0, &p01, &p2, _state); - ae_vector_set_length(&cpx1, n1, _state); - for(i=0; i<=n1-1; i++) - { - cpx1.ptr.p_double[i] = x1->ptr.p_double[i]; - } - tagsort(&cpx1, n1, &p11, &p2, _state); - - /* - *calculate function's value - */ - for(i=0; i<=s->nc-1; i++) - { - radius = s->wr.ptr.pp_double[i][0]; - for(d=0; d<=s->nl-1; d++) - { - omega = s->wr.ptr.pp_double[i][1+d]; - rlimit = radius*rbf_rbffarradius; - - /* - *search lower and upper indexes - */ - i00 = lowerbound(&cpx0, n0, s->xc.ptr.pp_double[i][0]-rlimit, _state); - i01 = upperbound(&cpx0, n0, s->xc.ptr.pp_double[i][0]+rlimit, _state); - i10 = lowerbound(&cpx1, n1, s->xc.ptr.pp_double[i][1]-rlimit, _state); - i11 = upperbound(&cpx1, n1, s->xc.ptr.pp_double[i][1]+rlimit, _state); - xc0 = s->xc.ptr.pp_double[i][0]; - xc1 = s->xc.ptr.pp_double[i][1]; - for(j=i00; j<=i01-1; j++) - { - hcpx0 = cpx0.ptr.p_double[j]; - hp01 = p01.ptr.p_int[j]; - for(k=i10; k<=i11-1; k++) - { - xcnorm2 = ae_sqr(hcpx0-xc0, _state)+ae_sqr(cpx1.ptr.p_double[k]-xc1, _state); - if( ae_fp_less_eq(xcnorm2,rlimit*rlimit) ) - { - y->ptr.pp_double[hp01][p11.ptr.p_int[k]] = y->ptr.pp_double[hp01][p11.ptr.p_int[k]]+ae_exp(-xcnorm2/ae_sqr(radius, _state), _state)*omega; - } - } - } - radius = 0.5*radius; - } - } - - /* - *add linear term - */ - for(i=0; i<=n0-1; i++) - { - for(j=0; j<=n1-1; j++) - { - y->ptr.pp_double[i][j] = y->ptr.pp_double[i][j]+s->v.ptr.pp_double[0][0]*x0->ptr.p_double[i]+s->v.ptr.pp_double[0][1]*x1->ptr.p_double[j]+s->v.ptr.pp_double[0][rbf_mxnx]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function "unpacks" RBF model by extracting its coefficients. - -INPUT PARAMETERS: - S - RBF model - -OUTPUT PARAMETERS: - NX - dimensionality of argument - NY - dimensionality of the target function - XWR - model information, array[NC,NX+NY+1]. - One row of the array corresponds to one basis function: - * first NX columns - coordinates of the center - * next NY columns - weights, one per dimension of the - function being modelled - * last column - radius, same for all dimensions of - the function being modelled - NC - number of the centers - V - polynomial term , array[NY,NX+1]. One row per one - dimension of the function being modelled. First NX - elements are linear coefficients, V[NX] is equal to the - constant part. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfunpack(rbfmodel* s, - ae_int_t* nx, - ae_int_t* ny, - /* Real */ ae_matrix* xwr, - ae_int_t* nc, - /* Real */ ae_matrix* v, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double rcur; - - *nx = 0; - *ny = 0; - ae_matrix_clear(xwr); - *nc = 0; - ae_matrix_clear(v); - - *nx = s->nx; - *ny = s->ny; - *nc = s->nc; - - /* - * Fill V - */ - ae_matrix_set_length(v, s->ny, s->nx+1, _state); - for(i=0; i<=s->ny-1; i++) - { - ae_v_move(&v->ptr.pp_double[i][0], 1, &s->v.ptr.pp_double[i][0], 1, ae_v_len(0,s->nx-1)); - v->ptr.pp_double[i][s->nx] = s->v.ptr.pp_double[i][rbf_mxnx]; - } - - /* - * Fill XWR and V - */ - if( *nc*s->nl>0 ) - { - ae_matrix_set_length(xwr, s->nc*s->nl, s->nx+s->ny+1, _state); - for(i=0; i<=s->nc-1; i++) - { - rcur = s->wr.ptr.pp_double[i][0]; - for(j=0; j<=s->nl-1; j++) - { - ae_v_move(&xwr->ptr.pp_double[i*s->nl+j][0], 1, &s->xc.ptr.pp_double[i][0], 1, ae_v_len(0,s->nx-1)); - ae_v_move(&xwr->ptr.pp_double[i*s->nl+j][s->nx], 1, &s->wr.ptr.pp_double[i][1+j*s->ny], 1, ae_v_len(s->nx,s->nx+s->ny-1)); - xwr->ptr.pp_double[i*s->nl+j][s->nx+s->ny] = rcur; - rcur = 0.5*rcur; - } - } - } -} - - -/************************************************************************* -Serializer: allocation - - -- ALGLIB -- - Copyright 02.02.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfalloc(ae_serializer* s, rbfmodel* model, ae_state *_state) -{ - - - - /* - * Header - */ - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - - /* - * Data - */ - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - ae_serializer_alloc_entry(s); - kdtreealloc(s, &model->tree, _state); - allocrealmatrix(s, &model->xc, -1, -1, _state); - allocrealmatrix(s, &model->wr, -1, -1, _state); - ae_serializer_alloc_entry(s); - allocrealmatrix(s, &model->v, -1, -1, _state); -} - - -/************************************************************************* -Serializer: serialization - - -- ALGLIB -- - Copyright 02.02.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfserialize(ae_serializer* s, rbfmodel* model, ae_state *_state) -{ - - - - /* - * Header - */ - ae_serializer_serialize_int(s, getrbfserializationcode(_state), _state); - ae_serializer_serialize_int(s, rbf_rbffirstversion, _state); - - /* - * Data - */ - ae_serializer_serialize_int(s, model->nx, _state); - ae_serializer_serialize_int(s, model->ny, _state); - ae_serializer_serialize_int(s, model->nc, _state); - ae_serializer_serialize_int(s, model->nl, _state); - kdtreeserialize(s, &model->tree, _state); - serializerealmatrix(s, &model->xc, -1, -1, _state); - serializerealmatrix(s, &model->wr, -1, -1, _state); - ae_serializer_serialize_double(s, model->rmax, _state); - serializerealmatrix(s, &model->v, -1, -1, _state); -} - - -/************************************************************************* -Serializer: unserialization - - -- ALGLIB -- - Copyright 02.02.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfunserialize(ae_serializer* s, rbfmodel* model, ae_state *_state) -{ - ae_int_t i0; - ae_int_t i1; - ae_int_t nx; - ae_int_t ny; - - _rbfmodel_clear(model); - - - /* - * Header - */ - ae_serializer_unserialize_int(s, &i0, _state); - ae_assert(i0==getrbfserializationcode(_state), "RBFUnserialize: stream header corrupted", _state); - ae_serializer_unserialize_int(s, &i1, _state); - ae_assert(i1==rbf_rbffirstversion, "RBFUnserialize: stream header corrupted", _state); - - /* - * Unserialize primary model parameters, initialize model. - * - * It is necessary to call RBFCreate() because some internal fields - * which are NOT unserialized will need initialization. - */ - ae_serializer_unserialize_int(s, &nx, _state); - ae_serializer_unserialize_int(s, &ny, _state); - rbfcreate(nx, ny, model, _state); - ae_serializer_unserialize_int(s, &model->nc, _state); - ae_serializer_unserialize_int(s, &model->nl, _state); - kdtreeunserialize(s, &model->tree, _state); - unserializerealmatrix(s, &model->xc, _state); - unserializerealmatrix(s, &model->wr, _state); - ae_serializer_unserialize_double(s, &model->rmax, _state); - unserializerealmatrix(s, &model->v, _state); -} - - -/************************************************************************* -This function changes centers allocation algorithm to one which allocates -centers exactly at the dataset points (one input point = one center). This -function won't have effect until next call to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -static void rbf_rbfgridpoints(rbfmodel* s, ae_state *_state) -{ - - - s->gridtype = 2; -} - - -/************************************************************************* -This function changes radii calculation algorithm to one which makes -radius for I-th node equal to R[i]=DistNN[i]*Q, where: -* R[i] is a radius calculated by the algorithm -* DistNN[i] is distance from I-th center to its nearest neighbor center -* Q is a scale parameter, which should be within [0.75,1.50], with - recommended value equal to 1.0 -* after performing radii calculation, radii are transformed in order to - avoid situation when single outlier has very large radius and influences - many points across entire dataset. Transformation has following form: - new_r[i] = min(r[i],Z*median(r[])) - where r[i] is I-th radius, median() is a median radius across entire - dataset, Z is user-specified value which controls amount of deviation - from median radius. - -This function won't have effect until next call to RBFBuildModel(). - -The idea behind this algorithm is to choose radii corresponding to basis -functions is such way that I-th radius is approximately equal to distance -from I-th center to its nearest neighbor. In this case interactions with -distant points will be insignificant, and we will get well conditioned -basis. - -Properties of this basis depend on the value of Q: -* Q<0.75 will give perfectly conditioned basis, but terrible smoothness - properties (RBF interpolant will have sharp peaks around function values) -* Q>1.5 will lead to badly conditioned systems and slow convergence of the - underlying linear solver (although smoothness will be very good) -* Q around 1.0 gives good balance between smoothness and condition number - - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Q - radius coefficient, Q>0 - Z - z-parameter, Z>0 - -Default value of Q is equal to 1.0 -Default value of Z is equal to 5.0 - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -static void rbf_rbfradnn(rbfmodel* s, - double q, - double z, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(q, _state)&&ae_fp_greater(q,0), "RBFRadNN: Q<=0, infinite or NAN", _state); - ae_assert(ae_isfinite(z, _state)&&ae_fp_greater(z,0), "RBFRadNN: Z<=0, infinite or NAN", _state); - s->fixrad = ae_false; - s->radvalue = q; - s->radzvalue = z; -} - - -static ae_bool rbf_buildlinearmodel(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t ny, - ae_int_t modeltype, - /* Real */ ae_matrix* v, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmpy; - ae_matrix a; - double scaling; - ae_vector shifting; - double mn; - double mx; - ae_vector c; - lsfitreport rep; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t info; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(v); - ae_vector_init(&tmpy, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&shifting, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c, 0, DT_REAL, _state, ae_true); - _lsfitreport_init(&rep, _state, ae_true); - - ae_assert(n>=0, "BuildLinearModel: N<0", _state); - ae_assert(ny>0, "BuildLinearModel: NY<=0", _state); - - /* - * Handle degenerate case (N=0) - */ - result = ae_true; - ae_matrix_set_length(v, ny, rbf_mxnx+1, _state); - if( n==0 ) - { - for(j=0; j<=rbf_mxnx; j++) - { - for(i=0; i<=ny-1; i++) - { - v->ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return result; - } - - /* - * Allocate temporaries - */ - ae_vector_set_length(&tmpy, n, _state); - - /* - * General linear model. - */ - if( modeltype==1 ) - { - - /* - * Calculate scaling/shifting, transform variables, prepare LLS problem - */ - ae_matrix_set_length(&a, n, rbf_mxnx+1, _state); - ae_vector_set_length(&shifting, rbf_mxnx, _state); - scaling = 0; - for(i=0; i<=rbf_mxnx-1; i++) - { - mn = x->ptr.pp_double[0][i]; - mx = mn; - for(j=1; j<=n-1; j++) - { - if( ae_fp_greater(mn,x->ptr.pp_double[j][i]) ) - { - mn = x->ptr.pp_double[j][i]; - } - if( ae_fp_less(mx,x->ptr.pp_double[j][i]) ) - { - mx = x->ptr.pp_double[j][i]; - } - } - scaling = ae_maxreal(scaling, mx-mn, _state); - shifting.ptr.p_double[i] = 0.5*(mx+mn); - } - if( ae_fp_eq(scaling,0) ) - { - scaling = 1; - } - else - { - scaling = 0.5*scaling; - } - for(i=0; i<=n-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - a.ptr.pp_double[i][j] = (x->ptr.pp_double[i][j]-shifting.ptr.p_double[j])/scaling; - } - } - for(i=0; i<=n-1; i++) - { - a.ptr.pp_double[i][rbf_mxnx] = 1; - } - - /* - * Solve linear system in transformed variables, make backward - */ - for(i=0; i<=ny-1; i++) - { - for(j=0; j<=n-1; j++) - { - tmpy.ptr.p_double[j] = y->ptr.pp_double[j][i]; - } - lsfitlinear(&tmpy, &a, n, rbf_mxnx+1, &info, &c, &rep, _state); - if( info<=0 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - for(j=0; j<=rbf_mxnx-1; j++) - { - v->ptr.pp_double[i][j] = c.ptr.p_double[j]/scaling; - } - v->ptr.pp_double[i][rbf_mxnx] = c.ptr.p_double[rbf_mxnx]; - for(j=0; j<=rbf_mxnx-1; j++) - { - v->ptr.pp_double[i][rbf_mxnx] = v->ptr.pp_double[i][rbf_mxnx]-shifting.ptr.p_double[j]*v->ptr.pp_double[i][j]; - } - for(j=0; j<=n-1; j++) - { - for(k=0; k<=rbf_mxnx-1; k++) - { - y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-x->ptr.pp_double[j][k]*v->ptr.pp_double[i][k]; - } - y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-v->ptr.pp_double[i][rbf_mxnx]; - } - } - ae_frame_leave(_state); - return result; - } - - /* - * Constant model, very simple - */ - if( modeltype==2 ) - { - for(i=0; i<=ny-1; i++) - { - for(j=0; j<=rbf_mxnx; j++) - { - v->ptr.pp_double[i][j] = 0; - } - for(j=0; j<=n-1; j++) - { - v->ptr.pp_double[i][rbf_mxnx] = v->ptr.pp_double[i][rbf_mxnx]+y->ptr.pp_double[j][i]; - } - if( n>0 ) - { - v->ptr.pp_double[i][rbf_mxnx] = v->ptr.pp_double[i][rbf_mxnx]/n; - } - for(j=0; j<=n-1; j++) - { - y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-v->ptr.pp_double[i][rbf_mxnx]; - } - } - ae_frame_leave(_state); - return result; - } - - /* - * Zero model - */ - ae_assert(modeltype==3, "BuildLinearModel: unknown model type", _state); - for(i=0; i<=ny-1; i++) - { - for(j=0; j<=rbf_mxnx; j++) - { - v->ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return result; -} - - -static void rbf_buildrbfmodellsqr(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - /* Real */ ae_matrix* xc, - /* Real */ ae_vector* r, - ae_int_t n, - ae_int_t nc, - ae_int_t ny, - kdtree* pointstree, - kdtree* centerstree, - double epsort, - double epserr, - ae_int_t maxits, - ae_int_t* gnnz, - ae_int_t* snnz, - /* Real */ ae_matrix* w, - ae_int_t* info, - ae_int_t* iterationscount, - ae_int_t* nmv, - ae_state *_state) -{ - ae_frame _frame_block; - linlsqrstate state; - linlsqrreport lsqrrep; - sparsematrix spg; - sparsematrix sps; - ae_vector nearcenterscnt; - ae_vector nearpointscnt; - ae_vector skipnearpointscnt; - ae_vector farpointscnt; - ae_int_t maxnearcenterscnt; - ae_int_t maxnearpointscnt; - ae_int_t maxfarpointscnt; - ae_int_t sumnearcenterscnt; - ae_int_t sumnearpointscnt; - ae_int_t sumfarpointscnt; - double maxrad; - ae_vector pointstags; - ae_vector centerstags; - ae_matrix nearpoints; - ae_matrix nearcenters; - ae_matrix farpoints; - ae_int_t tmpi; - ae_int_t pointscnt; - ae_int_t centerscnt; - ae_vector xcx; - ae_vector tmpy; - ae_vector tc; - ae_vector g; - ae_vector c; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t sind; - ae_matrix a; - double vv; - double vx; - double vy; - double vz; - double vr; - double gnorm2; - ae_vector tmp0; - ae_vector tmp1; - ae_vector tmp2; - double fx; - ae_matrix xx; - ae_matrix cx; - double mrad; - - ae_frame_make(_state, &_frame_block); - *gnnz = 0; - *snnz = 0; - ae_matrix_clear(w); - *info = 0; - *iterationscount = 0; - *nmv = 0; - _linlsqrstate_init(&state, _state, ae_true); - _linlsqrreport_init(&lsqrrep, _state, ae_true); - _sparsematrix_init(&spg, _state, ae_true); - _sparsematrix_init(&sps, _state, ae_true); - ae_vector_init(&nearcenterscnt, 0, DT_INT, _state, ae_true); - ae_vector_init(&nearpointscnt, 0, DT_INT, _state, ae_true); - ae_vector_init(&skipnearpointscnt, 0, DT_INT, _state, ae_true); - ae_vector_init(&farpointscnt, 0, DT_INT, _state, ae_true); - ae_vector_init(&pointstags, 0, DT_INT, _state, ae_true); - ae_vector_init(¢erstags, 0, DT_INT, _state, ae_true); - ae_matrix_init(&nearpoints, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&nearcenters, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&farpoints, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xcx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpy, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&g, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp2, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xx, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&cx, 0, 0, DT_REAL, _state, ae_true); - - - /* - * Handle special cases: NC=0 - */ - if( nc==0 ) - { - *info = 1; - *iterationscount = 0; - *nmv = 0; - ae_frame_leave(_state); - return; - } - - /* - * Prepare for general case, NC>0 - */ - ae_vector_set_length(&xcx, rbf_mxnx, _state); - ae_vector_set_length(&pointstags, n, _state); - ae_vector_set_length(¢erstags, nc, _state); - *info = -1; - *iterationscount = 0; - *nmv = 0; - - /* - * This block prepares quantities used to compute approximate cardinal basis functions (ACBFs): - * * NearCentersCnt[] - array[NC], whose elements store number of near centers used to build ACBF - * * NearPointsCnt[] - array[NC], number of near points used to build ACBF - * * FarPointsCnt[] - array[NC], number of far points (ones where ACBF is nonzero) - * * MaxNearCentersCnt - max(NearCentersCnt) - * * MaxNearPointsCnt - max(NearPointsCnt) - * * SumNearCentersCnt - sum(NearCentersCnt) - * * SumNearPointsCnt - sum(NearPointsCnt) - * * SumFarPointsCnt - sum(FarPointsCnt) - */ - ae_vector_set_length(&nearcenterscnt, nc, _state); - ae_vector_set_length(&nearpointscnt, nc, _state); - ae_vector_set_length(&skipnearpointscnt, nc, _state); - ae_vector_set_length(&farpointscnt, nc, _state); - maxnearcenterscnt = 0; - maxnearpointscnt = 0; - maxfarpointscnt = 0; - sumnearcenterscnt = 0; - sumnearpointscnt = 0; - sumfarpointscnt = 0; - for(i=0; i<=nc-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xcx.ptr.p_double[j] = xc->ptr.pp_double[i][j]; - } - - /* - * Determine number of near centers and maximum radius of near centers - */ - nearcenterscnt.ptr.p_int[i] = kdtreequeryrnn(centerstree, &xcx, r->ptr.p_double[i]*rbf_rbfnearradius, ae_true, _state); - kdtreequeryresultstags(centerstree, ¢erstags, _state); - maxrad = 0; - for(j=0; j<=nearcenterscnt.ptr.p_int[i]-1; j++) - { - maxrad = ae_maxreal(maxrad, ae_fabs(r->ptr.p_double[centerstags.ptr.p_int[j]], _state), _state); - } - - /* - * Determine number of near points (ones which used to build ACBF) - * and skipped points (the most near points which are NOT used to build ACBF - * and are NOT included in the near points count - */ - skipnearpointscnt.ptr.p_int[i] = kdtreequeryrnn(pointstree, &xcx, 0.1*r->ptr.p_double[i], ae_true, _state); - nearpointscnt.ptr.p_int[i] = kdtreequeryrnn(pointstree, &xcx, (r->ptr.p_double[i]+maxrad)*rbf_rbfnearradius, ae_true, _state)-skipnearpointscnt.ptr.p_int[i]; - ae_assert(nearpointscnt.ptr.p_int[i]>=0, "BuildRBFModelLSQR: internal error", _state); - - /* - * Determine number of far points - */ - farpointscnt.ptr.p_int[i] = kdtreequeryrnn(pointstree, &xcx, ae_maxreal(r->ptr.p_double[i]*rbf_rbfnearradius+maxrad*rbf_rbffarradius, r->ptr.p_double[i]*rbf_rbffarradius, _state), ae_true, _state); - - /* - * calculate sum and max, make some basic checks - */ - ae_assert(nearcenterscnt.ptr.p_int[i]>0, "BuildRBFModelLSQR: internal error", _state); - maxnearcenterscnt = ae_maxint(maxnearcenterscnt, nearcenterscnt.ptr.p_int[i], _state); - maxnearpointscnt = ae_maxint(maxnearpointscnt, nearpointscnt.ptr.p_int[i], _state); - maxfarpointscnt = ae_maxint(maxfarpointscnt, farpointscnt.ptr.p_int[i], _state); - sumnearcenterscnt = sumnearcenterscnt+nearcenterscnt.ptr.p_int[i]; - sumnearpointscnt = sumnearpointscnt+nearpointscnt.ptr.p_int[i]; - sumfarpointscnt = sumfarpointscnt+farpointscnt.ptr.p_int[i]; - } - *snnz = sumnearcenterscnt; - *gnnz = sumfarpointscnt; - ae_assert(maxnearcenterscnt>0, "BuildRBFModelLSQR: internal error", _state); - - /* - * Allocate temporaries. - * - * NOTE: we want to avoid allocation of zero-size arrays, so we - * use max(desired_size,1) instead of desired_size when performing - * memory allocation. - */ - ae_matrix_set_length(&a, maxnearpointscnt+maxnearcenterscnt, maxnearcenterscnt, _state); - ae_vector_set_length(&tmpy, maxnearpointscnt+maxnearcenterscnt, _state); - ae_vector_set_length(&g, maxnearcenterscnt, _state); - ae_vector_set_length(&c, maxnearcenterscnt, _state); - ae_matrix_set_length(&nearcenters, maxnearcenterscnt, rbf_mxnx, _state); - ae_matrix_set_length(&nearpoints, ae_maxint(maxnearpointscnt, 1, _state), rbf_mxnx, _state); - ae_matrix_set_length(&farpoints, ae_maxint(maxfarpointscnt, 1, _state), rbf_mxnx, _state); - - /* - * fill matrix SpG - */ - sparsecreate(n, nc, *gnnz, &spg, _state); - sparsecreate(nc, nc, *snnz, &sps, _state); - for(i=0; i<=nc-1; i++) - { - centerscnt = nearcenterscnt.ptr.p_int[i]; - - /* - * main center - */ - for(j=0; j<=rbf_mxnx-1; j++) - { - xcx.ptr.p_double[j] = xc->ptr.pp_double[i][j]; - } - - /* - * center's tree - */ - tmpi = kdtreequeryknn(centerstree, &xcx, centerscnt, ae_true, _state); - ae_assert(tmpi==centerscnt, "BuildRBFModelLSQR: internal error", _state); - kdtreequeryresultsx(centerstree, &cx, _state); - kdtreequeryresultstags(centerstree, ¢erstags, _state); - - /* - * point's tree - */ - mrad = 0; - for(j=0; j<=centerscnt-1; j++) - { - mrad = ae_maxreal(mrad, r->ptr.p_double[centerstags.ptr.p_int[j]], _state); - } - - /* - * we need to be sure that 'CTree' contains - * at least one side center - */ - sparseset(&sps, i, i, 1, _state); - c.ptr.p_double[0] = 1.0; - for(j=1; j<=centerscnt-1; j++) - { - c.ptr.p_double[j] = 0.0; - } - if( centerscnt>1&&nearpointscnt.ptr.p_int[i]>0 ) - { - - /* - * first KDTree request for points - */ - pointscnt = nearpointscnt.ptr.p_int[i]; - tmpi = kdtreequeryknn(pointstree, &xcx, skipnearpointscnt.ptr.p_int[i]+nearpointscnt.ptr.p_int[i], ae_true, _state); - ae_assert(tmpi==skipnearpointscnt.ptr.p_int[i]+nearpointscnt.ptr.p_int[i], "BuildRBFModelLSQR: internal error", _state); - kdtreequeryresultsx(pointstree, &xx, _state); - sind = skipnearpointscnt.ptr.p_int[i]; - for(j=0; j<=pointscnt-1; j++) - { - vx = xx.ptr.pp_double[sind+j][0]; - vy = xx.ptr.pp_double[sind+j][1]; - vz = xx.ptr.pp_double[sind+j][2]; - for(k=0; k<=centerscnt-1; k++) - { - vr = 0.0; - vv = vx-cx.ptr.pp_double[k][0]; - vr = vr+vv*vv; - vv = vy-cx.ptr.pp_double[k][1]; - vr = vr+vv*vv; - vv = vz-cx.ptr.pp_double[k][2]; - vr = vr+vv*vv; - vv = r->ptr.p_double[centerstags.ptr.p_int[k]]; - a.ptr.pp_double[j][k] = ae_exp(-vr/(vv*vv), _state); - } - } - for(j=0; j<=centerscnt-1; j++) - { - g.ptr.p_double[j] = ae_exp(-(ae_sqr(xcx.ptr.p_double[0]-cx.ptr.pp_double[j][0], _state)+ae_sqr(xcx.ptr.p_double[1]-cx.ptr.pp_double[j][1], _state)+ae_sqr(xcx.ptr.p_double[2]-cx.ptr.pp_double[j][2], _state))/ae_sqr(r->ptr.p_double[centerstags.ptr.p_int[j]], _state), _state); - } - - /* - * calculate the problem - */ - gnorm2 = ae_v_dotproduct(&g.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1)); - for(j=0; j<=pointscnt-1; j++) - { - vv = ae_v_dotproduct(&a.ptr.pp_double[j][0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1)); - vv = vv/gnorm2; - tmpy.ptr.p_double[j] = -vv; - ae_v_subd(&a.ptr.pp_double[j][0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1), vv); - } - for(j=pointscnt; j<=pointscnt+centerscnt-1; j++) - { - for(k=0; k<=centerscnt-1; k++) - { - a.ptr.pp_double[j][k] = 0.0; - } - a.ptr.pp_double[j][j-pointscnt] = 1.0E-6; - tmpy.ptr.p_double[j] = 0.0; - } - fblssolvels(&a, &tmpy, pointscnt+centerscnt, centerscnt, &tmp0, &tmp1, &tmp2, _state); - ae_v_move(&c.ptr.p_double[0], 1, &tmpy.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1)); - vv = ae_v_dotproduct(&g.ptr.p_double[0], 1, &c.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1)); - vv = vv/gnorm2; - ae_v_subd(&c.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1), vv); - vv = 1/gnorm2; - ae_v_addd(&c.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,centerscnt-1), vv); - for(j=0; j<=centerscnt-1; j++) - { - sparseset(&sps, i, centerstags.ptr.p_int[j], c.ptr.p_double[j], _state); - } - } - - /* - * second KDTree request for points - */ - pointscnt = farpointscnt.ptr.p_int[i]; - tmpi = kdtreequeryknn(pointstree, &xcx, pointscnt, ae_true, _state); - ae_assert(tmpi==pointscnt, "BuildRBFModelLSQR: internal error", _state); - kdtreequeryresultsx(pointstree, &xx, _state); - kdtreequeryresultstags(pointstree, &pointstags, _state); - - /* - *fill SpG matrix - */ - for(j=0; j<=pointscnt-1; j++) - { - fx = 0; - vx = xx.ptr.pp_double[j][0]; - vy = xx.ptr.pp_double[j][1]; - vz = xx.ptr.pp_double[j][2]; - for(k=0; k<=centerscnt-1; k++) - { - vr = 0.0; - vv = vx-cx.ptr.pp_double[k][0]; - vr = vr+vv*vv; - vv = vy-cx.ptr.pp_double[k][1]; - vr = vr+vv*vv; - vv = vz-cx.ptr.pp_double[k][2]; - vr = vr+vv*vv; - vv = r->ptr.p_double[centerstags.ptr.p_int[k]]; - vv = vv*vv; - fx = fx+c.ptr.p_double[k]*ae_exp(-vr/vv, _state); - } - sparseset(&spg, pointstags.ptr.p_int[j], i, fx, _state); - } - } - sparseconverttocrs(&spg, _state); - sparseconverttocrs(&sps, _state); - - /* - * solve by LSQR method - */ - ae_vector_set_length(&tmpy, n, _state); - ae_vector_set_length(&tc, nc, _state); - ae_matrix_set_length(w, nc, ny, _state); - linlsqrcreate(n, nc, &state, _state); - linlsqrsetcond(&state, epsort, epserr, maxits, _state); - for(i=0; i<=ny-1; i++) - { - for(j=0; j<=n-1; j++) - { - tmpy.ptr.p_double[j] = y->ptr.pp_double[j][i]; - } - linlsqrsolvesparse(&state, &spg, &tmpy, _state); - linlsqrresults(&state, &c, &lsqrrep, _state); - if( lsqrrep.terminationtype<=0 ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - sparsemtv(&sps, &c, &tc, _state); - for(j=0; j<=nc-1; j++) - { - w->ptr.pp_double[j][i] = tc.ptr.p_double[j]; - } - *iterationscount = *iterationscount+lsqrrep.iterationscount; - *nmv = *nmv+lsqrrep.nmv; - } - *info = 1; - ae_frame_leave(_state); -} - - -static void rbf_buildrbfmlayersmodellsqr(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - /* Real */ ae_matrix* xc, - double rval, - /* Real */ ae_vector* r, - ae_int_t n, - ae_int_t* nc, - ae_int_t ny, - ae_int_t nlayers, - kdtree* centerstree, - double epsort, - double epserr, - ae_int_t maxits, - double lambdav, - ae_int_t* annz, - /* Real */ ae_matrix* w, - ae_int_t* info, - ae_int_t* iterationscount, - ae_int_t* nmv, - ae_state *_state) -{ - ae_frame _frame_block; - linlsqrstate state; - linlsqrreport lsqrrep; - sparsematrix spa; - double anorm; - ae_vector omega; - ae_vector xx; - ae_vector tmpy; - ae_matrix cx; - double yval; - ae_int_t nec; - ae_vector centerstags; - ae_int_t layer; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double v; - double rmaxbefore; - double rmaxafter; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(xc); - ae_vector_clear(r); - *nc = 0; - *annz = 0; - ae_matrix_clear(w); - *info = 0; - *iterationscount = 0; - *nmv = 0; - _linlsqrstate_init(&state, _state, ae_true); - _linlsqrreport_init(&lsqrrep, _state, ae_true); - _sparsematrix_init(&spa, _state, ae_true); - ae_vector_init(&omega, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpy, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&cx, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(¢erstags, 0, DT_INT, _state, ae_true); - - ae_assert(nlayers>=0, "BuildRBFMLayersModelLSQR: invalid argument(NLayers<0)", _state); - ae_assert(n>=0, "BuildRBFMLayersModelLSQR: invalid argument(N<0)", _state); - ae_assert(rbf_mxnx>0&&rbf_mxnx<=3, "BuildRBFMLayersModelLSQR: internal error(invalid global const MxNX: either MxNX<=0 or MxNX>3)", _state); - *annz = 0; - if( n==0||nlayers==0 ) - { - *info = 1; - *iterationscount = 0; - *nmv = 0; - ae_frame_leave(_state); - return; - } - *nc = n*nlayers; - ae_vector_set_length(&xx, rbf_mxnx, _state); - ae_vector_set_length(¢erstags, n, _state); - ae_matrix_set_length(xc, *nc, rbf_mxnx, _state); - ae_vector_set_length(r, *nc, _state); - for(i=0; i<=*nc-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xc->ptr.pp_double[i][j] = x->ptr.pp_double[i%n][j]; - } - } - for(i=0; i<=*nc-1; i++) - { - r->ptr.p_double[i] = rval/ae_pow(2, i/n, _state); - } - for(i=0; i<=n-1; i++) - { - centerstags.ptr.p_int[i] = i; - } - kdtreebuildtagged(xc, ¢erstags, n, rbf_mxnx, 0, 2, centerstree, _state); - ae_vector_set_length(&omega, n, _state); - ae_vector_set_length(&tmpy, n, _state); - ae_matrix_set_length(w, *nc, ny, _state); - *info = -1; - *iterationscount = 0; - *nmv = 0; - linlsqrcreate(n, n, &state, _state); - linlsqrsetcond(&state, epsort, epserr, maxits, _state); - linlsqrsetlambdai(&state, 1.0E-6, _state); - - /* - * calculate number of non-zero elements for sparse matrix - */ - for(i=0; i<=n-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xx.ptr.p_double[j] = x->ptr.pp_double[i][j]; - } - *annz = *annz+kdtreequeryrnn(centerstree, &xx, r->ptr.p_double[0]*rbf_rbfmlradius, ae_true, _state); - } - for(layer=0; layer<=nlayers-1; layer++) - { - - /* - * Fill sparse matrix, calculate norm(A) - */ - anorm = 0.0; - sparsecreate(n, n, *annz, &spa, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=rbf_mxnx-1; j++) - { - xx.ptr.p_double[j] = x->ptr.pp_double[i][j]; - } - nec = kdtreequeryrnn(centerstree, &xx, r->ptr.p_double[layer*n]*rbf_rbfmlradius, ae_true, _state); - kdtreequeryresultsx(centerstree, &cx, _state); - kdtreequeryresultstags(centerstree, ¢erstags, _state); - for(j=0; j<=nec-1; j++) - { - v = ae_exp(-(ae_sqr(xx.ptr.p_double[0]-cx.ptr.pp_double[j][0], _state)+ae_sqr(xx.ptr.p_double[1]-cx.ptr.pp_double[j][1], _state)+ae_sqr(xx.ptr.p_double[2]-cx.ptr.pp_double[j][2], _state))/ae_sqr(r->ptr.p_double[layer*n+centerstags.ptr.p_int[j]], _state), _state); - sparseset(&spa, i, centerstags.ptr.p_int[j], v, _state); - anorm = anorm+ae_sqr(v, _state); - } - } - anorm = ae_sqrt(anorm, _state); - sparseconverttocrs(&spa, _state); - - /* - * Calculate maximum residual before adding new layer. - * This value is not used by algorithm, the only purpose is to make debugging easier. - */ - rmaxbefore = 0.0; - for(j=0; j<=n-1; j++) - { - for(i=0; i<=ny-1; i++) - { - rmaxbefore = ae_maxreal(rmaxbefore, ae_fabs(y->ptr.pp_double[j][i], _state), _state); - } - } - - /* - * Process NY dimensions of the target function - */ - for(i=0; i<=ny-1; i++) - { - for(j=0; j<=n-1; j++) - { - tmpy.ptr.p_double[j] = y->ptr.pp_double[j][i]; - } - - /* - * calculate Omega for current layer - */ - linlsqrsetlambdai(&state, lambdav*anorm/n, _state); - linlsqrsolvesparse(&state, &spa, &tmpy, _state); - linlsqrresults(&state, &omega, &lsqrrep, _state); - if( lsqrrep.terminationtype<=0 ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - - /* - * calculate error for current layer - */ - for(j=0; j<=n-1; j++) - { - yval = 0; - for(k=0; k<=rbf_mxnx-1; k++) - { - xx.ptr.p_double[k] = x->ptr.pp_double[j][k]; - } - nec = kdtreequeryrnn(centerstree, &xx, r->ptr.p_double[layer*n]*rbf_rbffarradius, ae_true, _state); - kdtreequeryresultsx(centerstree, &cx, _state); - kdtreequeryresultstags(centerstree, ¢erstags, _state); - for(k=0; k<=nec-1; k++) - { - yval = yval+omega.ptr.p_double[centerstags.ptr.p_int[k]]*ae_exp(-(ae_sqr(xx.ptr.p_double[0]-cx.ptr.pp_double[k][0], _state)+ae_sqr(xx.ptr.p_double[1]-cx.ptr.pp_double[k][1], _state)+ae_sqr(xx.ptr.p_double[2]-cx.ptr.pp_double[k][2], _state))/ae_sqr(r->ptr.p_double[layer*n+centerstags.ptr.p_int[k]], _state), _state); - } - y->ptr.pp_double[j][i] = y->ptr.pp_double[j][i]-yval; - } - - /* - * write Omega in out parameter W - */ - for(j=0; j<=n-1; j++) - { - w->ptr.pp_double[layer*n+j][i] = omega.ptr.p_double[j]; - } - *iterationscount = *iterationscount+lsqrrep.iterationscount; - *nmv = *nmv+lsqrrep.nmv; - } - - /* - * Calculate maximum residual before adding new layer. - * This value is not used by algorithm, the only purpose is to make debugging easier. - */ - rmaxafter = 0.0; - for(j=0; j<=n-1; j++) - { - for(i=0; i<=ny-1; i++) - { - rmaxafter = ae_maxreal(rmaxafter, ae_fabs(y->ptr.pp_double[j][i], _state), _state); - } - } - } - *info = 1; - ae_frame_leave(_state); -} - - -ae_bool _rbfmodel_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - rbfmodel *p = (rbfmodel*)_p; - ae_touch_ptr((void*)p); - if( !_kdtree_init(&p->tree, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->xc, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->wr, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->v, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->x, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->y, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->calcbufxcx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->calcbufx, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->calcbuftags, 0, DT_INT, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _rbfmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - rbfmodel *dst = (rbfmodel*)_dst; - rbfmodel *src = (rbfmodel*)_src; - dst->ny = src->ny; - dst->nx = src->nx; - dst->nc = src->nc; - dst->nl = src->nl; - if( !_kdtree_init_copy(&dst->tree, &src->tree, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->xc, &src->xc, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->wr, &src->wr, _state, make_automatic) ) - return ae_false; - dst->rmax = src->rmax; - if( !ae_matrix_init_copy(&dst->v, &src->v, _state, make_automatic) ) - return ae_false; - dst->gridtype = src->gridtype; - dst->fixrad = src->fixrad; - dst->lambdav = src->lambdav; - dst->radvalue = src->radvalue; - dst->radzvalue = src->radzvalue; - dst->nlayers = src->nlayers; - dst->aterm = src->aterm; - dst->algorithmtype = src->algorithmtype; - dst->epsort = src->epsort; - dst->epserr = src->epserr; - dst->maxits = src->maxits; - dst->h = src->h; - dst->n = src->n; - if( !ae_matrix_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->calcbufxcx, &src->calcbufxcx, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->calcbufx, &src->calcbufx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->calcbuftags, &src->calcbuftags, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _rbfmodel_clear(void* _p) -{ - rbfmodel *p = (rbfmodel*)_p; - ae_touch_ptr((void*)p); - _kdtree_clear(&p->tree); - ae_matrix_clear(&p->xc); - ae_matrix_clear(&p->wr); - ae_matrix_clear(&p->v); - ae_matrix_clear(&p->x); - ae_matrix_clear(&p->y); - ae_vector_clear(&p->calcbufxcx); - ae_matrix_clear(&p->calcbufx); - ae_vector_clear(&p->calcbuftags); -} - - -void _rbfmodel_destroy(void* _p) -{ - rbfmodel *p = (rbfmodel*)_p; - ae_touch_ptr((void*)p); - _kdtree_destroy(&p->tree); - ae_matrix_destroy(&p->xc); - ae_matrix_destroy(&p->wr); - ae_matrix_destroy(&p->v); - ae_matrix_destroy(&p->x); - ae_matrix_destroy(&p->y); - ae_vector_destroy(&p->calcbufxcx); - ae_matrix_destroy(&p->calcbufx); - ae_vector_destroy(&p->calcbuftags); -} - - -ae_bool _rbfreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - rbfreport *p = (rbfreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _rbfreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - rbfreport *dst = (rbfreport*)_dst; - rbfreport *src = (rbfreport*)_src; - dst->arows = src->arows; - dst->acols = src->acols; - dst->annz = src->annz; - dst->iterationscount = src->iterationscount; - dst->nmv = src->nmv; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _rbfreport_clear(void* _p) -{ - rbfreport *p = (rbfreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _rbfreport_destroy(void* _p) -{ - rbfreport *p = (rbfreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -This subroutine calculates the value of the bilinear or bicubic spline at -the given point X. - -Input parameters: - C - coefficients table. - Built by BuildBilinearSpline or BuildBicubicSpline. - X, Y- point - -Result: - S(x,y) - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -double spline2dcalc(spline2dinterpolant* c, - double x, - double y, - ae_state *_state) -{ - double v; - double vx; - double vy; - double vxy; - double result; - - - ae_assert(c->stype==-1||c->stype==-3, "Spline2DCalc: incorrect C (incorrect parameter C.SType)", _state); - ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DCalc: X or Y contains NaN or Infinite value", _state); - if( c->d!=1 ) - { - result = 0; - return result; - } - spline2ddiff(c, x, y, &v, &vx, &vy, &vxy, _state); - result = v; - return result; -} - - -/************************************************************************* -This subroutine calculates the value of the bilinear or bicubic spline at -the given point X and its derivatives. - -Input parameters: - C - spline interpolant. - X, Y- point - -Output parameters: - F - S(x,y) - FX - dS(x,y)/dX - FY - dS(x,y)/dY - FXY - d2S(x,y)/dXdY - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2ddiff(spline2dinterpolant* c, - double x, - double y, - double* f, - double* fx, - double* fy, - double* fxy, - ae_state *_state) -{ - double t; - double dt; - double u; - double du; - ae_int_t ix; - ae_int_t iy; - ae_int_t l; - ae_int_t r; - ae_int_t h; - ae_int_t s1; - ae_int_t s2; - ae_int_t s3; - ae_int_t s4; - ae_int_t sfx; - ae_int_t sfy; - ae_int_t sfxy; - double y1; - double y2; - double y3; - double y4; - double v; - double t0; - double t1; - double t2; - double t3; - double u0; - double u1; - double u2; - double u3; - - *f = 0; - *fx = 0; - *fy = 0; - *fxy = 0; - - ae_assert(c->stype==-1||c->stype==-3, "Spline2DDiff: incorrect C (incorrect parameter C.SType)", _state); - ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DDiff: X or Y contains NaN or Infinite value", _state); - - /* - * Prepare F, dF/dX, dF/dY, d2F/dXdY - */ - *f = 0; - *fx = 0; - *fy = 0; - *fxy = 0; - if( c->d!=1 ) - { - return; - } - - /* - * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included) - */ - l = 0; - r = c->n-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) ) - { - r = h; - } - else - { - l = h; - } - } - t = (x-c->x.ptr.p_double[l])/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]); - dt = 1.0/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]); - ix = l; - - /* - * Binary search in the [ y[0], ..., y[m-2] ] (y[m-1] is not included) - */ - l = 0; - r = c->m-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) ) - { - r = h; - } - else - { - l = h; - } - } - u = (y-c->y.ptr.p_double[l])/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]); - du = 1.0/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]); - iy = l; - - /* - * Bilinear interpolation - */ - if( c->stype==-1 ) - { - y1 = c->f.ptr.p_double[c->n*iy+ix]; - y2 = c->f.ptr.p_double[c->n*iy+(ix+1)]; - y3 = c->f.ptr.p_double[c->n*(iy+1)+(ix+1)]; - y4 = c->f.ptr.p_double[c->n*(iy+1)+ix]; - *f = (1-t)*(1-u)*y1+t*(1-u)*y2+t*u*y3+(1-t)*u*y4; - *fx = (-(1-u)*y1+(1-u)*y2+u*y3-u*y4)*dt; - *fy = (-(1-t)*y1-t*y2+t*y3+(1-t)*y4)*du; - *fxy = (y1-y2+y3-y4)*du*dt; - return; - } - - /* - * Bicubic interpolation - */ - if( c->stype==-3 ) - { - - /* - * Prepare info - */ - t0 = 1; - t1 = t; - t2 = ae_sqr(t, _state); - t3 = t*t2; - u0 = 1; - u1 = u; - u2 = ae_sqr(u, _state); - u3 = u*u2; - sfx = c->n*c->m; - sfy = 2*c->n*c->m; - sfxy = 3*c->n*c->m; - s1 = c->n*iy+ix; - s2 = c->n*iy+(ix+1); - s3 = c->n*(iy+1)+(ix+1); - s4 = c->n*(iy+1)+ix; - - /* - * Calculate - */ - v = c->f.ptr.p_double[s1]; - *f = *f+v*t0*u0; - v = c->f.ptr.p_double[sfy+s1]/du; - *f = *f+v*t0*u1; - *fy = *fy+v*t0*u0*du; - v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du; - *f = *f+v*t0*u2; - *fy = *fy+2*v*t0*u1*du; - v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du; - *f = *f+v*t0*u3; - *fy = *fy+3*v*t0*u2*du; - v = c->f.ptr.p_double[sfx+s1]/dt; - *f = *f+v*t1*u0; - *fx = *fx+v*t0*u0*dt; - v = c->f.ptr.p_double[sfxy+s1]/(dt*du); - *f = *f+v*t1*u1; - *fx = *fx+v*t0*u1*dt; - *fy = *fy+v*t1*u0*du; - *fxy = *fxy+v*t0*u0*dt*du; - v = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - *f = *f+v*t1*u2; - *fx = *fx+v*t0*u2*dt; - *fy = *fy+2*v*t1*u1*du; - *fxy = *fxy+2*v*t0*u1*dt*du; - v = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - *f = *f+v*t1*u3; - *fx = *fx+v*t0*u3*dt; - *fy = *fy+3*v*t1*u2*du; - *fxy = *fxy+3*v*t0*u2*dt*du; - v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt; - *f = *f+v*t2*u0; - *fx = *fx+2*v*t1*u0*dt; - v = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du); - *f = *f+v*t2*u1; - *fx = *fx+2*v*t1*u1*dt; - *fy = *fy+v*t2*u0*du; - *fxy = *fxy+2*v*t1*u0*dt*du; - v = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - *f = *f+v*t2*u2; - *fx = *fx+2*v*t1*u2*dt; - *fy = *fy+2*v*t2*u1*du; - *fxy = *fxy+4*v*t1*u1*dt*du; - v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - *f = *f+v*t2*u3; - *fx = *fx+2*v*t1*u3*dt; - *fy = *fy+3*v*t2*u2*du; - *fxy = *fxy+6*v*t1*u2*dt*du; - v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt; - *f = *f+v*t3*u0; - *fx = *fx+3*v*t2*u0*dt; - v = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du); - *f = *f+v*t3*u1; - *fx = *fx+3*v*t2*u1*dt; - *fy = *fy+v*t3*u0*du; - *fxy = *fxy+3*v*t2*u0*dt*du; - v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - *f = *f+v*t3*u2; - *fx = *fx+3*v*t2*u2*dt; - *fy = *fy+2*v*t3*u1*du; - *fxy = *fxy+6*v*t2*u1*dt*du; - v = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - *f = *f+v*t3*u3; - *fx = *fx+3*v*t2*u3*dt; - *fy = *fy+3*v*t3*u2*du; - *fxy = *fxy+9*v*t2*u2*dt*du; - return; - } -} - - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -Input parameters: - C - spline interpolant - AX, BX - transformation coefficients: x = A*t + B - AY, BY - transformation coefficients: y = A*u + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dlintransxy(spline2dinterpolant* c, - double ax, - double bx, - double ay, - double by, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_vector f; - ae_vector v; - ae_int_t i; - ae_int_t j; - ae_int_t k; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&f, 0, DT_REAL, _state, ae_true); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - - ae_assert(c->stype==-3||c->stype==-1, "Spline2DLinTransXY: incorrect C (incorrect parameter C.SType)", _state); - ae_assert(ae_isfinite(ax, _state), "Spline2DLinTransXY: AX is infinite or NaN", _state); - ae_assert(ae_isfinite(bx, _state), "Spline2DLinTransXY: BX is infinite or NaN", _state); - ae_assert(ae_isfinite(ay, _state), "Spline2DLinTransXY: AY is infinite or NaN", _state); - ae_assert(ae_isfinite(by, _state), "Spline2DLinTransXY: BY is infinite or NaN", _state); - ae_vector_set_length(&x, c->n, _state); - ae_vector_set_length(&y, c->m, _state); - ae_vector_set_length(&f, c->m*c->n*c->d, _state); - for(j=0; j<=c->n-1; j++) - { - x.ptr.p_double[j] = c->x.ptr.p_double[j]; - } - for(i=0; i<=c->m-1; i++) - { - y.ptr.p_double[i] = c->y.ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->n-1; j++) - { - for(k=0; k<=c->d-1; k++) - { - f.ptr.p_double[c->d*(i*c->n+j)+k] = c->f.ptr.p_double[c->d*(i*c->n+j)+k]; - } - } - } - - /* - * Handle different combinations of AX/AY - */ - if( ae_fp_eq(ax,0)&&ae_fp_neq(ay,0) ) - { - for(i=0; i<=c->m-1; i++) - { - spline2dcalcvbuf(c, bx, y.ptr.p_double[i], &v, _state); - y.ptr.p_double[i] = (y.ptr.p_double[i]-by)/ay; - for(j=0; j<=c->n-1; j++) - { - for(k=0; k<=c->d-1; k++) - { - f.ptr.p_double[c->d*(i*c->n+j)+k] = v.ptr.p_double[k]; - } - } - } - } - if( ae_fp_neq(ax,0)&&ae_fp_eq(ay,0) ) - { - for(j=0; j<=c->n-1; j++) - { - spline2dcalcvbuf(c, x.ptr.p_double[j], by, &v, _state); - x.ptr.p_double[j] = (x.ptr.p_double[j]-bx)/ax; - for(i=0; i<=c->m-1; i++) - { - for(k=0; k<=c->d-1; k++) - { - f.ptr.p_double[c->d*(i*c->n+j)+k] = v.ptr.p_double[k]; - } - } - } - } - if( ae_fp_neq(ax,0)&&ae_fp_neq(ay,0) ) - { - for(j=0; j<=c->n-1; j++) - { - x.ptr.p_double[j] = (x.ptr.p_double[j]-bx)/ax; - } - for(i=0; i<=c->m-1; i++) - { - y.ptr.p_double[i] = (y.ptr.p_double[i]-by)/ay; - } - } - if( ae_fp_eq(ax,0)&&ae_fp_eq(ay,0) ) - { - spline2dcalcvbuf(c, bx, by, &v, _state); - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->n-1; j++) - { - for(k=0; k<=c->d-1; k++) - { - f.ptr.p_double[c->d*(i*c->n+j)+k] = v.ptr.p_double[k]; - } - } - } - } - - /* - * Rebuild spline - */ - if( c->stype==-3 ) - { - spline2dbuildbicubicv(&x, c->n, &y, c->m, &f, c->d, c, _state); - } - if( c->stype==-1 ) - { - spline2dbuildbilinearv(&x, c->n, &y, c->m, &f, c->d, c, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -Input parameters: - C - spline interpolant. - A, B- transformation coefficients: S2(x,y) = A*S(x,y) + B - -Output parameters: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dlintransf(spline2dinterpolant* c, - double a, - double b, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_vector f; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&f, 0, DT_REAL, _state, ae_true); - - ae_assert(c->stype==-3||c->stype==-1, "Spline2DLinTransF: incorrect C (incorrect parameter C.SType)", _state); - ae_vector_set_length(&x, c->n, _state); - ae_vector_set_length(&y, c->m, _state); - ae_vector_set_length(&f, c->m*c->n*c->d, _state); - for(j=0; j<=c->n-1; j++) - { - x.ptr.p_double[j] = c->x.ptr.p_double[j]; - } - for(i=0; i<=c->m-1; i++) - { - y.ptr.p_double[i] = c->y.ptr.p_double[i]; - } - for(i=0; i<=c->m*c->n*c->d-1; i++) - { - f.ptr.p_double[i] = a*c->f.ptr.p_double[i]+b; - } - if( c->stype==-3 ) - { - spline2dbuildbicubicv(&x, c->n, &y, c->m, &f, c->d, c, _state); - } - if( c->stype==-1 ) - { - spline2dbuildbilinearv(&x, c->n, &y, c->m, &f, c->d, c, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine makes the copy of the spline model. - -Input parameters: - C - spline interpolant - -Output parameters: - CC - spline copy - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dcopy(spline2dinterpolant* c, - spline2dinterpolant* cc, - ae_state *_state) -{ - ae_int_t tblsize; - - _spline2dinterpolant_clear(cc); - - ae_assert(c->k==1||c->k==3, "Spline2DCopy: incorrect C (incorrect parameter C.K)", _state); - cc->k = c->k; - cc->n = c->n; - cc->m = c->m; - cc->d = c->d; - cc->stype = c->stype; - tblsize = -1; - if( c->stype==-3 ) - { - tblsize = 4*c->n*c->m*c->d; - } - if( c->stype==-1 ) - { - tblsize = c->n*c->m*c->d; - } - ae_assert(tblsize>0, "Spline2DCopy: internal error", _state); - ae_vector_set_length(&cc->x, cc->n, _state); - ae_vector_set_length(&cc->y, cc->m, _state); - ae_vector_set_length(&cc->f, tblsize, _state); - ae_v_move(&cc->x.ptr.p_double[0], 1, &c->x.ptr.p_double[0], 1, ae_v_len(0,cc->n-1)); - ae_v_move(&cc->y.ptr.p_double[0], 1, &c->y.ptr.p_double[0], 1, ae_v_len(0,cc->m-1)); - ae_v_move(&cc->f.ptr.p_double[0], 1, &c->f.ptr.p_double[0], 1, ae_v_len(0,tblsize-1)); -} - - -/************************************************************************* -Bicubic spline resampling - -Input parameters: - A - function values at the old grid, - array[0..OldHeight-1, 0..OldWidth-1] - OldHeight - old grid height, OldHeight>1 - OldWidth - old grid width, OldWidth>1 - NewHeight - new grid height, NewHeight>1 - NewWidth - new grid width, NewWidth>1 - -Output parameters: - B - function values at the new grid, - array[0..NewHeight-1, 0..NewWidth-1] - - -- ALGLIB routine -- - 15 May, 2007 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline2dresamplebicubic(/* Real */ ae_matrix* a, - ae_int_t oldheight, - ae_int_t oldwidth, - /* Real */ ae_matrix* b, - ae_int_t newheight, - ae_int_t newwidth, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix buf; - ae_vector x; - ae_vector y; - spline1dinterpolant c; - ae_int_t mw; - ae_int_t mh; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(b); - ae_matrix_init(&buf, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - _spline1dinterpolant_init(&c, _state, ae_true); - - ae_assert(oldwidth>1&&oldheight>1, "Spline2DResampleBicubic: width/height less than 1", _state); - ae_assert(newwidth>1&&newheight>1, "Spline2DResampleBicubic: width/height less than 1", _state); - - /* - * Prepare - */ - mw = ae_maxint(oldwidth, newwidth, _state); - mh = ae_maxint(oldheight, newheight, _state); - ae_matrix_set_length(b, newheight, newwidth, _state); - ae_matrix_set_length(&buf, oldheight, newwidth, _state); - ae_vector_set_length(&x, ae_maxint(mw, mh, _state), _state); - ae_vector_set_length(&y, ae_maxint(mw, mh, _state), _state); - - /* - * Horizontal interpolation - */ - for(i=0; i<=oldheight-1; i++) - { - - /* - * Fill X, Y - */ - for(j=0; j<=oldwidth-1; j++) - { - x.ptr.p_double[j] = (double)j/(double)(oldwidth-1); - y.ptr.p_double[j] = a->ptr.pp_double[i][j]; - } - - /* - * Interpolate and place result into temporary matrix - */ - spline1dbuildcubic(&x, &y, oldwidth, 0, 0.0, 0, 0.0, &c, _state); - for(j=0; j<=newwidth-1; j++) - { - buf.ptr.pp_double[i][j] = spline1dcalc(&c, (double)j/(double)(newwidth-1), _state); - } - } - - /* - * Vertical interpolation - */ - for(j=0; j<=newwidth-1; j++) - { - - /* - * Fill X, Y - */ - for(i=0; i<=oldheight-1; i++) - { - x.ptr.p_double[i] = (double)i/(double)(oldheight-1); - y.ptr.p_double[i] = buf.ptr.pp_double[i][j]; - } - - /* - * Interpolate and place result into B - */ - spline1dbuildcubic(&x, &y, oldheight, 0, 0.0, 0, 0.0, &c, _state); - for(i=0; i<=newheight-1; i++) - { - b->ptr.pp_double[i][j] = spline1dcalc(&c, (double)i/(double)(newheight-1), _state); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Bilinear spline resampling - -Input parameters: - A - function values at the old grid, - array[0..OldHeight-1, 0..OldWidth-1] - OldHeight - old grid height, OldHeight>1 - OldWidth - old grid width, OldWidth>1 - NewHeight - new grid height, NewHeight>1 - NewWidth - new grid width, NewWidth>1 - -Output parameters: - B - function values at the new grid, - array[0..NewHeight-1, 0..NewWidth-1] - - -- ALGLIB routine -- - 09.07.2007 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline2dresamplebilinear(/* Real */ ae_matrix* a, - ae_int_t oldheight, - ae_int_t oldwidth, - /* Real */ ae_matrix* b, - ae_int_t newheight, - ae_int_t newwidth, - ae_state *_state) -{ - ae_int_t l; - ae_int_t c; - double t; - double u; - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(b); - - ae_assert(oldwidth>1&&oldheight>1, "Spline2DResampleBilinear: width/height less than 1", _state); - ae_assert(newwidth>1&&newheight>1, "Spline2DResampleBilinear: width/height less than 1", _state); - ae_matrix_set_length(b, newheight, newwidth, _state); - for(i=0; i<=newheight-1; i++) - { - for(j=0; j<=newwidth-1; j++) - { - l = i*(oldheight-1)/(newheight-1); - if( l==oldheight-1 ) - { - l = oldheight-2; - } - u = (double)i/(double)(newheight-1)*(oldheight-1)-l; - c = j*(oldwidth-1)/(newwidth-1); - if( c==oldwidth-1 ) - { - c = oldwidth-2; - } - t = (double)(j*(oldwidth-1))/(double)(newwidth-1)-c; - b->ptr.pp_double[i][j] = (1-t)*(1-u)*a->ptr.pp_double[l][c]+t*(1-u)*a->ptr.pp_double[l][c+1]+t*u*a->ptr.pp_double[l+1][c+1]+(1-t)*u*a->ptr.pp_double[l+1][c]; - } - } -} - - -/************************************************************************* -This subroutine builds bilinear vector-valued spline. - -Input parameters: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - F - function values, array[0..M*N*D-1]: - * first D elements store D values at (X[0],Y[0]) - * next D elements store D values at (X[1],Y[0]) - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(J*N+I)...D*(J*N+I)+D-1]. - M,N - grid size, M>=2, N>=2 - D - vector dimension, D>=1 - -Output parameters: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbilinearv(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - /* Real */ ae_vector* f, - ae_int_t d, - spline2dinterpolant* c, - ae_state *_state) -{ - double t; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t i0; - - _spline2dinterpolant_clear(c); - - ae_assert(n>=2, "Spline2DBuildBilinearV: N is less then 2", _state); - ae_assert(m>=2, "Spline2DBuildBilinearV: M is less then 2", _state); - ae_assert(d>=1, "Spline2DBuildBilinearV: invalid argument D (D<1)", _state); - ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBilinearV: length of X or Y is too short (Length(X/Y)cnt>=k, "Spline2DBuildBilinearV: length of F is too short (Length(F)k = 1; - c->n = n; - c->m = m; - c->d = d; - c->stype = -1; - ae_vector_set_length(&c->x, c->n, _state); - ae_vector_set_length(&c->y, c->m, _state); - ae_vector_set_length(&c->f, k, _state); - for(i=0; i<=c->n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - c->y.ptr.p_double[i] = y->ptr.p_double[i]; - } - for(i=0; i<=k-1; i++) - { - c->f.ptr.p_double[i] = f->ptr.p_double[i]; - } - - /* - * Sort points - */ - for(j=0; j<=c->n-1; j++) - { - k = j; - for(i=j+1; i<=c->n-1; i++) - { - if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) ) - { - k = i; - } - } - if( k!=j ) - { - for(i=0; i<=c->m-1; i++) - { - for(i0=0; i0<=c->d-1; i0++) - { - t = c->f.ptr.p_double[c->d*(i*c->n+j)+i0]; - c->f.ptr.p_double[c->d*(i*c->n+j)+i0] = c->f.ptr.p_double[c->d*(i*c->n+k)+i0]; - c->f.ptr.p_double[c->d*(i*c->n+k)+i0] = t; - } - } - t = c->x.ptr.p_double[j]; - c->x.ptr.p_double[j] = c->x.ptr.p_double[k]; - c->x.ptr.p_double[k] = t; - } - } - for(i=0; i<=c->m-1; i++) - { - k = i; - for(j=i+1; j<=c->m-1; j++) - { - if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) ) - { - k = j; - } - } - if( k!=i ) - { - for(j=0; j<=c->n-1; j++) - { - for(i0=0; i0<=c->d-1; i0++) - { - t = c->f.ptr.p_double[c->d*(i*c->n+j)+i0]; - c->f.ptr.p_double[c->d*(i*c->n+j)+i0] = c->f.ptr.p_double[c->d*(k*c->n+j)+i0]; - c->f.ptr.p_double[c->d*(k*c->n+j)+i0] = t; - } - } - t = c->y.ptr.p_double[i]; - c->y.ptr.p_double[i] = c->y.ptr.p_double[k]; - c->y.ptr.p_double[k] = t; - } - } -} - - -/************************************************************************* -This subroutine builds bicubic vector-valued spline. - -Input parameters: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - F - function values, array[0..M*N*D-1]: - * first D elements store D values at (X[0],Y[0]) - * next D elements store D values at (X[1],Y[0]) - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(J*N+I)...D*(J*N+I)+D-1]. - M,N - grid size, M>=2, N>=2 - D - vector dimension, D>=1 - -Output parameters: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbicubicv(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - /* Real */ ae_vector* f, - ae_int_t d, - spline2dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _f; - ae_matrix tf; - ae_matrix dx; - ae_matrix dy; - ae_matrix dxy; - double t; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t di; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_f, f, _state, ae_true); - f = &_f; - _spline2dinterpolant_clear(c); - ae_matrix_init(&tf, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&dx, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&dy, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&dxy, 0, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=2, "Spline2DBuildBicubicV: N is less than 2", _state); - ae_assert(m>=2, "Spline2DBuildBicubicV: M is less than 2", _state); - ae_assert(d>=1, "Spline2DBuildBicubicV: invalid argument D (D<1)", _state); - ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBicubicV: length of X or Y is too short (Length(X/Y)cnt>=k, "Spline2DBuildBicubicV: length of F is too short (Length(F)k = 3; - c->d = d; - c->n = n; - c->m = m; - c->stype = -3; - k = 4*k; - ae_vector_set_length(&c->x, c->n, _state); - ae_vector_set_length(&c->y, c->m, _state); - ae_vector_set_length(&c->f, k, _state); - ae_matrix_set_length(&tf, c->m, c->n, _state); - for(i=0; i<=c->n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - c->y.ptr.p_double[i] = y->ptr.p_double[i]; - } - - /* - * Sort points - */ - for(j=0; j<=c->n-1; j++) - { - k = j; - for(i=j+1; i<=c->n-1; i++) - { - if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) ) - { - k = i; - } - } - if( k!=j ) - { - for(i=0; i<=c->m-1; i++) - { - for(di=0; di<=c->d-1; di++) - { - t = f->ptr.p_double[c->d*(i*c->n+j)+di]; - f->ptr.p_double[c->d*(i*c->n+j)+di] = f->ptr.p_double[c->d*(i*c->n+k)+di]; - f->ptr.p_double[c->d*(i*c->n+k)+di] = t; - } - } - t = c->x.ptr.p_double[j]; - c->x.ptr.p_double[j] = c->x.ptr.p_double[k]; - c->x.ptr.p_double[k] = t; - } - } - for(i=0; i<=c->m-1; i++) - { - k = i; - for(j=i+1; j<=c->m-1; j++) - { - if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) ) - { - k = j; - } - } - if( k!=i ) - { - for(j=0; j<=c->n-1; j++) - { - for(di=0; di<=c->d-1; di++) - { - t = f->ptr.p_double[c->d*(i*c->n+j)+di]; - f->ptr.p_double[c->d*(i*c->n+j)+di] = f->ptr.p_double[c->d*(k*c->n+j)+di]; - f->ptr.p_double[c->d*(k*c->n+j)+di] = t; - } - } - t = c->y.ptr.p_double[i]; - c->y.ptr.p_double[i] = c->y.ptr.p_double[k]; - c->y.ptr.p_double[k] = t; - } - } - for(di=0; di<=c->d-1; di++) - { - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->n-1; j++) - { - tf.ptr.pp_double[i][j] = f->ptr.p_double[c->d*(i*c->n+j)+di]; - } - } - spline2d_bicubiccalcderivatives(&tf, &c->x, &c->y, c->m, c->n, &dx, &dy, &dxy, _state); - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->n-1; j++) - { - k = c->d*(i*c->n+j)+di; - c->f.ptr.p_double[k] = tf.ptr.pp_double[i][j]; - c->f.ptr.p_double[c->n*c->m*c->d+k] = dx.ptr.pp_double[i][j]; - c->f.ptr.p_double[2*c->n*c->m*c->d+k] = dy.ptr.pp_double[i][j]; - c->f.ptr.p_double[3*c->n*c->m*c->d+k] = dxy.ptr.pp_double[i][j]; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y- point - F - output buffer, possibly preallocated array. In case array size - is large enough to store result, it is not reallocated. Array - which is too short will be reallocated - -OUTPUT PARAMETERS: - F - array[D] (or larger) which stores function values - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dcalcvbuf(spline2dinterpolant* c, - double x, - double y, - /* Real */ ae_vector* f, - ae_state *_state) -{ - double t; - double dt; - double u; - double du; - ae_int_t ix; - ae_int_t iy; - ae_int_t l; - ae_int_t r; - ae_int_t h; - ae_int_t s1; - ae_int_t s2; - ae_int_t s3; - ae_int_t s4; - ae_int_t sfx; - ae_int_t sfy; - ae_int_t sfxy; - double y1; - double y2; - double y3; - double y4; - double v; - double t0; - double t1; - double t2; - double t3; - double u0; - double u1; - double u2; - double u3; - ae_int_t i; - - - ae_assert(c->stype==-1||c->stype==-3, "Spline2DCalcVBuf: incorrect C (incorrect parameter C.SType)", _state); - ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DCalcVBuf: either X=NaN/Infinite or Y=NaN/Infinite", _state); - rvectorsetlengthatleast(f, c->d, _state); - - /* - * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included) - */ - l = 0; - r = c->n-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) ) - { - r = h; - } - else - { - l = h; - } - } - t = (x-c->x.ptr.p_double[l])/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]); - dt = 1.0/(c->x.ptr.p_double[l+1]-c->x.ptr.p_double[l]); - ix = l; - - /* - * Binary search in the [ y[0], ..., y[m-2] ] (y[m-1] is not included) - */ - l = 0; - r = c->m-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) ) - { - r = h; - } - else - { - l = h; - } - } - u = (y-c->y.ptr.p_double[l])/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]); - du = 1.0/(c->y.ptr.p_double[l+1]-c->y.ptr.p_double[l]); - iy = l; - - /* - * Bilinear interpolation - */ - if( c->stype==-1 ) - { - for(i=0; i<=c->d-1; i++) - { - y1 = c->f.ptr.p_double[c->d*(c->n*iy+ix)+i]; - y2 = c->f.ptr.p_double[c->d*(c->n*iy+(ix+1))+i]; - y3 = c->f.ptr.p_double[c->d*(c->n*(iy+1)+(ix+1))+i]; - y4 = c->f.ptr.p_double[c->d*(c->n*(iy+1)+ix)+i]; - f->ptr.p_double[i] = (1-t)*(1-u)*y1+t*(1-u)*y2+t*u*y3+(1-t)*u*y4; - } - return; - } - - /* - * Bicubic interpolation - */ - if( c->stype==-3 ) - { - - /* - * Prepare info - */ - t0 = 1; - t1 = t; - t2 = ae_sqr(t, _state); - t3 = t*t2; - u0 = 1; - u1 = u; - u2 = ae_sqr(u, _state); - u3 = u*u2; - sfx = c->n*c->m*c->d; - sfy = 2*c->n*c->m*c->d; - sfxy = 3*c->n*c->m*c->d; - for(i=0; i<=c->d-1; i++) - { - - /* - * Prepare F, dF/dX, dF/dY, d2F/dXdY - */ - f->ptr.p_double[i] = 0; - s1 = c->d*(c->n*iy+ix)+i; - s2 = c->d*(c->n*iy+(ix+1))+i; - s3 = c->d*(c->n*(iy+1)+(ix+1))+i; - s4 = c->d*(c->n*(iy+1)+ix)+i; - - /* - * Calculate - */ - v = c->f.ptr.p_double[s1]; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u0; - v = c->f.ptr.p_double[sfy+s1]/du; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u1; - v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u2; - v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t0*u3; - v = c->f.ptr.p_double[sfx+s1]/dt; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u0; - v = c->f.ptr.p_double[sfxy+s1]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u1; - v = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u2; - v = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t1*u3; - v = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u0; - v = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u1; - v = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u2; - v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t2*u3; - v = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt; - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u0; - v = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u1; - v = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u2; - v = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - f->ptr.p_double[i] = f->ptr.p_double[i]+v*t3*u3; - } - return; - } -} - - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y- point - -OUTPUT PARAMETERS: - F - array[D] which stores function values. F is out-parameter and - it is reallocated after call to this function. In case you - want to reuse previously allocated F, you may use - Spline2DCalcVBuf(), which reallocates F only when it is too - small. - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dcalcv(spline2dinterpolant* c, - double x, - double y, - /* Real */ ae_vector* f, - ae_state *_state) -{ - - ae_vector_clear(f); - - ae_assert(c->stype==-1||c->stype==-3, "Spline2DCalcV: incorrect C (incorrect parameter C.SType)", _state); - ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline2DCalcV: either X=NaN/Infinite or Y=NaN/Infinite", _state); - ae_vector_set_length(f, c->d, _state); - spline2dcalcvbuf(c, x, y, f, _state); -} - - -/************************************************************************* -This subroutine unpacks two-dimensional spline into the coefficients table - -Input parameters: - C - spline interpolant. - -Result: - M, N- grid size (x-axis and y-axis) - D - number of components - Tbl - coefficients table, unpacked format, - D - components: [0..(N-1)*(M-1)*D-1, 0..19]. - For T=0..D-1 (component index), I = 0...N-2 (x index), - J=0..M-2 (y index): - K := T + I*D + J*D*(N-1) - - K-th row stores decomposition for T-th component of the - vector-valued function - - Tbl[K,0] = X[i] - Tbl[K,1] = X[i+1] - Tbl[K,2] = Y[j] - Tbl[K,3] = Y[j+1] - Tbl[K,4] = C00 - Tbl[K,5] = C01 - Tbl[K,6] = C02 - Tbl[K,7] = C03 - Tbl[K,8] = C10 - Tbl[K,9] = C11 - ... - Tbl[K,19] = C33 - On each grid square spline is equals to: - S(x) = SUM(c[i,j]*(t^i)*(u^j), i=0..3, j=0..3) - t = x-x[j] - u = y-y[i] - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dunpackv(spline2dinterpolant* c, - ae_int_t* m, - ae_int_t* n, - ae_int_t* d, - /* Real */ ae_matrix* tbl, - ae_state *_state) -{ - ae_int_t k; - ae_int_t p; - ae_int_t ci; - ae_int_t cj; - ae_int_t s1; - ae_int_t s2; - ae_int_t s3; - ae_int_t s4; - ae_int_t sfx; - ae_int_t sfy; - ae_int_t sfxy; - double y1; - double y2; - double y3; - double y4; - double dt; - double du; - ae_int_t i; - ae_int_t j; - ae_int_t k0; - - *m = 0; - *n = 0; - *d = 0; - ae_matrix_clear(tbl); - - ae_assert(c->stype==-3||c->stype==-1, "Spline2DUnpackV: incorrect C (incorrect parameter C.SType)", _state); - *n = c->n; - *m = c->m; - *d = c->d; - ae_matrix_set_length(tbl, (*n-1)*(*m-1)*(*d), 20, _state); - sfx = *n*(*m)*(*d); - sfy = 2*(*n)*(*m)*(*d); - sfxy = 3*(*n)*(*m)*(*d); - for(i=0; i<=*m-2; i++) - { - for(j=0; j<=*n-2; j++) - { - for(k=0; k<=*d-1; k++) - { - p = *d*(i*(*n-1)+j)+k; - tbl->ptr.pp_double[p][0] = c->x.ptr.p_double[j]; - tbl->ptr.pp_double[p][1] = c->x.ptr.p_double[j+1]; - tbl->ptr.pp_double[p][2] = c->y.ptr.p_double[i]; - tbl->ptr.pp_double[p][3] = c->y.ptr.p_double[i+1]; - dt = 1/(tbl->ptr.pp_double[p][1]-tbl->ptr.pp_double[p][0]); - du = 1/(tbl->ptr.pp_double[p][3]-tbl->ptr.pp_double[p][2]); - - /* - * Bilinear interpolation - */ - if( c->stype==-1 ) - { - for(k0=4; k0<=19; k0++) - { - tbl->ptr.pp_double[p][k0] = 0; - } - y1 = c->f.ptr.p_double[*d*(*n*i+j)+k]; - y2 = c->f.ptr.p_double[*d*(*n*i+(j+1))+k]; - y3 = c->f.ptr.p_double[*d*(*n*(i+1)+(j+1))+k]; - y4 = c->f.ptr.p_double[*d*(*n*(i+1)+j)+k]; - tbl->ptr.pp_double[p][4] = y1; - tbl->ptr.pp_double[p][4+1*4+0] = y2-y1; - tbl->ptr.pp_double[p][4+0*4+1] = y4-y1; - tbl->ptr.pp_double[p][4+1*4+1] = y3-y2-y4+y1; - } - - /* - * Bicubic interpolation - */ - if( c->stype==-3 ) - { - s1 = *d*(*n*i+j)+k; - s2 = *d*(*n*i+(j+1))+k; - s3 = *d*(*n*(i+1)+(j+1))+k; - s4 = *d*(*n*(i+1)+j)+k; - tbl->ptr.pp_double[p][4+0*4+0] = c->f.ptr.p_double[s1]; - tbl->ptr.pp_double[p][4+0*4+1] = c->f.ptr.p_double[sfy+s1]/du; - tbl->ptr.pp_double[p][4+0*4+2] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du; - tbl->ptr.pp_double[p][4+0*4+3] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du; - tbl->ptr.pp_double[p][4+1*4+0] = c->f.ptr.p_double[sfx+s1]/dt; - tbl->ptr.pp_double[p][4+1*4+1] = c->f.ptr.p_double[sfxy+s1]/(dt*du); - tbl->ptr.pp_double[p][4+1*4+2] = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+1*4+3] = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+2*4+0] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt; - tbl->ptr.pp_double[p][4+2*4+1] = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du); - tbl->ptr.pp_double[p][4+2*4+2] = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+2*4+3] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+3*4+0] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt; - tbl->ptr.pp_double[p][4+3*4+1] = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du); - tbl->ptr.pp_double[p][4+3*4+2] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+3*4+3] = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - } - - /* - * Rescale Cij - */ - for(ci=0; ci<=3; ci++) - { - for(cj=0; cj<=3; cj++) - { - tbl->ptr.pp_double[p][4+ci*4+cj] = tbl->ptr.pp_double[p][4+ci*4+cj]*ae_pow(dt, ci, _state)*ae_pow(du, cj, _state); - } - } - } - } - } -} - - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DBuildBilinearV(), which is more -flexible and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbilinear(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_matrix* f, - ae_int_t m, - ae_int_t n, - spline2dinterpolant* c, - ae_state *_state) -{ - double t; - ae_int_t i; - ae_int_t j; - ae_int_t k; - - _spline2dinterpolant_clear(c); - - ae_assert(n>=2, "Spline2DBuildBilinear: N<2", _state); - ae_assert(m>=2, "Spline2DBuildBilinear: M<2", _state); - ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBilinear: length of X or Y is too short (Length(X/Y)rows>=m&&f->cols>=n, "Spline2DBuildBilinear: size of F is too small (rows(F)k = 1; - c->n = n; - c->m = m; - c->d = 1; - c->stype = -1; - ae_vector_set_length(&c->x, c->n, _state); - ae_vector_set_length(&c->y, c->m, _state); - ae_vector_set_length(&c->f, c->n*c->m, _state); - for(i=0; i<=c->n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - c->y.ptr.p_double[i] = y->ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->n-1; j++) - { - c->f.ptr.p_double[i*c->n+j] = f->ptr.pp_double[i][j]; - } - } - - /* - * Sort points - */ - for(j=0; j<=c->n-1; j++) - { - k = j; - for(i=j+1; i<=c->n-1; i++) - { - if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) ) - { - k = i; - } - } - if( k!=j ) - { - for(i=0; i<=c->m-1; i++) - { - t = c->f.ptr.p_double[i*c->n+j]; - c->f.ptr.p_double[i*c->n+j] = c->f.ptr.p_double[i*c->n+k]; - c->f.ptr.p_double[i*c->n+k] = t; - } - t = c->x.ptr.p_double[j]; - c->x.ptr.p_double[j] = c->x.ptr.p_double[k]; - c->x.ptr.p_double[k] = t; - } - } - for(i=0; i<=c->m-1; i++) - { - k = i; - for(j=i+1; j<=c->m-1; j++) - { - if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) ) - { - k = j; - } - } - if( k!=i ) - { - for(j=0; j<=c->n-1; j++) - { - t = c->f.ptr.p_double[i*c->n+j]; - c->f.ptr.p_double[i*c->n+j] = c->f.ptr.p_double[k*c->n+j]; - c->f.ptr.p_double[k*c->n+j] = t; - } - t = c->y.ptr.p_double[i]; - c->y.ptr.p_double[i] = c->y.ptr.p_double[k]; - c->y.ptr.p_double[k] = t; - } - } -} - - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DBuildBicubicV(), which is more -flexible and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbicubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_matrix* f, - ae_int_t m, - ae_int_t n, - spline2dinterpolant* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _f; - ae_int_t sfx; - ae_int_t sfy; - ae_int_t sfxy; - ae_matrix dx; - ae_matrix dy; - ae_matrix dxy; - double t; - ae_int_t i; - ae_int_t j; - ae_int_t k; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_f, f, _state, ae_true); - f = &_f; - _spline2dinterpolant_clear(c); - ae_matrix_init(&dx, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&dy, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&dxy, 0, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=2, "Spline2DBuildBicubicSpline: N<2", _state); - ae_assert(m>=2, "Spline2DBuildBicubicSpline: M<2", _state); - ae_assert(x->cnt>=n&&y->cnt>=m, "Spline2DBuildBicubic: length of X or Y is too short (Length(X/Y)rows>=m&&f->cols>=n, "Spline2DBuildBicubic: size of F is too small (rows(F)k = 3; - c->d = 1; - c->n = n; - c->m = m; - c->stype = -3; - sfx = c->n*c->m; - sfy = 2*c->n*c->m; - sfxy = 3*c->n*c->m; - ae_vector_set_length(&c->x, c->n, _state); - ae_vector_set_length(&c->y, c->m, _state); - ae_vector_set_length(&c->f, 4*c->n*c->m, _state); - for(i=0; i<=c->n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - c->y.ptr.p_double[i] = y->ptr.p_double[i]; - } - - /* - * Sort points - */ - for(j=0; j<=c->n-1; j++) - { - k = j; - for(i=j+1; i<=c->n-1; i++) - { - if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) ) - { - k = i; - } - } - if( k!=j ) - { - for(i=0; i<=c->m-1; i++) - { - t = f->ptr.pp_double[i][j]; - f->ptr.pp_double[i][j] = f->ptr.pp_double[i][k]; - f->ptr.pp_double[i][k] = t; - } - t = c->x.ptr.p_double[j]; - c->x.ptr.p_double[j] = c->x.ptr.p_double[k]; - c->x.ptr.p_double[k] = t; - } - } - for(i=0; i<=c->m-1; i++) - { - k = i; - for(j=i+1; j<=c->m-1; j++) - { - if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) ) - { - k = j; - } - } - if( k!=i ) - { - for(j=0; j<=c->n-1; j++) - { - t = f->ptr.pp_double[i][j]; - f->ptr.pp_double[i][j] = f->ptr.pp_double[k][j]; - f->ptr.pp_double[k][j] = t; - } - t = c->y.ptr.p_double[i]; - c->y.ptr.p_double[i] = c->y.ptr.p_double[k]; - c->y.ptr.p_double[k] = t; - } - } - spline2d_bicubiccalcderivatives(f, &c->x, &c->y, c->m, c->n, &dx, &dy, &dxy, _state); - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->n-1; j++) - { - k = i*c->n+j; - c->f.ptr.p_double[k] = f->ptr.pp_double[i][j]; - c->f.ptr.p_double[sfx+k] = dx.ptr.pp_double[i][j]; - c->f.ptr.p_double[sfy+k] = dy.ptr.pp_double[i][j]; - c->f.ptr.p_double[sfxy+k] = dxy.ptr.pp_double[i][j]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DUnpackV(), which is more flexible -and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dunpack(spline2dinterpolant* c, - ae_int_t* m, - ae_int_t* n, - /* Real */ ae_matrix* tbl, - ae_state *_state) -{ - ae_int_t k; - ae_int_t p; - ae_int_t ci; - ae_int_t cj; - ae_int_t s1; - ae_int_t s2; - ae_int_t s3; - ae_int_t s4; - ae_int_t sfx; - ae_int_t sfy; - ae_int_t sfxy; - double y1; - double y2; - double y3; - double y4; - double dt; - double du; - ae_int_t i; - ae_int_t j; - - *m = 0; - *n = 0; - ae_matrix_clear(tbl); - - ae_assert(c->stype==-3||c->stype==-1, "Spline2DUnpack: incorrect C (incorrect parameter C.SType)", _state); - if( c->d!=1 ) - { - *n = 0; - *m = 0; - return; - } - *n = c->n; - *m = c->m; - ae_matrix_set_length(tbl, (*n-1)*(*m-1), 20, _state); - sfx = *n*(*m); - sfy = 2*(*n)*(*m); - sfxy = 3*(*n)*(*m); - - /* - * Fill - */ - for(i=0; i<=*m-2; i++) - { - for(j=0; j<=*n-2; j++) - { - p = i*(*n-1)+j; - tbl->ptr.pp_double[p][0] = c->x.ptr.p_double[j]; - tbl->ptr.pp_double[p][1] = c->x.ptr.p_double[j+1]; - tbl->ptr.pp_double[p][2] = c->y.ptr.p_double[i]; - tbl->ptr.pp_double[p][3] = c->y.ptr.p_double[i+1]; - dt = 1/(tbl->ptr.pp_double[p][1]-tbl->ptr.pp_double[p][0]); - du = 1/(tbl->ptr.pp_double[p][3]-tbl->ptr.pp_double[p][2]); - - /* - * Bilinear interpolation - */ - if( c->stype==-1 ) - { - for(k=4; k<=19; k++) - { - tbl->ptr.pp_double[p][k] = 0; - } - y1 = c->f.ptr.p_double[*n*i+j]; - y2 = c->f.ptr.p_double[*n*i+(j+1)]; - y3 = c->f.ptr.p_double[*n*(i+1)+(j+1)]; - y4 = c->f.ptr.p_double[*n*(i+1)+j]; - tbl->ptr.pp_double[p][4] = y1; - tbl->ptr.pp_double[p][4+1*4+0] = y2-y1; - tbl->ptr.pp_double[p][4+0*4+1] = y4-y1; - tbl->ptr.pp_double[p][4+1*4+1] = y3-y2-y4+y1; - } - - /* - * Bicubic interpolation - */ - if( c->stype==-3 ) - { - s1 = *n*i+j; - s2 = *n*i+(j+1); - s3 = *n*(i+1)+(j+1); - s4 = *n*(i+1)+j; - tbl->ptr.pp_double[p][4+0*4+0] = c->f.ptr.p_double[s1]; - tbl->ptr.pp_double[p][4+0*4+1] = c->f.ptr.p_double[sfy+s1]/du; - tbl->ptr.pp_double[p][4+0*4+2] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s4]-2*c->f.ptr.p_double[sfy+s1]/du-c->f.ptr.p_double[sfy+s4]/du; - tbl->ptr.pp_double[p][4+0*4+3] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s4]+c->f.ptr.p_double[sfy+s1]/du+c->f.ptr.p_double[sfy+s4]/du; - tbl->ptr.pp_double[p][4+1*4+0] = c->f.ptr.p_double[sfx+s1]/dt; - tbl->ptr.pp_double[p][4+1*4+1] = c->f.ptr.p_double[sfxy+s1]/(dt*du); - tbl->ptr.pp_double[p][4+1*4+2] = -3*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+1*4+3] = 2*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+2*4+0] = -3*c->f.ptr.p_double[s1]+3*c->f.ptr.p_double[s2]-2*c->f.ptr.p_double[sfx+s1]/dt-c->f.ptr.p_double[sfx+s2]/dt; - tbl->ptr.pp_double[p][4+2*4+1] = -3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du); - tbl->ptr.pp_double[p][4+2*4+2] = 9*c->f.ptr.p_double[s1]-9*c->f.ptr.p_double[s2]+9*c->f.ptr.p_double[s3]-9*c->f.ptr.p_double[s4]+6*c->f.ptr.p_double[sfx+s1]/dt+3*c->f.ptr.p_double[sfx+s2]/dt-3*c->f.ptr.p_double[sfx+s3]/dt-6*c->f.ptr.p_double[sfx+s4]/dt+6*c->f.ptr.p_double[sfy+s1]/du-6*c->f.ptr.p_double[sfy+s2]/du-3*c->f.ptr.p_double[sfy+s3]/du+3*c->f.ptr.p_double[sfy+s4]/du+4*c->f.ptr.p_double[sfxy+s1]/(dt*du)+2*c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+2*4+3] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-4*c->f.ptr.p_double[sfx+s1]/dt-2*c->f.ptr.p_double[sfx+s2]/dt+2*c->f.ptr.p_double[sfx+s3]/dt+4*c->f.ptr.p_double[sfx+s4]/dt-3*c->f.ptr.p_double[sfy+s1]/du+3*c->f.ptr.p_double[sfy+s2]/du+3*c->f.ptr.p_double[sfy+s3]/du-3*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-2*c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+3*4+0] = 2*c->f.ptr.p_double[s1]-2*c->f.ptr.p_double[s2]+c->f.ptr.p_double[sfx+s1]/dt+c->f.ptr.p_double[sfx+s2]/dt; - tbl->ptr.pp_double[p][4+3*4+1] = 2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du); - tbl->ptr.pp_double[p][4+3*4+2] = -6*c->f.ptr.p_double[s1]+6*c->f.ptr.p_double[s2]-6*c->f.ptr.p_double[s3]+6*c->f.ptr.p_double[s4]-3*c->f.ptr.p_double[sfx+s1]/dt-3*c->f.ptr.p_double[sfx+s2]/dt+3*c->f.ptr.p_double[sfx+s3]/dt+3*c->f.ptr.p_double[sfx+s4]/dt-4*c->f.ptr.p_double[sfy+s1]/du+4*c->f.ptr.p_double[sfy+s2]/du+2*c->f.ptr.p_double[sfy+s3]/du-2*c->f.ptr.p_double[sfy+s4]/du-2*c->f.ptr.p_double[sfxy+s1]/(dt*du)-2*c->f.ptr.p_double[sfxy+s2]/(dt*du)-c->f.ptr.p_double[sfxy+s3]/(dt*du)-c->f.ptr.p_double[sfxy+s4]/(dt*du); - tbl->ptr.pp_double[p][4+3*4+3] = 4*c->f.ptr.p_double[s1]-4*c->f.ptr.p_double[s2]+4*c->f.ptr.p_double[s3]-4*c->f.ptr.p_double[s4]+2*c->f.ptr.p_double[sfx+s1]/dt+2*c->f.ptr.p_double[sfx+s2]/dt-2*c->f.ptr.p_double[sfx+s3]/dt-2*c->f.ptr.p_double[sfx+s4]/dt+2*c->f.ptr.p_double[sfy+s1]/du-2*c->f.ptr.p_double[sfy+s2]/du-2*c->f.ptr.p_double[sfy+s3]/du+2*c->f.ptr.p_double[sfy+s4]/du+c->f.ptr.p_double[sfxy+s1]/(dt*du)+c->f.ptr.p_double[sfxy+s2]/(dt*du)+c->f.ptr.p_double[sfxy+s3]/(dt*du)+c->f.ptr.p_double[sfxy+s4]/(dt*du); - } - - /* - * Rescale Cij - */ - for(ci=0; ci<=3; ci++) - { - for(cj=0; cj<=3; cj++) - { - tbl->ptr.pp_double[p][4+ci*4+cj] = tbl->ptr.pp_double[p][4+ci*4+cj]*ae_pow(dt, ci, _state)*ae_pow(du, cj, _state); - } - } - } - } -} - - -/************************************************************************* -Internal subroutine. -Calculation of the first derivatives and the cross-derivative. -*************************************************************************/ -static void spline2d_bicubiccalcderivatives(/* Real */ ae_matrix* a, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* dx, - /* Real */ ae_matrix* dy, - /* Real */ ae_matrix* dxy, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector xt; - ae_vector ft; - double s; - double ds; - double d2s; - spline1dinterpolant c; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(dx); - ae_matrix_clear(dy); - ae_matrix_clear(dxy); - ae_vector_init(&xt, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ft, 0, DT_REAL, _state, ae_true); - _spline1dinterpolant_init(&c, _state, ae_true); - - ae_matrix_set_length(dx, m, n, _state); - ae_matrix_set_length(dy, m, n, _state); - ae_matrix_set_length(dxy, m, n, _state); - - /* - * dF/dX - */ - ae_vector_set_length(&xt, n, _state); - ae_vector_set_length(&ft, n, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - xt.ptr.p_double[j] = x->ptr.p_double[j]; - ft.ptr.p_double[j] = a->ptr.pp_double[i][j]; - } - spline1dbuildcubic(&xt, &ft, n, 0, 0.0, 0, 0.0, &c, _state); - for(j=0; j<=n-1; j++) - { - spline1ddiff(&c, x->ptr.p_double[j], &s, &ds, &d2s, _state); - dx->ptr.pp_double[i][j] = ds; - } - } - - /* - * dF/dY - */ - ae_vector_set_length(&xt, m, _state); - ae_vector_set_length(&ft, m, _state); - for(j=0; j<=n-1; j++) - { - for(i=0; i<=m-1; i++) - { - xt.ptr.p_double[i] = y->ptr.p_double[i]; - ft.ptr.p_double[i] = a->ptr.pp_double[i][j]; - } - spline1dbuildcubic(&xt, &ft, m, 0, 0.0, 0, 0.0, &c, _state); - for(i=0; i<=m-1; i++) - { - spline1ddiff(&c, y->ptr.p_double[i], &s, &ds, &d2s, _state); - dy->ptr.pp_double[i][j] = ds; - } - } - - /* - * d2F/dXdY - */ - ae_vector_set_length(&xt, n, _state); - ae_vector_set_length(&ft, n, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - xt.ptr.p_double[j] = x->ptr.p_double[j]; - ft.ptr.p_double[j] = dy->ptr.pp_double[i][j]; - } - spline1dbuildcubic(&xt, &ft, n, 0, 0.0, 0, 0.0, &c, _state); - for(j=0; j<=n-1; j++) - { - spline1ddiff(&c, x->ptr.p_double[j], &s, &ds, &d2s, _state); - dxy->ptr.pp_double[i][j] = ds; - } - } - ae_frame_leave(_state); -} - - -ae_bool _spline2dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - spline2dinterpolant *p = (spline2dinterpolant*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->f, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _spline2dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - spline2dinterpolant *dst = (spline2dinterpolant*)_dst; - spline2dinterpolant *src = (spline2dinterpolant*)_src; - dst->k = src->k; - dst->stype = src->stype; - dst->n = src->n; - dst->m = src->m; - dst->d = src->d; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->f, &src->f, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _spline2dinterpolant_clear(void* _p) -{ - spline2dinterpolant *p = (spline2dinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->y); - ae_vector_clear(&p->f); -} - - -void _spline2dinterpolant_destroy(void* _p) -{ - spline2dinterpolant *p = (spline2dinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->y); - ae_vector_destroy(&p->f); -} - - - - -/************************************************************************* -This subroutine calculates the value of the trilinear or tricubic spline at -the given point (X,Y,Z). - -INPUT PARAMETERS: - C - coefficients table. - Built by BuildBilinearSpline or BuildBicubicSpline. - X, Y, - Z - point - -Result: - S(x,y,z) - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -double spline3dcalc(spline3dinterpolant* c, - double x, - double y, - double z, - ae_state *_state) -{ - double v; - double vx; - double vy; - double vxy; - double result; - - - ae_assert(c->stype==-1||c->stype==-3, "Spline3DCalc: incorrect C (incorrect parameter C.SType)", _state); - ae_assert((ae_isfinite(x, _state)&&ae_isfinite(y, _state))&&ae_isfinite(z, _state), "Spline3DCalc: X=NaN/Infinite, Y=NaN/Infinite or Z=NaN/Infinite", _state); - if( c->d!=1 ) - { - result = 0; - return result; - } - spline3d_spline3ddiff(c, x, y, z, &v, &vx, &vy, &vxy, _state); - result = v; - return result; -} - - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -INPUT PARAMETERS: - C - spline interpolant - AX, BX - transformation coefficients: x = A*u + B - AY, BY - transformation coefficients: y = A*v + B - AZ, BZ - transformation coefficients: z = A*w + B - -OUTPUT PARAMETERS: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dlintransxyz(spline3dinterpolant* c, - double ax, - double bx, - double ay, - double by, - double az, - double bz, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_vector z; - ae_vector f; - ae_vector v; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t di; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&z, 0, DT_REAL, _state, ae_true); - ae_vector_init(&f, 0, DT_REAL, _state, ae_true); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - - ae_assert(c->stype==-3||c->stype==-1, "Spline3DLinTransXYZ: incorrect C (incorrect parameter C.SType)", _state); - ae_vector_set_length(&x, c->n, _state); - ae_vector_set_length(&y, c->m, _state); - ae_vector_set_length(&z, c->l, _state); - ae_vector_set_length(&f, c->m*c->n*c->l*c->d, _state); - for(j=0; j<=c->n-1; j++) - { - x.ptr.p_double[j] = c->x.ptr.p_double[j]; - } - for(i=0; i<=c->m-1; i++) - { - y.ptr.p_double[i] = c->y.ptr.p_double[i]; - } - for(i=0; i<=c->l-1; i++) - { - z.ptr.p_double[i] = c->z.ptr.p_double[i]; - } - - /* - * Handle different combinations of zero/nonzero AX/AY/AZ - */ - if( (ae_fp_neq(ax,0)&&ae_fp_neq(ay,0))&&ae_fp_neq(az,0) ) - { - ae_v_move(&f.ptr.p_double[0], 1, &c->f.ptr.p_double[0], 1, ae_v_len(0,c->m*c->n*c->l*c->d-1)); - } - if( (ae_fp_eq(ax,0)&&ae_fp_neq(ay,0))&&ae_fp_neq(az,0) ) - { - for(i=0; i<=c->m-1; i++) - { - for(j=0; j<=c->l-1; j++) - { - spline3dcalcv(c, bx, y.ptr.p_double[i], z.ptr.p_double[j], &v, _state); - for(k=0; k<=c->n-1; k++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*j+i)+k)+di] = v.ptr.p_double[di]; - } - } - } - } - ax = 1; - bx = 0; - } - if( (ae_fp_neq(ax,0)&&ae_fp_eq(ay,0))&&ae_fp_neq(az,0) ) - { - for(i=0; i<=c->n-1; i++) - { - for(j=0; j<=c->l-1; j++) - { - spline3dcalcv(c, x.ptr.p_double[i], by, z.ptr.p_double[j], &v, _state); - for(k=0; k<=c->m-1; k++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*j+k)+i)+di] = v.ptr.p_double[di]; - } - } - } - } - ay = 1; - by = 0; - } - if( (ae_fp_neq(ax,0)&&ae_fp_neq(ay,0))&&ae_fp_eq(az,0) ) - { - for(i=0; i<=c->n-1; i++) - { - for(j=0; j<=c->m-1; j++) - { - spline3dcalcv(c, x.ptr.p_double[i], y.ptr.p_double[j], bz, &v, _state); - for(k=0; k<=c->l-1; k++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*k+j)+i)+di] = v.ptr.p_double[di]; - } - } - } - } - az = 1; - bz = 0; - } - if( (ae_fp_eq(ax,0)&&ae_fp_eq(ay,0))&&ae_fp_neq(az,0) ) - { - for(i=0; i<=c->l-1; i++) - { - spline3dcalcv(c, bx, by, z.ptr.p_double[i], &v, _state); - for(k=0; k<=c->m-1; k++) - { - for(j=0; j<=c->n-1; j++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*i+k)+j)+di] = v.ptr.p_double[di]; - } - } - } - } - ax = 1; - bx = 0; - ay = 1; - by = 0; - } - if( (ae_fp_eq(ax,0)&&ae_fp_neq(ay,0))&&ae_fp_eq(az,0) ) - { - for(i=0; i<=c->m-1; i++) - { - spline3dcalcv(c, bx, y.ptr.p_double[i], bz, &v, _state); - for(k=0; k<=c->l-1; k++) - { - for(j=0; j<=c->n-1; j++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*k+i)+j)+di] = v.ptr.p_double[di]; - } - } - } - } - ax = 1; - bx = 0; - az = 1; - bz = 0; - } - if( (ae_fp_neq(ax,0)&&ae_fp_eq(ay,0))&&ae_fp_eq(az,0) ) - { - for(i=0; i<=c->n-1; i++) - { - spline3dcalcv(c, x.ptr.p_double[i], by, bz, &v, _state); - for(k=0; k<=c->l-1; k++) - { - for(j=0; j<=c->m-1; j++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*k+j)+i)+di] = v.ptr.p_double[di]; - } - } - } - } - ay = 1; - by = 0; - az = 1; - bz = 0; - } - if( (ae_fp_eq(ax,0)&&ae_fp_eq(ay,0))&&ae_fp_eq(az,0) ) - { - spline3dcalcv(c, bx, by, bz, &v, _state); - for(k=0; k<=c->l-1; k++) - { - for(j=0; j<=c->m-1; j++) - { - for(i=0; i<=c->n-1; i++) - { - for(di=0; di<=c->d-1; di++) - { - f.ptr.p_double[c->d*(c->n*(c->m*k+j)+i)+di] = v.ptr.p_double[di]; - } - } - } - } - ax = 1; - bx = 0; - ay = 1; - by = 0; - az = 1; - bz = 0; - } - - /* - * General case: AX<>0, AY<>0, AZ<>0 - * Unpack, scale and pack again. - */ - for(i=0; i<=c->n-1; i++) - { - x.ptr.p_double[i] = (x.ptr.p_double[i]-bx)/ax; - } - for(i=0; i<=c->m-1; i++) - { - y.ptr.p_double[i] = (y.ptr.p_double[i]-by)/ay; - } - for(i=0; i<=c->l-1; i++) - { - z.ptr.p_double[i] = (z.ptr.p_double[i]-bz)/az; - } - if( c->stype==-1 ) - { - spline3dbuildtrilinearv(&x, c->n, &y, c->m, &z, c->l, &f, c->d, c, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: S2(x,y) = A*S(x,y,z) + B - -OUTPUT PARAMETERS: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dlintransf(spline3dinterpolant* c, - double a, - double b, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector x; - ae_vector y; - ae_vector z; - ae_vector f; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&x, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&z, 0, DT_REAL, _state, ae_true); - ae_vector_init(&f, 0, DT_REAL, _state, ae_true); - - ae_assert(c->stype==-3||c->stype==-1, "Spline3DLinTransF: incorrect C (incorrect parameter C.SType)", _state); - ae_vector_set_length(&x, c->n, _state); - ae_vector_set_length(&y, c->m, _state); - ae_vector_set_length(&z, c->l, _state); - ae_vector_set_length(&f, c->m*c->n*c->l*c->d, _state); - for(j=0; j<=c->n-1; j++) - { - x.ptr.p_double[j] = c->x.ptr.p_double[j]; - } - for(i=0; i<=c->m-1; i++) - { - y.ptr.p_double[i] = c->y.ptr.p_double[i]; - } - for(i=0; i<=c->l-1; i++) - { - z.ptr.p_double[i] = c->z.ptr.p_double[i]; - } - for(i=0; i<=c->m*c->n*c->l*c->d-1; i++) - { - f.ptr.p_double[i] = a*c->f.ptr.p_double[i]+b; - } - if( c->stype==-1 ) - { - spline3dbuildtrilinearv(&x, c->n, &y, c->m, &z, c->l, &f, c->d, c, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine makes the copy of the spline model. - -INPUT PARAMETERS: - C - spline interpolant - -OUTPUT PARAMETERS: - CC - spline copy - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcopy(spline3dinterpolant* c, - spline3dinterpolant* cc, - ae_state *_state) -{ - ae_int_t tblsize; - - _spline3dinterpolant_clear(cc); - - ae_assert(c->k==1||c->k==3, "Spline3DCopy: incorrect C (incorrect parameter C.K)", _state); - cc->k = c->k; - cc->n = c->n; - cc->m = c->m; - cc->l = c->l; - cc->d = c->d; - tblsize = c->n*c->m*c->l*c->d; - cc->stype = c->stype; - ae_vector_set_length(&cc->x, cc->n, _state); - ae_vector_set_length(&cc->y, cc->m, _state); - ae_vector_set_length(&cc->z, cc->l, _state); - ae_vector_set_length(&cc->f, tblsize, _state); - ae_v_move(&cc->x.ptr.p_double[0], 1, &c->x.ptr.p_double[0], 1, ae_v_len(0,cc->n-1)); - ae_v_move(&cc->y.ptr.p_double[0], 1, &c->y.ptr.p_double[0], 1, ae_v_len(0,cc->m-1)); - ae_v_move(&cc->z.ptr.p_double[0], 1, &c->z.ptr.p_double[0], 1, ae_v_len(0,cc->l-1)); - ae_v_move(&cc->f.ptr.p_double[0], 1, &c->f.ptr.p_double[0], 1, ae_v_len(0,tblsize-1)); -} - - -/************************************************************************* -Trilinear spline resampling - -INPUT PARAMETERS: - A - array[0..OldXCount*OldYCount*OldZCount-1], function - values at the old grid, : - A[0] x=0,y=0,z=0 - A[1] x=1,y=0,z=0 - A[..] ... - A[..] x=oldxcount-1,y=0,z=0 - A[..] x=0,y=1,z=0 - A[..] ... - ... - OldZCount - old Z-count, OldZCount>1 - OldYCount - old Y-count, OldYCount>1 - OldXCount - old X-count, OldXCount>1 - NewZCount - new Z-count, NewZCount>1 - NewYCount - new Y-count, NewYCount>1 - NewXCount - new X-count, NewXCount>1 - -OUTPUT PARAMETERS: - B - array[0..NewXCount*NewYCount*NewZCount-1], function - values at the new grid: - B[0] x=0,y=0,z=0 - B[1] x=1,y=0,z=0 - B[..] ... - B[..] x=newxcount-1,y=0,z=0 - B[..] x=0,y=1,z=0 - B[..] ... - ... - - -- ALGLIB routine -- - 26.04.2012 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline3dresampletrilinear(/* Real */ ae_vector* a, - ae_int_t oldzcount, - ae_int_t oldycount, - ae_int_t oldxcount, - ae_int_t newzcount, - ae_int_t newycount, - ae_int_t newxcount, - /* Real */ ae_vector* b, - ae_state *_state) -{ - double xd; - double yd; - double zd; - double c0; - double c1; - double c2; - double c3; - ae_int_t ix; - ae_int_t iy; - ae_int_t iz; - ae_int_t i; - ae_int_t j; - ae_int_t k; - - ae_vector_clear(b); - - ae_assert((oldycount>1&&oldzcount>1)&&oldxcount>1, "Spline3DResampleTrilinear: length/width/height less than 1", _state); - ae_assert((newycount>1&&newzcount>1)&&newxcount>1, "Spline3DResampleTrilinear: length/width/height less than 1", _state); - ae_assert(a->cnt>=oldycount*oldzcount*oldxcount, "Spline3DResampleTrilinear: length/width/height less than 1", _state); - ae_vector_set_length(b, newxcount*newycount*newzcount, _state); - for(i=0; i<=newxcount-1; i++) - { - for(j=0; j<=newycount-1; j++) - { - for(k=0; k<=newzcount-1; k++) - { - ix = i*(oldxcount-1)/(newxcount-1); - if( ix==oldxcount-1 ) - { - ix = oldxcount-2; - } - xd = (double)(i*(oldxcount-1))/(double)(newxcount-1)-ix; - iy = j*(oldycount-1)/(newycount-1); - if( iy==oldycount-1 ) - { - iy = oldycount-2; - } - yd = (double)(j*(oldycount-1))/(double)(newycount-1)-iy; - iz = k*(oldzcount-1)/(newzcount-1); - if( iz==oldzcount-1 ) - { - iz = oldzcount-2; - } - zd = (double)(k*(oldzcount-1))/(double)(newzcount-1)-iz; - c0 = a->ptr.p_double[oldxcount*(oldycount*iz+iy)+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*iz+iy)+(ix+1)]*xd; - c1 = a->ptr.p_double[oldxcount*(oldycount*iz+(iy+1))+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*iz+(iy+1))+(ix+1)]*xd; - c2 = a->ptr.p_double[oldxcount*(oldycount*(iz+1)+iy)+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*(iz+1)+iy)+(ix+1)]*xd; - c3 = a->ptr.p_double[oldxcount*(oldycount*(iz+1)+(iy+1))+ix]*(1-xd)+a->ptr.p_double[oldxcount*(oldycount*(iz+1)+(iy+1))+(ix+1)]*xd; - c0 = c0*(1-yd)+c1*yd; - c1 = c2*(1-yd)+c3*yd; - b->ptr.p_double[newxcount*(newycount*k+j)+i] = c0*(1-zd)+c1*zd; - } - } - } -} - - -/************************************************************************* -This subroutine builds trilinear vector-valued spline. - -INPUT PARAMETERS: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - Z - spline applicates, array[0..L-1] - F - function values, array[0..M*N*L*D-1]: - * first D elements store D values at (X[0],Y[0],Z[0]) - * next D elements store D values at (X[1],Y[0],Z[0]) - * next D elements store D values at (X[2],Y[0],Z[0]) - * ... - * next D elements store D values at (X[0],Y[1],Z[0]) - * next D elements store D values at (X[1],Y[1],Z[0]) - * next D elements store D values at (X[2],Y[1],Z[0]) - * ... - * next D elements store D values at (X[0],Y[0],Z[1]) - * next D elements store D values at (X[1],Y[0],Z[1]) - * next D elements store D values at (X[2],Y[0],Z[1]) - * ... - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(N*(M*K+J)+I)...D*(N*(M*K+J)+I)+D-1]. - M,N, - L - grid size, M>=2, N>=2, L>=2 - D - vector dimension, D>=1 - -OUTPUT PARAMETERS: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dbuildtrilinearv(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - /* Real */ ae_vector* z, - ae_int_t l, - /* Real */ ae_vector* f, - ae_int_t d, - spline3dinterpolant* c, - ae_state *_state) -{ - double t; - ae_int_t tblsize; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t i0; - ae_int_t j0; - - _spline3dinterpolant_clear(c); - - ae_assert(m>=2, "Spline3DBuildTrilinearV: M<2", _state); - ae_assert(n>=2, "Spline3DBuildTrilinearV: N<2", _state); - ae_assert(l>=2, "Spline3DBuildTrilinearV: L<2", _state); - ae_assert(d>=1, "Spline3DBuildTrilinearV: D<1", _state); - ae_assert((x->cnt>=n&&y->cnt>=m)&&z->cnt>=l, "Spline3DBuildTrilinearV: length of X, Y or Z is too short (Length(X/Y/Z)cnt>=tblsize, "Spline3DBuildTrilinearV: length of F is too short (Length(F)k = 1; - c->n = n; - c->m = m; - c->l = l; - c->d = d; - c->stype = -1; - ae_vector_set_length(&c->x, c->n, _state); - ae_vector_set_length(&c->y, c->m, _state); - ae_vector_set_length(&c->z, c->l, _state); - ae_vector_set_length(&c->f, tblsize, _state); - for(i=0; i<=c->n-1; i++) - { - c->x.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=c->m-1; i++) - { - c->y.ptr.p_double[i] = y->ptr.p_double[i]; - } - for(i=0; i<=c->l-1; i++) - { - c->z.ptr.p_double[i] = z->ptr.p_double[i]; - } - for(i=0; i<=tblsize-1; i++) - { - c->f.ptr.p_double[i] = f->ptr.p_double[i]; - } - - /* - * Sort points: - * * sort x; - * * sort y; - * * sort z. - */ - for(j=0; j<=c->n-1; j++) - { - k = j; - for(i=j+1; i<=c->n-1; i++) - { - if( ae_fp_less(c->x.ptr.p_double[i],c->x.ptr.p_double[k]) ) - { - k = i; - } - } - if( k!=j ) - { - for(i=0; i<=c->m-1; i++) - { - for(j0=0; j0<=c->l-1; j0++) - { - for(i0=0; i0<=c->d-1; i0++) - { - t = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0]; - c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0] = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+k)+i0]; - c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+k)+i0] = t; - } - } - } - t = c->x.ptr.p_double[j]; - c->x.ptr.p_double[j] = c->x.ptr.p_double[k]; - c->x.ptr.p_double[k] = t; - } - } - for(i=0; i<=c->m-1; i++) - { - k = i; - for(j=i+1; j<=c->m-1; j++) - { - if( ae_fp_less(c->y.ptr.p_double[j],c->y.ptr.p_double[k]) ) - { - k = j; - } - } - if( k!=i ) - { - for(j=0; j<=c->n-1; j++) - { - for(j0=0; j0<=c->l-1; j0++) - { - for(i0=0; i0<=c->d-1; i0++) - { - t = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0]; - c->f.ptr.p_double[c->d*(c->n*(c->m*j0+i)+j)+i0] = c->f.ptr.p_double[c->d*(c->n*(c->m*j0+k)+j)+i0]; - c->f.ptr.p_double[c->d*(c->n*(c->m*j0+k)+j)+i0] = t; - } - } - } - t = c->y.ptr.p_double[i]; - c->y.ptr.p_double[i] = c->y.ptr.p_double[k]; - c->y.ptr.p_double[k] = t; - } - } - for(k=0; k<=c->l-1; k++) - { - i = k; - for(j=i+1; j<=c->l-1; j++) - { - if( ae_fp_less(c->z.ptr.p_double[j],c->z.ptr.p_double[i]) ) - { - i = j; - } - } - if( i!=k ) - { - for(j=0; j<=c->m-1; j++) - { - for(j0=0; j0<=c->n-1; j0++) - { - for(i0=0; i0<=c->d-1; i0++) - { - t = c->f.ptr.p_double[c->d*(c->n*(c->m*k+j)+j0)+i0]; - c->f.ptr.p_double[c->d*(c->n*(c->m*k+j)+j0)+i0] = c->f.ptr.p_double[c->d*(c->n*(c->m*i+j)+j0)+i0]; - c->f.ptr.p_double[c->d*(c->n*(c->m*i+j)+j0)+i0] = t; - } - } - } - t = c->z.ptr.p_double[k]; - c->z.ptr.p_double[k] = c->z.ptr.p_double[i]; - c->z.ptr.p_double[i] = t; - } - } -} - - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y,Z). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, - Z - point - F - output buffer, possibly preallocated array. In case array size - is large enough to store result, it is not reallocated. Array - which is too short will be reallocated - -OUTPUT PARAMETERS: - F - array[D] (or larger) which stores function values - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcalcvbuf(spline3dinterpolant* c, - double x, - double y, - double z, - /* Real */ ae_vector* f, - ae_state *_state) -{ - double xd; - double yd; - double zd; - double c0; - double c1; - double c2; - double c3; - ae_int_t ix; - ae_int_t iy; - ae_int_t iz; - ae_int_t l; - ae_int_t r; - ae_int_t h; - ae_int_t i; - - - ae_assert(c->stype==-1||c->stype==-3, "Spline3DCalcVBuf: incorrect C (incorrect parameter C.SType)", _state); - ae_assert((ae_isfinite(x, _state)&&ae_isfinite(y, _state))&&ae_isfinite(z, _state), "Spline3DCalcVBuf: X, Y or Z contains NaN/Infinite", _state); - rvectorsetlengthatleast(f, c->d, _state); - - /* - * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included) - */ - l = 0; - r = c->n-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) ) - { - r = h; - } - else - { - l = h; - } - } - ix = l; - - /* - * Binary search in the [ y[0], ..., y[n-2] ] (y[n-1] is not included) - */ - l = 0; - r = c->m-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) ) - { - r = h; - } - else - { - l = h; - } - } - iy = l; - - /* - * Binary search in the [ z[0], ..., z[n-2] ] (z[n-1] is not included) - */ - l = 0; - r = c->l-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->z.ptr.p_double[h],z) ) - { - r = h; - } - else - { - l = h; - } - } - iz = l; - xd = (x-c->x.ptr.p_double[ix])/(c->x.ptr.p_double[ix+1]-c->x.ptr.p_double[ix]); - yd = (y-c->y.ptr.p_double[iy])/(c->y.ptr.p_double[iy+1]-c->y.ptr.p_double[iy]); - zd = (z-c->z.ptr.p_double[iz])/(c->z.ptr.p_double[iz+1]-c->z.ptr.p_double[iz]); - for(i=0; i<=c->d-1; i++) - { - - /* - * Trilinear interpolation - */ - if( c->stype==-1 ) - { - c0 = c->f.ptr.p_double[c->d*(c->n*(c->m*iz+iy)+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*iz+iy)+(ix+1))+i]*xd; - c1 = c->f.ptr.p_double[c->d*(c->n*(c->m*iz+(iy+1))+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*iz+(iy+1))+(ix+1))+i]*xd; - c2 = c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+iy)+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+iy)+(ix+1))+i]*xd; - c3 = c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+(iy+1))+ix)+i]*(1-xd)+c->f.ptr.p_double[c->d*(c->n*(c->m*(iz+1)+(iy+1))+(ix+1))+i]*xd; - c0 = c0*(1-yd)+c1*yd; - c1 = c2*(1-yd)+c3*yd; - f->ptr.p_double[i] = c0*(1-zd)+c1*zd; - } - } -} - - -/************************************************************************* -This subroutine calculates trilinear or tricubic vector-valued spline at the -given point (X,Y,Z). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, - Z - point - -OUTPUT PARAMETERS: - F - array[D] which stores function values. F is out-parameter and - it is reallocated after call to this function. In case you - want to reuse previously allocated F, you may use - Spline2DCalcVBuf(), which reallocates F only when it is too - small. - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcalcv(spline3dinterpolant* c, - double x, - double y, - double z, - /* Real */ ae_vector* f, - ae_state *_state) -{ - - ae_vector_clear(f); - - ae_assert(c->stype==-1||c->stype==-3, "Spline3DCalcV: incorrect C (incorrect parameter C.SType)", _state); - ae_assert((ae_isfinite(x, _state)&&ae_isfinite(y, _state))&&ae_isfinite(z, _state), "Spline3DCalcV: X=NaN/Infinite, Y=NaN/Infinite or Z=NaN/Infinite", _state); - ae_vector_set_length(f, c->d, _state); - spline3dcalcvbuf(c, x, y, z, f, _state); -} - - -/************************************************************************* -This subroutine unpacks tri-dimensional spline into the coefficients table - -INPUT PARAMETERS: - C - spline interpolant. - -Result: - N - grid size (X) - M - grid size (Y) - L - grid size (Z) - D - number of components - SType- spline type. Currently, only one spline type is supported: - trilinear spline, as indicated by SType=1. - Tbl - spline coefficients: [0..(N-1)*(M-1)*(L-1)*D-1, 0..13]. - For T=0..D-1 (component index), I = 0...N-2 (x index), - J=0..M-2 (y index), K=0..L-2 (z index): - Q := T + I*D + J*D*(N-1) + K*D*(N-1)*(M-1), - - Q-th row stores decomposition for T-th component of the - vector-valued function - - Tbl[Q,0] = X[i] - Tbl[Q,1] = X[i+1] - Tbl[Q,2] = Y[j] - Tbl[Q,3] = Y[j+1] - Tbl[Q,4] = Z[k] - Tbl[Q,5] = Z[k+1] - - Tbl[Q,6] = C000 - Tbl[Q,7] = C100 - Tbl[Q,8] = C010 - Tbl[Q,9] = C110 - Tbl[Q,10]= C001 - Tbl[Q,11]= C101 - Tbl[Q,12]= C011 - Tbl[Q,13]= C111 - On each grid square spline is equals to: - S(x) = SUM(c[i,j,k]*(x^i)*(y^j)*(z^k), i=0..1, j=0..1, k=0..1) - t = x-x[j] - u = y-y[i] - v = z-z[k] - - NOTE: format of Tbl is given for SType=1. Future versions of - ALGLIB can use different formats for different values of - SType. - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dunpackv(spline3dinterpolant* c, - ae_int_t* n, - ae_int_t* m, - ae_int_t* l, - ae_int_t* d, - ae_int_t* stype, - /* Real */ ae_matrix* tbl, - ae_state *_state) -{ - ae_int_t p; - ae_int_t ci; - ae_int_t cj; - ae_int_t ck; - double du; - double dv; - double dw; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t di; - ae_int_t i0; - - *n = 0; - *m = 0; - *l = 0; - *d = 0; - *stype = 0; - ae_matrix_clear(tbl); - - ae_assert(c->stype==-1, "Spline3DUnpackV: incorrect C (incorrect parameter C.SType)", _state); - *n = c->n; - *m = c->m; - *l = c->l; - *d = c->d; - *stype = ae_iabs(c->stype, _state); - ae_matrix_set_length(tbl, (*n-1)*(*m-1)*(*l-1)*(*d), 14, _state); - - /* - * Fill - */ - for(i=0; i<=*n-2; i++) - { - for(j=0; j<=*m-2; j++) - { - for(k=0; k<=*l-2; k++) - { - for(di=0; di<=*d-1; di++) - { - p = *d*((*n-1)*((*m-1)*k+j)+i)+di; - tbl->ptr.pp_double[p][0] = c->x.ptr.p_double[i]; - tbl->ptr.pp_double[p][1] = c->x.ptr.p_double[i+1]; - tbl->ptr.pp_double[p][2] = c->y.ptr.p_double[j]; - tbl->ptr.pp_double[p][3] = c->y.ptr.p_double[j+1]; - tbl->ptr.pp_double[p][4] = c->z.ptr.p_double[k]; - tbl->ptr.pp_double[p][5] = c->z.ptr.p_double[k+1]; - du = 1/(tbl->ptr.pp_double[p][1]-tbl->ptr.pp_double[p][0]); - dv = 1/(tbl->ptr.pp_double[p][3]-tbl->ptr.pp_double[p][2]); - dw = 1/(tbl->ptr.pp_double[p][5]-tbl->ptr.pp_double[p][4]); - - /* - * Trilinear interpolation - */ - if( c->stype==-1 ) - { - for(i0=6; i0<=13; i0++) - { - tbl->ptr.pp_double[p][i0] = 0; - } - tbl->ptr.pp_double[p][6+2*(2*0+0)+0] = c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*0+0)+1] = c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*0+1)+0] = c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*0+1)+1] = c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*1+0)+0] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*1+0)+1] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*1+1)+0] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - tbl->ptr.pp_double[p][6+2*(2*1+1)+1] = c->f.ptr.p_double[*d*(*n*(*m*(k+1)+(j+1))+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+(j+1))+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*(k+1)+j)+i)+di]-c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+(i+1))+di]+c->f.ptr.p_double[*d*(*n*(*m*k+(j+1))+i)+di]+c->f.ptr.p_double[*d*(*n*(*m*k+j)+(i+1))+di]-c->f.ptr.p_double[*d*(*n*(*m*k+j)+i)+di]; - } - - /* - * Rescale Cij - */ - for(ci=0; ci<=1; ci++) - { - for(cj=0; cj<=1; cj++) - { - for(ck=0; ck<=1; ck++) - { - tbl->ptr.pp_double[p][6+2*(2*ck+cj)+ci] = tbl->ptr.pp_double[p][6+2*(2*ck+cj)+ci]*ae_pow(du, ci, _state)*ae_pow(dv, cj, _state)*ae_pow(dw, ck, _state); - } - } - } - } - } - } - } -} - - -/************************************************************************* -This subroutine calculates the value of the trilinear(or tricubic;possible -will be later) spline at the given point X(and its derivatives; possible -will be later). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, Z - point - -OUTPUT PARAMETERS: - F - S(x,y,z) - FX - dS(x,y,z)/dX - FY - dS(x,y,z)/dY - FXY - d2S(x,y,z)/dXdY - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -static void spline3d_spline3ddiff(spline3dinterpolant* c, - double x, - double y, - double z, - double* f, - double* fx, - double* fy, - double* fxy, - ae_state *_state) -{ - double xd; - double yd; - double zd; - double c0; - double c1; - double c2; - double c3; - ae_int_t ix; - ae_int_t iy; - ae_int_t iz; - ae_int_t l; - ae_int_t r; - ae_int_t h; - - *f = 0; - *fx = 0; - *fy = 0; - *fxy = 0; - - ae_assert(c->stype==-1||c->stype==-3, "Spline3DDiff: incorrect C (incorrect parameter C.SType)", _state); - ae_assert(ae_isfinite(x, _state)&&ae_isfinite(y, _state), "Spline3DDiff: X or Y contains NaN or Infinite value", _state); - - /* - * Prepare F, dF/dX, dF/dY, d2F/dXdY - */ - *f = 0; - *fx = 0; - *fy = 0; - *fxy = 0; - if( c->d!=1 ) - { - return; - } - - /* - * Binary search in the [ x[0], ..., x[n-2] ] (x[n-1] is not included) - */ - l = 0; - r = c->n-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->x.ptr.p_double[h],x) ) - { - r = h; - } - else - { - l = h; - } - } - ix = l; - - /* - * Binary search in the [ y[0], ..., y[n-2] ] (y[n-1] is not included) - */ - l = 0; - r = c->m-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->y.ptr.p_double[h],y) ) - { - r = h; - } - else - { - l = h; - } - } - iy = l; - - /* - * Binary search in the [ z[0], ..., z[n-2] ] (z[n-1] is not included) - */ - l = 0; - r = c->l-1; - while(l!=r-1) - { - h = (l+r)/2; - if( ae_fp_greater_eq(c->z.ptr.p_double[h],z) ) - { - r = h; - } - else - { - l = h; - } - } - iz = l; - xd = (x-c->x.ptr.p_double[ix])/(c->x.ptr.p_double[ix+1]-c->x.ptr.p_double[ix]); - yd = (y-c->y.ptr.p_double[iy])/(c->y.ptr.p_double[iy+1]-c->y.ptr.p_double[iy]); - zd = (z-c->z.ptr.p_double[iz])/(c->z.ptr.p_double[iz+1]-c->z.ptr.p_double[iz]); - - /* - * Trilinear interpolation - */ - if( c->stype==-1 ) - { - c0 = c->f.ptr.p_double[c->n*(c->m*iz+iy)+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*iz+iy)+(ix+1)]*xd; - c1 = c->f.ptr.p_double[c->n*(c->m*iz+(iy+1))+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*iz+(iy+1))+(ix+1)]*xd; - c2 = c->f.ptr.p_double[c->n*(c->m*(iz+1)+iy)+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*(iz+1)+iy)+(ix+1)]*xd; - c3 = c->f.ptr.p_double[c->n*(c->m*(iz+1)+(iy+1))+ix]*(1-xd)+c->f.ptr.p_double[c->n*(c->m*(iz+1)+(iy+1))+(ix+1)]*xd; - c0 = c0*(1-yd)+c1*yd; - c1 = c2*(1-yd)+c3*yd; - *f = c0*(1-zd)+c1*zd; - } -} - - -ae_bool _spline3dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - spline3dinterpolant *p = (spline3dinterpolant*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->y, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->z, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->f, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _spline3dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - spline3dinterpolant *dst = (spline3dinterpolant*)_dst; - spline3dinterpolant *src = (spline3dinterpolant*)_src; - dst->k = src->k; - dst->stype = src->stype; - dst->n = src->n; - dst->m = src->m; - dst->l = src->l; - dst->d = src->d; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->y, &src->y, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->z, &src->z, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->f, &src->f, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _spline3dinterpolant_clear(void* _p) -{ - spline3dinterpolant *p = (spline3dinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->y); - ae_vector_clear(&p->z); - ae_vector_clear(&p->f); -} - - -void _spline3dinterpolant_destroy(void* _p) -{ - spline3dinterpolant *p = (spline3dinterpolant*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->y); - ae_vector_destroy(&p->z); - ae_vector_destroy(&p->f); -} - - - -} - diff --git a/src/inc/alglib/interpolation.h b/src/inc/alglib/interpolation.h deleted file mode 100644 index f2c9d34..0000000 --- a/src/inc/alglib/interpolation.h +++ /dev/null @@ -1,5906 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _interpolation_pkg_h -#define _interpolation_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "alglibmisc.h" -#include "linalg.h" -#include "solvers.h" -#include "optimization.h" -#include "specialfunctions.h" -#include "integration.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - ae_int_t n; - ae_int_t nx; - ae_int_t d; - double r; - ae_int_t nw; - kdtree tree; - ae_int_t modeltype; - ae_matrix q; - ae_vector xbuf; - ae_vector tbuf; - ae_vector rbuf; - ae_matrix xybuf; - ae_int_t debugsolverfailures; - double debugworstrcond; - double debugbestrcond; -} idwinterpolant; -typedef struct -{ - ae_int_t n; - double sy; - ae_vector x; - ae_vector y; - ae_vector w; -} barycentricinterpolant; -typedef struct -{ - ae_bool periodic; - ae_int_t n; - ae_int_t k; - ae_int_t continuity; - ae_vector x; - ae_vector c; -} spline1dinterpolant; -typedef struct -{ - double taskrcond; - double rmserror; - double avgerror; - double avgrelerror; - double maxerror; -} polynomialfitreport; -typedef struct -{ - double taskrcond; - ae_int_t dbest; - double rmserror; - double avgerror; - double avgrelerror; - double maxerror; -} barycentricfitreport; -typedef struct -{ - double taskrcond; - double rmserror; - double avgerror; - double avgrelerror; - double maxerror; -} spline1dfitreport; -typedef struct -{ - double taskrcond; - ae_int_t iterationscount; - ae_int_t varidx; - double rmserror; - double avgerror; - double avgrelerror; - double maxerror; - double wrmserror; - ae_matrix covpar; - ae_vector errpar; - ae_vector errcurve; - ae_vector noise; - double r2; -} lsfitreport; -typedef struct -{ - ae_int_t optalgo; - ae_int_t m; - ae_int_t k; - double epsf; - double epsx; - ae_int_t maxits; - double stpmax; - ae_bool xrep; - ae_vector s; - ae_vector bndl; - ae_vector bndu; - ae_matrix taskx; - ae_vector tasky; - ae_int_t npoints; - ae_vector taskw; - ae_int_t nweights; - ae_int_t wkind; - ae_int_t wits; - double diffstep; - double teststep; - ae_bool xupdated; - ae_bool needf; - ae_bool needfg; - ae_bool needfgh; - ae_int_t pointindex; - ae_vector x; - ae_vector c; - double f; - ae_vector g; - ae_matrix h; - ae_vector wcur; - ae_vector tmp; - ae_vector tmpf; - ae_matrix tmpjac; - ae_matrix tmpjacw; - double tmpnoise; - matinvreport invrep; - ae_int_t repiterationscount; - ae_int_t repterminationtype; - ae_int_t repvaridx; - double reprmserror; - double repavgerror; - double repavgrelerror; - double repmaxerror; - double repwrmserror; - lsfitreport rep; - minlmstate optstate; - minlmreport optrep; - ae_int_t prevnpt; - ae_int_t prevalgo; - rcommstate rstate; -} lsfitstate; -typedef struct -{ - ae_int_t n; - ae_bool periodic; - ae_vector p; - spline1dinterpolant x; - spline1dinterpolant y; -} pspline2interpolant; -typedef struct -{ - ae_int_t n; - ae_bool periodic; - ae_vector p; - spline1dinterpolant x; - spline1dinterpolant y; - spline1dinterpolant z; -} pspline3interpolant; -typedef struct -{ - ae_int_t ny; - ae_int_t nx; - ae_int_t nc; - ae_int_t nl; - kdtree tree; - ae_matrix xc; - ae_matrix wr; - double rmax; - ae_matrix v; - ae_int_t gridtype; - ae_bool fixrad; - double lambdav; - double radvalue; - double radzvalue; - ae_int_t nlayers; - ae_int_t aterm; - ae_int_t algorithmtype; - double epsort; - double epserr; - ae_int_t maxits; - double h; - ae_int_t n; - ae_matrix x; - ae_matrix y; - ae_vector calcbufxcx; - ae_matrix calcbufx; - ae_vector calcbuftags; -} rbfmodel; -typedef struct -{ - ae_int_t arows; - ae_int_t acols; - ae_int_t annz; - ae_int_t iterationscount; - ae_int_t nmv; - ae_int_t terminationtype; -} rbfreport; -typedef struct -{ - ae_int_t k; - ae_int_t stype; - ae_int_t n; - ae_int_t m; - ae_int_t d; - ae_vector x; - ae_vector y; - ae_vector f; -} spline2dinterpolant; -typedef struct -{ - ae_int_t k; - ae_int_t stype; - ae_int_t n; - ae_int_t m; - ae_int_t l; - ae_int_t d; - ae_vector x; - ae_vector y; - ae_vector z; - ae_vector f; -} spline3dinterpolant; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - -/************************************************************************* -IDW interpolant. -*************************************************************************/ -class _idwinterpolant_owner -{ -public: - _idwinterpolant_owner(); - _idwinterpolant_owner(const _idwinterpolant_owner &rhs); - _idwinterpolant_owner& operator=(const _idwinterpolant_owner &rhs); - virtual ~_idwinterpolant_owner(); - alglib_impl::idwinterpolant* c_ptr(); - alglib_impl::idwinterpolant* c_ptr() const; -protected: - alglib_impl::idwinterpolant *p_struct; -}; -class idwinterpolant : public _idwinterpolant_owner -{ -public: - idwinterpolant(); - idwinterpolant(const idwinterpolant &rhs); - idwinterpolant& operator=(const idwinterpolant &rhs); - virtual ~idwinterpolant(); - -}; - -/************************************************************************* -Barycentric interpolant. -*************************************************************************/ -class _barycentricinterpolant_owner -{ -public: - _barycentricinterpolant_owner(); - _barycentricinterpolant_owner(const _barycentricinterpolant_owner &rhs); - _barycentricinterpolant_owner& operator=(const _barycentricinterpolant_owner &rhs); - virtual ~_barycentricinterpolant_owner(); - alglib_impl::barycentricinterpolant* c_ptr(); - alglib_impl::barycentricinterpolant* c_ptr() const; -protected: - alglib_impl::barycentricinterpolant *p_struct; -}; -class barycentricinterpolant : public _barycentricinterpolant_owner -{ -public: - barycentricinterpolant(); - barycentricinterpolant(const barycentricinterpolant &rhs); - barycentricinterpolant& operator=(const barycentricinterpolant &rhs); - virtual ~barycentricinterpolant(); - -}; - - - -/************************************************************************* -1-dimensional spline interpolant -*************************************************************************/ -class _spline1dinterpolant_owner -{ -public: - _spline1dinterpolant_owner(); - _spline1dinterpolant_owner(const _spline1dinterpolant_owner &rhs); - _spline1dinterpolant_owner& operator=(const _spline1dinterpolant_owner &rhs); - virtual ~_spline1dinterpolant_owner(); - alglib_impl::spline1dinterpolant* c_ptr(); - alglib_impl::spline1dinterpolant* c_ptr() const; -protected: - alglib_impl::spline1dinterpolant *p_struct; -}; -class spline1dinterpolant : public _spline1dinterpolant_owner -{ -public: - spline1dinterpolant(); - spline1dinterpolant(const spline1dinterpolant &rhs); - spline1dinterpolant& operator=(const spline1dinterpolant &rhs); - virtual ~spline1dinterpolant(); - -}; - -/************************************************************************* -Polynomial fitting report: - TaskRCond reciprocal of task's condition number - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error -*************************************************************************/ -class _polynomialfitreport_owner -{ -public: - _polynomialfitreport_owner(); - _polynomialfitreport_owner(const _polynomialfitreport_owner &rhs); - _polynomialfitreport_owner& operator=(const _polynomialfitreport_owner &rhs); - virtual ~_polynomialfitreport_owner(); - alglib_impl::polynomialfitreport* c_ptr(); - alglib_impl::polynomialfitreport* c_ptr() const; -protected: - alglib_impl::polynomialfitreport *p_struct; -}; -class polynomialfitreport : public _polynomialfitreport_owner -{ -public: - polynomialfitreport(); - polynomialfitreport(const polynomialfitreport &rhs); - polynomialfitreport& operator=(const polynomialfitreport &rhs); - virtual ~polynomialfitreport(); - double &taskrcond; - double &rmserror; - double &avgerror; - double &avgrelerror; - double &maxerror; - -}; - - -/************************************************************************* -Barycentric fitting report: - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error - TaskRCond reciprocal of task's condition number -*************************************************************************/ -class _barycentricfitreport_owner -{ -public: - _barycentricfitreport_owner(); - _barycentricfitreport_owner(const _barycentricfitreport_owner &rhs); - _barycentricfitreport_owner& operator=(const _barycentricfitreport_owner &rhs); - virtual ~_barycentricfitreport_owner(); - alglib_impl::barycentricfitreport* c_ptr(); - alglib_impl::barycentricfitreport* c_ptr() const; -protected: - alglib_impl::barycentricfitreport *p_struct; -}; -class barycentricfitreport : public _barycentricfitreport_owner -{ -public: - barycentricfitreport(); - barycentricfitreport(const barycentricfitreport &rhs); - barycentricfitreport& operator=(const barycentricfitreport &rhs); - virtual ~barycentricfitreport(); - double &taskrcond; - ae_int_t &dbest; - double &rmserror; - double &avgerror; - double &avgrelerror; - double &maxerror; - -}; - - -/************************************************************************* -Spline fitting report: - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error - -Fields below are filled by obsolete functions (Spline1DFitCubic, -Spline1DFitHermite). Modern fitting functions do NOT fill these fields: - TaskRCond reciprocal of task's condition number -*************************************************************************/ -class _spline1dfitreport_owner -{ -public: - _spline1dfitreport_owner(); - _spline1dfitreport_owner(const _spline1dfitreport_owner &rhs); - _spline1dfitreport_owner& operator=(const _spline1dfitreport_owner &rhs); - virtual ~_spline1dfitreport_owner(); - alglib_impl::spline1dfitreport* c_ptr(); - alglib_impl::spline1dfitreport* c_ptr() const; -protected: - alglib_impl::spline1dfitreport *p_struct; -}; -class spline1dfitreport : public _spline1dfitreport_owner -{ -public: - spline1dfitreport(); - spline1dfitreport(const spline1dfitreport &rhs); - spline1dfitreport& operator=(const spline1dfitreport &rhs); - virtual ~spline1dfitreport(); - double &taskrcond; - double &rmserror; - double &avgerror; - double &avgrelerror; - double &maxerror; - -}; - - -/************************************************************************* -Least squares fitting report. This structure contains informational fields -which are set by fitting functions provided by this unit. - -Different functions initialize different sets of fields, so you should -read documentation on specific function you used in order to know which -fields are initialized. - - TaskRCond reciprocal of task's condition number - IterationsCount number of internal iterations - - VarIdx if user-supplied gradient contains errors which were - detected by nonlinear fitter, this field is set to - index of the first component of gradient which is - suspected to be spoiled by bugs. - - RMSError RMS error - AvgError average error - AvgRelError average relative error (for non-zero Y[I]) - MaxError maximum error - - WRMSError weighted RMS error - - CovPar covariance matrix for parameters, filled by some solvers - ErrPar vector of errors in parameters, filled by some solvers - ErrCurve vector of fit errors - variability of the best-fit - curve, filled by some solvers. - Noise vector of per-point noise estimates, filled by - some solvers. - R2 coefficient of determination (non-weighted, non-adjusted), - filled by some solvers. -*************************************************************************/ -class _lsfitreport_owner -{ -public: - _lsfitreport_owner(); - _lsfitreport_owner(const _lsfitreport_owner &rhs); - _lsfitreport_owner& operator=(const _lsfitreport_owner &rhs); - virtual ~_lsfitreport_owner(); - alglib_impl::lsfitreport* c_ptr(); - alglib_impl::lsfitreport* c_ptr() const; -protected: - alglib_impl::lsfitreport *p_struct; -}; -class lsfitreport : public _lsfitreport_owner -{ -public: - lsfitreport(); - lsfitreport(const lsfitreport &rhs); - lsfitreport& operator=(const lsfitreport &rhs); - virtual ~lsfitreport(); - double &taskrcond; - ae_int_t &iterationscount; - ae_int_t &varidx; - double &rmserror; - double &avgerror; - double &avgrelerror; - double &maxerror; - double &wrmserror; - real_2d_array covpar; - real_1d_array errpar; - real_1d_array errcurve; - real_1d_array noise; - double &r2; - -}; - - -/************************************************************************* -Nonlinear fitter. - -You should use ALGLIB functions to work with fitter. -Never try to access its fields directly! -*************************************************************************/ -class _lsfitstate_owner -{ -public: - _lsfitstate_owner(); - _lsfitstate_owner(const _lsfitstate_owner &rhs); - _lsfitstate_owner& operator=(const _lsfitstate_owner &rhs); - virtual ~_lsfitstate_owner(); - alglib_impl::lsfitstate* c_ptr(); - alglib_impl::lsfitstate* c_ptr() const; -protected: - alglib_impl::lsfitstate *p_struct; -}; -class lsfitstate : public _lsfitstate_owner -{ -public: - lsfitstate(); - lsfitstate(const lsfitstate &rhs); - lsfitstate& operator=(const lsfitstate &rhs); - virtual ~lsfitstate(); - ae_bool &needf; - ae_bool &needfg; - ae_bool &needfgh; - ae_bool &xupdated; - real_1d_array c; - double &f; - real_1d_array g; - real_2d_array h; - real_1d_array x; - -}; - -/************************************************************************* -Parametric spline inteprolant: 2-dimensional curve. - -You should not try to access its members directly - use PSpline2XXXXXXXX() -functions instead. -*************************************************************************/ -class _pspline2interpolant_owner -{ -public: - _pspline2interpolant_owner(); - _pspline2interpolant_owner(const _pspline2interpolant_owner &rhs); - _pspline2interpolant_owner& operator=(const _pspline2interpolant_owner &rhs); - virtual ~_pspline2interpolant_owner(); - alglib_impl::pspline2interpolant* c_ptr(); - alglib_impl::pspline2interpolant* c_ptr() const; -protected: - alglib_impl::pspline2interpolant *p_struct; -}; -class pspline2interpolant : public _pspline2interpolant_owner -{ -public: - pspline2interpolant(); - pspline2interpolant(const pspline2interpolant &rhs); - pspline2interpolant& operator=(const pspline2interpolant &rhs); - virtual ~pspline2interpolant(); - -}; - - -/************************************************************************* -Parametric spline inteprolant: 3-dimensional curve. - -You should not try to access its members directly - use PSpline3XXXXXXXX() -functions instead. -*************************************************************************/ -class _pspline3interpolant_owner -{ -public: - _pspline3interpolant_owner(); - _pspline3interpolant_owner(const _pspline3interpolant_owner &rhs); - _pspline3interpolant_owner& operator=(const _pspline3interpolant_owner &rhs); - virtual ~_pspline3interpolant_owner(); - alglib_impl::pspline3interpolant* c_ptr(); - alglib_impl::pspline3interpolant* c_ptr() const; -protected: - alglib_impl::pspline3interpolant *p_struct; -}; -class pspline3interpolant : public _pspline3interpolant_owner -{ -public: - pspline3interpolant(); - pspline3interpolant(const pspline3interpolant &rhs); - pspline3interpolant& operator=(const pspline3interpolant &rhs); - virtual ~pspline3interpolant(); - -}; - -/************************************************************************* -RBF model. - -Never try to directly work with fields of this object - always use ALGLIB -functions to use this object. -*************************************************************************/ -class _rbfmodel_owner -{ -public: - _rbfmodel_owner(); - _rbfmodel_owner(const _rbfmodel_owner &rhs); - _rbfmodel_owner& operator=(const _rbfmodel_owner &rhs); - virtual ~_rbfmodel_owner(); - alglib_impl::rbfmodel* c_ptr(); - alglib_impl::rbfmodel* c_ptr() const; -protected: - alglib_impl::rbfmodel *p_struct; -}; -class rbfmodel : public _rbfmodel_owner -{ -public: - rbfmodel(); - rbfmodel(const rbfmodel &rhs); - rbfmodel& operator=(const rbfmodel &rhs); - virtual ~rbfmodel(); - -}; - - -/************************************************************************* -RBF solution report: -* TerminationType - termination type, positive values - success, - non-positive - failure. -*************************************************************************/ -class _rbfreport_owner -{ -public: - _rbfreport_owner(); - _rbfreport_owner(const _rbfreport_owner &rhs); - _rbfreport_owner& operator=(const _rbfreport_owner &rhs); - virtual ~_rbfreport_owner(); - alglib_impl::rbfreport* c_ptr(); - alglib_impl::rbfreport* c_ptr() const; -protected: - alglib_impl::rbfreport *p_struct; -}; -class rbfreport : public _rbfreport_owner -{ -public: - rbfreport(); - rbfreport(const rbfreport &rhs); - rbfreport& operator=(const rbfreport &rhs); - virtual ~rbfreport(); - ae_int_t &arows; - ae_int_t &acols; - ae_int_t &annz; - ae_int_t &iterationscount; - ae_int_t &nmv; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -2-dimensional spline inteprolant -*************************************************************************/ -class _spline2dinterpolant_owner -{ -public: - _spline2dinterpolant_owner(); - _spline2dinterpolant_owner(const _spline2dinterpolant_owner &rhs); - _spline2dinterpolant_owner& operator=(const _spline2dinterpolant_owner &rhs); - virtual ~_spline2dinterpolant_owner(); - alglib_impl::spline2dinterpolant* c_ptr(); - alglib_impl::spline2dinterpolant* c_ptr() const; -protected: - alglib_impl::spline2dinterpolant *p_struct; -}; -class spline2dinterpolant : public _spline2dinterpolant_owner -{ -public: - spline2dinterpolant(); - spline2dinterpolant(const spline2dinterpolant &rhs); - spline2dinterpolant& operator=(const spline2dinterpolant &rhs); - virtual ~spline2dinterpolant(); - -}; - -/************************************************************************* -3-dimensional spline inteprolant -*************************************************************************/ -class _spline3dinterpolant_owner -{ -public: - _spline3dinterpolant_owner(); - _spline3dinterpolant_owner(const _spline3dinterpolant_owner &rhs); - _spline3dinterpolant_owner& operator=(const _spline3dinterpolant_owner &rhs); - virtual ~_spline3dinterpolant_owner(); - alglib_impl::spline3dinterpolant* c_ptr(); - alglib_impl::spline3dinterpolant* c_ptr() const; -protected: - alglib_impl::spline3dinterpolant *p_struct; -}; -class spline3dinterpolant : public _spline3dinterpolant_owner -{ -public: - spline3dinterpolant(); - spline3dinterpolant(const spline3dinterpolant &rhs); - spline3dinterpolant& operator=(const spline3dinterpolant &rhs); - virtual ~spline3dinterpolant(); - -}; - -/************************************************************************* -IDW interpolation - -INPUT PARAMETERS: - Z - IDW interpolant built with one of model building - subroutines. - X - array[0..NX-1], interpolation point - -Result: - IDW interpolant Z(X) - - -- ALGLIB -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -double idwcalc(const idwinterpolant &z, const real_1d_array &x); - - -/************************************************************************* -IDW interpolant using modified Shepard method for uniform point -distributions. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - D - nodal function type, either: - * 0 constant model. Just for demonstration only, worst - model ever. - * 1 linear model, least squares fitting. Simpe model for - datasets too small for quadratic models - * 2 quadratic model, least squares fitting. Best model - available (if your dataset is large enough). - * -1 "fast" linear model, use with caution!!! It is - significantly faster than linear/quadratic and better - than constant model. But it is less robust (especially - in the presence of noise). - NQ - number of points used to calculate nodal functions (ignored - for constant models). NQ should be LARGER than: - * max(1.5*(1+NX),2^NX+1) for linear model, - * max(3/4*(NX+2)*(NX+1),2^NX+1) for quadratic model. - Values less than this threshold will be silently increased. - NW - number of points used to calculate weights and to interpolate. - Required: >=2^NX+1, values less than this threshold will be - silently increased. - Recommended value: about 2*NQ - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: - * best results are obtained with quadratic models, worst - with constant - models - * when N is large, NQ and NW must be significantly smaller than N both - to obtain optimal performance and to obtain optimal accuracy. In 2 or - 3-dimensional tasks NQ=15 and NW=25 are good values to start with. - * NQ and NW may be greater than N. In such cases they will be - automatically decreased. - * this subroutine is always succeeds (as long as correct parameters are - passed). - * see 'Multivariate Interpolation of Large Sets of Scattered Data' by - Robert J. Renka for more information on this algorithm. - * this subroutine assumes that point distribution is uniform at the small - scales. If it isn't - for example, points are concentrated along - "lines", but "lines" distribution is uniform at the larger scale - then - you should use IDWBuildModifiedShepardR() - - - -- ALGLIB PROJECT -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildmodifiedshepard(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t d, const ae_int_t nq, const ae_int_t nw, idwinterpolant &z); - - -/************************************************************************* -IDW interpolant using modified Shepard method for non-uniform datasets. - -This type of model uses constant nodal functions and interpolates using -all nodes which are closer than user-specified radius R. It may be used -when points distribution is non-uniform at the small scale, but it is at -the distances as large as R. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - R - radius, R>0 - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: -* if there is less than IDWKMin points within R-ball, algorithm selects - IDWKMin closest ones, so that continuity properties of interpolant are - preserved even far from points. - - -- ALGLIB PROJECT -- - Copyright 11.04.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildmodifiedshepardr(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const double r, idwinterpolant &z); - - -/************************************************************************* -IDW model for noisy data. - -This subroutine may be used to handle noisy data, i.e. data with noise in -OUTPUT values. It differs from IDWBuildModifiedShepard() in the following -aspects: -* nodal functions are not constrained to pass through nodes: Qi(xi)<>yi, - i.e. we have fitting instead of interpolation. -* weights which are used during least squares fitting stage are all equal - to 1.0 (independently of distance) -* "fast"-linear or constant nodal functions are not supported (either not - robust enough or too rigid) - -This problem require far more complex tuning than interpolation problems. -Below you can find some recommendations regarding this problem: -* focus on tuning NQ; it controls noise reduction. As for NW, you can just - make it equal to 2*NQ. -* you can use cross-validation to determine optimal NQ. -* optimal NQ is a result of complex tradeoff between noise level (more - noise = larger NQ required) and underlying function complexity (given - fixed N, larger NQ means smoothing of compex features in the data). For - example, NQ=N will reduce noise to the minimum level possible, but you - will end up with just constant/linear/quadratic (depending on D) least - squares model for the whole dataset. - -INPUT PARAMETERS: - XY - X and Y values, array[0..N-1,0..NX]. - First NX columns contain X-values, last column contain - Y-values. - N - number of nodes, N>0. - NX - space dimension, NX>=1. - D - nodal function degree, either: - * 1 linear model, least squares fitting. Simpe model for - datasets too small for quadratic models (or for very - noisy problems). - * 2 quadratic model, least squares fitting. Best model - available (if your dataset is large enough). - NQ - number of points used to calculate nodal functions. NQ should - be significantly larger than 1.5 times the number of - coefficients in a nodal function to overcome effects of noise: - * larger than 1.5*(1+NX) for linear model, - * larger than 3/4*(NX+2)*(NX+1) for quadratic model. - Values less than this threshold will be silently increased. - NW - number of points used to calculate weights and to interpolate. - Required: >=2^NX+1, values less than this threshold will be - silently increased. - Recommended value: about 2*NQ or larger - -OUTPUT PARAMETERS: - Z - IDW interpolant. - -NOTES: - * best results are obtained with quadratic models, linear models are not - recommended to use unless you are pretty sure that it is what you want - * this subroutine is always succeeds (as long as correct parameters are - passed). - * see 'Multivariate Interpolation of Large Sets of Scattered Data' by - Robert J. Renka for more information on this algorithm. - - - -- ALGLIB PROJECT -- - Copyright 02.03.2010 by Bochkanov Sergey -*************************************************************************/ -void idwbuildnoisy(const real_2d_array &xy, const ae_int_t n, const ae_int_t nx, const ae_int_t d, const ae_int_t nq, const ae_int_t nw, idwinterpolant &z); - -/************************************************************************* -Rational interpolation using barycentric formula - -F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i])) - -Input parameters: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -Result: - barycentric interpolant F(t) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -double barycentriccalc(const barycentricinterpolant &b, const double t); - - -/************************************************************************* -Differentiation of barycentric interpolant: first derivative. - -Algorithm used in this subroutine is very robust and should not fail until -provided with values too close to MaxRealNumber (usually MaxRealNumber/N -or greater will overflow). - -INPUT PARAMETERS: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -OUTPUT PARAMETERS: - F - barycentric interpolant at T - DF - first derivative - -NOTE - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricdiff1(const barycentricinterpolant &b, const double t, double &f, double &df); - - -/************************************************************************* -Differentiation of barycentric interpolant: first/second derivatives. - -INPUT PARAMETERS: - B - barycentric interpolant built with one of model building - subroutines. - T - interpolation point - -OUTPUT PARAMETERS: - F - barycentric interpolant at T - DF - first derivative - D2F - second derivative - -NOTE: this algorithm may fail due to overflow/underflor if used on data -whose values are close to MaxRealNumber or MinRealNumber. Use more robust -BarycentricDiff1() subroutine in such cases. - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricdiff2(const barycentricinterpolant &b, const double t, double &f, double &df, double &d2f); - - -/************************************************************************* -This subroutine performs linear transformation of the argument. - -INPUT PARAMETERS: - B - rational interpolant in barycentric form - CA, CB - transformation coefficients: x = CA*t + CB - -OUTPUT PARAMETERS: - B - transformed interpolant with X replaced by T - - -- ALGLIB PROJECT -- - Copyright 19.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriclintransx(const barycentricinterpolant &b, const double ca, const double cb); - - -/************************************************************************* -This subroutine performs linear transformation of the barycentric -interpolant. - -INPUT PARAMETERS: - B - rational interpolant in barycentric form - CA, CB - transformation coefficients: B2(x) = CA*B(x) + CB - -OUTPUT PARAMETERS: - B - transformed interpolant - - -- ALGLIB PROJECT -- - Copyright 19.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentriclintransy(const barycentricinterpolant &b, const double ca, const double cb); - - -/************************************************************************* -Extracts X/Y/W arrays from rational interpolant - -INPUT PARAMETERS: - B - barycentric interpolant - -OUTPUT PARAMETERS: - N - nodes count, N>0 - X - interpolation nodes, array[0..N-1] - F - function values, array[0..N-1] - W - barycentric weights, array[0..N-1] - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricunpack(const barycentricinterpolant &b, ae_int_t &n, real_1d_array &x, real_1d_array &y, real_1d_array &w); - - -/************************************************************************* -Rational interpolant from X/Y/W arrays - -F(t) = SUM(i=0,n-1,w[i]*f[i]/(t-x[i])) / SUM(i=0,n-1,w[i]/(t-x[i])) - -INPUT PARAMETERS: - X - interpolation nodes, array[0..N-1] - F - function values, array[0..N-1] - W - barycentric weights, array[0..N-1] - N - nodes count, N>0 - -OUTPUT PARAMETERS: - B - barycentric interpolant built from (X, Y, W) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricbuildxyw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, barycentricinterpolant &b); - - -/************************************************************************* -Rational interpolant without poles - -The subroutine constructs the rational interpolating function without real -poles (see 'Barycentric rational interpolation with no poles and high -rates of approximation', Michael S. Floater. and Kai Hormann, for more -information on this subject). - -Input parameters: - X - interpolation nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of nodes, N>0. - D - order of the interpolation scheme, 0 <= D <= N-1. - D<0 will cause an error. - D>=N it will be replaced with D=N-1. - if you don't know what D to choose, use small value about 3-5. - -Output parameters: - B - barycentric interpolant. - -Note: - this algorithm always succeeds and calculates the weights with close - to machine precision. - - -- ALGLIB PROJECT -- - Copyright 17.06.2007 by Bochkanov Sergey -*************************************************************************/ -void barycentricbuildfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t d, barycentricinterpolant &b); - -/************************************************************************* -Conversion from barycentric representation to Chebyshev basis. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - P - polynomial in barycentric form - A,B - base interval for Chebyshev polynomials (see below) - A<>B - -OUTPUT PARAMETERS - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N-1 }, - where Ti - I-th Chebyshev polynomial. - -NOTES: - barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2cheb(const barycentricinterpolant &p, const double a, const double b, real_1d_array &t); - - -/************************************************************************* -Conversion from Chebyshev basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - T - coefficients of Chebyshev representation; - P(x) = sum { T[i]*Ti(2*(x-A)/(B-A)-1), i=0..N }, - where Ti - I-th Chebyshev polynomial. - N - number of coefficients: - * if given, only leading N elements of T are used - * if not given, automatically determined from size of T - A,B - base interval for Chebyshev polynomials (see above) - A0. - -OUTPUT PARAMETERS - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if P was obtained as - result of interpolation on [-1,+1], you can set C=0 and S=1 and - represent P as sum of 1, x, x^2, x^3 and so on. In most cases you it - is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as basis. Representing P as sum of 1, (x-1000), (x-1000)^2, (x-1000)^3 - will be better option. Such representation can be obtained by using - 1000.0 as offset C and 1.0 as scale S. - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return coefficients in - any case, but for N>8 they will become unreliable. However, N's - less than 5 are pretty safe. - -3. barycentric interpolant passed as P may be either polynomial obtained - from polynomial interpolation/ fitting or rational function which is - NOT polynomial. We can't distinguish between these two cases, and this - algorithm just tries to work assuming that P IS a polynomial. If not, - algorithm will return results, but they won't have any meaning. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialbar2pow(const barycentricinterpolant &p, const double c, const double s, real_1d_array &a); -void polynomialbar2pow(const barycentricinterpolant &p, real_1d_array &a); - - -/************************************************************************* -Conversion from power basis to barycentric representation. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - A - coefficients, P(x) = sum { A[i]*((X-C)/S)^i, i=0..N-1 } - N - number of coefficients (polynomial degree plus 1) - * if given, only leading N elements of A are used - * if not given, automatically determined from size of A - C - offset (see below); 0.0 is used as default value. - S - scale (see below); 1.0 is used as default value. S<>0. - -OUTPUT PARAMETERS - P - polynomial in barycentric form - - -NOTES: -1. this function accepts offset and scale, which can be set to improve - numerical properties of polynomial. For example, if you interpolate on - [-1,+1], you can set C=0 and S=1 and convert from sum of 1, x, x^2, - x^3 and so on. In most cases you it is exactly what you need. - - However, if your interpolation model was built on [999,1001], you will - see significant growth of numerical errors when using {1, x, x^2, x^3} - as input basis. Converting from sum of 1, (x-1000), (x-1000)^2, - (x-1000)^3 will be better option (you have to specify 1000.0 as offset - C and 1.0 as scale S). - -2. power basis is ill-conditioned and tricks described above can't solve - this problem completely. This function will return barycentric model - in any case, but for N>8 accuracy well degrade. However, N's less than - 5 are pretty safe. - - -- ALGLIB -- - Copyright 30.09.2010 by Bochkanov Sergey -*************************************************************************/ -void polynomialpow2bar(const real_1d_array &a, const ae_int_t n, const double c, const double s, barycentricinterpolant &p); -void polynomialpow2bar(const real_1d_array &a, barycentricinterpolant &p); - - -/************************************************************************* -Lagrange intepolant: generation of the model on the general grid. -This function has O(N^2) complexity. - -INPUT PARAMETERS: - X - abscissas, array[0..N-1] - Y - function values, array[0..N-1] - N - number of points, N>=1 - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuild(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p); -void polynomialbuild(const real_1d_array &x, const real_1d_array &y, barycentricinterpolant &p); - - -/************************************************************************* -Lagrange intepolant: generation of the model on equidistant grid. -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1] - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p); -void polynomialbuildeqdist(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p); - - -/************************************************************************* -Lagrange intepolant on Chebyshev grid (first kind). -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1], - Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n))) - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p); -void polynomialbuildcheb1(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p); - - -/************************************************************************* -Lagrange intepolant on Chebyshev grid (second kind). -This function has O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - Y - function values at the nodes, array[0..N-1], - Y[I] = Y(0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1))) - N - number of points, N>=1 - for N=1 a constant model is constructed. - -OUTPUT PARAMETERS - P - barycentric model which represents Lagrange interpolant - (see ratint unit info and BarycentricCalc() description for - more information). - - -- ALGLIB -- - Copyright 03.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, const ae_int_t n, barycentricinterpolant &p); -void polynomialbuildcheb2(const double a, const double b, const real_1d_array &y, barycentricinterpolant &p); - - -/************************************************************************* -Fast equidistant polynomial interpolation function with O(N) complexity - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on equidistant grid, N>=1 - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolynomialBuildEqDist()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t); -double polynomialcalceqdist(const double a, const double b, const real_1d_array &f, const double t); - - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (first kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (first kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*(2*i+1)/(2*n)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb1()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t); -double polynomialcalccheb1(const double a, const double b, const real_1d_array &f, const double t); - - -/************************************************************************* -Fast polynomial interpolation function on Chebyshev points (second kind) -with O(N) complexity. - -INPUT PARAMETERS: - A - left boundary of [A,B] - B - right boundary of [A,B] - F - function values, array[0..N-1] - N - number of points on Chebyshev grid (second kind), - X[i] = 0.5*(B+A) + 0.5*(B-A)*Cos(PI*i/(n-1)) - for N=1 a constant model is constructed. - T - position where P(x) is calculated - -RESULT - value of the Lagrange interpolant at T - -IMPORTANT - this function provides fast interface which is not overflow-safe - nor it is very precise. - the best option is to use PolIntBuildCheb2()/BarycentricCalc() - subroutines unless you are pretty sure that your data will not result - in overflow. - - -- ALGLIB -- - Copyright 02.12.2009 by Bochkanov Sergey -*************************************************************************/ -double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const ae_int_t n, const double t); -double polynomialcalccheb2(const double a, const double b, const real_1d_array &f, const double t); - -/************************************************************************* -This subroutine builds linear spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c); -void spline1dbuildlinear(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c); - - -/************************************************************************* -This subroutine builds cubic spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - C - spline interpolant - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, spline1dinterpolant &c); -void spline1dbuildcubic(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c); - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns table of function derivatives d[] -(calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D - derivative values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, real_1d_array &d); -void spline1dgriddiffcubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d); - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at nodes x[], it calculates and returns tables of first and second -function derivatives d1[] and d2[] (calculated at the same nodes x[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - spline nodes - Y - function values - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - -OUTPUT PARAMETERS: - D1 - S' values at X[] - D2 - S'' values at X[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Derivative values are correctly reordered on return, so D[I] is always -equal to S'(X[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, real_1d_array &d1, real_1d_array &d2); -void spline1dgriddiff2cubic(const real_1d_array &x, const real_1d_array &y, real_1d_array &d1, real_1d_array &d2); - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2); -void spline1dconvcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2); - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[] and derivatives d2[] (calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiffcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2, real_1d_array &d2); -void spline1dconvdiffcubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, real_1d_array &d2); - - -/************************************************************************* -This function solves following problem: given table y[] of function values -at old nodes x[] and new nodes x2[], it calculates and returns table of -function values y2[], first and second derivatives d2[] and dd2[] -(calculated at x2[]). - -This function yields same result as Spline1DBuildCubic() call followed by -sequence of Spline1DDiff() calls, but it can be several times faster when -called for ordered X[] and X2[]. - -INPUT PARAMETERS: - X - old spline nodes - Y - function values - X2 - new spline nodes - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points from X/Y are used - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundLType - boundary condition type for the left boundary - BoundL - left boundary condition (first or second derivative, - depending on the BoundLType) - BoundRType - boundary condition type for the right boundary - BoundR - right boundary condition (first or second derivative, - depending on the BoundRType) - N2 - new points count: - * N2>=2 - * if given, only first N2 points from X2 are used - * if not given, automatically detected from X2 size - -OUTPUT PARAMETERS: - F2 - function values at X2[] - D2 - first derivatives at X2[] - DD2 - second derivatives at X2[] - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. -Function values are correctly reordered on return, so F2[I] is always -equal to S(X2[I]) independently of points order. - -SETTING BOUNDARY VALUES: - -The BoundLType/BoundRType parameters can have the following values: - * -1, which corresonds to the periodic (cyclic) boundary conditions. - In this case: - * both BoundLType and BoundRType must be equal to -1. - * BoundL/BoundR are ignored - * Y[last] is ignored (it is assumed to be equal to Y[first]). - * 0, which corresponds to the parabolically terminated spline - (BoundL and/or BoundR are ignored). - * 1, which corresponds to the first derivative boundary condition - * 2, which corresponds to the second derivative boundary condition - * by default, BoundType=0 is used - -PROBLEMS WITH PERIODIC BOUNDARY CONDITIONS: - -Problems with periodic boundary conditions have Y[first_point]=Y[last_point]. -However, this subroutine doesn't require you to specify equal values for -the first and last points - it automatically forces them to be equal by -copying Y[first_point] (corresponds to the leftmost, minimal X[]) to -Y[last_point]. However it is recommended to pass consistent values of Y[], -i.e. to make Y[first_point]=Y[last_point]. - - -- ALGLIB PROJECT -- - Copyright 03.09.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dconvdiff2cubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t boundltype, const double boundl, const ae_int_t boundrtype, const double boundr, const real_1d_array &x2, const ae_int_t n2, real_1d_array &y2, real_1d_array &d2, real_1d_array &dd2); -void spline1dconvdiff2cubic(const real_1d_array &x, const real_1d_array &y, const real_1d_array &x2, real_1d_array &y2, real_1d_array &d2, real_1d_array &dd2); - - -/************************************************************************* -This subroutine builds Catmull-Rom spline interpolant. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. - Y - function values, array[0..N-1]. - -OPTIONAL PARAMETERS: - N - points count: - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - BoundType - boundary condition type: - * -1 for periodic boundary condition - * 0 for parabolically terminated spline (default) - Tension - tension parameter: - * tension=0 corresponds to classic Catmull-Rom spline (default) - * 0=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, const ae_int_t n, spline1dinterpolant &c); -void spline1dbuildhermite(const real_1d_array &x, const real_1d_array &y, const real_1d_array &d, spline1dinterpolant &c); - - -/************************************************************************* -This subroutine builds Akima spline interpolant - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1] - Y - function values, array[0..N-1] - N - points count (optional): - * N>=2 - * if given, only first N points are used to build spline - * if not given, automatically detected from X/Y sizes - (len(X) must be equal to len(Y)) - -OUTPUT PARAMETERS: - C - spline interpolant - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c); -void spline1dbuildakima(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c); - - -/************************************************************************* -This subroutine calculates the value of the spline at the given point X. - -INPUT PARAMETERS: - C - spline interpolant - X - point - -Result: - S(x) - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -double spline1dcalc(const spline1dinterpolant &c, const double x); - - -/************************************************************************* -This subroutine differentiates the spline. - -INPUT PARAMETERS: - C - spline interpolant. - X - point - -Result: - S - S(x) - DS - S'(x) - D2S - S''(x) - - -- ALGLIB PROJECT -- - Copyright 24.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1ddiff(const spline1dinterpolant &c, const double x, double &s, double &ds, double &d2s); - - -/************************************************************************* -This subroutine unpacks the spline into the coefficients table. - -INPUT PARAMETERS: - C - spline interpolant. - X - point - -OUTPUT PARAMETERS: - Tbl - coefficients table, unpacked format, array[0..N-2, 0..5]. - For I = 0...N-2: - Tbl[I,0] = X[i] - Tbl[I,1] = X[i+1] - Tbl[I,2] = C0 - Tbl[I,3] = C1 - Tbl[I,4] = C2 - Tbl[I,5] = C3 - On [x[i], x[i+1]] spline is equals to: - S(x) = C0 + C1*t + C2*t^2 + C3*t^3 - t = x-x[i] - -NOTE: - You can rebuild spline with Spline1DBuildHermite() function, which - accepts as inputs function values and derivatives at nodes, which are - easy to calculate when you have coefficients. - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dunpack(const spline1dinterpolant &c, ae_int_t &n, real_2d_array &tbl); - - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: x = A*t + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dlintransx(const spline1dinterpolant &c, const double a, const double b); - - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: S2(x) = A*S(x) + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline1dlintransy(const spline1dinterpolant &c, const double a, const double b); - - -/************************************************************************* -This subroutine integrates the spline. - -INPUT PARAMETERS: - C - spline interpolant. - X - right bound of the integration interval [a, x], - here 'a' denotes min(x[]) -Result: - integral(S(t)dt,a,x) - - -- ALGLIB PROJECT -- - Copyright 23.06.2007 by Bochkanov Sergey -*************************************************************************/ -double spline1dintegrate(const spline1dinterpolant &c, const double x); - - -/************************************************************************* -This function builds monotone cubic Hermite interpolant. This interpolant -is monotonic in [x(0),x(n-1)] and is constant outside of this interval. - -In case y[] form non-monotonic sequence, interpolant is piecewise -monotonic. Say, for x=(0,1,2,3,4) and y=(0,1,2,1,0) interpolant will -monotonically grow at [0..2] and monotonically decrease at [2..4]. - -INPUT PARAMETERS: - X - spline nodes, array[0..N-1]. Subroutine automatically - sorts points, so caller may pass unsorted array. - Y - function values, array[0..N-1] - N - the number of points(N>=2). - -OUTPUT PARAMETERS: - C - spline interpolant. - - -- ALGLIB PROJECT -- - Copyright 21.06.2012 by Bochkanov Sergey -*************************************************************************/ -void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, spline1dinterpolant &c); -void spline1dbuildmonotone(const real_1d_array &x, const real_1d_array &y, spline1dinterpolant &c); - -/************************************************************************* -Fitting by polynomials in barycentric form. This function provides simple -unterface for unconstrained unweighted fitting. See PolynomialFitWC() if -you need constrained fitting. - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFitWC() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0 - * if given, only leading N elements of X/Y are used - * if not given, automatically determined from sizes of X/Y - M - number of basis functions (= polynomial_degree + 1), M>=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep); -void polynomialfit(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep); - - -/************************************************************************* -Weighted fitting by polynomials in barycentric form, with constraints on -function values or first derivatives. - -Small regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO: - PolynomialFit() - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points, N>0. - * if given, only leading N elements of X/Y/W are used - * if not given, automatically determined from sizes of X/Y/W - XC - points where polynomial values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that P(XC[i])=YC[i] - * DC[i]=1 means that P'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=1 - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - P - interpolant in barycentric form. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTES: - you can convert P from barycentric form to the power or Chebyshev - basis with PolynomialBar2Pow() or PolynomialBar2Cheb() functions from - POLINT subpackage. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* even simple constraints can be inconsistent, see Wikipedia article on - this subject: http://en.wikipedia.org/wiki/Birkhoff_interpolation -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the one special cases, however, we can guarantee consistency. This - case is: M>1 and constraints on the function values (NOT DERIVATIVES) - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 10.12.2009 by Bochkanov Sergey -*************************************************************************/ -void polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep); -void polynomialfitwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, barycentricinterpolant &p, polynomialfitreport &rep); - - -/************************************************************************* -Weghted rational least squares fitting using Floater-Hormann rational -functions with optimal D chosen from [0,9], with constraints and -individual weights. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least WEIGHTED root -mean square error) is chosen. Task is linear, so linear least squares -solver is used. Complexity of this computational scheme is O(N*M^2) -(mostly dominated by the least squares solver). - -SEE ALSO -* BarycentricFitFloaterHormann(), "lightweight" fitting without invididual - weights and constraints. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points, N>0. - XC - points where function values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints, 0<=K=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -1 means another errors in parameters passed - (N<=0, for example) - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroutine doesn't calculate task's condition number for K<>0. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained barycentric interpolants: -* excessive constraints can be inconsistent. Floater-Hormann basis - functions aren't as flexible as splines (although they are very smooth). -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function VALUES at the interval - boundaries. Note that consustency of the constraints on the function - DERIVATIVES is NOT guaranteed (you can use in such cases cubic splines - which are more flexible). -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricfitfloaterhormannwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep); - - -/************************************************************************* -Rational least squares fitting using Floater-Hormann rational functions -with optimal D chosen from [0,9]. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least root mean -square error) is chosen. Task is linear, so linear least squares solver -is used. Complexity of this computational scheme is O(N*M^2) (mostly -dominated by the least squares solver). - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void barycentricfitfloaterhormann(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, barycentricinterpolant &b, barycentricfitreport &rep); - - -/************************************************************************* -Rational least squares fitting using Floater-Hormann rational functions -with optimal D chosen from [0,9]. - -Equidistant grid with M node on [min(x),max(x)] is used to build basis -functions. Different values of D are tried, optimal D (least root mean -square error) is chosen. Task is linear, so linear least squares solver -is used. Complexity of this computational scheme is O(N*M^2) (mostly -dominated by the least squares solver). - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - N - number of points, N>0. - M - number of basis functions ( = number_of_nodes), M>=2. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - B - barycentric interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * DBest best value of the D parameter - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); -void spline1dfitpenalized(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); - - -/************************************************************************* -Weighted fitting by penalized cubic spline. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with natural boundary -conditions. Problem is regularized by adding non-linearity penalty to the -usual least squares penalty function: - - S(x) = arg min { LS + P }, where - LS = SUM { w[i]^2*(y[i] - S(x[i]))^2 } - least squares penalty - P = C*10^rho*integral{ S''(x)^2*dx } - non-linearity penalty - rho - tunable constant given by user - C - automatically determined scale parameter, - makes penalty invariant with respect to scaling of X, Y, W. - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - problem. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - M - number of basis functions ( = number_of_nodes), M>=4. - Rho - regularization constant passed by user. It penalizes - nonlinearity in the regression spline. It is logarithmically - scaled, i.e. actual value of regularization constant is - calculated as 10^Rho. It is automatically scaled so that: - * Rho=2.0 corresponds to moderate amount of nonlinearity - * generally, it should be somewhere in the [-8.0,+8.0] - If you do not want to penalize nonlineary, - pass small Rho. Values as low as -15 should work. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD or - Cholesky decomposition; problem may be - too ill-conditioned (very rare) - S - spline interpolant. - Rep - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -NOTE 1: additional nodes are added to the spline outside of the fitting -interval to force linearity when xmax(x,xc). It is done -for consistency - we penalize non-linearity at [min(x,xc),max(x,xc)], so -it is natural to force linearity outside of this interval. - -NOTE 2: function automatically sorts points, so caller may pass unsorted -array. - - -- ALGLIB PROJECT -- - Copyright 19.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); -void spline1dfitpenalizedw(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t m, const double rho, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); - - -/************************************************************************* -Weighted fitting by cubic spline, with constraints on function values or -derivatives. - -Equidistant grid with M-2 nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are cubic splines with continuous second -derivatives and non-fixed first derivatives at interval ends. Small -regularizing term is used when solving constrained tasks (to improve -stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitHermiteWC() - fitting by Hermite splines (more flexible, - less smooth) - Spline1DFitCubic() - "lightweight" fitting by cubic splines, - without invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4. - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearWC() subroutine. - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - S - spline interpolant. - Rep - report, same format as in LSFitLinearWC() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints IS NOT GUARANTEED. -* in the several special cases, however, we CAN guarantee consistency. -* one of this cases is constraints on the function values AND/OR its - derivatives at the interval boundaries. -* another special case is ONE constraint on the function value (OR, but - not AND, derivative) anywhere in the interval - -Our final recommendation is to use constraints WHEN AND ONLY WHEN you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); -void spline1dfitcubicwc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); - - -/************************************************************************* -Weighted fitting by Hermite spline, with constraints on function values -or first derivatives. - -Equidistant grid with M nodes on [min(x,xc),max(x,xc)] is used to build -basis functions. Basis functions are Hermite splines. Small regularizing -term is used when solving constrained tasks (to improve stability). - -Task is linear, so linear least squares solver is used. Complexity of this -computational scheme is O(N*M^2), mostly dominated by least squares solver - -SEE ALSO - Spline1DFitCubicWC() - fitting by Cubic splines (less flexible, - more smooth) - Spline1DFitHermite() - "lightweight" Hermite fitting, without - invididual weights and constraints - -INPUT PARAMETERS: - X - points, array[0..N-1]. - Y - function values, array[0..N-1]. - W - weights, array[0..N-1] - Each summand in square sum of approximation deviations from - given values is multiplied by the square of corresponding - weight. Fill it by 1's if you don't want to solve weighted - task. - N - number of points (optional): - * N>0 - * if given, only first N elements of X/Y/W are processed - * if not given, automatically determined from X/Y/W sizes - XC - points where spline values/derivatives are constrained, - array[0..K-1]. - YC - values of constraints, array[0..K-1] - DC - array[0..K-1], types of constraints: - * DC[i]=0 means that S(XC[i])=YC[i] - * DC[i]=1 means that S'(XC[i])=YC[i] - SEE BELOW FOR IMPORTANT INFORMATION ON CONSTRAINTS - K - number of constraints (optional): - * 0<=K=4, - M IS EVEN! - -OUTPUT PARAMETERS: - Info- same format as in LSFitLinearW() subroutine: - * Info>0 task is solved - * Info<=0 an error occured: - -4 means inconvergence of internal SVD - -3 means inconsistent constraints - -2 means odd M was passed (which is not supported) - -1 means another errors in parameters passed - (N<=0, for example) - S - spline interpolant. - Rep - report, same format as in LSFitLinearW() subroutine. - Following fields are set: - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -IMPORTANT: - this subroitine supports only even M's - - -ORDER OF POINTS - -Subroutine automatically sorts points, so caller may pass unsorted array. - -SETTING CONSTRAINTS - DANGERS AND OPPORTUNITIES: - -Setting constraints can lead to undesired results, like ill-conditioned -behavior, or inconsistency being detected. From the other side, it allows -us to improve quality of the fit. Here we summarize our experience with -constrained regression splines: -* excessive constraints can be inconsistent. Splines are piecewise cubic - functions, and it is easy to create an example, where large number of - constraints concentrated in small area will result in inconsistency. - Just because spline is not flexible enough to satisfy all of them. And - same constraints spread across the [min(x),max(x)] will be perfectly - consistent. -* the more evenly constraints are spread across [min(x),max(x)], the more - chances that they will be consistent -* the greater is M (given fixed constraints), the more chances that - constraints will be consistent -* in the general case, consistency of constraints is NOT GUARANTEED. -* in the several special cases, however, we can guarantee consistency. -* one of this cases is M>=4 and constraints on the function value - (AND/OR its derivative) at the interval boundaries. -* another special case is M>=4 and ONE constraint on the function value - (OR, BUT NOT AND, derivative) anywhere in [min(x),max(x)] - -Our final recommendation is to use constraints WHEN AND ONLY when you -can't solve your task without them. Anything beyond special cases given -above is not guaranteed and may result in inconsistency. - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const ae_int_t n, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t k, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); -void spline1dfithermitewc(const real_1d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &xc, const real_1d_array &yc, const integer_1d_array &dc, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); - - -/************************************************************************* -Least squares fitting by cubic spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitCubicWC(). See Spline1DFitCubicWC() for more information -about subroutine parameters (we don't duplicate it here because of length) - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); -void spline1dfitcubic(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); - - -/************************************************************************* -Least squares fitting by Hermite spline. - -This subroutine is "lightweight" alternative for more complex and feature- -rich Spline1DFitHermiteWC(). See Spline1DFitHermiteWC() description for -more information about subroutine parameters (we don't duplicate it here -because of length). - - -- ALGLIB PROJECT -- - Copyright 18.08.2009 by Bochkanov Sergey -*************************************************************************/ -void spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t n, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); -void spline1dfithermite(const real_1d_array &x, const real_1d_array &y, const ae_int_t m, ae_int_t &info, spline1dinterpolant &s, spline1dfitreport &rep); - - -/************************************************************************* -Weighted linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - W - array[0..N-1] Weights corresponding to function values. - Each summand in square sum of approximation deviations - from given values is multiplied by the square of - corresponding weight. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -1 incorrect N/M were specified - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep); -void lsfitlinearw(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep); - - -/************************************************************************* -Weighted constained linear least squares fitting. - -This is variation of LSFitLinearW(), which searchs for min|A*x=b| given -that K additional constaints C*x=bc are satisfied. It reduces original -task to modified one: min|B*y-d| WITHOUT constraints, then LSFitLinearW() -is called. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - W - array[0..N-1] Weights corresponding to function values. - Each summand in square sum of approximation deviations - from given values is multiplied by the square of - corresponding weight. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I,J] - value of J-th basis function in I-th point. - CMatrix - a table of constaints, array[0..K-1,0..M]. - I-th row of CMatrix corresponds to I-th linear constraint: - CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M] - N - number of points used. N>=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep); -void lsfitlinearwc(const real_1d_array &y, const real_1d_array &w, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep); - - -/************************************************************************* -Linear least squares fitting. - -QR decomposition is used to reduce task to MxM, then triangular solver or -SVD-based solver is used depending on condition number of the system. It -allows to maximize speed and retain decent accuracy. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I, J] - value of J-th basis function in I-th point. - N - number of points used. N>=1. - M - number of basis functions, M>=1. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * Rep.TaskRCond reciprocal of condition number - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, const ae_int_t n, const ae_int_t m, ae_int_t &info, real_1d_array &c, lsfitreport &rep); -void lsfitlinear(const real_1d_array &y, const real_2d_array &fmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep); - - -/************************************************************************* -Constained linear least squares fitting. - -This is variation of LSFitLinear(), which searchs for min|A*x=b| given -that K additional constaints C*x=bc are satisfied. It reduces original -task to modified one: min|B*y-d| WITHOUT constraints, then LSFitLinear() -is called. - -IMPORTANT: if you want to perform polynomial fitting, it may be more - convenient to use PolynomialFit() function. This function gives - best results on polynomial problems and solves numerical - stability issues which arise when you fit high-degree - polynomials to your data. - -INPUT PARAMETERS: - Y - array[0..N-1] Function values in N points. - FMatrix - a table of basis functions values, array[0..N-1, 0..M-1]. - FMatrix[I,J] - value of J-th basis function in I-th point. - CMatrix - a table of constaints, array[0..K-1,0..M]. - I-th row of CMatrix corresponds to I-th linear constraint: - CMatrix[I,0]*C[0] + ... + CMatrix[I,M-1]*C[M-1] = CMatrix[I,M] - N - number of points used. N>=1. - M - number of basis functions, M>=1. - K - number of constraints, 0 <= K < M - K=0 corresponds to absence of constraints. - -OUTPUT PARAMETERS: - Info - error code: - * -4 internal SVD decomposition subroutine failed (very - rare and for degenerate systems only) - * -3 either too many constraints (M or more), - degenerate constraints (some constraints are - repetead twice) or inconsistent constraints were - specified. - * 1 task is solved - C - decomposition coefficients, array[0..M-1] - Rep - fitting report. Following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - -IMPORTANT: - this subroitine doesn't calculate task's condition number for K<>0. - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(F*CovPar*F')), - where F is functions matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 07.09.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, const ae_int_t n, const ae_int_t m, const ae_int_t k, ae_int_t &info, real_1d_array &c, lsfitreport &rep); -void lsfitlinearc(const real_1d_array &y, const real_2d_array &fmatrix, const real_2d_array &cmatrix, ae_int_t &info, real_1d_array &c, lsfitreport &rep); - - -/************************************************************************* -Weighted nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewf(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const double diffstep, lsfitstate &state); -void lsfitcreatewf(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const double diffstep, lsfitstate &state); - - -/************************************************************************* -Nonlinear least squares fitting using function values only. - -Combination of numerical differentiation and secant updates is used to -obtain function Jacobian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (f(c,x[0])-y[0])^2 + ... + (f(c,x[n-1])-y[n-1])^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]). - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - DiffStep- numerical differentiation step; - should not be very small or large; - large = loss of accuracy - small = growth of round-off errors - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 18.10.2008 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const double diffstep, lsfitstate &state); -void lsfitcreatef(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const double diffstep, lsfitstate &state); - - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient only. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also: - LSFitResults - LSFitCreateFG (fitting without weights) - LSFitCreateWFGH (fitting using Hessian) - LSFitCreateFGH (fitting using Hessian, without weights) - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const bool cheapfg, lsfitstate &state); -void lsfitcreatewfg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const bool cheapfg, lsfitstate &state); - - -/************************************************************************* -Nonlinear least squares fitting using gradient only, without individual -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses only f(c,x[i]) and its gradient. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - CheapFG - boolean flag, which is: - * True if both function and gradient calculation complexity - are less than O(M^2). An improved algorithm can - be used which corresponds to FGJ scheme from - MINLM unit. - * False otherwise. - Standard Jacibian-bases Levenberg-Marquardt algo - will be used (FJ scheme). - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, const bool cheapfg, lsfitstate &state); -void lsfitcreatefg(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const bool cheapfg, lsfitstate &state); - - -/************************************************************************* -Weighted nonlinear least squares fitting using gradient/Hessian. - -Nonlinear task min(F(c)) is solved, where - - F(c) = (w[0]*(f(c,x[0])-y[0]))^2 + ... + (w[n-1]*(f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * w is an N-dimensional vector of weight coefficients, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - W - weights, array[0..N-1] - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, lsfitstate &state); -void lsfitcreatewfgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &w, const real_1d_array &c, lsfitstate &state); - - -/************************************************************************* -Nonlinear least squares fitting using gradient/Hessian, without individial -weights. - -Nonlinear task min(F(c)) is solved, where - - F(c) = ((f(c,x[0])-y[0]))^2 + ... + ((f(c,x[n-1])-y[n-1]))^2, - - * N is a number of points, - * M is a dimension of a space points belong to, - * K is a dimension of a space of parameters being fitted, - * x is a set of N points, each of them is an M-dimensional vector, - * c is a K-dimensional vector of parameters being fitted - -This subroutine uses f(c,x[i]), its gradient and its Hessian. - -INPUT PARAMETERS: - X - array[0..N-1,0..M-1], points (one row = one point) - Y - array[0..N-1], function values. - C - array[0..K-1], initial approximation to the solution, - N - number of points, N>1 - M - dimension of space - K - number of parameters being fitted - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, const ae_int_t n, const ae_int_t m, const ae_int_t k, lsfitstate &state); -void lsfitcreatefgh(const real_2d_array &x, const real_1d_array &y, const real_1d_array &c, lsfitstate &state); - - -/************************************************************************* -Stopping conditions for nonlinear least squares fitting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsF - stopping criterion. Algorithm stops if - |F(k+1)-F(k)| <= EpsF*max{|F(k)|, |F(k+1)|, 1} - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by LSFitSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. Only Levenberg-Marquardt - iterations are counted (L-BFGS/CG iterations are NOT - counted because their cost is very low compared to that of - LM). - -NOTE - -Passing EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to automatic -stopping criterion selection (according to the scheme used by MINLM unit). - - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetcond(const lsfitstate &state, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - -NOTE: non-zero StpMax leads to moderate performance degradation because -intermediate step of preconditioned L-BFGS optimization is incompatible -with limits on step size. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetstpmax(const lsfitstate &state, const double stpmax); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -When reports are needed, State.C (current parameters) and State.F (current -value of fitting function) are reported. - - - -- ALGLIB -- - Copyright 15.08.2010 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetxrep(const lsfitstate &state, const bool needxrep); - - -/************************************************************************* -This function sets scaling coefficients for underlying optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Generally, scale is NOT considered to be a form of preconditioner. But LM -optimizer is unique in that it uses scaling matrix both in the stopping -condition tests and as Marquardt damping factor. - -Proper scaling is very important for the algorithm performance. It is less -important for the quality of results, but still has some influence (it is -easier to converge when variables are properly scaled, so premature -stopping is possible when very badly scalled variables are combined with -relaxed stopping conditions). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetscale(const lsfitstate &state, const real_1d_array &s); - - -/************************************************************************* -This function sets boundary constraints for underlying optimizer - -Boundary constraints are inactive by default (after initial creation). -They are preserved until explicitly turned off with another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[K]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[K]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: unlike other constrained optimization algorithms, this solver has -following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetbc(const lsfitstate &state, const real_1d_array &bndl, const real_1d_array &bndu); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool lsfititeration(const lsfitstate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear fitter - -These functions accept following parameters: - state - algorithm state - func - callback which calculates function (or merit function) - value func at given point x - grad - callback which calculates function (or merit function) - value func and gradient grad at given point x - hess - callback which calculates function (or merit function) - value func, gradient grad and Hessian hess at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - -NOTES: - -1. this algorithm is somewhat unusual because it works with parameterized - function f(C,X), where X is a function argument (we have many points - which are characterized by different argument values), and C is a - parameter to fit. - - For example, if we want to do linear fit by f(c0,c1,x) = c0*x+c1, then - x will be argument, and {c0,c1} will be parameters. - - It is important to understand that this algorithm finds minimum in the - space of function PARAMETERS (not arguments), so it needs derivatives - of f() with respect to C, not X. - - In the example above it will need f=c0*x+c1 and {df/dc0,df/dc1} = {x,1} - instead of {df/dx} = {c0}. - -2. Callback functions accept C as the first parameter, and X as the second - -3. If state was created with LSFitCreateFG(), algorithm needs just - function and its gradient, but if state was created with - LSFitCreateFGH(), algorithm will need function, gradient and Hessian. - - According to the said above, there ase several versions of this - function, which accept different sets of callbacks. - - This flexibility opens way to subtle errors - you may create state with - LSFitCreateFGH() (optimization using Hessian), but call function which - does not accept Hessian. So when algorithm will request Hessian, there - will be no callback to call. In this case exception will be thrown. - - Be careful to avoid such errors because there is no way to find them at - compile time - you can see them at runtime only. - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey - -*************************************************************************/ -void lsfitfit(lsfitstate &state, - void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &c, double func, void *ptr) = NULL, - void *ptr = NULL); -void lsfitfit(lsfitstate &state, - void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &c, double func, void *ptr) = NULL, - void *ptr = NULL); -void lsfitfit(lsfitstate &state, - void (*func)(const real_1d_array &c, const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*hess)(const real_1d_array &c, const real_1d_array &x, double &func, real_1d_array &grad, real_2d_array &hess, void *ptr), - void (*rep)(const real_1d_array &c, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -Nonlinear least squares fitting results. - -Called after return from LSFitFit(). - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - Info - completion code: - * -7 gradient verification failed. - See LSFitSetGradientCheck() for more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - C - array[0..K-1], solution - Rep - optimization report. On success following fields are set: - * R2 non-adjusted coefficient of determination - (non-weighted) - * RMSError rms error on the (X,Y). - * AvgError average error on the (X,Y). - * AvgRelError average relative error on the non-zero Y - * MaxError maximum error - NON-WEIGHTED ERRORS ARE CALCULATED - * WRMSError weighted rms error on the (X,Y). - -ERRORS IN PARAMETERS - -This solver also calculates different kinds of errors in parameters and -fills corresponding fields of report: -* Rep.CovPar covariance matrix for parameters, array[K,K]. -* Rep.ErrPar errors in parameters, array[K], - errpar = sqrt(diag(CovPar)) -* Rep.ErrCurve vector of fit errors - standard deviations of empirical - best-fit curve from "ideal" best-fit curve built with - infinite number of samples, array[N]. - errcurve = sqrt(diag(J*CovPar*J')), - where J is Jacobian matrix. -* Rep.Noise vector of per-point estimates of noise, array[N] - -IMPORTANT: errors in parameters are calculated without taking into - account boundary/linear constraints! Presence of constraints - changes distribution of errors, but there is no easy way to - account for constraints when you calculate covariance matrix. - -NOTE: noise in the data is estimated as follows: - * for fitting without user-supplied weights all points are - assumed to have same level of noise, which is estimated from - the data - * for fitting with user-supplied weights we assume that noise - level in I-th point is inversely proportional to Ith weight. - Coefficient of proportionality is estimated from the data. - -NOTE: we apply small amount of regularization when we invert squared - Jacobian and calculate covariance matrix. It guarantees that - algorithm won't divide by zero during inversion, but skews - error estimates a bit (fractional error is about 10^-9). - - However, we believe that this difference is insignificant for - all practical purposes except for the situation when you want - to compare ALGLIB results with "reference" implementation up - to the last significant digit. - -NOTE: covariance matrix is estimated using correction for degrees - of freedom (covariances are divided by N-M instead of dividing - by N). - - -- ALGLIB -- - Copyright 17.08.2009 by Bochkanov Sergey -*************************************************************************/ -void lsfitresults(const lsfitstate &state, ae_int_t &info, real_1d_array &c, lsfitreport &rep); - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before fitting begins -* LSFitFit() is called -* prior to actual fitting, for each point in data set X_i and each - component of parameters being fited C_j algorithm performs following - steps: - * two trial steps are made to C_j-TestStep*S[j] and C_j+TestStep*S[j], - where C_j is j-th parameter and S[j] is a scale of j-th parameter - * if needed, steps are bounded with respect to constraints on C[] - * F(X_i|C) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N*K (points count * parameters count) gradient - evaluations. It is very costly and you should use it only for low - dimensional problems, when you want to be sure that you've - correctly calculated analytic derivatives. You should not use it - in the production code (unless you want to check derivatives - provided by some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with LSFitSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -NOTE 4: this function works only for optimizers created with LSFitCreateWFG() - or LSFitCreateFG() constructors. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void lsfitsetgradientcheck(const lsfitstate &state, const double teststep); - -/************************************************************************* -This function builds non-periodic 2-dimensional parametric spline which -starts at (X[0],Y[0]) and ends at (X[N-1],Y[N-1]). - -INPUT PARAMETERS: - XY - points, array[0..N-1,0..1]. - XY[I,0:1] corresponds to the Ith point. - Order of points is important! - N - points count, N>=5 for Akima splines, N>=2 for other types of - splines. - ST - spline type: - * 0 Akima spline - * 1 parabolically terminated Catmull-Rom spline (Tension=0) - * 2 parabolically terminated cubic spline - PT - parameterization type: - * 0 uniform - * 1 chord length - * 2 centripetal - -OUTPUT PARAMETERS: - P - parametric spline interpolant - - -NOTES: -* this function assumes that there all consequent points are distinct. - I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2), (x2,y2)<>(x3,y3) and so on. - However, non-consequent points may coincide, i.e. we can have (x0,y0)= - =(x2,y2). - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2build(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline2interpolant &p); - - -/************************************************************************* -This function builds non-periodic 3-dimensional parametric spline which -starts at (X[0],Y[0],Z[0]) and ends at (X[N-1],Y[N-1],Z[N-1]). - -Same as PSpline2Build() function, but for 3D, so we won't duplicate its -description here. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3build(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline3interpolant &p); - - -/************************************************************************* -This function builds periodic 2-dimensional parametric spline which -starts at (X[0],Y[0]), goes through all points to (X[N-1],Y[N-1]) and then -back to (X[0],Y[0]). - -INPUT PARAMETERS: - XY - points, array[0..N-1,0..1]. - XY[I,0:1] corresponds to the Ith point. - XY[N-1,0:1] must be different from XY[0,0:1]. - Order of points is important! - N - points count, N>=3 for other types of splines. - ST - spline type: - * 1 Catmull-Rom spline (Tension=0) with cyclic boundary conditions - * 2 cubic spline with cyclic boundary conditions - PT - parameterization type: - * 0 uniform - * 1 chord length - * 2 centripetal - -OUTPUT PARAMETERS: - P - parametric spline interpolant - - -NOTES: -* this function assumes that there all consequent points are distinct. - I.e. (x0,y0)<>(x1,y1), (x1,y1)<>(x2,y2), (x2,y2)<>(x3,y3) and so on. - However, non-consequent points may coincide, i.e. we can have (x0,y0)= - =(x2,y2). -* last point of sequence is NOT equal to the first point. You shouldn't - make curve "explicitly periodic" by making them equal. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2buildperiodic(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline2interpolant &p); - - -/************************************************************************* -This function builds periodic 3-dimensional parametric spline which -starts at (X[0],Y[0],Z[0]), goes through all points to (X[N-1],Y[N-1],Z[N-1]) -and then back to (X[0],Y[0],Z[0]). - -Same as PSpline2Build() function, but for 3D, so we won't duplicate its -description here. - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3buildperiodic(const real_2d_array &xy, const ae_int_t n, const ae_int_t st, const ae_int_t pt, pspline3interpolant &p); - - -/************************************************************************* -This function returns vector of parameter values correspoding to points. - -I.e. for P created from (X[0],Y[0])...(X[N-1],Y[N-1]) and U=TValues(P) we -have - (X[0],Y[0]) = PSpline2Calc(P,U[0]), - (X[1],Y[1]) = PSpline2Calc(P,U[1]), - (X[2],Y[2]) = PSpline2Calc(P,U[2]), - ... - -INPUT PARAMETERS: - P - parametric spline interpolant - -OUTPUT PARAMETERS: - N - array size - T - array[0..N-1] - - -NOTES: -* for non-periodic splines U[0]=0, U[0]1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-position - Y - Y-position - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2calc(const pspline2interpolant &p, const double t, double &x, double &y); - - -/************************************************************************* -This function calculates the value of the parametric spline for a given -value of parameter T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-position - Y - Y-position - Z - Z-position - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3calc(const pspline3interpolant &p, const double t, double &x, double &y, double &z); - - -/************************************************************************* -This function calculates tangent vector for a given value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-component of tangent vector (normalized) - Y - Y-component of tangent vector (normalized) - -NOTE: - X^2+Y^2 is either 1 (for non-zero tangent vector) or 0. - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2tangent(const pspline2interpolant &p, const double t, double &x, double &y); - - -/************************************************************************* -This function calculates tangent vector for a given value of parameter T - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-component of tangent vector (normalized) - Y - Y-component of tangent vector (normalized) - Z - Z-component of tangent vector (normalized) - -NOTE: - X^2+Y^2+Z^2 is either 1 (for non-zero tangent vector) or 0. - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3tangent(const pspline3interpolant &p, const double t, double &x, double &y, double &z); - - -/************************************************************************* -This function calculates derivative, i.e. it returns (dX/dT,dY/dT). - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - X-derivative - Y - Y-value - DY - Y-derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2diff(const pspline2interpolant &p, const double t, double &x, double &dx, double &y, double &dy); - - -/************************************************************************* -This function calculates derivative, i.e. it returns (dX/dT,dY/dT,dZ/dT). - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - X-derivative - Y - Y-value - DY - Y-derivative - Z - Z-value - DZ - Z-derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3diff(const pspline3interpolant &p, const double t, double &x, double &dx, double &y, double &dy, double &z, double &dz); - - -/************************************************************************* -This function calculates first and second derivative with respect to T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - derivative - D2X - second derivative - Y - Y-value - DY - derivative - D2Y - second derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline2diff2(const pspline2interpolant &p, const double t, double &x, double &dx, double &d2x, double &y, double &dy, double &d2y); - - -/************************************************************************* -This function calculates first and second derivative with respect to T. - -INPUT PARAMETERS: - P - parametric spline interpolant - T - point: - * T in [0,1] corresponds to interval spanned by points - * for non-periodic splines T<0 (or T>1) correspond to parts of - the curve before the first (after the last) point - * for periodic splines T<0 (or T>1) are projected into [0,1] - by making T=T-floor(T). - -OUTPUT PARAMETERS: - X - X-value - DX - derivative - D2X - second derivative - Y - Y-value - DY - derivative - D2Y - second derivative - Z - Z-value - DZ - derivative - D2Z - second derivative - - - -- ALGLIB PROJECT -- - Copyright 28.05.2010 by Bochkanov Sergey -*************************************************************************/ -void pspline3diff2(const pspline3interpolant &p, const double t, double &x, double &dx, double &d2x, double &y, double &dy, double &d2y, double &z, double &dz, double &d2z); - - -/************************************************************************* -This function calculates arc length, i.e. length of curve between t=a -and t=b. - -INPUT PARAMETERS: - P - parametric spline interpolant - A,B - parameter values corresponding to arc ends: - * B>A will result in positive length returned - * BA will result in positive length returned - * B1) -function in a NX-dimensional space (NX=2 or NX=3). - -Newly created model is empty. It can be used for interpolation right after -creation, but it just returns zeros. You have to add points to the model, -tune interpolation settings, and then call model construction function -RBFBuildModel() which will update model according to your specification. - -USAGE: -1. User creates model with RBFCreate() -2. User adds dataset with RBFSetPoints() (points do NOT have to be on a - regular grid) -3. (OPTIONAL) User chooses polynomial term by calling: - * RBFLinTerm() to set linear term - * RBFConstTerm() to set constant term - * RBFZeroTerm() to set zero term - By default, linear term is used. -4. User chooses specific RBF algorithm to use: either QNN (RBFSetAlgoQNN) - or ML (RBFSetAlgoMultiLayer). -5. User calls RBFBuildModel() function which rebuilds model according to - the specification -6. User may call RBFCalc() to calculate model value at the specified point, - RBFGridCalc() to calculate model values at the points of the regular - grid. User may extract model coefficients with RBFUnpack() call. - -INPUT PARAMETERS: - NX - dimension of the space, NX=2 or NX=3 - NY - function dimension, NY>=1 - -OUTPUT PARAMETERS: - S - RBF model (initially equals to zero) - -NOTE 1: memory requirements. RBF models require amount of memory which is - proportional to the number of data points. Memory is allocated - during model construction, but most of this memory is freed after - model coefficients are calculated. - - Some approximate estimates for N centers with default settings are - given below: - * about 250*N*(sizeof(double)+2*sizeof(int)) bytes of memory is - needed during model construction stage. - * about 15*N*sizeof(double) bytes is needed after model is built. - For example, for N=100000 we may need 0.6 GB of memory to build - model, but just about 0.012 GB to store it. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcreate(const ae_int_t nx, const ae_int_t ny, rbfmodel &s); - - -/************************************************************************* -This function adds dataset. - -This function overrides results of the previous calls, i.e. multiple calls -of this function will result in only the last set being added. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call. - XY - points, array[N,NX+NY]. One row corresponds to one point - in the dataset. First NX elements are coordinates, next - NY elements are function values. Array may be larger than - specific, in this case only leading [N,NX+NY] elements - will be used. - N - number of points in the dataset - -After you've added dataset and (optionally) tuned algorithm settings you -should call RBFBuildModel() in order to build a model for you. - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy, const ae_int_t n); -void rbfsetpoints(const rbfmodel &s, const real_2d_array &xy); - - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-QNN and it is good for point sets with -following properties: -a) all points are distinct -b) all points are well separated. -c) points distribution is approximately uniform. There is no "contour - lines", clusters of points, or other small-scale structures. - -Algorithm description: -1) interpolation centers are allocated to data points -2) interpolation radii are calculated as distances to the nearest centers - times Q coefficient (where Q is a value from [0.75,1.50]). -3) after performing (2) radii are transformed in order to avoid situation - when single outlier has very large radius and influences many points - across all dataset. Transformation has following form: - new_r[i] = min(r[i],Z*median(r[])) - where r[i] is I-th radius, median() is a median radius across entire - dataset, Z is user-specified value which controls amount of deviation - from median radius. - -When (a) is violated, we will be unable to build RBF model. When (b) or -(c) are violated, model will be built, but interpolation quality will be -low. See http://www.alglib.net/interpolation/ for more information on this -subject. - -This algorithm is used by default. - -Additional Q parameter controls smoothness properties of the RBF basis: -* Q<0.75 will give perfectly conditioned basis, but terrible smoothness - properties (RBF interpolant will have sharp peaks around function values) -* Q around 1.0 gives good balance between smoothness and condition number -* Q>1.5 will lead to badly conditioned systems and slow convergence of the - underlying linear solver (although smoothness will be very good) -* Q>2.0 will effectively make optimizer useless because it won't converge - within reasonable amount of iterations. It is possible to set such large - Q, but it is advised not to do so. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Q - Q parameter, Q>0, recommended value - 1.0 - Z - Z parameter, Z>0, recommended value - 5.0 - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgoqnn(const rbfmodel &s, const double q, const double z); -void rbfsetalgoqnn(const rbfmodel &s); - - -/************************************************************************* -This function sets RBF interpolation algorithm. ALGLIB supports several -RBF algorithms with different properties. - -This algorithm is called RBF-ML. It builds multilayer RBF model, i.e. -model with subsequently decreasing radii, which allows us to combine -smoothness (due to large radii of the first layers) with exactness (due -to small radii of the last layers) and fast convergence. - -Internally RBF-ML uses many different means of acceleration, from sparse -matrices to KD-trees, which results in algorithm whose working time is -roughly proportional to N*log(N)*Density*RBase^2*NLayers, where N is a -number of points, Density is an average density if points per unit of the -interpolation space, RBase is an initial radius, NLayers is a number of -layers. - -RBF-ML is good for following kinds of interpolation problems: -1. "exact" problems (perfect fit) with well separated points -2. least squares problems with arbitrary distribution of points (algorithm - gives perfect fit where it is possible, and resorts to least squares - fit in the hard areas). -3. noisy problems where we want to apply some controlled amount of - smoothing. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - RBase - RBase parameter, RBase>0 - NLayers - NLayers parameter, NLayers>0, recommended value to start - with - about 5. - LambdaV - regularization value, can be useful when solving problem - in the least squares sense. Optimal lambda is problem- - dependent and require trial and error. In our experience, - good lambda can be as large as 0.1, and you can use 0.001 - as initial guess. - Default value - 0.01, which is used when LambdaV is not - given. You can specify zero value, but it is not - recommended to do so. - -TUNING ALGORITHM - -In order to use this algorithm you have to choose three parameters: -* initial radius RBase -* number of layers in the model NLayers -* regularization coefficient LambdaV - -Initial radius is easy to choose - you can pick any number several times -larger than the average distance between points. Algorithm won't break -down if you choose radius which is too large (model construction time will -increase, but model will be built correctly). - -Choose such number of layers that RLast=RBase/2^(NLayers-1) (radius used -by the last layer) will be smaller than the typical distance between -points. In case model error is too large, you can increase number of -layers. Having more layers will make model construction and evaluation -proportionally slower, but it will allow you to have model which precisely -fits your data. From the other side, if you want to suppress noise, you -can DECREASE number of layers to make your model less flexible. - -Regularization coefficient LambdaV controls smoothness of the individual -models built for each layer. We recommend you to use default value in case -you don't want to tune this parameter, because having non-zero LambdaV -accelerates and stabilizes internal iterative algorithm. In case you want -to suppress noise you can use LambdaV as additional parameter (larger -value = more smoothness) to tune. - -TYPICAL ERRORS - -1. Using initial radius which is too large. Memory requirements of the - RBF-ML are roughly proportional to N*Density*RBase^2 (where Density is - an average density of points per unit of the interpolation space). In - the extreme case of the very large RBase we will need O(N^2) units of - memory - and many layers in order to decrease radius to some reasonably - small value. - -2. Using too small number of layers - RBF models with large radius are not - flexible enough to reproduce small variations in the target function. - You need many layers with different radii, from large to small, in - order to have good model. - -3. Using initial radius which is too small. You will get model with - "holes" in the areas which are too far away from interpolation centers. - However, algorithm will work correctly (and quickly) in this case. - -4. Using too many layers - you will get too large and too slow model. This - model will perfectly reproduce your function, but maybe you will be - able to achieve similar results with less layers (and less memory). - - -- ALGLIB -- - Copyright 02.03.2012 by Bochkanov Sergey -*************************************************************************/ -void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers, const double lambdav); -void rbfsetalgomultilayer(const rbfmodel &s, const double rbase, const ae_int_t nlayers); - - -/************************************************************************* -This function sets linear term (model is a sum of radial basis functions -plus linear polynomial). This function won't have effect until next call -to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetlinterm(const rbfmodel &s); - - -/************************************************************************* -This function sets constant term (model is a sum of radial basis functions -plus constant). This function won't have effect until next call to -RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetconstterm(const rbfmodel &s); - - -/************************************************************************* -This function sets zero term (model is a sum of radial basis functions -without polynomial term). This function won't have effect until next call -to RBFBuildModel(). - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - -NOTE: this function has some serialization-related subtleties. We - recommend you to study serialization examples from ALGLIB Reference - Manual if you want to perform serialization of your models. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfsetzeroterm(const rbfmodel &s); - - -/************************************************************************* -This function builds RBF model and returns report (contains some -information which can be used for evaluation of the algorithm properties). - -Call to this function modifies RBF model by calculating its centers/radii/ -weights and saving them into RBFModel structure. Initially RBFModel -contain zero coefficients, but after call to this function we will have -coefficients which were calculated in order to fit our dataset. - -After you called this function you can call RBFCalc(), RBFGridCalc() and -other model calculation functions. - -INPUT PARAMETERS: - S - RBF model, initialized by RBFCreate() call - Rep - report: - * Rep.TerminationType: - * -5 - non-distinct basis function centers were detected, - interpolation aborted - * -4 - nonconvergence of the internal SVD solver - * 1 - successful termination - Fields are used for debugging purposes: - * Rep.IterationsCount - iterations count of the LSQR solver - * Rep.NMV - number of matrix-vector products - * Rep.ARows - rows count for the system matrix - * Rep.ACols - columns count for the system matrix - * Rep.ANNZ - number of significantly non-zero elements - (elements above some algorithm-determined threshold) - -NOTE: failure to build model will leave current state of the structure -unchanged. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfbuildmodel(const rbfmodel &s, rbfreport &rep); - - -/************************************************************************* -This function calculates values of the RBF model in the given point. - -This function should be used when we have NY=1 (scalar function) and NX=2 -(2-dimensional space). If you have 3-dimensional space, use RBFCalc3(). If -you have general situation (NX-dimensional space, NY-dimensional function) -you should use general, less efficient implementation RBFCalc(). - -If you want to calculate function values many times, consider using -RBFGridCalc2(), which is far more efficient than many subsequent calls to -RBFCalc2(). - -This function returns 0.0 when: -* model is not initialized -* NX<>2 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - first coordinate, finite number - X1 - second coordinate, finite number - -RESULT: - value of the model or 0.0 (as defined above) - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -double rbfcalc2(const rbfmodel &s, const double x0, const double x1); - - -/************************************************************************* -This function calculates values of the RBF model in the given point. - -This function should be used when we have NY=1 (scalar function) and NX=3 -(3-dimensional space). If you have 2-dimensional space, use RBFCalc2(). If -you have general situation (NX-dimensional space, NY-dimensional function) -you should use general, less efficient implementation RBFCalc(). - -This function returns 0.0 when: -* model is not initialized -* NX<>3 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - first coordinate, finite number - X1 - second coordinate, finite number - X2 - third coordinate, finite number - -RESULT: - value of the model or 0.0 (as defined above) - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -double rbfcalc3(const rbfmodel &s, const double x0, const double x1, const double x2); - - -/************************************************************************* -This function calculates values of the RBF model at the given point. - -This is general function which can be used for arbitrary NX (dimension of -the space of arguments) and NY (dimension of the function itself). However -when you have NY=1 you may find more convenient to use RBFCalc2() or -RBFCalc3(). - -This function returns 0.0 when model is not initialized. - -INPUT PARAMETERS: - S - RBF model - X - coordinates, array[NX]. - X may have more than NX elements, in this case only - leading NX will be used. - -OUTPUT PARAMETERS: - Y - function value, array[NY]. Y is out-parameter and - reallocated after call to this function. In case you want - to reuse previously allocated Y, you may use RBFCalcBuf(), - which reallocates Y only when it is too small. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcalc(const rbfmodel &s, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -This function calculates values of the RBF model at the given point. - -Same as RBFCalc(), but does not reallocate Y when in is large enough to -store function values. - -INPUT PARAMETERS: - S - RBF model - X - coordinates, array[NX]. - X may have more than NX elements, in this case only - leading NX will be used. - Y - possibly preallocated array - -OUTPUT PARAMETERS: - Y - function value, array[NY]. Y is not reallocated when it - is larger than NY. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfcalcbuf(const rbfmodel &s, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -This function calculates values of the RBF model at the regular grid. - -Grid have N0*N1 points, with Point[I,J] = (X0[I], X1[J]) - -This function returns 0.0 when: -* model is not initialized -* NX<>2 - *NY<>1 - -INPUT PARAMETERS: - S - RBF model - X0 - array of grid nodes, first coordinates, array[N0] - N0 - grid size (number of nodes) in the first dimension - X1 - array of grid nodes, second coordinates, array[N1] - N1 - grid size (number of nodes) in the second dimension - -OUTPUT PARAMETERS: - Y - function values, array[N0,N1]. Y is out-variable and - is reallocated by this function. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfgridcalc2(const rbfmodel &s, const real_1d_array &x0, const ae_int_t n0, const real_1d_array &x1, const ae_int_t n1, real_2d_array &y); - - -/************************************************************************* -This function "unpacks" RBF model by extracting its coefficients. - -INPUT PARAMETERS: - S - RBF model - -OUTPUT PARAMETERS: - NX - dimensionality of argument - NY - dimensionality of the target function - XWR - model information, array[NC,NX+NY+1]. - One row of the array corresponds to one basis function: - * first NX columns - coordinates of the center - * next NY columns - weights, one per dimension of the - function being modelled - * last column - radius, same for all dimensions of - the function being modelled - NC - number of the centers - V - polynomial term , array[NY,NX+1]. One row per one - dimension of the function being modelled. First NX - elements are linear coefficients, V[NX] is equal to the - constant part. - - -- ALGLIB -- - Copyright 13.12.2011 by Bochkanov Sergey -*************************************************************************/ -void rbfunpack(const rbfmodel &s, ae_int_t &nx, ae_int_t &ny, real_2d_array &xwr, ae_int_t &nc, real_2d_array &v); - -/************************************************************************* -This subroutine calculates the value of the bilinear or bicubic spline at -the given point X. - -Input parameters: - C - coefficients table. - Built by BuildBilinearSpline or BuildBicubicSpline. - X, Y- point - -Result: - S(x,y) - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -double spline2dcalc(const spline2dinterpolant &c, const double x, const double y); - - -/************************************************************************* -This subroutine calculates the value of the bilinear or bicubic spline at -the given point X and its derivatives. - -Input parameters: - C - spline interpolant. - X, Y- point - -Output parameters: - F - S(x,y) - FX - dS(x,y)/dX - FY - dS(x,y)/dY - FXY - d2S(x,y)/dXdY - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2ddiff(const spline2dinterpolant &c, const double x, const double y, double &f, double &fx, double &fy, double &fxy); - - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -Input parameters: - C - spline interpolant - AX, BX - transformation coefficients: x = A*t + B - AY, BY - transformation coefficients: y = A*u + B -Result: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dlintransxy(const spline2dinterpolant &c, const double ax, const double bx, const double ay, const double by); - - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -Input parameters: - C - spline interpolant. - A, B- transformation coefficients: S2(x,y) = A*S(x,y) + B - -Output parameters: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 30.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dlintransf(const spline2dinterpolant &c, const double a, const double b); - - -/************************************************************************* -This subroutine makes the copy of the spline model. - -Input parameters: - C - spline interpolant - -Output parameters: - CC - spline copy - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dcopy(const spline2dinterpolant &c, spline2dinterpolant &cc); - - -/************************************************************************* -Bicubic spline resampling - -Input parameters: - A - function values at the old grid, - array[0..OldHeight-1, 0..OldWidth-1] - OldHeight - old grid height, OldHeight>1 - OldWidth - old grid width, OldWidth>1 - NewHeight - new grid height, NewHeight>1 - NewWidth - new grid width, NewWidth>1 - -Output parameters: - B - function values at the new grid, - array[0..NewHeight-1, 0..NewWidth-1] - - -- ALGLIB routine -- - 15 May, 2007 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline2dresamplebicubic(const real_2d_array &a, const ae_int_t oldheight, const ae_int_t oldwidth, real_2d_array &b, const ae_int_t newheight, const ae_int_t newwidth); - - -/************************************************************************* -Bilinear spline resampling - -Input parameters: - A - function values at the old grid, - array[0..OldHeight-1, 0..OldWidth-1] - OldHeight - old grid height, OldHeight>1 - OldWidth - old grid width, OldWidth>1 - NewHeight - new grid height, NewHeight>1 - NewWidth - new grid width, NewWidth>1 - -Output parameters: - B - function values at the new grid, - array[0..NewHeight-1, 0..NewWidth-1] - - -- ALGLIB routine -- - 09.07.2007 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline2dresamplebilinear(const real_2d_array &a, const ae_int_t oldheight, const ae_int_t oldwidth, real_2d_array &b, const ae_int_t newheight, const ae_int_t newwidth); - - -/************************************************************************* -This subroutine builds bilinear vector-valued spline. - -Input parameters: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - F - function values, array[0..M*N*D-1]: - * first D elements store D values at (X[0],Y[0]) - * next D elements store D values at (X[1],Y[0]) - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(J*N+I)...D*(J*N+I)+D-1]. - M,N - grid size, M>=2, N>=2 - D - vector dimension, D>=1 - -Output parameters: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbilinearv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &f, const ae_int_t d, spline2dinterpolant &c); - - -/************************************************************************* -This subroutine builds bicubic vector-valued spline. - -Input parameters: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - F - function values, array[0..M*N*D-1]: - * first D elements store D values at (X[0],Y[0]) - * next D elements store D values at (X[1],Y[0]) - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(J*N+I)...D*(J*N+I)+D-1]. - M,N - grid size, M>=2, N>=2 - D - vector dimension, D>=1 - -Output parameters: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbicubicv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &f, const ae_int_t d, spline2dinterpolant &c); - - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y- point - F - output buffer, possibly preallocated array. In case array size - is large enough to store result, it is not reallocated. Array - which is too short will be reallocated - -OUTPUT PARAMETERS: - F - array[D] (or larger) which stores function values - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dcalcvbuf(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f); - - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y- point - -OUTPUT PARAMETERS: - F - array[D] which stores function values. F is out-parameter and - it is reallocated after call to this function. In case you - want to reuse previously allocated F, you may use - Spline2DCalcVBuf(), which reallocates F only when it is too - small. - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dcalcv(const spline2dinterpolant &c, const double x, const double y, real_1d_array &f); - - -/************************************************************************* -This subroutine unpacks two-dimensional spline into the coefficients table - -Input parameters: - C - spline interpolant. - -Result: - M, N- grid size (x-axis and y-axis) - D - number of components - Tbl - coefficients table, unpacked format, - D - components: [0..(N-1)*(M-1)*D-1, 0..19]. - For T=0..D-1 (component index), I = 0...N-2 (x index), - J=0..M-2 (y index): - K := T + I*D + J*D*(N-1) - - K-th row stores decomposition for T-th component of the - vector-valued function - - Tbl[K,0] = X[i] - Tbl[K,1] = X[i+1] - Tbl[K,2] = Y[j] - Tbl[K,3] = Y[j+1] - Tbl[K,4] = C00 - Tbl[K,5] = C01 - Tbl[K,6] = C02 - Tbl[K,7] = C03 - Tbl[K,8] = C10 - Tbl[K,9] = C11 - ... - Tbl[K,19] = C33 - On each grid square spline is equals to: - S(x) = SUM(c[i,j]*(t^i)*(u^j), i=0..3, j=0..3) - t = x-x[j] - u = y-y[i] - - -- ALGLIB PROJECT -- - Copyright 16.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline2dunpackv(const spline2dinterpolant &c, ae_int_t &m, ae_int_t &n, ae_int_t &d, real_2d_array &tbl); - - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DBuildBilinearV(), which is more -flexible and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbilinear(const real_1d_array &x, const real_1d_array &y, const real_2d_array &f, const ae_int_t m, const ae_int_t n, spline2dinterpolant &c); - - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DBuildBicubicV(), which is more -flexible and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 05.07.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dbuildbicubic(const real_1d_array &x, const real_1d_array &y, const real_2d_array &f, const ae_int_t m, const ae_int_t n, spline2dinterpolant &c); - - -/************************************************************************* -This subroutine was deprecated in ALGLIB 3.6.0 - -We recommend you to switch to Spline2DUnpackV(), which is more flexible -and accepts its arguments in more convenient order. - - -- ALGLIB PROJECT -- - Copyright 29.06.2007 by Bochkanov Sergey -*************************************************************************/ -void spline2dunpack(const spline2dinterpolant &c, ae_int_t &m, ae_int_t &n, real_2d_array &tbl); - -/************************************************************************* -This subroutine calculates the value of the trilinear or tricubic spline at -the given point (X,Y,Z). - -INPUT PARAMETERS: - C - coefficients table. - Built by BuildBilinearSpline or BuildBicubicSpline. - X, Y, - Z - point - -Result: - S(x,y,z) - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -double spline3dcalc(const spline3dinterpolant &c, const double x, const double y, const double z); - - -/************************************************************************* -This subroutine performs linear transformation of the spline argument. - -INPUT PARAMETERS: - C - spline interpolant - AX, BX - transformation coefficients: x = A*u + B - AY, BY - transformation coefficients: y = A*v + B - AZ, BZ - transformation coefficients: z = A*w + B - -OUTPUT PARAMETERS: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dlintransxyz(const spline3dinterpolant &c, const double ax, const double bx, const double ay, const double by, const double az, const double bz); - - -/************************************************************************* -This subroutine performs linear transformation of the spline. - -INPUT PARAMETERS: - C - spline interpolant. - A, B- transformation coefficients: S2(x,y) = A*S(x,y,z) + B - -OUTPUT PARAMETERS: - C - transformed spline - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dlintransf(const spline3dinterpolant &c, const double a, const double b); - - -/************************************************************************* -Trilinear spline resampling - -INPUT PARAMETERS: - A - array[0..OldXCount*OldYCount*OldZCount-1], function - values at the old grid, : - A[0] x=0,y=0,z=0 - A[1] x=1,y=0,z=0 - A[..] ... - A[..] x=oldxcount-1,y=0,z=0 - A[..] x=0,y=1,z=0 - A[..] ... - ... - OldZCount - old Z-count, OldZCount>1 - OldYCount - old Y-count, OldYCount>1 - OldXCount - old X-count, OldXCount>1 - NewZCount - new Z-count, NewZCount>1 - NewYCount - new Y-count, NewYCount>1 - NewXCount - new X-count, NewXCount>1 - -OUTPUT PARAMETERS: - B - array[0..NewXCount*NewYCount*NewZCount-1], function - values at the new grid: - B[0] x=0,y=0,z=0 - B[1] x=1,y=0,z=0 - B[..] ... - B[..] x=newxcount-1,y=0,z=0 - B[..] x=0,y=1,z=0 - B[..] ... - ... - - -- ALGLIB routine -- - 26.04.2012 - Copyright by Bochkanov Sergey -*************************************************************************/ -void spline3dresampletrilinear(const real_1d_array &a, const ae_int_t oldzcount, const ae_int_t oldycount, const ae_int_t oldxcount, const ae_int_t newzcount, const ae_int_t newycount, const ae_int_t newxcount, real_1d_array &b); - - -/************************************************************************* -This subroutine builds trilinear vector-valued spline. - -INPUT PARAMETERS: - X - spline abscissas, array[0..N-1] - Y - spline ordinates, array[0..M-1] - Z - spline applicates, array[0..L-1] - F - function values, array[0..M*N*L*D-1]: - * first D elements store D values at (X[0],Y[0],Z[0]) - * next D elements store D values at (X[1],Y[0],Z[0]) - * next D elements store D values at (X[2],Y[0],Z[0]) - * ... - * next D elements store D values at (X[0],Y[1],Z[0]) - * next D elements store D values at (X[1],Y[1],Z[0]) - * next D elements store D values at (X[2],Y[1],Z[0]) - * ... - * next D elements store D values at (X[0],Y[0],Z[1]) - * next D elements store D values at (X[1],Y[0],Z[1]) - * next D elements store D values at (X[2],Y[0],Z[1]) - * ... - * general form - D function values at (X[i],Y[j]) are stored - at F[D*(N*(M*K+J)+I)...D*(N*(M*K+J)+I)+D-1]. - M,N, - L - grid size, M>=2, N>=2, L>=2 - D - vector dimension, D>=1 - -OUTPUT PARAMETERS: - C - spline interpolant - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dbuildtrilinearv(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, const real_1d_array &z, const ae_int_t l, const real_1d_array &f, const ae_int_t d, spline3dinterpolant &c); - - -/************************************************************************* -This subroutine calculates bilinear or bicubic vector-valued spline at the -given point (X,Y,Z). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, - Z - point - F - output buffer, possibly preallocated array. In case array size - is large enough to store result, it is not reallocated. Array - which is too short will be reallocated - -OUTPUT PARAMETERS: - F - array[D] (or larger) which stores function values - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcalcvbuf(const spline3dinterpolant &c, const double x, const double y, const double z, real_1d_array &f); - - -/************************************************************************* -This subroutine calculates trilinear or tricubic vector-valued spline at the -given point (X,Y,Z). - -INPUT PARAMETERS: - C - spline interpolant. - X, Y, - Z - point - -OUTPUT PARAMETERS: - F - array[D] which stores function values. F is out-parameter and - it is reallocated after call to this function. In case you - want to reuse previously allocated F, you may use - Spline2DCalcVBuf(), which reallocates F only when it is too - small. - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dcalcv(const spline3dinterpolant &c, const double x, const double y, const double z, real_1d_array &f); - - -/************************************************************************* -This subroutine unpacks tri-dimensional spline into the coefficients table - -INPUT PARAMETERS: - C - spline interpolant. - -Result: - N - grid size (X) - M - grid size (Y) - L - grid size (Z) - D - number of components - SType- spline type. Currently, only one spline type is supported: - trilinear spline, as indicated by SType=1. - Tbl - spline coefficients: [0..(N-1)*(M-1)*(L-1)*D-1, 0..13]. - For T=0..D-1 (component index), I = 0...N-2 (x index), - J=0..M-2 (y index), K=0..L-2 (z index): - Q := T + I*D + J*D*(N-1) + K*D*(N-1)*(M-1), - - Q-th row stores decomposition for T-th component of the - vector-valued function - - Tbl[Q,0] = X[i] - Tbl[Q,1] = X[i+1] - Tbl[Q,2] = Y[j] - Tbl[Q,3] = Y[j+1] - Tbl[Q,4] = Z[k] - Tbl[Q,5] = Z[k+1] - - Tbl[Q,6] = C000 - Tbl[Q,7] = C100 - Tbl[Q,8] = C010 - Tbl[Q,9] = C110 - Tbl[Q,10]= C001 - Tbl[Q,11]= C101 - Tbl[Q,12]= C011 - Tbl[Q,13]= C111 - On each grid square spline is equals to: - S(x) = SUM(c[i,j,k]*(x^i)*(y^j)*(z^k), i=0..1, j=0..1, k=0..1) - t = x-x[j] - u = y-y[i] - v = z-z[k] - - NOTE: format of Tbl is given for SType=1. Future versions of - ALGLIB can use different formats for different values of - SType. - - -- ALGLIB PROJECT -- - Copyright 26.04.2012 by Bochkanov Sergey -*************************************************************************/ -void spline3dunpackv(const spline3dinterpolant &c, ae_int_t &n, ae_int_t &m, ae_int_t &l, ae_int_t &d, ae_int_t &stype, real_2d_array &tbl); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -double idwcalc(idwinterpolant* z, - /* Real */ ae_vector* x, - ae_state *_state); -void idwbuildmodifiedshepard(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - ae_int_t d, - ae_int_t nq, - ae_int_t nw, - idwinterpolant* z, - ae_state *_state); -void idwbuildmodifiedshepardr(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - double r, - idwinterpolant* z, - ae_state *_state); -void idwbuildnoisy(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t nx, - ae_int_t d, - ae_int_t nq, - ae_int_t nw, - idwinterpolant* z, - ae_state *_state); -ae_bool _idwinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _idwinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _idwinterpolant_clear(void* _p); -void _idwinterpolant_destroy(void* _p); -double barycentriccalc(barycentricinterpolant* b, - double t, - ae_state *_state); -void barycentricdiff1(barycentricinterpolant* b, - double t, - double* f, - double* df, - ae_state *_state); -void barycentricdiff2(barycentricinterpolant* b, - double t, - double* f, - double* df, - double* d2f, - ae_state *_state); -void barycentriclintransx(barycentricinterpolant* b, - double ca, - double cb, - ae_state *_state); -void barycentriclintransy(barycentricinterpolant* b, - double ca, - double cb, - ae_state *_state); -void barycentricunpack(barycentricinterpolant* b, - ae_int_t* n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_state *_state); -void barycentricbuildxyw(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - barycentricinterpolant* b, - ae_state *_state); -void barycentricbuildfloaterhormann(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t d, - barycentricinterpolant* b, - ae_state *_state); -void barycentriccopy(barycentricinterpolant* b, - barycentricinterpolant* b2, - ae_state *_state); -ae_bool _barycentricinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _barycentricinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _barycentricinterpolant_clear(void* _p); -void _barycentricinterpolant_destroy(void* _p); -void polynomialbar2cheb(barycentricinterpolant* p, - double a, - double b, - /* Real */ ae_vector* t, - ae_state *_state); -void polynomialcheb2bar(/* Real */ ae_vector* t, - ae_int_t n, - double a, - double b, - barycentricinterpolant* p, - ae_state *_state); -void polynomialbar2pow(barycentricinterpolant* p, - double c, - double s, - /* Real */ ae_vector* a, - ae_state *_state); -void polynomialpow2bar(/* Real */ ae_vector* a, - ae_int_t n, - double c, - double s, - barycentricinterpolant* p, - ae_state *_state); -void polynomialbuild(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state); -void polynomialbuildeqdist(double a, - double b, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state); -void polynomialbuildcheb1(double a, - double b, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state); -void polynomialbuildcheb2(double a, - double b, - /* Real */ ae_vector* y, - ae_int_t n, - barycentricinterpolant* p, - ae_state *_state); -double polynomialcalceqdist(double a, - double b, - /* Real */ ae_vector* f, - ae_int_t n, - double t, - ae_state *_state); -double polynomialcalccheb1(double a, - double b, - /* Real */ ae_vector* f, - ae_int_t n, - double t, - ae_state *_state); -double polynomialcalccheb2(double a, - double b, - /* Real */ ae_vector* f, - ae_int_t n, - double t, - ae_state *_state); -void spline1dbuildlinear(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state); -void spline1dbuildcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - spline1dinterpolant* c, - ae_state *_state); -void spline1dgriddiffcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* d, - ae_state *_state); -void spline1dgriddiff2cubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* d1, - /* Real */ ae_vector* d2, - ae_state *_state); -void spline1dconvcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y2, - ae_state *_state); -void spline1dconvdiffcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y2, - /* Real */ ae_vector* d2, - ae_state *_state); -void spline1dconvdiff2cubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundltype, - double boundl, - ae_int_t boundrtype, - double boundr, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y2, - /* Real */ ae_vector* d2, - /* Real */ ae_vector* dd2, - ae_state *_state); -void spline1dbuildcatmullrom(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t boundtype, - double tension, - spline1dinterpolant* c, - ae_state *_state); -void spline1dbuildhermite(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* d, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state); -void spline1dbuildakima(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state); -double spline1dcalc(spline1dinterpolant* c, double x, ae_state *_state); -void spline1ddiff(spline1dinterpolant* c, - double x, - double* s, - double* ds, - double* d2s, - ae_state *_state); -void spline1dcopy(spline1dinterpolant* c, - spline1dinterpolant* cc, - ae_state *_state); -void spline1dunpack(spline1dinterpolant* c, - ae_int_t* n, - /* Real */ ae_matrix* tbl, - ae_state *_state); -void spline1dlintransx(spline1dinterpolant* c, - double a, - double b, - ae_state *_state); -void spline1dlintransy(spline1dinterpolant* c, - double a, - double b, - ae_state *_state); -double spline1dintegrate(spline1dinterpolant* c, - double x, - ae_state *_state); -void spline1dconvdiffinternal(/* Real */ ae_vector* xold, - /* Real */ ae_vector* yold, - /* Real */ ae_vector* dold, - ae_int_t n, - /* Real */ ae_vector* x2, - ae_int_t n2, - /* Real */ ae_vector* y, - ae_bool needy, - /* Real */ ae_vector* d1, - ae_bool needd1, - /* Real */ ae_vector* d2, - ae_bool needd2, - ae_state *_state); -void spline1drootsandextrema(spline1dinterpolant* c, - /* Real */ ae_vector* r, - ae_int_t* nr, - ae_bool* dr, - /* Real */ ae_vector* e, - /* Integer */ ae_vector* et, - ae_int_t* ne, - ae_bool* de, - ae_state *_state); -void heapsortdpoints(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* d, - ae_int_t n, - ae_state *_state); -void solvepolinom2(double p0, - double m0, - double p1, - double m1, - double* x0, - double* x1, - ae_int_t* nr, - ae_state *_state); -void solvecubicpolinom(double pa, - double ma, - double pb, - double mb, - double a, - double b, - double* x0, - double* x1, - double* x2, - double* ex0, - double* ex1, - ae_int_t* nr, - ae_int_t* ne, - /* Real */ ae_vector* tempdata, - ae_state *_state); -ae_int_t bisectmethod(double pa, - double ma, - double pb, - double mb, - double a, - double b, - double* x, - ae_state *_state); -void spline1dbuildmonotone(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - spline1dinterpolant* c, - ae_state *_state); -ae_bool _spline1dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _spline1dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _spline1dinterpolant_clear(void* _p); -void _spline1dinterpolant_destroy(void* _p); -void polynomialfit(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* p, - polynomialfitreport* rep, - ae_state *_state); -void polynomialfitwc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* p, - polynomialfitreport* rep, - ae_state *_state); -void barycentricfitfloaterhormannwc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* b, - barycentricfitreport* rep, - ae_state *_state); -void barycentricfitfloaterhormann(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - barycentricinterpolant* b, - barycentricfitreport* rep, - ae_state *_state); -void spline1dfitpenalized(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - double rho, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -void spline1dfitpenalizedw(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - ae_int_t m, - double rho, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -void spline1dfitcubicwc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -void spline1dfithermitewc(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -void spline1dfitcubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -void spline1dfithermite(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - spline1dinterpolant* s, - spline1dfitreport* rep, - ae_state *_state); -void lsfitlinearw(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -void lsfitlinearwc(/* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* fmatrix, - /* Real */ ae_matrix* cmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -void lsfitlinear(/* Real */ ae_vector* y, - /* Real */ ae_matrix* fmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -void lsfitlinearc(/* Real */ ae_vector* y, - /* Real */ ae_matrix* fmatrix, - /* Real */ ae_matrix* cmatrix, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -void lsfitcreatewf(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - double diffstep, - lsfitstate* state, - ae_state *_state); -void lsfitcreatef(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - double diffstep, - lsfitstate* state, - ae_state *_state); -void lsfitcreatewfg(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_bool cheapfg, - lsfitstate* state, - ae_state *_state); -void lsfitcreatefg(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - ae_bool cheapfg, - lsfitstate* state, - ae_state *_state); -void lsfitcreatewfgh(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - lsfitstate* state, - ae_state *_state); -void lsfitcreatefgh(/* Real */ ae_matrix* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* c, - ae_int_t n, - ae_int_t m, - ae_int_t k, - lsfitstate* state, - ae_state *_state); -void lsfitsetcond(lsfitstate* state, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void lsfitsetstpmax(lsfitstate* state, double stpmax, ae_state *_state); -void lsfitsetxrep(lsfitstate* state, ae_bool needxrep, ae_state *_state); -void lsfitsetscale(lsfitstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void lsfitsetbc(lsfitstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state); -ae_bool lsfititeration(lsfitstate* state, ae_state *_state); -void lsfitresults(lsfitstate* state, - ae_int_t* info, - /* Real */ ae_vector* c, - lsfitreport* rep, - ae_state *_state); -void lsfitsetgradientcheck(lsfitstate* state, - double teststep, - ae_state *_state); -void lsfitscalexy(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* w, - ae_int_t n, - /* Real */ ae_vector* xc, - /* Real */ ae_vector* yc, - /* Integer */ ae_vector* dc, - ae_int_t k, - double* xa, - double* xb, - double* sa, - double* sb, - /* Real */ ae_vector* xoriginal, - /* Real */ ae_vector* yoriginal, - ae_state *_state); -ae_bool _polynomialfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _polynomialfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _polynomialfitreport_clear(void* _p); -void _polynomialfitreport_destroy(void* _p); -ae_bool _barycentricfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _barycentricfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _barycentricfitreport_clear(void* _p); -void _barycentricfitreport_destroy(void* _p); -ae_bool _spline1dfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _spline1dfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _spline1dfitreport_clear(void* _p); -void _spline1dfitreport_destroy(void* _p); -ae_bool _lsfitreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _lsfitreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _lsfitreport_clear(void* _p); -void _lsfitreport_destroy(void* _p); -ae_bool _lsfitstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _lsfitstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _lsfitstate_clear(void* _p); -void _lsfitstate_destroy(void* _p); -void pspline2build(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline2interpolant* p, - ae_state *_state); -void pspline3build(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline3interpolant* p, - ae_state *_state); -void pspline2buildperiodic(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline2interpolant* p, - ae_state *_state); -void pspline3buildperiodic(/* Real */ ae_matrix* xy, - ae_int_t n, - ae_int_t st, - ae_int_t pt, - pspline3interpolant* p, - ae_state *_state); -void pspline2parametervalues(pspline2interpolant* p, - ae_int_t* n, - /* Real */ ae_vector* t, - ae_state *_state); -void pspline3parametervalues(pspline3interpolant* p, - ae_int_t* n, - /* Real */ ae_vector* t, - ae_state *_state); -void pspline2calc(pspline2interpolant* p, - double t, - double* x, - double* y, - ae_state *_state); -void pspline3calc(pspline3interpolant* p, - double t, - double* x, - double* y, - double* z, - ae_state *_state); -void pspline2tangent(pspline2interpolant* p, - double t, - double* x, - double* y, - ae_state *_state); -void pspline3tangent(pspline3interpolant* p, - double t, - double* x, - double* y, - double* z, - ae_state *_state); -void pspline2diff(pspline2interpolant* p, - double t, - double* x, - double* dx, - double* y, - double* dy, - ae_state *_state); -void pspline3diff(pspline3interpolant* p, - double t, - double* x, - double* dx, - double* y, - double* dy, - double* z, - double* dz, - ae_state *_state); -void pspline2diff2(pspline2interpolant* p, - double t, - double* x, - double* dx, - double* d2x, - double* y, - double* dy, - double* d2y, - ae_state *_state); -void pspline3diff2(pspline3interpolant* p, - double t, - double* x, - double* dx, - double* d2x, - double* y, - double* dy, - double* d2y, - double* z, - double* dz, - double* d2z, - ae_state *_state); -double pspline2arclength(pspline2interpolant* p, - double a, - double b, - ae_state *_state); -double pspline3arclength(pspline3interpolant* p, - double a, - double b, - ae_state *_state); -ae_bool _pspline2interpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _pspline2interpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _pspline2interpolant_clear(void* _p); -void _pspline2interpolant_destroy(void* _p); -ae_bool _pspline3interpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _pspline3interpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _pspline3interpolant_clear(void* _p); -void _pspline3interpolant_destroy(void* _p); -void rbfcreate(ae_int_t nx, ae_int_t ny, rbfmodel* s, ae_state *_state); -void rbfsetpoints(rbfmodel* s, - /* Real */ ae_matrix* xy, - ae_int_t n, - ae_state *_state); -void rbfsetalgoqnn(rbfmodel* s, double q, double z, ae_state *_state); -void rbfsetalgomultilayer(rbfmodel* s, - double rbase, - ae_int_t nlayers, - double lambdav, - ae_state *_state); -void rbfsetlinterm(rbfmodel* s, ae_state *_state); -void rbfsetconstterm(rbfmodel* s, ae_state *_state); -void rbfsetzeroterm(rbfmodel* s, ae_state *_state); -void rbfsetcond(rbfmodel* s, - double epsort, - double epserr, - ae_int_t maxits, - ae_state *_state); -void rbfbuildmodel(rbfmodel* s, rbfreport* rep, ae_state *_state); -double rbfcalc2(rbfmodel* s, double x0, double x1, ae_state *_state); -double rbfcalc3(rbfmodel* s, - double x0, - double x1, - double x2, - ae_state *_state); -void rbfcalc(rbfmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void rbfcalcbuf(rbfmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void rbfgridcalc2(rbfmodel* s, - /* Real */ ae_vector* x0, - ae_int_t n0, - /* Real */ ae_vector* x1, - ae_int_t n1, - /* Real */ ae_matrix* y, - ae_state *_state); -void rbfunpack(rbfmodel* s, - ae_int_t* nx, - ae_int_t* ny, - /* Real */ ae_matrix* xwr, - ae_int_t* nc, - /* Real */ ae_matrix* v, - ae_state *_state); -void rbfalloc(ae_serializer* s, rbfmodel* model, ae_state *_state); -void rbfserialize(ae_serializer* s, rbfmodel* model, ae_state *_state); -void rbfunserialize(ae_serializer* s, rbfmodel* model, ae_state *_state); -ae_bool _rbfmodel_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _rbfmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _rbfmodel_clear(void* _p); -void _rbfmodel_destroy(void* _p); -ae_bool _rbfreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _rbfreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _rbfreport_clear(void* _p); -void _rbfreport_destroy(void* _p); -double spline2dcalc(spline2dinterpolant* c, - double x, - double y, - ae_state *_state); -void spline2ddiff(spline2dinterpolant* c, - double x, - double y, - double* f, - double* fx, - double* fy, - double* fxy, - ae_state *_state); -void spline2dlintransxy(spline2dinterpolant* c, - double ax, - double bx, - double ay, - double by, - ae_state *_state); -void spline2dlintransf(spline2dinterpolant* c, - double a, - double b, - ae_state *_state); -void spline2dcopy(spline2dinterpolant* c, - spline2dinterpolant* cc, - ae_state *_state); -void spline2dresamplebicubic(/* Real */ ae_matrix* a, - ae_int_t oldheight, - ae_int_t oldwidth, - /* Real */ ae_matrix* b, - ae_int_t newheight, - ae_int_t newwidth, - ae_state *_state); -void spline2dresamplebilinear(/* Real */ ae_matrix* a, - ae_int_t oldheight, - ae_int_t oldwidth, - /* Real */ ae_matrix* b, - ae_int_t newheight, - ae_int_t newwidth, - ae_state *_state); -void spline2dbuildbilinearv(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - /* Real */ ae_vector* f, - ae_int_t d, - spline2dinterpolant* c, - ae_state *_state); -void spline2dbuildbicubicv(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - /* Real */ ae_vector* f, - ae_int_t d, - spline2dinterpolant* c, - ae_state *_state); -void spline2dcalcvbuf(spline2dinterpolant* c, - double x, - double y, - /* Real */ ae_vector* f, - ae_state *_state); -void spline2dcalcv(spline2dinterpolant* c, - double x, - double y, - /* Real */ ae_vector* f, - ae_state *_state); -void spline2dunpackv(spline2dinterpolant* c, - ae_int_t* m, - ae_int_t* n, - ae_int_t* d, - /* Real */ ae_matrix* tbl, - ae_state *_state); -void spline2dbuildbilinear(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_matrix* f, - ae_int_t m, - ae_int_t n, - spline2dinterpolant* c, - ae_state *_state); -void spline2dbuildbicubic(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_matrix* f, - ae_int_t m, - ae_int_t n, - spline2dinterpolant* c, - ae_state *_state); -void spline2dunpack(spline2dinterpolant* c, - ae_int_t* m, - ae_int_t* n, - /* Real */ ae_matrix* tbl, - ae_state *_state); -ae_bool _spline2dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _spline2dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _spline2dinterpolant_clear(void* _p); -void _spline2dinterpolant_destroy(void* _p); -double spline3dcalc(spline3dinterpolant* c, - double x, - double y, - double z, - ae_state *_state); -void spline3dlintransxyz(spline3dinterpolant* c, - double ax, - double bx, - double ay, - double by, - double az, - double bz, - ae_state *_state); -void spline3dlintransf(spline3dinterpolant* c, - double a, - double b, - ae_state *_state); -void spline3dcopy(spline3dinterpolant* c, - spline3dinterpolant* cc, - ae_state *_state); -void spline3dresampletrilinear(/* Real */ ae_vector* a, - ae_int_t oldzcount, - ae_int_t oldycount, - ae_int_t oldxcount, - ae_int_t newzcount, - ae_int_t newycount, - ae_int_t newxcount, - /* Real */ ae_vector* b, - ae_state *_state); -void spline3dbuildtrilinearv(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - /* Real */ ae_vector* z, - ae_int_t l, - /* Real */ ae_vector* f, - ae_int_t d, - spline3dinterpolant* c, - ae_state *_state); -void spline3dcalcvbuf(spline3dinterpolant* c, - double x, - double y, - double z, - /* Real */ ae_vector* f, - ae_state *_state); -void spline3dcalcv(spline3dinterpolant* c, - double x, - double y, - double z, - /* Real */ ae_vector* f, - ae_state *_state); -void spline3dunpackv(spline3dinterpolant* c, - ae_int_t* n, - ae_int_t* m, - ae_int_t* l, - ae_int_t* d, - ae_int_t* stype, - /* Real */ ae_matrix* tbl, - ae_state *_state); -ae_bool _spline3dinterpolant_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _spline3dinterpolant_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _spline3dinterpolant_clear(void* _p); -void _spline3dinterpolant_destroy(void* _p); - -} -#endif - diff --git a/src/inc/alglib/linalg.cpp b/src/inc/alglib/linalg.cpp deleted file mode 100644 index 1ef178c..0000000 --- a/src/inc/alglib/linalg.cpp +++ /dev/null @@ -1,33805 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "linalg.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Cache-oblivous complex "copy-and-transpose" - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void cmatrixtranspose(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, complex_2d_array &b, const ae_int_t ib, const ae_int_t jb) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixtranspose(m, n, const_cast(a.c_ptr()), ia, ja, const_cast(b.c_ptr()), ib, jb, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cache-oblivous real "copy-and-transpose" - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void rmatrixtranspose(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, real_2d_array &b, const ae_int_t ib, const ae_int_t jb) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixtranspose(m, n, const_cast(a.c_ptr()), ia, ja, const_cast(b.c_ptr()), ib, jb, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This code enforces symmetricy of the matrix by copying Upper part to lower -one (or vice versa). - -INPUT PARAMETERS: - A - matrix - N - number of rows/columns - IsUpper - whether we want to copy upper triangle to lower one (True) - or vice versa (False). -*************************************************************************/ -void rmatrixenforcesymmetricity(const real_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixenforcesymmetricity(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Copy - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void cmatrixcopy(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, complex_2d_array &b, const ae_int_t ib, const ae_int_t jb) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixcopy(m, n, const_cast(a.c_ptr()), ia, ja, const_cast(b.c_ptr()), ib, jb, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Copy - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void rmatrixcopy(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, real_2d_array &b, const ae_int_t ib, const ae_int_t jb) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixcopy(m, n, const_cast(a.c_ptr()), ia, ja, const_cast(b.c_ptr()), ib, jb, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rank-1 correction: A := A + u*v' - -INPUT PARAMETERS: - M - number of rows - N - number of columns - A - target matrix, MxN submatrix is updated - IA - submatrix offset (row index) - JA - submatrix offset (column index) - U - vector #1 - IU - subvector offset - V - vector #2 - IV - subvector offset -*************************************************************************/ -void cmatrixrank1(const ae_int_t m, const ae_int_t n, complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, complex_1d_array &u, const ae_int_t iu, complex_1d_array &v, const ae_int_t iv) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixrank1(m, n, const_cast(a.c_ptr()), ia, ja, const_cast(u.c_ptr()), iu, const_cast(v.c_ptr()), iv, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Rank-1 correction: A := A + u*v' - -INPUT PARAMETERS: - M - number of rows - N - number of columns - A - target matrix, MxN submatrix is updated - IA - submatrix offset (row index) - JA - submatrix offset (column index) - U - vector #1 - IU - subvector offset - V - vector #2 - IV - subvector offset -*************************************************************************/ -void rmatrixrank1(const ae_int_t m, const ae_int_t n, real_2d_array &a, const ae_int_t ia, const ae_int_t ja, real_1d_array &u, const ae_int_t iu, real_1d_array &v, const ae_int_t iv) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixrank1(m, n, const_cast(a.c_ptr()), ia, ja, const_cast(u.c_ptr()), iu, const_cast(v.c_ptr()), iv, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Matrix-vector product: y := op(A)*x - -INPUT PARAMETERS: - M - number of rows of op(A) - M>=0 - N - number of columns of op(A) - N>=0 - A - target matrix - IA - submatrix offset (row index) - JA - submatrix offset (column index) - OpA - operation type: - * OpA=0 => op(A) = A - * OpA=1 => op(A) = A^T - * OpA=2 => op(A) = A^H - X - input vector - IX - subvector offset - IY - subvector offset - Y - preallocated matrix, must be large enough to store result - -OUTPUT PARAMETERS: - Y - vector which stores result - -if M=0, then subroutine does nothing. -if N=0, Y is filled by zeros. - - - -- ALGLIB routine -- - - 28.01.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixmv(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t opa, const complex_1d_array &x, const ae_int_t ix, complex_1d_array &y, const ae_int_t iy) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixmv(m, n, const_cast(a.c_ptr()), ia, ja, opa, const_cast(x.c_ptr()), ix, const_cast(y.c_ptr()), iy, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Matrix-vector product: y := op(A)*x - -INPUT PARAMETERS: - M - number of rows of op(A) - N - number of columns of op(A) - A - target matrix - IA - submatrix offset (row index) - JA - submatrix offset (column index) - OpA - operation type: - * OpA=0 => op(A) = A - * OpA=1 => op(A) = A^T - X - input vector - IX - subvector offset - IY - subvector offset - Y - preallocated matrix, must be large enough to store result - -OUTPUT PARAMETERS: - Y - vector which stores result - -if M=0, then subroutine does nothing. -if N=0, Y is filled by zeros. - - - -- ALGLIB routine -- - - 28.01.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixmv(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t opa, const real_1d_array &x, const ae_int_t ix, real_1d_array &y, const ae_int_t iy) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixmv(m, n, const_cast(a.c_ptr()), ia, ja, opa, const_cast(x.c_ptr()), ix, const_cast(y.c_ptr()), iy, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void cmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixrighttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_cmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_cmatrixrighttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void cmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlefttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_cmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_cmatrixlefttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixrighttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rmatrixrighttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlefttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rmatrixlefttrsm(m, n, const_cast(a.c_ptr()), i1, j1, isupper, isunit, optype, const_cast(x.c_ptr()), i2, j2, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void cmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixsyrk(n, k, alpha, const_cast(a.c_ptr()), ia, ja, optypea, beta, const_cast(c.c_ptr()), ic, jc, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_cmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_cmatrixsyrk(n, k, alpha, const_cast(a.c_ptr()), ia, ja, optypea, beta, const_cast(c.c_ptr()), ic, jc, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixsyrk(n, k, alpha, const_cast(a.c_ptr()), ia, ja, optypea, beta, const_cast(c.c_ptr()), ic, jc, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rmatrixsyrk(n, k, alpha, const_cast(a.c_ptr()), ia, ja, optypea, beta, const_cast(c.c_ptr()), ic, jc, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void cmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const alglib::complex alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const complex_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const alglib::complex beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixgemm(m, n, k, *alpha.c_ptr(), const_cast(a.c_ptr()), ia, ja, optypea, const_cast(b.c_ptr()), ib, jb, optypeb, *beta.c_ptr(), const_cast(c.c_ptr()), ic, jc, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_cmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const alglib::complex alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const complex_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const alglib::complex beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_cmatrixgemm(m, n, k, *alpha.c_ptr(), const_cast(a.c_ptr()), ia, ja, optypea, const_cast(b.c_ptr()), ib, jb, optypeb, *beta.c_ptr(), const_cast(c.c_ptr()), ic, jc, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const real_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixgemm(m, n, k, alpha, const_cast(a.c_ptr()), ia, ja, optypea, const_cast(b.c_ptr()), ib, jb, optypeb, beta, const_cast(c.c_ptr()), ic, jc, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const real_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rmatrixgemm(m, n, k, alpha, const_cast(a.c_ptr()), ia, ja, optypea, const_cast(b.c_ptr()), ib, jb, optypeb, beta, const_cast(c.c_ptr()), ic, jc, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -QR decomposition of a rectangular matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and R in compact form (see below). - Tau - array of scalar factors which are used to form - matrix Q. Array whose index ranges within [0.. Min(M-1,N-1)]. - -Matrix A is represented as A = QR, where Q is an orthogonal matrix of size -MxM, R - upper triangular (or upper trapezoid) matrix of size M x N. - -The elements of matrix R are located on and above the main diagonal of -matrix A. The elements which are located in Tau array and below the main -diagonal of matrix A are used to form matrix Q as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(0)*H(2)*...*H(k-1), - -where k = min(m,n), and each H(i) is in the form - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - real vector, -so that v(0:i-1) = 0, v(i) = 1, v(i+1:m-1) stored in A(i+1:m-1,i). - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqr(real_2d_array &a, const ae_int_t m, const ae_int_t n, real_1d_array &tau) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixqr(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -LQ decomposition of a rectangular matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices L and Q in compact form (see below) - Tau - array of scalar factors which are used to form - matrix Q. Array whose index ranges within [0..Min(M,N)-1]. - -Matrix A is represented as A = LQ, where Q is an orthogonal matrix of size -MxM, L - lower triangular (or lower trapezoid) matrix of size M x N. - -The elements of matrix L are located on and below the main diagonal of -matrix A. The elements which are located in Tau array and above the main -diagonal of matrix A are used to form matrix Q as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(k-1)*H(k-2)*...*H(1)*H(0), - -where k = min(m,n), and each H(i) is of the form - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - real vector, so that v(0:i-1)=0, -v(i) = 1, v(i+1:n-1) stored in A(i,i+1:n-1). - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlq(real_2d_array &a, const ae_int_t m, const ae_int_t n, real_1d_array &tau) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlq(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -QR decomposition of a rectangular complex matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and R in compact form - Tau - array of scalar factors which are used to form matrix Q. Array - whose indexes range within [0.. Min(M,N)-1] - -Matrix A is represented as A = QR, where Q is an orthogonal matrix of size -MxM, R - upper triangular (or upper trapezoid) matrix of size MxN. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void cmatrixqr(complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_1d_array &tau) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixqr(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -LQ decomposition of a rectangular complex matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and L in compact form - Tau - array of scalar factors which are used to form matrix Q. Array - whose indexes range within [0.. Min(M,N)-1] - -Matrix A is represented as A = LQ, where Q is an orthogonal matrix of size -MxM, L - lower triangular (or lower trapezoid) matrix of size MxN. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void cmatrixlq(complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_1d_array &tau) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlq(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Partial unpacking of matrix Q from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of RMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of the RMatrixQR subroutine. - QColumns - required number of columns of matrix Q. M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array whose indexes range within [0..M-1, 0..QColumns-1]. - If QColumns=0, the array remains unchanged. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqrunpackq(const real_2d_array &a, const ae_int_t m, const ae_int_t n, const real_1d_array &tau, const ae_int_t qcolumns, real_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixqrunpackq(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), qcolumns, const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking of matrix R from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of RMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - R - matrix R, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqrunpackr(const real_2d_array &a, const ae_int_t m, const ae_int_t n, real_2d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixqrunpackr(const_cast(a.c_ptr()), m, n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Partial unpacking of matrix Q from the LQ decomposition of a matrix A - -Input parameters: - A - matrices L and Q in compact form. - Output of RMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of the RMatrixLQ subroutine. - QRows - required number of rows in matrix Q. N>=QRows>=0. - -Output parameters: - Q - first QRows rows of matrix Q. Array whose indexes range - within [0..QRows-1, 0..N-1]. If QRows=0, the array remains - unchanged. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlqunpackq(const real_2d_array &a, const ae_int_t m, const ae_int_t n, const real_1d_array &tau, const ae_int_t qrows, real_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlqunpackq(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), qrows, const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking of matrix L from the LQ decomposition of a matrix A - -Input parameters: - A - matrices Q and L in compact form. - Output of RMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - L - matrix L, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlqunpackl(const real_2d_array &a, const ae_int_t m, const ae_int_t n, real_2d_array &l) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlqunpackl(const_cast(a.c_ptr()), m, n, const_cast(l.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Partial unpacking of matrix Q from QR decomposition of a complex matrix A. - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixQR subroutine . - M - number of rows in matrix A. M>=0. - N - number of columns in matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of CMatrixQR subroutine . - QColumns - required number of columns in matrix Q. M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array whose index ranges within [0..M-1, 0..QColumns-1]. - If QColumns=0, array isn't changed. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixqrunpackq(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, const complex_1d_array &tau, const ae_int_t qcolumns, complex_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixqrunpackq(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), qcolumns, const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking of matrix R from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - R - matrix R, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixqrunpackr(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_2d_array &r) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixqrunpackr(const_cast(a.c_ptr()), m, n, const_cast(r.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Partial unpacking of matrix Q from LQ decomposition of a complex matrix A. - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixLQ subroutine . - M - number of rows in matrix A. M>=0. - N - number of columns in matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of CMatrixLQ subroutine . - QRows - required number of rows in matrix Q. N>=QColumns>=0. - -Output parameters: - Q - first QRows rows of matrix Q. - Array whose index ranges within [0..QRows-1, 0..N-1]. - If QRows=0, array isn't changed. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlqunpackq(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, const complex_1d_array &tau, const ae_int_t qrows, complex_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlqunpackq(const_cast(a.c_ptr()), m, n, const_cast(tau.c_ptr()), qrows, const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking of matrix L from the LQ decomposition of a matrix A - -Input parameters: - A - matrices Q and L in compact form. - Output of CMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - L - matrix L, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlqunpackl(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_2d_array &l) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlqunpackl(const_cast(a.c_ptr()), m, n, const_cast(l.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Reduction of a rectangular matrix to bidiagonal form - -The algorithm reduces the rectangular matrix A to bidiagonal form by -orthogonal transformations P and Q: A = Q*B*P. - -Input parameters: - A - source matrix. array[0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q, B, P in compact form (see below). - TauQ - scalar factors which are used to form matrix Q. - TauP - scalar factors which are used to form matrix P. - -The main diagonal and one of the secondary diagonals of matrix A are -replaced with bidiagonal matrix B. Other elements contain elementary -reflections which form MxM matrix Q and NxN matrix P, respectively. - -If M>=N, B is the upper bidiagonal MxN matrix and is stored in the -corresponding elements of matrix A. Matrix Q is represented as a -product of elementary reflections Q = H(0)*H(1)*...*H(n-1), where -H(i) = 1-tau*v*v'. Here tau is a scalar which is stored in TauQ[i], and -vector v has the following structure: v(0:i-1)=0, v(i)=1, v(i+1:m-1) is -stored in elements A(i+1:m-1,i). Matrix P is as follows: P = -G(0)*G(1)*...*G(n-2), where G(i) = 1 - tau*u*u'. Tau is stored in TauP[i], -u(0:i)=0, u(i+1)=1, u(i+2:n-1) is stored in elements A(i,i+2:n-1). - -If M n): m=5, n=6 (m < n): - -( d e u1 u1 u1 ) ( d u1 u1 u1 u1 u1 ) -( v1 d e u2 u2 ) ( e d u2 u2 u2 u2 ) -( v1 v2 d e u3 ) ( v1 e d u3 u3 u3 ) -( v1 v2 v3 d e ) ( v1 v2 e d u4 u4 ) -( v1 v2 v3 v4 d ) ( v1 v2 v3 e d u5 ) -( v1 v2 v3 v4 v5 ) - -Here vi and ui are vectors which form H(i) and G(i), and d and e - -are the diagonal and off-diagonal elements of matrix B. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -void rmatrixbd(real_2d_array &a, const ae_int_t m, const ae_int_t n, real_1d_array &tauq, real_1d_array &taup) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixbd(const_cast(a.c_ptr()), m, n, const_cast(tauq.c_ptr()), const_cast(taup.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking matrix Q which reduces a matrix to bidiagonal form. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUQ - scalar factors which are used to form Q. - Output of ToBidiagonal subroutine. - QColumns - required number of columns in matrix Q. - M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array[0..M-1, 0..QColumns-1] - If QColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackq(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &tauq, const ae_int_t qcolumns, real_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixbdunpackq(const_cast(qp.c_ptr()), m, n, const_cast(tauq.c_ptr()), qcolumns, const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiplication by matrix Q which reduces matrix A to bidiagonal form. - -The algorithm allows pre- or post-multiply by Q or Q'. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUQ - scalar factors which are used to form Q. - Output of ToBidiagonal subroutine. - Z - multiplied matrix. - array[0..ZRows-1,0..ZColumns-1] - ZRows - number of rows in matrix Z. If FromTheRight=False, - ZRows=M, otherwise ZRows can be arbitrary. - ZColumns - number of columns in matrix Z. If FromTheRight=True, - ZColumns=M, otherwise ZColumns can be arbitrary. - FromTheRight - pre- or post-multiply. - DoTranspose - multiply by Q or Q'. - -Output parameters: - Z - product of Z and Q. - Array[0..ZRows-1,0..ZColumns-1] - If ZRows=0 or ZColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdmultiplybyq(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &tauq, real_2d_array &z, const ae_int_t zrows, const ae_int_t zcolumns, const bool fromtheright, const bool dotranspose) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixbdmultiplybyq(const_cast(qp.c_ptr()), m, n, const_cast(tauq.c_ptr()), const_cast(z.c_ptr()), zrows, zcolumns, fromtheright, dotranspose, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking matrix P which reduces matrix A to bidiagonal form. -The subroutine returns transposed matrix P. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUP - scalar factors which are used to form P. - Output of ToBidiagonal subroutine. - PTRows - required number of rows of matrix P^T. N >= PTRows >= 0. - -Output parameters: - PT - first PTRows columns of matrix P^T - Array[0..PTRows-1, 0..N-1] - If PTRows=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackpt(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &taup, const ae_int_t ptrows, real_2d_array &pt) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixbdunpackpt(const_cast(qp.c_ptr()), m, n, const_cast(taup.c_ptr()), ptrows, const_cast(pt.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiplication by matrix P which reduces matrix A to bidiagonal form. - -The algorithm allows pre- or post-multiply by P or P'. - -Input parameters: - QP - matrices Q and P in compact form. - Output of RMatrixBD subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUP - scalar factors which are used to form P. - Output of RMatrixBD subroutine. - Z - multiplied matrix. - Array whose indexes range within [0..ZRows-1,0..ZColumns-1]. - ZRows - number of rows in matrix Z. If FromTheRight=False, - ZRows=N, otherwise ZRows can be arbitrary. - ZColumns - number of columns in matrix Z. If FromTheRight=True, - ZColumns=N, otherwise ZColumns can be arbitrary. - FromTheRight - pre- or post-multiply. - DoTranspose - multiply by P or P'. - -Output parameters: - Z - product of Z and P. - Array whose indexes range within [0..ZRows-1,0..ZColumns-1]. - If ZRows=0 or ZColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdmultiplybyp(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &taup, real_2d_array &z, const ae_int_t zrows, const ae_int_t zcolumns, const bool fromtheright, const bool dotranspose) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixbdmultiplybyp(const_cast(qp.c_ptr()), m, n, const_cast(taup.c_ptr()), const_cast(z.c_ptr()), zrows, zcolumns, fromtheright, dotranspose, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking of the main and secondary diagonals of bidiagonal decomposition -of matrix A. - -Input parameters: - B - output of RMatrixBD subroutine. - M - number of rows in matrix B. - N - number of columns in matrix B. - -Output parameters: - IsUpper - True, if the matrix is upper bidiagonal. - otherwise IsUpper is False. - D - the main diagonal. - Array whose index ranges within [0..Min(M,N)-1]. - E - the secondary diagonal (upper or lower, depending on - the value of IsUpper). - Array index ranges within [0..Min(M,N)-1], the last - element is not used. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackdiagonals(const real_2d_array &b, const ae_int_t m, const ae_int_t n, bool &isupper, real_1d_array &d, real_1d_array &e) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixbdunpackdiagonals(const_cast(b.c_ptr()), m, n, &isupper, const_cast(d.c_ptr()), const_cast(e.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Reduction of a square matrix to upper Hessenberg form: Q'*A*Q = H, -where Q is an orthogonal matrix, H - Hessenberg matrix. - -Input parameters: - A - matrix A with elements [0..N-1, 0..N-1] - N - size of matrix A. - -Output parameters: - A - matrices Q and P in compact form (see below). - Tau - array of scalar factors which are used to form matrix Q. - Array whose index ranges within [0..N-2] - -Matrix H is located on the main diagonal, on the lower secondary diagonal -and above the main diagonal of matrix A. The elements which are used to -form matrix Q are situated in array Tau and below the lower secondary -diagonal of matrix A as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(0)*H(2)*...*H(n-2), - -where each H(i) is given by - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - is a real vector, -so that v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) stored in A(i+2:n-1,i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void rmatrixhessenberg(real_2d_array &a, const ae_int_t n, real_1d_array &tau) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixhessenberg(const_cast(a.c_ptr()), n, const_cast(tau.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking matrix Q which reduces matrix A to upper Hessenberg form - -Input parameters: - A - output of RMatrixHessenberg subroutine. - N - size of matrix A. - Tau - scalar factors which are used to form Q. - Output of RMatrixHessenberg subroutine. - -Output parameters: - Q - matrix Q. - Array whose indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixhessenbergunpackq(const real_2d_array &a, const ae_int_t n, const real_1d_array &tau, real_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixhessenbergunpackq(const_cast(a.c_ptr()), n, const_cast(tau.c_ptr()), const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking matrix H (the result of matrix A reduction to upper Hessenberg form) - -Input parameters: - A - output of RMatrixHessenberg subroutine. - N - size of matrix A. - -Output parameters: - H - matrix H. Array whose indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixhessenbergunpackh(const real_2d_array &a, const ae_int_t n, real_2d_array &h) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixhessenbergunpackh(const_cast(a.c_ptr()), n, const_cast(h.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Reduction of a symmetric matrix which is given by its higher or lower -triangular part to a tridiagonal matrix using orthogonal similarity -transformation: Q'*A*Q=T. - -Input parameters: - A - matrix to be transformed - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. If IsUpper = True, then matrix A is given - by its upper triangle, and the lower triangle is not used - and not modified by the algorithm, and vice versa - if IsUpper = False. - -Output parameters: - A - matrices T and Q in compact form (see lower) - Tau - array of factors which are forming matrices H(i) - array with elements [0..N-2]. - D - main diagonal of symmetric matrix T. - array with elements [0..N-1]. - E - secondary diagonal of symmetric matrix T. - array with elements [0..N-2]. - - - If IsUpper=True, the matrix Q is represented as a product of elementary - reflectors - - Q = H(n-2) . . . H(2) H(0). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a real scalar, and v is a real vector with - v(i+1:n-1) = 0, v(i) = 1, v(0:i-1) is stored on exit in - A(0:i-1,i+1), and tau in TAU(i). - - If IsUpper=False, the matrix Q is represented as a product of elementary - reflectors - - Q = H(0) H(2) . . . H(n-2). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a real scalar, and v is a real vector with - v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) is stored on exit in A(i+2:n-1,i), - and tau in TAU(i). - - The contents of A on exit are illustrated by the following examples - with n = 5: - - if UPLO = 'U': if UPLO = 'L': - - ( d e v1 v2 v3 ) ( d ) - ( d e v2 v3 ) ( e d ) - ( d e v3 ) ( v0 e d ) - ( d e ) ( v0 v1 e d ) - ( d ) ( v0 v1 v2 e d ) - - where d and e denote diagonal and off-diagonal elements of T, and vi - denotes an element of the vector defining H(i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void smatrixtd(real_2d_array &a, const ae_int_t n, const bool isupper, real_1d_array &tau, real_1d_array &d, real_1d_array &e) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::smatrixtd(const_cast(a.c_ptr()), n, isupper, const_cast(tau.c_ptr()), const_cast(d.c_ptr()), const_cast(e.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking matrix Q which reduces symmetric matrix to a tridiagonal -form. - -Input parameters: - A - the result of a SMatrixTD subroutine - N - size of matrix A. - IsUpper - storage format (a parameter of SMatrixTD subroutine) - Tau - the result of a SMatrixTD subroutine - -Output parameters: - Q - transformation matrix. - array with elements [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void smatrixtdunpackq(const real_2d_array &a, const ae_int_t n, const bool isupper, const real_1d_array &tau, real_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::smatrixtdunpackq(const_cast(a.c_ptr()), n, isupper, const_cast(tau.c_ptr()), const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Reduction of a Hermitian matrix which is given by its higher or lower -triangular part to a real tridiagonal matrix using unitary similarity -transformation: Q'*A*Q = T. - -Input parameters: - A - matrix to be transformed - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. If IsUpper = True, then matrix A is given - by its upper triangle, and the lower triangle is not used - and not modified by the algorithm, and vice versa - if IsUpper = False. - -Output parameters: - A - matrices T and Q in compact form (see lower) - Tau - array of factors which are forming matrices H(i) - array with elements [0..N-2]. - D - main diagonal of real symmetric matrix T. - array with elements [0..N-1]. - E - secondary diagonal of real symmetric matrix T. - array with elements [0..N-2]. - - - If IsUpper=True, the matrix Q is represented as a product of elementary - reflectors - - Q = H(n-2) . . . H(2) H(0). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a complex scalar, and v is a complex vector with - v(i+1:n-1) = 0, v(i) = 1, v(0:i-1) is stored on exit in - A(0:i-1,i+1), and tau in TAU(i). - - If IsUpper=False, the matrix Q is represented as a product of elementary - reflectors - - Q = H(0) H(2) . . . H(n-2). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a complex scalar, and v is a complex vector with - v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) is stored on exit in A(i+2:n-1,i), - and tau in TAU(i). - - The contents of A on exit are illustrated by the following examples - with n = 5: - - if UPLO = 'U': if UPLO = 'L': - - ( d e v1 v2 v3 ) ( d ) - ( d e v2 v3 ) ( e d ) - ( d e v3 ) ( v0 e d ) - ( d e ) ( v0 v1 e d ) - ( d ) ( v0 v1 v2 e d ) - -where d and e denote diagonal and off-diagonal elements of T, and vi -denotes an element of the vector defining H(i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void hmatrixtd(complex_2d_array &a, const ae_int_t n, const bool isupper, complex_1d_array &tau, real_1d_array &d, real_1d_array &e) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hmatrixtd(const_cast(a.c_ptr()), n, isupper, const_cast(tau.c_ptr()), const_cast(d.c_ptr()), const_cast(e.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Unpacking matrix Q which reduces a Hermitian matrix to a real tridiagonal -form. - -Input parameters: - A - the result of a HMatrixTD subroutine - N - size of matrix A. - IsUpper - storage format (a parameter of HMatrixTD subroutine) - Tau - the result of a HMatrixTD subroutine - -Output parameters: - Q - transformation matrix. - array with elements [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void hmatrixtdunpackq(const complex_2d_array &a, const ae_int_t n, const bool isupper, const complex_1d_array &tau, complex_2d_array &q) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hmatrixtdunpackq(const_cast(a.c_ptr()), n, isupper, const_cast(tau.c_ptr()), const_cast(q.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Singular value decomposition of a bidiagonal matrix (extended algorithm) - -The algorithm performs the singular value decomposition of a bidiagonal -matrix B (upper or lower) representing it as B = Q*S*P^T, where Q and P - -orthogonal matrices, S - diagonal matrix with non-negative elements on the -main diagonal, in descending order. - -The algorithm finds singular values. In addition, the algorithm can -calculate matrices Q and P (more precisely, not the matrices, but their -product with given matrices U and VT - U*Q and (P^T)*VT)). Of course, -matrices U and VT can be of any type, including identity. Furthermore, the -algorithm can calculate Q'*C (this product is calculated more effectively -than U*Q, because this calculation operates with rows instead of matrix -columns). - -The feature of the algorithm is its ability to find all singular values -including those which are arbitrarily close to 0 with relative accuracy -close to machine precision. If the parameter IsFractionalAccuracyRequired -is set to True, all singular values will have high relative accuracy close -to machine precision. If the parameter is set to False, only the biggest -singular value will have relative accuracy close to machine precision. -The absolute error of other singular values is equal to the absolute error -of the biggest singular value. - -Input parameters: - D - main diagonal of matrix B. - Array whose index ranges within [0..N-1]. - E - superdiagonal (or subdiagonal) of matrix B. - Array whose index ranges within [0..N-2]. - N - size of matrix B. - IsUpper - True, if the matrix is upper bidiagonal. - IsFractionalAccuracyRequired - - THIS PARAMETER IS IGNORED SINCE ALGLIB 3.5.0 - SINGULAR VALUES ARE ALWAYS SEARCHED WITH HIGH ACCURACY. - U - matrix to be multiplied by Q. - Array whose indexes range within [0..NRU-1, 0..N-1]. - The matrix can be bigger, in that case only the submatrix - [0..NRU-1, 0..N-1] will be multiplied by Q. - NRU - number of rows in matrix U. - C - matrix to be multiplied by Q'. - Array whose indexes range within [0..N-1, 0..NCC-1]. - The matrix can be bigger, in that case only the submatrix - [0..N-1, 0..NCC-1] will be multiplied by Q'. - NCC - number of columns in matrix C. - VT - matrix to be multiplied by P^T. - Array whose indexes range within [0..N-1, 0..NCVT-1]. - The matrix can be bigger, in that case only the submatrix - [0..N-1, 0..NCVT-1] will be multiplied by P^T. - NCVT - number of columns in matrix VT. - -Output parameters: - D - singular values of matrix B in descending order. - U - if NRU>0, contains matrix U*Q. - VT - if NCVT>0, contains matrix (P^T)*VT. - C - if NCC>0, contains matrix Q'*C. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - -Additional information: - The type of convergence is controlled by the internal parameter TOL. - If the parameter is greater than 0, the singular values will have - relative accuracy TOL. If TOL<0, the singular values will have - absolute accuracy ABS(TOL)*norm(B). - By default, |TOL| falls within the range of 10*Epsilon and 100*Epsilon, - where Epsilon is the machine precision. It is not recommended to use - TOL less than 10*Epsilon since this will considerably slow down the - algorithm and may not lead to error decreasing. -History: - * 31 March, 2007. - changed MAXITR from 6 to 12. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1999. -*************************************************************************/ -bool rmatrixbdsvd(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const bool isupper, const bool isfractionalaccuracyrequired, real_2d_array &u, const ae_int_t nru, real_2d_array &c, const ae_int_t ncc, real_2d_array &vt, const ae_int_t ncvt) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::rmatrixbdsvd(const_cast(d.c_ptr()), const_cast(e.c_ptr()), n, isupper, isfractionalaccuracyrequired, const_cast(u.c_ptr()), nru, const_cast(c.c_ptr()), ncc, const_cast(vt.c_ptr()), ncvt, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Singular value decomposition of a rectangular matrix. - -The algorithm calculates the singular value decomposition of a matrix of -size MxN: A = U * S * V^T - -The algorithm finds the singular values and, optionally, matrices U and V^T. -The algorithm can find both first min(M,N) columns of matrix U and rows of -matrix V^T (singular vectors), and matrices U and V^T wholly (of sizes MxM -and NxN respectively). - -Take into account that the subroutine does not return matrix V but V^T. - -Input parameters: - A - matrix to be decomposed. - Array whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - UNeeded - 0, 1 or 2. See the description of the parameter U. - VTNeeded - 0, 1 or 2. See the description of the parameter VT. - AdditionalMemory - - If the parameter: - * equals 0, the algorithm doesn’t use additional - memory (lower requirements, lower performance). - * equals 1, the algorithm uses additional - memory of size min(M,N)*min(M,N) of real numbers. - It often speeds up the algorithm. - * equals 2, the algorithm uses additional - memory of size M*min(M,N) of real numbers. - It allows to get a maximum performance. - The recommended value of the parameter is 2. - -Output parameters: - W - contains singular values in descending order. - U - if UNeeded=0, U isn't changed, the left singular vectors - are not calculated. - if Uneeded=1, U contains left singular vectors (first - min(M,N) columns of matrix U). Array whose indexes range - within [0..M-1, 0..Min(M,N)-1]. - if UNeeded=2, U contains matrix U wholly. Array whose - indexes range within [0..M-1, 0..M-1]. - VT - if VTNeeded=0, VT isn’t changed, the right singular vectors - are not calculated. - if VTNeeded=1, VT contains right singular vectors (first - min(M,N) rows of matrix V^T). Array whose indexes range - within [0..min(M,N)-1, 0..N-1]. - if VTNeeded=2, VT contains matrix V^T wholly. Array whose - indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -bool rmatrixsvd(const real_2d_array &a, const ae_int_t m, const ae_int_t n, const ae_int_t uneeded, const ae_int_t vtneeded, const ae_int_t additionalmemory, real_1d_array &w, real_2d_array &u, real_2d_array &vt) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::rmatrixsvd(const_cast(a.c_ptr()), m, n, uneeded, vtneeded, additionalmemory, const_cast(w.c_ptr()), const_cast(u.c_ptr()), const_cast(vt.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a symmetric matrix - -The algorithm finds eigen pairs of a symmetric matrix by reducing it to -tridiagonal form and using the QL/QR algorithm. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpper - storage format. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -bool smatrixevd(const real_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, real_1d_array &d, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixevd(const_cast(a.c_ptr()), n, zneeded, isupper, const_cast(d.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine for finding the eigenvalues (and eigenvectors) of a symmetric -matrix in a given half open interval (A, B] by using a bisection and -inverse iteration - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. Array [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - B1, B2 - half open interval (B1, B2] to search eigenvalues in. - -Output parameters: - M - number of eigenvalues found in a given half-interval (M>=0). - W - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..M-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if successful. M contains the number of eigenvalues in the given - half-interval (could be equal to 0), W contains the eigenvalues, - Z contains the eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned, - M is equal to 0. - - -- ALGLIB -- - Copyright 07.01.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixevdr(const real_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const double b1, const double b2, ae_int_t &m, real_1d_array &w, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixevdr(const_cast(a.c_ptr()), n, zneeded, isupper, b1, b2, &m, const_cast(w.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine for finding the eigenvalues and eigenvectors of a symmetric -matrix with given indexes by using bisection and inverse iteration methods. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - -Output parameters: - W - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..I2-I1]. - In that case, the eigenvectors are stored in the matrix columns. - -Result: - True, if successful. W contains the eigenvalues, Z contains the - eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned. - - -- ALGLIB -- - Copyright 07.01.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixevdi(const real_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const ae_int_t i1, const ae_int_t i2, real_1d_array &w, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixevdi(const_cast(a.c_ptr()), n, zneeded, isupper, i1, i2, const_cast(w.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a Hermitian matrix - -The algorithm finds eigen pairs of a Hermitian matrix by reducing it to -real tridiagonal form and using the QL/QR algorithm. - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - -Note: - eigenvectors of Hermitian matrix are defined up to multiplication by - a complex number L, such that |L|=1. - - -- ALGLIB -- - Copyright 2005, 23 March 2007 by Bochkanov Sergey -*************************************************************************/ -bool hmatrixevd(const complex_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, real_1d_array &d, complex_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::hmatrixevd(const_cast(a.c_ptr()), n, zneeded, isupper, const_cast(d.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine for finding the eigenvalues (and eigenvectors) of a Hermitian -matrix in a given half-interval (A, B] by using a bisection and inverse -iteration - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. Array whose indexes range within - [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - B1, B2 - half-interval (B1, B2] to search eigenvalues in. - -Output parameters: - M - number of eigenvalues found in a given half-interval, M>=0 - W - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..M-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if successful. M contains the number of eigenvalues in the given - half-interval (could be equal to 0), W contains the eigenvalues, - Z contains the eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration - subroutine wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned, M is - equal to 0. - -Note: - eigen vectors of Hermitian matrix are defined up to multiplication by - a complex number L, such as |L|=1. - - -- ALGLIB -- - Copyright 07.01.2006, 24.03.2007 by Bochkanov Sergey. -*************************************************************************/ -bool hmatrixevdr(const complex_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const double b1, const double b2, ae_int_t &m, real_1d_array &w, complex_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::hmatrixevdr(const_cast(a.c_ptr()), n, zneeded, isupper, b1, b2, &m, const_cast(w.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine for finding the eigenvalues and eigenvectors of a Hermitian -matrix with given indexes by using bisection and inverse iteration methods - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - -Output parameters: - W - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..I2-I1]. - In that case, the eigenvectors are stored in the matrix - columns. - -Result: - True, if successful. W contains the eigenvalues, Z contains the - eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration - subroutine wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned. - -Note: - eigen vectors of Hermitian matrix are defined up to multiplication by - a complex number L, such as |L|=1. - - -- ALGLIB -- - Copyright 07.01.2006, 24.03.2007 by Bochkanov Sergey. -*************************************************************************/ -bool hmatrixevdi(const complex_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const ae_int_t i1, const ae_int_t i2, real_1d_array &w, complex_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::hmatrixevdi(const_cast(a.c_ptr()), n, zneeded, isupper, i1, i2, const_cast(w.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a tridiagonal symmetric matrix - -The algorithm finds the eigen pairs of a tridiagonal symmetric matrix by -using an QL/QR algorithm with implicit shifts. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix - are multiplied by the square matrix Z. It is used if the - tridiagonal matrix is obtained by the similarity - transformation of a symmetric matrix; - * 2, the eigenvectors of a tridiagonal matrix replace the - square matrix Z; - * 3, matrix Z contains the first row of the eigenvectors - matrix. - Z - if ZNeeded=1, Z contains the square matrix by which the - eigenvectors are multiplied. - Array whose indexes range within [0..N-1, 0..N-1]. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the product of a given matrix (from the left) - and the eigenvectors matrix (from the right); - * 2, Z contains the eigenvectors. - * 3, Z contains the first row of the eigenvectors matrix. - If ZNeeded<3, Z is the array whose indexes range within [0..N-1, 0..N-1]. - In that case, the eigenvectors are stored in the matrix columns. - If ZNeeded=3, Z is the array whose indexes range within [0..0, 0..N-1]. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -bool smatrixtdevd(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const ae_int_t zneeded, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixtdevd(const_cast(d.c_ptr()), const_cast(e.c_ptr()), n, zneeded, const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine for finding the tridiagonal matrix eigenvalues/vectors in a -given half-interval (A, B] by using bisection and inverse iteration. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix, N>=0. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix are multiplied - by the square matrix Z. It is used if the tridiagonal - matrix is obtained by the similarity transformation - of a symmetric matrix. - * 2, the eigenvectors of a tridiagonal matrix replace matrix Z. - A, B - half-interval (A, B] to search eigenvalues in. - Z - if ZNeeded is equal to: - * 0, Z isn't used and remains unchanged; - * 1, Z contains the square matrix (array whose indexes range - within [0..N-1, 0..N-1]) which reduces the given symmetric - matrix to tridiagonal form; - * 2, Z isn't used (but changed on the exit). - -Output parameters: - D - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - M - number of eigenvalues found in the given half-interval (M>=0). - Z - if ZNeeded is equal to: - * 0, doesn't contain any information; - * 1, contains the product of a given NxN matrix Z (from the - left) and NxM matrix of the eigenvectors found (from the - right). Array whose indexes range within [0..N-1, 0..M-1]. - * 2, contains the matrix of the eigenvectors found. - Array whose indexes range within [0..N-1, 0..M-1]. - -Result: - - True, if successful. In that case, M contains the number of eigenvalues - in the given half-interval (could be equal to 0), D contains the eigenvalues, - Z contains the eigenvectors (if needed). - It should be noted that the subroutine changes the size of arrays D and Z. - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. In that case, - the eigenvalues and eigenvectors are not returned, M is equal to 0. - - -- ALGLIB -- - Copyright 31.03.2008 by Bochkanov Sergey -*************************************************************************/ -bool smatrixtdevdr(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const ae_int_t zneeded, const double a, const double b, ae_int_t &m, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixtdevdr(const_cast(d.c_ptr()), const_cast(e.c_ptr()), n, zneeded, a, b, &m, const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine for finding tridiagonal matrix eigenvalues/vectors with given -indexes (in ascending order) by using the bisection and inverse iteraion. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix. N>=0. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix are multiplied - by the square matrix Z. It is used if the - tridiagonal matrix is obtained by the similarity transformation - of a symmetric matrix. - * 2, the eigenvectors of a tridiagonal matrix replace - matrix Z. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - Z - if ZNeeded is equal to: - * 0, Z isn't used and remains unchanged; - * 1, Z contains the square matrix (array whose indexes range within [0..N-1, 0..N-1]) - which reduces the given symmetric matrix to tridiagonal form; - * 2, Z isn't used (but changed on the exit). - -Output parameters: - D - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, doesn't contain any information; - * 1, contains the product of a given NxN matrix Z (from the left) and - Nx(I2-I1) matrix of the eigenvectors found (from the right). - Array whose indexes range within [0..N-1, 0..I2-I1]. - * 2, contains the matrix of the eigenvalues found. - Array whose indexes range within [0..N-1, 0..I2-I1]. - - -Result: - - True, if successful. In that case, D contains the eigenvalues, - Z contains the eigenvectors (if needed). - It should be noted that the subroutine changes the size of arrays D and Z. - - False, if the bisection method subroutine wasn't able to find the eigenvalues - in the given interval or if the inverse iteration subroutine wasn't able - to find all the corresponding eigenvectors. In that case, the eigenvalues - and eigenvectors are not returned. - - -- ALGLIB -- - Copyright 25.12.2005 by Bochkanov Sergey -*************************************************************************/ -bool smatrixtdevdi(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const ae_int_t zneeded, const ae_int_t i1, const ae_int_t i2, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixtdevdi(const_cast(d.c_ptr()), const_cast(e.c_ptr()), n, zneeded, i1, i2, const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Finding eigenvalues and eigenvectors of a general matrix - -The algorithm finds eigenvalues and eigenvectors of a general matrix by -using the QR algorithm with multiple shifts. The algorithm can find -eigenvalues and both left and right eigenvectors. - -The right eigenvector is a vector x such that A*x = w*x, and the left -eigenvector is a vector y such that y'*A = w*y' (here y' implies a complex -conjugate transposition of vector y). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - VNeeded - flag controlling whether eigenvectors are needed or not. - If VNeeded is equal to: - * 0, eigenvectors are not returned; - * 1, right eigenvectors are returned; - * 2, left eigenvectors are returned; - * 3, both left and right eigenvectors are returned. - -Output parameters: - WR - real parts of eigenvalues. - Array whose index ranges within [0..N-1]. - WR - imaginary parts of eigenvalues. - Array whose index ranges within [0..N-1]. - VL, VR - arrays of left and right eigenvectors (if they are needed). - If WI[i]=0, the respective eigenvalue is a real number, - and it corresponds to the column number I of matrices VL/VR. - If WI[i]>0, we have a pair of complex conjugate numbers with - positive and negative imaginary parts: - the first eigenvalue WR[i] + sqrt(-1)*WI[i]; - the second eigenvalue WR[i+1] + sqrt(-1)*WI[i+1]; - WI[i]>0 - WI[i+1] = -WI[i] < 0 - In that case, the eigenvector corresponding to the first - eigenvalue is located in i and i+1 columns of matrices - VL/VR (the column number i contains the real part, and the - column number i+1 contains the imaginary part), and the vector - corresponding to the second eigenvalue is a complex conjugate to - the first vector. - Arrays whose indexes range within [0..N-1, 0..N-1]. - -Result: - True, if the algorithm has converged. - False, if the algorithm has not converged. - -Note 1: - Some users may ask the following question: what if WI[N-1]>0? - WI[N] must contain an eigenvalue which is complex conjugate to the - N-th eigenvalue, but the array has only size N? - The answer is as follows: such a situation cannot occur because the - algorithm finds a pairs of eigenvalues, therefore, if WI[i]>0, I is - strictly less than N-1. - -Note 2: - The algorithm performance depends on the value of the internal parameter - NS of the InternalSchurDecomposition subroutine which defines the number - of shifts in the QR algorithm (similarly to the block width in block-matrix - algorithms of linear algebra). If you require maximum performance - on your machine, it is recommended to adjust this parameter manually. - - -See also the InternalTREVC subroutine. - -The algorithm is based on the LAPACK 3.0 library. -*************************************************************************/ -bool rmatrixevd(const real_2d_array &a, const ae_int_t n, const ae_int_t vneeded, real_1d_array &wr, real_1d_array &wi, real_2d_array &vl, real_2d_array &vr) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::rmatrixevd(const_cast(a.c_ptr()), n, vneeded, const_cast(wr.c_ptr()), const_cast(wi.c_ptr()), const_cast(vl.c_ptr()), const_cast(vr.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of a random uniformly distributed (Haar) orthogonal matrix - -INPUT PARAMETERS: - N - matrix size, N>=1 - -OUTPUT PARAMETERS: - A - orthogonal NxN matrix, array[0..N-1,0..N-1] - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonal(const ae_int_t n, real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixrndorthogonal(n, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of random NxN matrix with given condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndcond(const ae_int_t n, const double c, real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixrndcond(n, c, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of a random Haar distributed orthogonal complex matrix - -INPUT PARAMETERS: - N - matrix size, N>=1 - -OUTPUT PARAMETERS: - A - orthogonal NxN matrix, array[0..N-1,0..N-1] - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonal(const ae_int_t n, complex_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixrndorthogonal(n, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of random NxN complex matrix with given condition number C and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndcond(const ae_int_t n, const double c, complex_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixrndcond(n, c, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of random NxN symmetric matrix with given condition number and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void smatrixrndcond(const ae_int_t n, const double c, real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::smatrixrndcond(n, c, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of random NxN symmetric positive definite matrix with given -condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random SPD matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixrndcond(const ae_int_t n, const double c, real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixrndcond(n, c, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of random NxN Hermitian matrix with given condition number and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hmatrixrndcond(const ae_int_t n, const double c, complex_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hmatrixrndcond(n, c, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Generation of random NxN Hermitian positive definite matrix with given -condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random HPD matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixrndcond(const ae_int_t n, const double c, complex_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixrndcond(n, c, const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiplication of MxN matrix by NxN random Haar distributed orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonalfromtheright(real_2d_array &a, const ae_int_t m, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixrndorthogonalfromtheright(const_cast(a.c_ptr()), m, n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiplication of MxN matrix by MxM random Haar distributed orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - Q*A, where Q is random MxM orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonalfromtheleft(real_2d_array &a, const ae_int_t m, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixrndorthogonalfromtheleft(const_cast(a.c_ptr()), m, n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiplication of MxN complex matrix by NxN random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonalfromtheright(complex_2d_array &a, const ae_int_t m, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixrndorthogonalfromtheright(const_cast(a.c_ptr()), m, n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Multiplication of MxN complex matrix by MxM random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - Q*A, where Q is random MxM orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonalfromtheleft(complex_2d_array &a, const ae_int_t m, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixrndorthogonalfromtheleft(const_cast(a.c_ptr()), m, n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Symmetric multiplication of NxN matrix by random Haar distributed -orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..N-1, 0..N-1] - N - matrix size - -OUTPUT PARAMETERS: - A - Q'*A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void smatrixrndmultiply(real_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::smatrixrndmultiply(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Hermitian multiplication of NxN matrix by random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..N-1, 0..N-1] - N - matrix size - -OUTPUT PARAMETERS: - A - Q^H*A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hmatrixrndmultiply(complex_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hmatrixrndmultiply(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -LU decomposition of a general real matrix with row pivoting - -A is represented as A = P*L*U, where: -* L is lower unitriangular matrix -* U is upper triangular matrix -* P = P0*P1*...*PK, K=min(M,N)-1, - Pi - permutation matrix for I and Pivots[I] - -This is cache-oblivous implementation of LU decomposition. -It is optimized for square matrices. As for rectangular matrices: -* best case - M>>N -* worst case - N>>M, small M, large N, matrix does not fit in CPU cache - -INPUT PARAMETERS: - A - array[0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - - -OUTPUT PARAMETERS: - A - matrices L and U in compact form: - * L is stored under main diagonal - * U is stored on and above main diagonal - Pivots - permutation matrix in compact form. - array[0..Min(M-1,N-1)]. - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlu(real_2d_array &a, const ae_int_t m, const ae_int_t n, integer_1d_array &pivots) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlu(const_cast(a.c_ptr()), m, n, const_cast(pivots.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -LU decomposition of a general complex matrix with row pivoting - -A is represented as A = P*L*U, where: -* L is lower unitriangular matrix -* U is upper triangular matrix -* P = P0*P1*...*PK, K=min(M,N)-1, - Pi - permutation matrix for I and Pivots[I] - -This is cache-oblivous implementation of LU decomposition. It is optimized -for square matrices. As for rectangular matrices: -* best case - M>>N -* worst case - N>>M, small M, large N, matrix does not fit in CPU cache - -INPUT PARAMETERS: - A - array[0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - - -OUTPUT PARAMETERS: - A - matrices L and U in compact form: - * L is stored under main diagonal - * U is stored on and above main diagonal - Pivots - permutation matrix in compact form. - array[0..Min(M-1,N-1)]. - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlu(complex_2d_array &a, const ae_int_t m, const ae_int_t n, integer_1d_array &pivots) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlu(const_cast(a.c_ptr()), m, n, const_cast(pivots.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cache-oblivious Cholesky decomposition - -The algorithm computes Cholesky decomposition of a Hermitian positive- -definite matrix. The result of an algorithm is a representation of A as -A=U'*U or A=L*L' (here X' detones conj(X^T)). - -INPUT PARAMETERS: - A - upper or lower triangle of a factorized matrix. - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - if IsUpper=True, then A contains an upper triangle of - a symmetric matrix, otherwise A contains a lower one. - -OUTPUT PARAMETERS: - A - the result of factorization. If IsUpper=True, then - the upper triangle contains matrix U, so that A = U'*U, - and the elements below the main diagonal are not modified. - Similarly, if IsUpper = False. - -RESULT: - If the matrix is positive-definite, the function returns True. - Otherwise, the function returns False. Contents of A is not determined - in such case. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -bool hpdmatrixcholesky(complex_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::hpdmatrixcholesky(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cache-oblivious Cholesky decomposition - -The algorithm computes Cholesky decomposition of a symmetric positive- -definite matrix. The result of an algorithm is a representation of A as -A=U^T*U or A=L*L^T - -INPUT PARAMETERS: - A - upper or lower triangle of a factorized matrix. - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - if IsUpper=True, then A contains an upper triangle of - a symmetric matrix, otherwise A contains a lower one. - -OUTPUT PARAMETERS: - A - the result of factorization. If IsUpper=True, then - the upper triangle contains matrix U, so that A = U^T*U, - and the elements below the main diagonal are not modified. - Similarly, if IsUpper = False. - -RESULT: - If the matrix is positive-definite, the function returns True. - Otherwise, the function returns False. Contents of A is not determined - in such case. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -bool spdmatrixcholesky(real_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::spdmatrixcholesky(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of a matrix condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixrcond1(const real_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixrcond1(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixrcondinf(const real_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixrcondinf(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Condition number estimate of a symmetric positive definite matrix. - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm of condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - A - symmetric positive definite matrix which is given by its - upper or lower triangle depending on the value of - IsUpper. Array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - -Result: - 1/LowerBound(cond(A)), if matrix A is positive definite, - -1, if matrix A is not positive definite, and its condition number - could not be found by this algorithm. - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double spdmatrixrcond(const real_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spdmatrixrcond(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix: estimate of a condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array[0..N-1, 0..N-1]. - N - size of A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixtrrcond1(const real_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixtrrcond1(const_cast(a.c_ptr()), n, isupper, isunit, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix: estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixtrrcondinf(const real_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixtrrcondinf(const_cast(a.c_ptr()), n, isupper, isunit, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Condition number estimate of a Hermitian positive definite matrix. - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm of condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - A - Hermitian positive definite matrix which is given by its - upper or lower triangle depending on the value of - IsUpper. Array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - -Result: - 1/LowerBound(cond(A)), if matrix A is positive definite, - -1, if matrix A is not positive definite, and its condition number - could not be found by this algorithm. - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double hpdmatrixrcond(const complex_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hpdmatrixrcond(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of a matrix condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixrcond1(const complex_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cmatrixrcond1(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixrcondinf(const complex_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cmatrixrcondinf(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the RMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixlurcond1(const real_2d_array &lua, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixlurcond1(const_cast(lua.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition -(infinity norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the RMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixlurcondinf(const real_2d_array &lua, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixlurcondinf(const_cast(lua.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Condition number estimate of a symmetric positive definite matrix given by -Cholesky decomposition. - -The algorithm calculates a lower bound of the condition number. In this -case, the algorithm does not return a lower bound of the condition number, -but an inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - CD - Cholesky decomposition of matrix A, - output of SMatrixCholesky subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double spdmatrixcholeskyrcond(const real_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spdmatrixcholeskyrcond(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Condition number estimate of a Hermitian positive definite matrix given by -Cholesky decomposition. - -The algorithm calculates a lower bound of the condition number. In this -case, the algorithm does not return a lower bound of the condition number, -but an inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - CD - Cholesky decomposition of matrix A, - output of SMatrixCholesky subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double hpdmatrixcholeskyrcond(const complex_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hpdmatrixcholeskyrcond(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the CMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixlurcond1(const complex_2d_array &lua, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cmatrixlurcond1(const_cast(lua.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition -(infinity norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the CMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixlurcondinf(const complex_2d_array &lua, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cmatrixlurcondinf(const_cast(lua.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix: estimate of a condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array[0..N-1, 0..N-1]. - N - size of A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixtrrcond1(const complex_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cmatrixtrrcond1(const_cast(a.c_ptr()), n, isupper, isunit, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix: estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixtrrcondinf(const complex_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cmatrixtrrcondinf(const_cast(a.c_ptr()), n, isupper, isunit, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Matrix inverse report: -* R1 reciprocal of condition number in 1-norm -* RInf reciprocal of condition number in inf-norm -*************************************************************************/ -_matinvreport_owner::_matinvreport_owner() -{ - p_struct = (alglib_impl::matinvreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::matinvreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_matinvreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_matinvreport_owner::_matinvreport_owner(const _matinvreport_owner &rhs) -{ - p_struct = (alglib_impl::matinvreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::matinvreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_matinvreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_matinvreport_owner& _matinvreport_owner::operator=(const _matinvreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_matinvreport_clear(p_struct); - if( !alglib_impl::_matinvreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_matinvreport_owner::~_matinvreport_owner() -{ - alglib_impl::_matinvreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::matinvreport* _matinvreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::matinvreport* _matinvreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -matinvreport::matinvreport() : _matinvreport_owner() ,r1(p_struct->r1),rinf(p_struct->rinf) -{ -} - -matinvreport::matinvreport(const matinvreport &rhs):_matinvreport_owner(rhs) ,r1(p_struct->r1),rinf(p_struct->rinf) -{ -} - -matinvreport& matinvreport::operator=(const matinvreport &rhs) -{ - if( this==&rhs ) - return *this; - _matinvreport_owner::operator=(rhs); - return *this; -} - -matinvreport::~matinvreport() -{ -} - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of RMatrixLU subroutine). - Pivots - table of permutations - (the output of RMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code: - * -3 A is singular, or VERY close to singular. - it is filled by zeros in such cases. - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - A - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixluinverse(real_2d_array &a, const integer_1d_array &pivots, const ae_int_t n, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixluinverse(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of RMatrixLU subroutine). - Pivots - table of permutations - (the output of RMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code: - * -3 A is singular, or VERY close to singular. - it is filled by zeros in such cases. - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - A - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixluinverse(real_2d_array &a, const integer_1d_array &pivots, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.cols()!=a.rows()) || (a.cols()!=pivots.length())) - throw ap_error("Error while calling 'rmatrixluinverse': looks like one of arguments has wrong size"); - n = a.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixluinverse(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - -Result: - True, if the matrix is not singular. - False, if the matrix is singular. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinverse(real_2d_array &a, const ae_int_t n, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixinverse(const_cast(a.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - -Result: - True, if the matrix is not singular. - False, if the matrix is singular. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinverse(real_2d_array &a, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'rmatrixinverse': looks like one of arguments has wrong size"); - n = a.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixinverse(const_cast(a.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of CMatrixLU subroutine). - Pivots - table of permutations - (the output of CMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixluinverse(complex_2d_array &a, const integer_1d_array &pivots, const ae_int_t n, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixluinverse(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of CMatrixLU subroutine). - Pivots - table of permutations - (the output of CMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixluinverse(complex_2d_array &a, const integer_1d_array &pivots, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.cols()!=a.rows()) || (a.cols()!=pivots.length())) - throw ap_error("Error while calling 'cmatrixluinverse': looks like one of arguments has wrong size"); - n = a.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixluinverse(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void cmatrixinverse(complex_2d_array &a, const ae_int_t n, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixinverse(const_cast(a.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void cmatrixinverse(complex_2d_array &a, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'cmatrixinverse': looks like one of arguments has wrong size"); - n = a.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixinverse(const_cast(a.c_ptr()), n, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a symmetric positive definite matrix which is given -by Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition of the matrix to be inverted: - A=U’*U or A = L*L'. - Output of SPDMatrixCholesky subroutine. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, lower half is used. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskyinverse(real_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixcholeskyinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a symmetric positive definite matrix which is given -by Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition of the matrix to be inverted: - A=U’*U or A = L*L'. - Output of SPDMatrixCholesky subroutine. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, lower half is used. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskyinverse(real_2d_array &a, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isupper; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'spdmatrixcholeskyinverse': looks like one of arguments has wrong size"); - n = a.cols(); - isupper = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixcholeskyinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a symmetric positive definite matrix. - -Given an upper or lower triangle of a symmetric positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixinverse(real_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a symmetric positive definite matrix. - -Given an upper or lower triangle of a symmetric positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixinverse(real_2d_array &a, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isupper; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'spdmatrixinverse': looks like one of arguments has wrong size"); - if( !alglib_impl::ae_is_symmetric(const_cast(a.c_ptr())) ) - throw ap_error("'a' parameter is not symmetric matrix"); - n = a.cols(); - isupper = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - if( !alglib_impl::ae_force_symmetric(const_cast(a.c_ptr())) ) - throw ap_error("Internal error while forcing symmetricity of 'a' parameter"); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a Hermitian positive definite matrix which is given -by Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition of the matrix to be inverted: - A=U’*U or A = L*L'. - Output of HPDMatrixCholesky subroutine. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, lower half is used. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskyinverse(complex_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixcholeskyinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a Hermitian positive definite matrix which is given -by Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition of the matrix to be inverted: - A=U’*U or A = L*L'. - Output of HPDMatrixCholesky subroutine. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, lower half is used. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskyinverse(complex_2d_array &a, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isupper; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'hpdmatrixcholeskyinverse': looks like one of arguments has wrong size"); - n = a.cols(); - isupper = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixcholeskyinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a Hermitian positive definite matrix. - -Given an upper or lower triangle of a Hermitian positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixinverse(complex_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inversion of a Hermitian positive definite matrix. - -Given an upper or lower triangle of a Hermitian positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixinverse(complex_2d_array &a, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isupper; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'hpdmatrixinverse': looks like one of arguments has wrong size"); - if( !alglib_impl::ae_is_hermitian(const_cast(a.c_ptr())) ) - throw ap_error("'a' parameter is not Hermitian matrix"); - n = a.cols(); - isupper = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixinverse(const_cast(a.c_ptr()), n, isupper, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - if( !alglib_impl::ae_force_hermitian(const_cast(a.c_ptr())) ) - throw ap_error("Internal error while forcing Hermitian properties of 'a' parameter"); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix inverse (real) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixtrinverse(real_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixtrinverse(const_cast(a.c_ptr()), n, isupper, isunit, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix inverse (real) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixtrinverse(real_2d_array &a, const bool isupper, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isunit; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'rmatrixtrinverse': looks like one of arguments has wrong size"); - n = a.cols(); - isunit = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixtrinverse(const_cast(a.c_ptr()), n, isupper, isunit, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix inverse (complex) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixtrinverse(complex_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixtrinverse(const_cast(a.c_ptr()), n, isupper, isunit, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Triangular matrix inverse (complex) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixtrinverse(complex_2d_array &a, const bool isupper, ae_int_t &info, matinvreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isunit; - if( (a.cols()!=a.rows())) - throw ap_error("Error while calling 'cmatrixtrinverse': looks like one of arguments has wrong size"); - n = a.cols(); - isunit = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixtrinverse(const_cast(a.c_ptr()), n, isupper, isunit, &info, const_cast(rep.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Sparse matrix - -You should use ALGLIB functions to work with sparse matrix. -Never try to access its fields directly! -*************************************************************************/ -_sparsematrix_owner::_sparsematrix_owner() -{ - p_struct = (alglib_impl::sparsematrix*)alglib_impl::ae_malloc(sizeof(alglib_impl::sparsematrix), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_sparsematrix_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_sparsematrix_owner::_sparsematrix_owner(const _sparsematrix_owner &rhs) -{ - p_struct = (alglib_impl::sparsematrix*)alglib_impl::ae_malloc(sizeof(alglib_impl::sparsematrix), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_sparsematrix_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_sparsematrix_owner& _sparsematrix_owner::operator=(const _sparsematrix_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_sparsematrix_clear(p_struct); - if( !alglib_impl::_sparsematrix_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_sparsematrix_owner::~_sparsematrix_owner() -{ - alglib_impl::_sparsematrix_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::sparsematrix* _sparsematrix_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::sparsematrix* _sparsematrix_owner::c_ptr() const -{ - return const_cast(p_struct); -} -sparsematrix::sparsematrix() : _sparsematrix_owner() -{ -} - -sparsematrix::sparsematrix(const sparsematrix &rhs):_sparsematrix_owner(rhs) -{ -} - -sparsematrix& sparsematrix::operator=(const sparsematrix &rhs) -{ - if( this==&rhs ) - return *this; - _sparsematrix_owner::operator=(rhs); - return *this; -} - -sparsematrix::~sparsematrix() -{ -} - -/************************************************************************* -This function creates sparse matrix in a Hash-Table format. - -This function creates Hast-Table matrix, which can be converted to CRS -format after its initialization is over. Typical usage scenario for a -sparse matrix is: -1. creation in a Hash-Table format -2. insertion of the matrix elements -3. conversion to the CRS representation -4. matrix is passed to some linear algebra algorithm - -Some information about different matrix formats can be found below, in -the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - K - K>=0, expected number of non-zero elements in a matrix. - K can be inexact approximation, can be less than actual - number of elements (table will grow when needed) or - even zero). - It is important to understand that although hash-table - may grow automatically, it is better to provide good - estimate of data size. - -OUTPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - All elements of the matrix are zero. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreate(const ae_int_t m, const ae_int_t n, const ae_int_t k, sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsecreate(m, n, k, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function creates sparse matrix in a Hash-Table format. - -This function creates Hast-Table matrix, which can be converted to CRS -format after its initialization is over. Typical usage scenario for a -sparse matrix is: -1. creation in a Hash-Table format -2. insertion of the matrix elements -3. conversion to the CRS representation -4. matrix is passed to some linear algebra algorithm - -Some information about different matrix formats can be found below, in -the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - K - K>=0, expected number of non-zero elements in a matrix. - K can be inexact approximation, can be less than actual - number of elements (table will grow when needed) or - even zero). - It is important to understand that although hash-table - may grow automatically, it is better to provide good - estimate of data size. - -OUTPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - All elements of the matrix are zero. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreate(const ae_int_t m, const ae_int_t n, sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t k; - - k = 0; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsecreate(m, n, k, const_cast(s.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function creates sparse matrix in a CRS format (expert function for -situations when you are running out of memory). - -This function creates CRS matrix. Typical usage scenario for a CRS matrix -is: -1. creation (you have to tell number of non-zero elements at each row at - this moment) -2. insertion of the matrix elements (row by row, from left to right) -3. matrix is passed to some linear algebra algorithm - -This function is a memory-efficient alternative to SparseCreate(), but it -is more complex because it requires you to know in advance how large your -matrix is. Some information about different matrix formats can be found -below, in the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - NER - number of elements at each row, array[M], NER[I]>=0 - -OUTPUT PARAMETERS - S - sparse M*N matrix in CRS representation. - You have to fill ALL non-zero elements by calling - SparseSet() BEFORE you try to use this matrix. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreatecrs(const ae_int_t m, const ae_int_t n, const integer_1d_array &ner, sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsecreatecrs(m, n, const_cast(ner.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function copies S0 to S1. - -NOTE: this function does not verify its arguments, it just copies all -fields of the structure. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecopy(const sparsematrix &s0, sparsematrix &s1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsecopy(const_cast(s0.c_ptr()), const_cast(s1.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function adds value to S[i,j] - element of the sparse matrix. Matrix -must be in a Hash-Table mode. - -In case S[i,j] already exists in the table, V i added to its value. In -case S[i,j] is non-existent, it is inserted in the table. Table -automatically grows when necessary. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - Exception will be thrown for CRS matrix. - I - row index of the element to modify, 0<=I(s.c_ptr()), i, j, v, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function modifies S[i,j] - element of the sparse matrix. - -For Hash-based storage format: -* new value can be zero or non-zero. In case new value of S[i,j] is zero, - this element is deleted from the table. -* this function has no effect when called with zero V for non-existent - element. - -For CRS-bases storage format: -* new value MUST be non-zero. Exception will be thrown for zero V. -* elements must be initialized in correct order - from top row to bottom, - within row - from left to right. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - I - row index of the element to modify, 0<=I(s.c_ptr()), i, j, v, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns S[i,j] - element of the sparse matrix. Matrix can -be in any mode (Hash-Table or CRS), but this function is less efficient -for CRS matrices. Hash-Table matrices can find element in O(1) time, -while CRS matrices need O(log(RS)) time, where RS is an number of non- -zero elements in a row. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - Exception will be thrown for CRS matrix. - I - row index of the element to modify, 0<=I(s.c_ptr()), i, j, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns I-th diagonal element of the sparse matrix. - -Matrix can be in any mode (Hash-Table or CRS storage), but this function -is most efficient for CRS matrices - it requires less than 50 CPU cycles -to extract diagonal element. For Hash-Table matrices we still have O(1) -query time, but function is many times slower. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - Exception will be thrown for CRS matrix. - I - index of the element to modify, 0<=I(s.c_ptr()), i, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function converts matrix to CRS format. - -Some algorithms (linear algebra ones, for example) require matrices in -CRS format. - -INPUT PARAMETERS - S - sparse M*N matrix in any format - -OUTPUT PARAMETERS - S - matrix in CRS format - -NOTE: this function has no effect when called with matrix which is -already in CRS mode. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparseconverttocrs(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparseconverttocrs(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates matrix-vector product S*x. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[M], S*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemv(const sparsematrix &s, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsemv(const_cast(s.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates matrix-vector product S^T*x. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - X - array[M], input vector. For performance reasons we - make only quick checks - we check that array size is - at least M, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[N], S^T*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemtv(const sparsematrix &s, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsemtv(const_cast(s.c_ptr()), const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function simultaneously calculates two matrix-vector products: - S*x and S^T*x. -S must be square (non-rectangular) matrix stored in CRS format (exception -will be thrown otherwise). - -INPUT PARAMETERS - S - sparse N*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y0 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - Y1 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y0 - array[N], S*x - Y1 - array[N], S^T*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. It also throws exception when S is non-square. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemv2(const sparsematrix &s, const real_1d_array &x, real_1d_array &y0, real_1d_array &y1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsemv2(const_cast(s.c_ptr()), const_cast(x.c_ptr()), const_cast(y0.c_ptr()), const_cast(y1.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates matrix-vector product S*x, when S is symmetric -matrix. Matrix S must be stored in CRS format (exception will be -thrown otherwise). - -INPUT PARAMETERS - S - sparse M*M matrix in CRS format (you MUST convert it - to CRS before calling this function). - IsUpper - whether upper or lower triangle of S is given: - * if upper triangle is given, only S[i,j] for j>=i - are used, and lower triangle is ignored (it can be - empty - these elements are not referenced at all). - * if lower triangle is given, only S[i,j] for j<=i - are used, and upper triangle is ignored. - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[M], S*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsesmv(const sparsematrix &s, const bool isupper, const real_1d_array &x, real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsesmv(const_cast(s.c_ptr()), isupper, const_cast(x.c_ptr()), const_cast(y.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates matrix-matrix product S*A. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size - is at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[M][K], S*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemm(const sparsematrix &s, const real_2d_array &a, const ae_int_t k, real_2d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsemm(const_cast(s.c_ptr()), const_cast(a.c_ptr()), k, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates matrix-matrix product S^T*A. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[M][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least M, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[N][K], S^T*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemtm(const sparsematrix &s, const real_2d_array &a, const ae_int_t k, real_2d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsemtm(const_cast(s.c_ptr()), const_cast(a.c_ptr()), k, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function simultaneously calculates two matrix-matrix products: - S*A and S^T*A. -S must be square (non-rectangular) matrix stored in CRS format (exception -will be thrown otherwise). - -INPUT PARAMETERS - S - sparse N*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B0 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - B1 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B0 - array[N][K], S*A - B1 - array[N][K], S^T*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. It also throws exception when S is non-square. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemm2(const sparsematrix &s, const real_2d_array &a, const ae_int_t k, real_2d_array &b0, real_2d_array &b1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsemm2(const_cast(s.c_ptr()), const_cast(a.c_ptr()), k, const_cast(b0.c_ptr()), const_cast(b1.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function calculates matrix-matrix product S*A, when S is symmetric -matrix. Matrix S must be stored in CRS format (exception will be -thrown otherwise). - -INPUT PARAMETERS - S - sparse M*M matrix in CRS format (you MUST convert it - to CRS before calling this function). - IsUpper - whether upper or lower triangle of S is given: - * if upper triangle is given, only S[i,j] for j>=i - are used, and lower triangle is ignored (it can be - empty - these elements are not referenced at all). - * if lower triangle is given, only S[i,j] for j<=i - are used, and upper triangle is ignored. - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[M][K], S*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsesmm(const sparsematrix &s, const bool isupper, const real_2d_array &a, const ae_int_t k, real_2d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsesmm(const_cast(s.c_ptr()), isupper, const_cast(a.c_ptr()), k, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This procedure resizes Hash-Table matrix. It can be called when you have -deleted too many elements from the matrix, and you want to free unneeded -memory. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparseresizematrix(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparseresizematrix(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to enumerate all elements of the sparse matrix. -Before first call user initializes T0 and T1 counters by zero. These -counters are used to remember current position in a matrix; after each -call they are updated by the function. - -Subsequent calls to this function return non-zero elements of the sparse -matrix, one by one. If you enumerate CRS matrix, matrix is traversed from -left to right, from top to bottom. In case you enumerate matrix stored as -Hash table, elements are returned in random order. - -EXAMPLE - > T0=0 - > T1=0 - > while SparseEnumerate(S,T0,T1,I,J,V) do - > ....do something with I,J,V - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - T0 - internal counter - T1 - internal counter - -OUTPUT PARAMETERS - T0 - new value of the internal counter - T1 - new value of the internal counter - I - row index of non-zero element, 0<=I(s.c_ptr()), &t0, &t1, &i, &j, &v, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function rewrites existing (non-zero) element. It returns True if -element exists or False, when it is called for non-existing (zero) -element. - -The purpose of this function is to provide convenient thread-safe way to -modify sparse matrix. Such modification (already existing element is -rewritten) is guaranteed to be thread-safe without any synchronization, as -long as different threads modify different elements. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - I - row index of non-zero element to modify, 0<=I(s.c_ptr()), i, j, v, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns I-th row of the sparse matrix stored in CRS format. - -NOTE: when incorrect I (outside of [0,M-1]) or matrix (non-CRS) are - passed, this function throws exception. - -INPUT PARAMETERS: - S - sparse M*N matrix in CRS format - I - row index, 0<=I(s.c_ptr()), i, const_cast(irow.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function performs in-place conversion from CRS format to Hash table -storage. - -INPUT PARAMETERS - S - sparse matrix in CRS format. - -OUTPUT PARAMETERS - S - sparse matrix in Hash table format. - -NOTE: this function has no effect when called with matrix which is -already in Hash table mode. - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparseconverttohash(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparseconverttohash(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function performs out-of-place conversion to Hash table storage -format. S0 is copied to S1 and converted on-the-fly. - -INPUT PARAMETERS - S0 - sparse matrix in any format. - -OUTPUT PARAMETERS - S1 - sparse matrix in Hash table format. - -NOTE: if S0 is stored as Hash-table, it is just copied without conversion. - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparsecopytohash(const sparsematrix &s0, sparsematrix &s1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsecopytohash(const_cast(s0.c_ptr()), const_cast(s1.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function performs out-of-place conversion to CRS format. S0 is -copied to S1 and converted on-the-fly. - -INPUT PARAMETERS - S0 - sparse matrix in any format. - -OUTPUT PARAMETERS - S1 - sparse matrix in CRS format. - -NOTE: if S0 is stored as CRS, it is just copied without conversion. - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparsecopytocrs(const sparsematrix &s0, sparsematrix &s1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsecopytocrs(const_cast(s0.c_ptr()), const_cast(s1.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function returns type of the matrix storage format. - -INPUT PARAMETERS: - S - sparse matrix. - -RESULT: - sparse storage format used by matrix: - 0 - Hash-table - 1 - CRS-format - -NOTE: future versions of ALGLIB may include additional sparse storage - formats. - - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sparsegetmatrixtype(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::sparsegetmatrixtype(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function checks matrix storage format and returns True when matrix is -stored using Hash table representation. - -INPUT PARAMETERS: - S - sparse matrix. - -RESULT: - True if matrix type is Hash table - False if matrix type is not Hash table - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -bool sparseishash(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::sparseishash(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function checks matrix storage format and returns True when matrix is -stored using CRS representation. - -INPUT PARAMETERS: - S - sparse matrix. - -RESULT: - True if matrix type is CRS - False if matrix type is not CRS - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -bool sparseiscrs(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::sparseiscrs(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The function frees all memory occupied by sparse matrix. Sparse matrix -structure becomes unusable after this call. - -OUTPUT PARAMETERS - S - sparse matrix to delete - - -- ALGLIB PROJECT -- - Copyright 24.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparsefree(sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sparsefree(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The function returns number of rows of a sparse matrix. - -RESULT: number of rows of a sparse matrix. - - -- ALGLIB PROJECT -- - Copyright 23.08.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sparsegetnrows(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::sparsegetnrows(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The function returns number of columns of a sparse matrix. - -RESULT: number of columns of a sparse matrix. - - -- ALGLIB PROJECT -- - Copyright 23.08.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sparsegetncols(const sparsematrix &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_int_t result = alglib_impl::sparsegetncols(const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -This object stores state of the iterative norm estimation algorithm. - -You should use ALGLIB functions to work with this object. -*************************************************************************/ -_normestimatorstate_owner::_normestimatorstate_owner() -{ - p_struct = (alglib_impl::normestimatorstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::normestimatorstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_normestimatorstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_normestimatorstate_owner::_normestimatorstate_owner(const _normestimatorstate_owner &rhs) -{ - p_struct = (alglib_impl::normestimatorstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::normestimatorstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_normestimatorstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_normestimatorstate_owner& _normestimatorstate_owner::operator=(const _normestimatorstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_normestimatorstate_clear(p_struct); - if( !alglib_impl::_normestimatorstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_normestimatorstate_owner::~_normestimatorstate_owner() -{ - alglib_impl::_normestimatorstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::normestimatorstate* _normestimatorstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::normestimatorstate* _normestimatorstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -normestimatorstate::normestimatorstate() : _normestimatorstate_owner() -{ -} - -normestimatorstate::normestimatorstate(const normestimatorstate &rhs):_normestimatorstate_owner(rhs) -{ -} - -normestimatorstate& normestimatorstate::operator=(const normestimatorstate &rhs) -{ - if( this==&rhs ) - return *this; - _normestimatorstate_owner::operator=(rhs); - return *this; -} - -normestimatorstate::~normestimatorstate() -{ -} - -/************************************************************************* -This procedure initializes matrix norm estimator. - -USAGE: -1. User initializes algorithm state with NormEstimatorCreate() call -2. User calls NormEstimatorEstimateSparse() (or NormEstimatorIteration()) -3. User calls NormEstimatorResults() to get solution. - -INPUT PARAMETERS: - M - number of rows in the matrix being estimated, M>0 - N - number of columns in the matrix being estimated, N>0 - NStart - number of random starting vectors - recommended value - at least 5. - NIts - number of iterations to do with best starting vector - recommended value - at least 5. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTE: this algorithm is effectively deterministic, i.e. it always returns -same result when repeatedly called for the same matrix. In fact, algorithm -uses randomized starting vectors, but internal random numbers generator -always generates same sequence of the random values (it is a feature, not -bug). - -Algorithm can be made non-deterministic with NormEstimatorSetSeed(0) call. - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorcreate(const ae_int_t m, const ae_int_t n, const ae_int_t nstart, const ae_int_t nits, normestimatorstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::normestimatorcreate(m, n, nstart, nits, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function changes seed value used by algorithm. In some cases we need -deterministic processing, i.e. subsequent calls must return equal results, -in other cases we need non-deterministic algorithm which returns different -results for the same matrix on every pass. - -Setting zero seed will lead to non-deterministic algorithm, while non-zero -value will make our algorithm deterministic. - -INPUT PARAMETERS: - State - norm estimator state, must be initialized with a call - to NormEstimatorCreate() - SeedVal - seed value, >=0. Zero value = non-deterministic algo. - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorsetseed(const normestimatorstate &state, const ae_int_t seedval) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::normestimatorsetseed(const_cast(state.c_ptr()), seedval, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function estimates norm of the sparse M*N matrix A. - -INPUT PARAMETERS: - State - norm estimator state, must be initialized with a call - to NormEstimatorCreate() - A - sparse M*N matrix, must be converted to CRS format - prior to calling this function. - -After this function is over you can call NormEstimatorResults() to get -estimate of the norm(A). - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorestimatesparse(const normestimatorstate &state, const sparsematrix &a) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::normestimatorestimatesparse(const_cast(state.c_ptr()), const_cast(a.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Matrix norm estimation results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - Nrm - estimate of the matrix norm, Nrm>=0 - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorresults(const normestimatorstate &state, double &nrm) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::normestimatorresults(const_cast(state.c_ptr()), &nrm, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixludet(const real_2d_array &a, const integer_1d_array &pivots, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixludet(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixludet(const real_2d_array &a, const integer_1d_array &pivots) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.rows()!=a.cols()) || (a.rows()!=pivots.length())) - throw ap_error("Error while calling 'rmatrixludet': looks like one of arguments has wrong size"); - n = a.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixludet(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixdet(const real_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixdet(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixdet(const real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.rows()!=a.cols())) - throw ap_error("Error while calling 'rmatrixdet': looks like one of arguments has wrong size"); - n = a.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::rmatrixdet(const_cast(a.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -alglib::complex cmatrixludet(const complex_2d_array &a, const integer_1d_array &pivots, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_complex result = alglib_impl::cmatrixludet(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -alglib::complex cmatrixludet(const complex_2d_array &a, const integer_1d_array &pivots) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.rows()!=a.cols()) || (a.rows()!=pivots.length())) - throw ap_error("Error while calling 'cmatrixludet': looks like one of arguments has wrong size"); - n = a.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_complex result = alglib_impl::cmatrixludet(const_cast(a.c_ptr()), const_cast(pivots.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -alglib::complex cmatrixdet(const complex_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_complex result = alglib_impl::cmatrixdet(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -alglib::complex cmatrixdet(const complex_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.rows()!=a.cols())) - throw ap_error("Error while calling 'cmatrixdet': looks like one of arguments has wrong size"); - n = a.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ae_complex result = alglib_impl::cmatrixdet(const_cast(a.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the matrix given by the Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition, - output of SMatrixCholesky subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -As the determinant is equal to the product of squares of diagonal elements, -it’s not necessary to specify which triangle - lower or upper - the matrix -is stored in. - -Result: - matrix determinant. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixcholeskydet(const real_2d_array &a, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spdmatrixcholeskydet(const_cast(a.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the matrix given by the Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition, - output of SMatrixCholesky subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -As the determinant is equal to the product of squares of diagonal elements, -it’s not necessary to specify which triangle - lower or upper - the matrix -is stored in. - -Result: - matrix determinant. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixcholeskydet(const real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (a.rows()!=a.cols())) - throw ap_error("Error while calling 'spdmatrixcholeskydet': looks like one of arguments has wrong size"); - n = a.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spdmatrixcholeskydet(const_cast(a.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the symmetric positive definite matrix. - -Input parameters: - A - matrix. Array with elements [0..N-1, 0..N-1]. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Result: - determinant of matrix A. - If matrix A is not positive definite, exception is thrown. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixdet(const real_2d_array &a, const ae_int_t n, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spdmatrixdet(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Determinant calculation of the symmetric positive definite matrix. - -Input parameters: - A - matrix. Array with elements [0..N-1, 0..N-1]. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Result: - determinant of matrix A. - If matrix A is not positive definite, exception is thrown. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixdet(const real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - bool isupper; - if( (a.rows()!=a.cols())) - throw ap_error("Error while calling 'spdmatrixdet': looks like one of arguments has wrong size"); - if( !alglib_impl::ae_is_symmetric(const_cast(a.c_ptr())) ) - throw ap_error("'a' parameter is not symmetric matrix"); - n = a.rows(); - isupper = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spdmatrixdet(const_cast(a.c_ptr()), n, isupper, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Algorithm for solving the following generalized symmetric positive-definite -eigenproblem: - A*x = lambda*B*x (1) or - A*B*x = lambda*x (2) or - B*A*x = lambda*x (3). -where A is a symmetric matrix, B - symmetric positive-definite matrix. -The problem is solved by reducing it to an ordinary symmetric eigenvalue -problem. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrices A and B. - IsUpperA - storage format of matrix A. - B - symmetric positive-definite matrix which is given by - its upper or lower triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperB - storage format of matrix B. - ZNeeded - if ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - ProblemType - if ProblemType is equal to: - * 1, the following problem is solved: A*x = lambda*B*x; - * 2, the following problem is solved: A*B*x = lambda*x; - * 3, the following problem is solved: B*A*x = lambda*x. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in matrix columns. It should - be noted that the eigenvectors in such problems do not - form an orthogonal system. - -Result: - True, if the problem was solved successfully. - False, if the error occurred during the Cholesky decomposition of matrix - B (the matrix isn’t positive-definite) or during the work of the iterative - algorithm for solving the symmetric eigenproblem. - -See also the GeneralizedSymmetricDefiniteEVDReduce subroutine. - - -- ALGLIB -- - Copyright 1.28.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixgevd(const real_2d_array &a, const ae_int_t n, const bool isuppera, const real_2d_array &b, const bool isupperb, const ae_int_t zneeded, const ae_int_t problemtype, real_1d_array &d, real_2d_array &z) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixgevd(const_cast(a.c_ptr()), n, isuppera, const_cast(b.c_ptr()), isupperb, zneeded, problemtype, const_cast(d.c_ptr()), const_cast(z.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Algorithm for reduction of the following generalized symmetric positive- -definite eigenvalue problem: - A*x = lambda*B*x (1) or - A*B*x = lambda*x (2) or - B*A*x = lambda*x (3) -to the symmetric eigenvalues problem C*y = lambda*y (eigenvalues of this and -the given problems are the same, and the eigenvectors of the given problem -could be obtained by multiplying the obtained eigenvectors by the -transformation matrix x = R*y). - -Here A is a symmetric matrix, B - symmetric positive-definite matrix. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrices A and B. - IsUpperA - storage format of matrix A. - B - symmetric positive-definite matrix which is given by - its upper or lower triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperB - storage format of matrix B. - ProblemType - if ProblemType is equal to: - * 1, the following problem is solved: A*x = lambda*B*x; - * 2, the following problem is solved: A*B*x = lambda*x; - * 3, the following problem is solved: B*A*x = lambda*x. - -Output parameters: - A - symmetric matrix which is given by its upper or lower - triangle depending on IsUpperA. Contains matrix C. - Array whose indexes range within [0..N-1, 0..N-1]. - R - upper triangular or low triangular transformation matrix - which is used to obtain the eigenvectors of a given problem - as the product of eigenvectors of C (from the right) and - matrix R (from the left). If the matrix is upper - triangular, the elements below the main diagonal - are equal to 0 (and vice versa). Thus, we can perform - the multiplication without taking into account the - internal structure (which is an easier though less - effective way). - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperR - type of matrix R (upper or lower triangular). - -Result: - True, if the problem was reduced successfully. - False, if the error occurred during the Cholesky decomposition of - matrix B (the matrix is not positive-definite). - - -- ALGLIB -- - Copyright 1.28.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixgevdreduce(real_2d_array &a, const ae_int_t n, const bool isuppera, const real_2d_array &b, const bool isupperb, const ae_int_t problemtype, real_2d_array &r, bool &isupperr) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::smatrixgevdreduce(const_cast(a.c_ptr()), n, isuppera, const_cast(b.c_ptr()), isupperb, problemtype, const_cast(r.c_ptr()), &isupperr, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a number to an element -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdRow - row where the element to be updated is stored. - UpdColumn - column where the element to be updated is stored. - UpdVal - a number to be added to the element. - - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdatesimple(real_2d_array &inva, const ae_int_t n, const ae_int_t updrow, const ae_int_t updcolumn, const double updval) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixinvupdatesimple(const_cast(inva.c_ptr()), n, updrow, updcolumn, updval, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a vector to a row -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdRow - the row of A whose vector V was added. - 0 <= Row <= N-1 - V - the vector to be added to a row. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdaterow(real_2d_array &inva, const ae_int_t n, const ae_int_t updrow, const real_1d_array &v) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixinvupdaterow(const_cast(inva.c_ptr()), n, updrow, const_cast(v.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a vector to a column -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdColumn - the column of A whose vector U was added. - 0 <= UpdColumn <= N-1 - U - the vector to be added to a column. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdatecolumn(real_2d_array &inva, const ae_int_t n, const ae_int_t updcolumn, const real_1d_array &u) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixinvupdatecolumn(const_cast(inva.c_ptr()), n, updcolumn, const_cast(u.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm computes the inverse of matrix A+u*v’ by using the given matrix -A^-1 and the vectors u and v. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - U - the vector modifying the matrix. - Array whose index ranges within [0..N-1]. - V - the vector modifying the matrix. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of matrix A + u*v'. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdateuv(real_2d_array &inva, const ae_int_t n, const real_1d_array &u, const real_1d_array &v) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixinvupdateuv(const_cast(inva.c_ptr()), n, const_cast(u.c_ptr()), const_cast(v.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Subroutine performing the Schur decomposition of a general matrix by using -the QR algorithm with multiple shifts. - -The source matrix A is represented as S'*A*S = T, where S is an orthogonal -matrix (Schur vectors), T - upper quasi-triangular matrix (with blocks of -sizes 1x1 and 2x2 on the main diagonal). - -Input parameters: - A - matrix to be decomposed. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of A, N>=0. - - -Output parameters: - A - contains matrix T. - Array whose indexes range within [0..N-1, 0..N-1]. - S - contains Schur vectors. - Array whose indexes range within [0..N-1, 0..N-1]. - -Note 1: - The block structure of matrix T can be easily recognized: since all - the elements below the blocks are zeros, the elements a[i+1,i] which - are equal to 0 show the block border. - -Note 2: - The algorithm performance depends on the value of the internal parameter - NS of the InternalSchurDecomposition subroutine which defines the number - of shifts in the QR algorithm (similarly to the block width in block-matrix - algorithms in linear algebra). If you require maximum performance on - your machine, it is recommended to adjust this parameter manually. - -Result: - True, - if the algorithm has converged and parameters A and S contain the result. - False, - if the algorithm has not converged. - -Algorithm implemented on the basis of the DHSEQR subroutine (LAPACK 3.0 library). -*************************************************************************/ -bool rmatrixschur(real_2d_array &a, const ae_int_t n, real_2d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::rmatrixschur(const_cast(a.c_ptr()), n, const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static ae_int_t ablas_rgemmparallelsize = 64; -static ae_int_t ablas_cgemmparallelsize = 64; -static void ablas_ablasinternalsplitlength(ae_int_t n, - ae_int_t nb, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state); -static void ablas_cmatrixrighttrsm2(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -static void ablas_cmatrixlefttrsm2(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -static void ablas_rmatrixrighttrsm2(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -static void ablas_rmatrixlefttrsm2(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -static void ablas_cmatrixsyrk2(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); -static void ablas_rmatrixsyrk2(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); - - -static void ortfac_cmatrixqrbasecase(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* work, - /* Complex */ ae_vector* t, - /* Complex */ ae_vector* tau, - ae_state *_state); -static void ortfac_cmatrixlqbasecase(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* work, - /* Complex */ ae_vector* t, - /* Complex */ ae_vector* tau, - ae_state *_state); -static void ortfac_rmatrixblockreflector(/* Real */ ae_matrix* a, - /* Real */ ae_vector* tau, - ae_bool columnwisea, - ae_int_t lengtha, - ae_int_t blocksize, - /* Real */ ae_matrix* t, - /* Real */ ae_vector* work, - ae_state *_state); -static void ortfac_cmatrixblockreflector(/* Complex */ ae_matrix* a, - /* Complex */ ae_vector* tau, - ae_bool columnwisea, - ae_int_t lengtha, - ae_int_t blocksize, - /* Complex */ ae_matrix* t, - /* Complex */ ae_vector* work, - ae_state *_state); - - -static ae_bool bdsvd_bidiagonalsvddecompositioninternal(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_bool isupper, - ae_bool isfractionalaccuracyrequired, - /* Real */ ae_matrix* u, - ae_int_t ustart, - ae_int_t nru, - /* Real */ ae_matrix* c, - ae_int_t cstart, - ae_int_t ncc, - /* Real */ ae_matrix* vt, - ae_int_t vstart, - ae_int_t ncvt, - ae_state *_state); -static double bdsvd_extsignbdsqr(double a, double b, ae_state *_state); -static void bdsvd_svd2x2(double f, - double g, - double h, - double* ssmin, - double* ssmax, - ae_state *_state); -static void bdsvd_svdv2x2(double f, - double g, - double h, - double* ssmin, - double* ssmax, - double* snr, - double* csr, - double* snl, - double* csl, - ae_state *_state); - - - - -static ae_bool evd_tridiagonalevd(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - /* Real */ ae_matrix* z, - ae_state *_state); -static void evd_tdevde2(double a, - double b, - double c, - double* rt1, - double* rt2, - ae_state *_state); -static void evd_tdevdev2(double a, - double b, - double c, - double* rt1, - double* rt2, - double* cs1, - double* sn1, - ae_state *_state); -static double evd_tdevdpythag(double a, double b, ae_state *_state); -static double evd_tdevdextsign(double a, double b, ae_state *_state); -static ae_bool evd_internalbisectioneigenvalues(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t irange, - ae_int_t iorder, - double vl, - double vu, - ae_int_t il, - ae_int_t iu, - double abstol, - /* Real */ ae_vector* w, - ae_int_t* m, - ae_int_t* nsplit, - /* Integer */ ae_vector* iblock, - /* Integer */ ae_vector* isplit, - ae_int_t* errorcode, - ae_state *_state); -static void evd_internaldstein(ae_int_t n, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t m, - /* Real */ ae_vector* w, - /* Integer */ ae_vector* iblock, - /* Integer */ ae_vector* isplit, - /* Real */ ae_matrix* z, - /* Integer */ ae_vector* ifail, - ae_int_t* info, - ae_state *_state); -static void evd_tdininternaldlagtf(ae_int_t n, - /* Real */ ae_vector* a, - double lambdav, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - double tol, - /* Real */ ae_vector* d, - /* Integer */ ae_vector* iin, - ae_int_t* info, - ae_state *_state); -static void evd_tdininternaldlagts(ae_int_t n, - /* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - /* Real */ ae_vector* d, - /* Integer */ ae_vector* iin, - /* Real */ ae_vector* y, - double* tol, - ae_int_t* info, - ae_state *_state); -static void evd_internaldlaebz(ae_int_t ijob, - ae_int_t nitmax, - ae_int_t n, - ae_int_t mmax, - ae_int_t minp, - double abstol, - double reltol, - double pivmin, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - /* Real */ ae_vector* e2, - /* Integer */ ae_vector* nval, - /* Real */ ae_matrix* ab, - /* Real */ ae_vector* c, - ae_int_t* mout, - /* Integer */ ae_matrix* nab, - /* Real */ ae_vector* work, - /* Integer */ ae_vector* iwork, - ae_int_t* info, - ae_state *_state); -static void evd_internaltrevc(/* Real */ ae_matrix* t, - ae_int_t n, - ae_int_t side, - ae_int_t howmny, - /* Boolean */ ae_vector* vselect, - /* Real */ ae_matrix* vl, - /* Real */ ae_matrix* vr, - ae_int_t* m, - ae_int_t* info, - ae_state *_state); -static void evd_internalhsevdlaln2(ae_bool ltrans, - ae_int_t na, - ae_int_t nw, - double smin, - double ca, - /* Real */ ae_matrix* a, - double d1, - double d2, - /* Real */ ae_matrix* b, - double wr, - double wi, - /* Boolean */ ae_vector* rswap4, - /* Boolean */ ae_vector* zswap4, - /* Integer */ ae_matrix* ipivot44, - /* Real */ ae_vector* civ4, - /* Real */ ae_vector* crv4, - /* Real */ ae_matrix* x, - double* scl, - double* xnorm, - ae_int_t* info, - ae_state *_state); -static void evd_internalhsevdladiv(double a, - double b, - double c, - double d, - double* p, - double* q, - ae_state *_state); -static ae_bool evd_nonsymmetricevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t vneeded, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - /* Real */ ae_matrix* vl, - /* Real */ ae_matrix* vr, - ae_state *_state); -static void evd_toupperhessenberg(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state); -static void evd_unpackqfromupperhessenberg(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - /* Real */ ae_matrix* q, - ae_state *_state); - - - - -static void trfac_cmatrixluprec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static void trfac_rmatrixluprec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void trfac_cmatrixplurec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static void trfac_rmatrixplurec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void trfac_cmatrixlup2(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static void trfac_rmatrixlup2(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void trfac_cmatrixplu2(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static void trfac_rmatrixplu2(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state); -static ae_bool trfac_hpdmatrixcholeskyrec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static ae_bool trfac_hpdmatrixcholesky2(/* Complex */ ae_matrix* aaa, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static ae_bool trfac_spdmatrixcholesky2(/* Real */ ae_matrix* aaa, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tmp, - ae_state *_state); - - -static void rcond_rmatrixrcondtrinternal(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_bool onenorm, - double anorm, - double* rc, - ae_state *_state); -static void rcond_cmatrixrcondtrinternal(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_bool onenorm, - double anorm, - double* rc, - ae_state *_state); -static void rcond_spdmatrixrcondcholeskyinternal(/* Real */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - ae_bool isnormprovided, - double anorm, - double* rc, - ae_state *_state); -static void rcond_hpdmatrixrcondcholeskyinternal(/* Complex */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - ae_bool isnormprovided, - double anorm, - double* rc, - ae_state *_state); -static void rcond_rmatrixrcondluinternal(/* Real */ ae_matrix* lua, - ae_int_t n, - ae_bool onenorm, - ae_bool isanormprovided, - double anorm, - double* rc, - ae_state *_state); -static void rcond_cmatrixrcondluinternal(/* Complex */ ae_matrix* lua, - ae_int_t n, - ae_bool onenorm, - ae_bool isanormprovided, - double anorm, - double* rc, - ae_state *_state); -static void rcond_rmatrixestimatenorm(ae_int_t n, - /* Real */ ae_vector* v, - /* Real */ ae_vector* x, - /* Integer */ ae_vector* isgn, - double* est, - ae_int_t* kase, - ae_state *_state); -static void rcond_cmatrixestimatenorm(ae_int_t n, - /* Complex */ ae_vector* v, - /* Complex */ ae_vector* x, - double* est, - ae_int_t* kase, - /* Integer */ ae_vector* isave, - /* Real */ ae_vector* rsave, - ae_state *_state); -static double rcond_internalcomplexrcondscsum1(/* Complex */ ae_vector* x, - ae_int_t n, - ae_state *_state); -static ae_int_t rcond_internalcomplexrcondicmax1(/* Complex */ ae_vector* x, - ae_int_t n, - ae_state *_state); -static void rcond_internalcomplexrcondsaveall(/* Integer */ ae_vector* isave, - /* Real */ ae_vector* rsave, - ae_int_t* i, - ae_int_t* iter, - ae_int_t* j, - ae_int_t* jlast, - ae_int_t* jump, - double* absxi, - double* altsgn, - double* estold, - double* temp, - ae_state *_state); -static void rcond_internalcomplexrcondloadall(/* Integer */ ae_vector* isave, - /* Real */ ae_vector* rsave, - ae_int_t* i, - ae_int_t* iter, - ae_int_t* j, - ae_int_t* jlast, - ae_int_t* jump, - double* absxi, - double* altsgn, - double* estold, - double* temp, - ae_state *_state); - - -static void matinv_rmatrixtrinverserec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - /* Real */ ae_vector* tmp, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -static void matinv_cmatrixtrinverserec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - /* Complex */ ae_vector* tmp, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -static void matinv_rmatrixluinverserec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - /* Real */ ae_vector* work, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -static void matinv_cmatrixluinverserec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - /* Complex */ ae_vector* work, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -static void matinv_spdmatrixcholeskyinverserec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void matinv_hpdmatrixcholeskyinverserec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tmp, - ae_state *_state); - - -static double sparse_desiredloadfactor = 0.66; -static double sparse_maxloadfactor = 0.75; -static double sparse_growfactor = 2.00; -static ae_int_t sparse_additional = 10; -static ae_int_t sparse_linalgswitch = 16; -static void sparse_sparseinitduidx(sparsematrix* s, ae_state *_state); -static ae_int_t sparse_hash(ae_int_t i, - ae_int_t j, - ae_int_t tabsize, - ae_state *_state); - - - - - - - - - - - - - - - - - -/************************************************************************* -Splits matrix length in two parts, left part should match ABLAS block size - -INPUT PARAMETERS - A - real matrix, is passed to ensure that we didn't split - complex matrix using real splitting subroutine. - matrix itself is not changed. - N - length, N>0 - -OUTPUT PARAMETERS - N1 - length - N2 - length - -N1+N2=N, N1>=N2, N2 may be zero - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -void ablassplitlength(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state) -{ - - *n1 = 0; - *n2 = 0; - - if( n>ablasblocksize(a, _state) ) - { - ablas_ablasinternalsplitlength(n, ablasblocksize(a, _state), n1, n2, _state); - } - else - { - ablas_ablasinternalsplitlength(n, ablasmicroblocksize(_state), n1, n2, _state); - } -} - - -/************************************************************************* -Complex ABLASSplitLength - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -void ablascomplexsplitlength(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state) -{ - - *n1 = 0; - *n2 = 0; - - if( n>ablascomplexblocksize(a, _state) ) - { - ablas_ablasinternalsplitlength(n, ablascomplexblocksize(a, _state), n1, n2, _state); - } - else - { - ablas_ablasinternalsplitlength(n, ablasmicroblocksize(_state), n1, n2, _state); - } -} - - -/************************************************************************* -Returns block size - subdivision size where cache-oblivious soubroutines -switch to the optimized kernel. - -INPUT PARAMETERS - A - real matrix, is passed to ensure that we didn't split - complex matrix using real splitting subroutine. - matrix itself is not changed. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -ae_int_t ablasblocksize(/* Real */ ae_matrix* a, ae_state *_state) -{ - ae_int_t result; - - - result = 32; - return result; -} - - -/************************************************************************* -Block size for complex subroutines. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -ae_int_t ablascomplexblocksize(/* Complex */ ae_matrix* a, - ae_state *_state) -{ - ae_int_t result; - - - result = 24; - return result; -} - - -/************************************************************************* -Microblock size - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -ae_int_t ablasmicroblocksize(ae_state *_state) -{ - ae_int_t result; - - - result = 8; - return result; -} - - -/************************************************************************* -Cache-oblivous complex "copy-and-transpose" - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void cmatrixtranspose(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state) -{ - ae_int_t i; - ae_int_t s1; - ae_int_t s2; - - - if( m<=2*ablascomplexblocksize(a, _state)&&n<=2*ablascomplexblocksize(a, _state) ) - { - - /* - * base case - */ - for(i=0; i<=m-1; i++) - { - ae_v_cmove(&b->ptr.pp_complex[ib][jb+i], b->stride, &a->ptr.pp_complex[ia+i][ja], 1, "N", ae_v_len(ib,ib+n-1)); - } - } - else - { - - /* - * Cache-oblivious recursion - */ - if( m>n ) - { - ablascomplexsplitlength(a, m, &s1, &s2, _state); - cmatrixtranspose(s1, n, a, ia, ja, b, ib, jb, _state); - cmatrixtranspose(s2, n, a, ia+s1, ja, b, ib, jb+s1, _state); - } - else - { - ablascomplexsplitlength(a, n, &s1, &s2, _state); - cmatrixtranspose(m, s1, a, ia, ja, b, ib, jb, _state); - cmatrixtranspose(m, s2, a, ia, ja+s1, b, ib+s1, jb, _state); - } - } -} - - -/************************************************************************* -Cache-oblivous real "copy-and-transpose" - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void rmatrixtranspose(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state) -{ - ae_int_t i; - ae_int_t s1; - ae_int_t s2; - - - if( m<=2*ablasblocksize(a, _state)&&n<=2*ablasblocksize(a, _state) ) - { - - /* - * base case - */ - for(i=0; i<=m-1; i++) - { - ae_v_move(&b->ptr.pp_double[ib][jb+i], b->stride, &a->ptr.pp_double[ia+i][ja], 1, ae_v_len(ib,ib+n-1)); - } - } - else - { - - /* - * Cache-oblivious recursion - */ - if( m>n ) - { - ablassplitlength(a, m, &s1, &s2, _state); - rmatrixtranspose(s1, n, a, ia, ja, b, ib, jb, _state); - rmatrixtranspose(s2, n, a, ia+s1, ja, b, ib, jb+s1, _state); - } - else - { - ablassplitlength(a, n, &s1, &s2, _state); - rmatrixtranspose(m, s1, a, ia, ja, b, ib, jb, _state); - rmatrixtranspose(m, s2, a, ia, ja+s1, b, ib+s1, jb, _state); - } - } -} - - -/************************************************************************* -This code enforces symmetricy of the matrix by copying Upper part to lower -one (or vice versa). - -INPUT PARAMETERS: - A - matrix - N - number of rows/columns - IsUpper - whether we want to copy upper triangle to lower one (True) - or vice versa (False). -*************************************************************************/ -void rmatrixenforcesymmetricity(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - if( isupper ) - { - for(i=0; i<=n-1; i++) - { - for(j=i+1; j<=n-1; j++) - { - a->ptr.pp_double[j][i] = a->ptr.pp_double[i][j]; - } - } - } - else - { - for(i=0; i<=n-1; i++) - { - for(j=i+1; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = a->ptr.pp_double[j][i]; - } - } - } -} - - -/************************************************************************* -Copy - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void cmatrixcopy(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state) -{ - ae_int_t i; - - - if( m==0||n==0 ) - { - return; - } - for(i=0; i<=m-1; i++) - { - ae_v_cmove(&b->ptr.pp_complex[ib+i][jb], 1, &a->ptr.pp_complex[ia+i][ja], 1, "N", ae_v_len(jb,jb+n-1)); - } -} - - -/************************************************************************* -Copy - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void rmatrixcopy(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state) -{ - ae_int_t i; - - - if( m==0||n==0 ) - { - return; - } - for(i=0; i<=m-1; i++) - { - ae_v_move(&b->ptr.pp_double[ib+i][jb], 1, &a->ptr.pp_double[ia+i][ja], 1, ae_v_len(jb,jb+n-1)); - } -} - - -/************************************************************************* -Rank-1 correction: A := A + u*v' - -INPUT PARAMETERS: - M - number of rows - N - number of columns - A - target matrix, MxN submatrix is updated - IA - submatrix offset (row index) - JA - submatrix offset (column index) - U - vector #1 - IU - subvector offset - V - vector #2 - IV - subvector offset -*************************************************************************/ -void cmatrixrank1(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_vector* u, - ae_int_t iu, - /* Complex */ ae_vector* v, - ae_int_t iv, - ae_state *_state) -{ - ae_int_t i; - ae_complex s; - - - if( m==0||n==0 ) - { - return; - } - if( cmatrixrank1f(m, n, a, ia, ja, u, iu, v, iv, _state) ) - { - return; - } - for(i=0; i<=m-1; i++) - { - s = u->ptr.p_complex[iu+i]; - ae_v_caddc(&a->ptr.pp_complex[ia+i][ja], 1, &v->ptr.p_complex[iv], 1, "N", ae_v_len(ja,ja+n-1), s); - } -} - - -/************************************************************************* -Rank-1 correction: A := A + u*v' - -INPUT PARAMETERS: - M - number of rows - N - number of columns - A - target matrix, MxN submatrix is updated - IA - submatrix offset (row index) - JA - submatrix offset (column index) - U - vector #1 - IU - subvector offset - V - vector #2 - IV - subvector offset -*************************************************************************/ -void rmatrixrank1(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_vector* u, - ae_int_t iu, - /* Real */ ae_vector* v, - ae_int_t iv, - ae_state *_state) -{ - ae_int_t i; - double s; - - - if( m==0||n==0 ) - { - return; - } - if( rmatrixrank1f(m, n, a, ia, ja, u, iu, v, iv, _state) ) - { - return; - } - for(i=0; i<=m-1; i++) - { - s = u->ptr.p_double[iu+i]; - ae_v_addd(&a->ptr.pp_double[ia+i][ja], 1, &v->ptr.p_double[iv], 1, ae_v_len(ja,ja+n-1), s); - } -} - - -/************************************************************************* -Matrix-vector product: y := op(A)*x - -INPUT PARAMETERS: - M - number of rows of op(A) - M>=0 - N - number of columns of op(A) - N>=0 - A - target matrix - IA - submatrix offset (row index) - JA - submatrix offset (column index) - OpA - operation type: - * OpA=0 => op(A) = A - * OpA=1 => op(A) = A^T - * OpA=2 => op(A) = A^H - X - input vector - IX - subvector offset - IY - subvector offset - Y - preallocated matrix, must be large enough to store result - -OUTPUT PARAMETERS: - Y - vector which stores result - -if M=0, then subroutine does nothing. -if N=0, Y is filled by zeros. - - - -- ALGLIB routine -- - - 28.01.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixmv(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Complex */ ae_vector* x, - ae_int_t ix, - /* Complex */ ae_vector* y, - ae_int_t iy, - ae_state *_state) -{ - ae_int_t i; - ae_complex v; - - - if( m==0 ) - { - return; - } - if( n==0 ) - { - for(i=0; i<=m-1; i++) - { - y->ptr.p_complex[iy+i] = ae_complex_from_d(0); - } - return; - } - if( cmatrixmvf(m, n, a, ia, ja, opa, x, ix, y, iy, _state) ) - { - return; - } - if( opa==0 ) - { - - /* - * y = A*x - */ - for(i=0; i<=m-1; i++) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia+i][ja], 1, "N", &x->ptr.p_complex[ix], 1, "N", ae_v_len(ja,ja+n-1)); - y->ptr.p_complex[iy+i] = v; - } - return; - } - if( opa==1 ) - { - - /* - * y = A^T*x - */ - for(i=0; i<=m-1; i++) - { - y->ptr.p_complex[iy+i] = ae_complex_from_d(0); - } - for(i=0; i<=n-1; i++) - { - v = x->ptr.p_complex[ix+i]; - ae_v_caddc(&y->ptr.p_complex[iy], 1, &a->ptr.pp_complex[ia+i][ja], 1, "N", ae_v_len(iy,iy+m-1), v); - } - return; - } - if( opa==2 ) - { - - /* - * y = A^H*x - */ - for(i=0; i<=m-1; i++) - { - y->ptr.p_complex[iy+i] = ae_complex_from_d(0); - } - for(i=0; i<=n-1; i++) - { - v = x->ptr.p_complex[ix+i]; - ae_v_caddc(&y->ptr.p_complex[iy], 1, &a->ptr.pp_complex[ia+i][ja], 1, "Conj", ae_v_len(iy,iy+m-1), v); - } - return; - } -} - - -/************************************************************************* -Matrix-vector product: y := op(A)*x - -INPUT PARAMETERS: - M - number of rows of op(A) - N - number of columns of op(A) - A - target matrix - IA - submatrix offset (row index) - JA - submatrix offset (column index) - OpA - operation type: - * OpA=0 => op(A) = A - * OpA=1 => op(A) = A^T - X - input vector - IX - subvector offset - IY - subvector offset - Y - preallocated matrix, must be large enough to store result - -OUTPUT PARAMETERS: - Y - vector which stores result - -if M=0, then subroutine does nothing. -if N=0, Y is filled by zeros. - - - -- ALGLIB routine -- - - 28.01.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixmv(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Real */ ae_vector* x, - ae_int_t ix, - /* Real */ ae_vector* y, - ae_int_t iy, - ae_state *_state) -{ - ae_int_t i; - double v; - - - if( m==0 ) - { - return; - } - if( n==0 ) - { - for(i=0; i<=m-1; i++) - { - y->ptr.p_double[iy+i] = 0; - } - return; - } - if( rmatrixmvf(m, n, a, ia, ja, opa, x, ix, y, iy, _state) ) - { - return; - } - if( opa==0 ) - { - - /* - * y = A*x - */ - for(i=0; i<=m-1; i++) - { - v = ae_v_dotproduct(&a->ptr.pp_double[ia+i][ja], 1, &x->ptr.p_double[ix], 1, ae_v_len(ja,ja+n-1)); - y->ptr.p_double[iy+i] = v; - } - return; - } - if( opa==1 ) - { - - /* - * y = A^T*x - */ - for(i=0; i<=m-1; i++) - { - y->ptr.p_double[iy+i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = x->ptr.p_double[ix+i]; - ae_v_addd(&y->ptr.p_double[iy], 1, &a->ptr.pp_double[ia+i][ja], 1, ae_v_len(iy,iy+m-1), v); - } - return; - } -} - - -void cmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablascomplexblocksize(a, _state); - if( m<=bs&&n<=bs ) - { - ablas_cmatrixrighttrsm2(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( m>=n ) - { - - /* - * Split X: X*A = (X1 X2)^T*A - */ - ablascomplexsplitlength(a, m, &s1, &s2, _state); - cmatrixrighttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - cmatrixrighttrsm(s2, n, a, i1, j1, isupper, isunit, optype, x, i2+s1, j2, _state); - return; - } - else - { - - /* - * Split A: - * (A1 A12) - * X*op(A) = X*op( ) - * ( A2) - * - * Different variants depending on - * IsUpper/OpType combinations - */ - ablascomplexsplitlength(a, n, &s1, &s2, _state); - if( isupper&&optype==0 ) - { - - /* - * (A1 A12)-1 - * X*A^-1 = (X1 X2)*( ) - * ( A2) - */ - cmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - cmatrixgemm(m, s2, s1, ae_complex_from_d(-1.0), x, i2, j2, 0, a, i1, j1+s1, 0, ae_complex_from_d(1.0), x, i2, j2+s1, _state); - cmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - return; - } - if( isupper&&optype!=0 ) - { - - /* - * (A1' )-1 - * X*A^-1 = (X1 X2)*( ) - * (A12' A2') - */ - cmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - cmatrixgemm(m, s1, s2, ae_complex_from_d(-1.0), x, i2, j2+s1, 0, a, i1, j1+s1, optype, ae_complex_from_d(1.0), x, i2, j2, _state); - cmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( !isupper&&optype==0 ) - { - - /* - * (A1 )-1 - * X*A^-1 = (X1 X2)*( ) - * (A21 A2) - */ - cmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - cmatrixgemm(m, s1, s2, ae_complex_from_d(-1.0), x, i2, j2+s1, 0, a, i1+s1, j1, 0, ae_complex_from_d(1.0), x, i2, j2, _state); - cmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( !isupper&&optype!=0 ) - { - - /* - * (A1' A21')-1 - * X*A^-1 = (X1 X2)*( ) - * ( A2') - */ - cmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - cmatrixgemm(m, s2, s1, ae_complex_from_d(-1.0), x, i2, j2, 0, a, i1+s1, j1, optype, ae_complex_from_d(1.0), x, i2, j2+s1, _state); - cmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - return; - } - } -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_cmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state) -{ - cmatrixrighttrsm(m,n,a,i1,j1,isupper,isunit,optype,x,i2,j2, _state); -} - - -void cmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablascomplexblocksize(a, _state); - if( m<=bs&&n<=bs ) - { - ablas_cmatrixlefttrsm2(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( n>=m ) - { - - /* - * Split X: op(A)^-1*X = op(A)^-1*(X1 X2) - */ - ablascomplexsplitlength(x, n, &s1, &s2, _state); - cmatrixlefttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - cmatrixlefttrsm(m, s2, a, i1, j1, isupper, isunit, optype, x, i2, j2+s1, _state); - return; - } - else - { - - /* - * Split A - */ - ablascomplexsplitlength(a, m, &s1, &s2, _state); - if( isupper&&optype==0 ) - { - - /* - * (A1 A12)-1 ( X1 ) - * A^-1*X* = ( ) *( ) - * ( A2) ( X2 ) - */ - cmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - cmatrixgemm(s1, n, s2, ae_complex_from_d(-1.0), a, i1, j1+s1, 0, x, i2+s1, j2, 0, ae_complex_from_d(1.0), x, i2, j2, _state); - cmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( isupper&&optype!=0 ) - { - - /* - * (A1' )-1 ( X1 ) - * A^-1*X = ( ) *( ) - * (A12' A2') ( X2 ) - */ - cmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - cmatrixgemm(s2, n, s1, ae_complex_from_d(-1.0), a, i1, j1+s1, optype, x, i2, j2, 0, ae_complex_from_d(1.0), x, i2+s1, j2, _state); - cmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - return; - } - if( !isupper&&optype==0 ) - { - - /* - * (A1 )-1 ( X1 ) - * A^-1*X = ( ) *( ) - * (A21 A2) ( X2 ) - */ - cmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - cmatrixgemm(s2, n, s1, ae_complex_from_d(-1.0), a, i1+s1, j1, 0, x, i2, j2, 0, ae_complex_from_d(1.0), x, i2+s1, j2, _state); - cmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - return; - } - if( !isupper&&optype!=0 ) - { - - /* - * (A1' A21')-1 ( X1 ) - * A^-1*X = ( ) *( ) - * ( A2') ( X2 ) - */ - cmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - cmatrixgemm(s1, n, s2, ae_complex_from_d(-1.0), a, i1+s1, j1, optype, x, i2+s1, j2, 0, ae_complex_from_d(1.0), x, i2, j2, _state); - cmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - } -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_cmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state) -{ - cmatrixlefttrsm(m,n,a,i1,j1,isupper,isunit,optype,x,i2,j2, _state); -} - - -void rmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablasblocksize(a, _state); - if( m<=bs&&n<=bs ) - { - ablas_rmatrixrighttrsm2(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( m>=n ) - { - - /* - * Split X: X*A = (X1 X2)^T*A - */ - ablassplitlength(a, m, &s1, &s2, _state); - rmatrixrighttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - rmatrixrighttrsm(s2, n, a, i1, j1, isupper, isunit, optype, x, i2+s1, j2, _state); - return; - } - else - { - - /* - * Split A: - * (A1 A12) - * X*op(A) = X*op( ) - * ( A2) - * - * Different variants depending on - * IsUpper/OpType combinations - */ - ablassplitlength(a, n, &s1, &s2, _state); - if( isupper&&optype==0 ) - { - - /* - * (A1 A12)-1 - * X*A^-1 = (X1 X2)*( ) - * ( A2) - */ - rmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - rmatrixgemm(m, s2, s1, -1.0, x, i2, j2, 0, a, i1, j1+s1, 0, 1.0, x, i2, j2+s1, _state); - rmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - return; - } - if( isupper&&optype!=0 ) - { - - /* - * (A1' )-1 - * X*A^-1 = (X1 X2)*( ) - * (A12' A2') - */ - rmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - rmatrixgemm(m, s1, s2, -1.0, x, i2, j2+s1, 0, a, i1, j1+s1, optype, 1.0, x, i2, j2, _state); - rmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( !isupper&&optype==0 ) - { - - /* - * (A1 )-1 - * X*A^-1 = (X1 X2)*( ) - * (A21 A2) - */ - rmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - rmatrixgemm(m, s1, s2, -1.0, x, i2, j2+s1, 0, a, i1+s1, j1, 0, 1.0, x, i2, j2, _state); - rmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( !isupper&&optype!=0 ) - { - - /* - * (A1' A21')-1 - * X*A^-1 = (X1 X2)*( ) - * ( A2') - */ - rmatrixrighttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - rmatrixgemm(m, s2, s1, -1.0, x, i2, j2, 0, a, i1+s1, j1, optype, 1.0, x, i2, j2+s1, _state); - rmatrixrighttrsm(m, s2, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2, j2+s1, _state); - return; - } - } -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_rmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state) -{ - rmatrixrighttrsm(m,n,a,i1,j1,isupper,isunit,optype,x,i2,j2, _state); -} - - -void rmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablasblocksize(a, _state); - if( m<=bs&&n<=bs ) - { - ablas_rmatrixlefttrsm2(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( n>=m ) - { - - /* - * Split X: op(A)^-1*X = op(A)^-1*(X1 X2) - */ - ablassplitlength(x, n, &s1, &s2, _state); - rmatrixlefttrsm(m, s1, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - rmatrixlefttrsm(m, s2, a, i1, j1, isupper, isunit, optype, x, i2, j2+s1, _state); - } - else - { - - /* - * Split A - */ - ablassplitlength(a, m, &s1, &s2, _state); - if( isupper&&optype==0 ) - { - - /* - * (A1 A12)-1 ( X1 ) - * A^-1*X* = ( ) *( ) - * ( A2) ( X2 ) - */ - rmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - rmatrixgemm(s1, n, s2, -1.0, a, i1, j1+s1, 0, x, i2+s1, j2, 0, 1.0, x, i2, j2, _state); - rmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - if( isupper&&optype!=0 ) - { - - /* - * (A1' )-1 ( X1 ) - * A^-1*X = ( ) *( ) - * (A12' A2') ( X2 ) - */ - rmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - rmatrixgemm(s2, n, s1, -1.0, a, i1, j1+s1, optype, x, i2, j2, 0, 1.0, x, i2+s1, j2, _state); - rmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - return; - } - if( !isupper&&optype==0 ) - { - - /* - * (A1 )-1 ( X1 ) - * A^-1*X = ( ) *( ) - * (A21 A2) ( X2 ) - */ - rmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - rmatrixgemm(s2, n, s1, -1.0, a, i1+s1, j1, 0, x, i2, j2, 0, 1.0, x, i2+s1, j2, _state); - rmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - return; - } - if( !isupper&&optype!=0 ) - { - - /* - * (A1' A21')-1 ( X1 ) - * A^-1*X = ( ) *( ) - * ( A2') ( X2 ) - */ - rmatrixlefttrsm(s2, n, a, i1+s1, j1+s1, isupper, isunit, optype, x, i2+s1, j2, _state); - rmatrixgemm(s1, n, s2, -1.0, a, i1+s1, j1, optype, x, i2+s1, j2, 0, 1.0, x, i2, j2, _state); - rmatrixlefttrsm(s1, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state); - return; - } - } -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_rmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state) -{ - rmatrixlefttrsm(m,n,a,i1,j1,isupper,isunit,optype,x,i2,j2, _state); -} - - -void cmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablascomplexblocksize(a, _state); - if( n<=bs&&k<=bs ) - { - ablas_cmatrixsyrk2(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - return; - } - if( k>=n ) - { - - /* - * Split K - */ - ablascomplexsplitlength(a, k, &s1, &s2, _state); - if( optypea==0 ) - { - cmatrixsyrk(n, s1, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - cmatrixsyrk(n, s2, alpha, a, ia, ja+s1, optypea, 1.0, c, ic, jc, isupper, _state); - } - else - { - cmatrixsyrk(n, s1, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - cmatrixsyrk(n, s2, alpha, a, ia+s1, ja, optypea, 1.0, c, ic, jc, isupper, _state); - } - } - else - { - - /* - * Split N - */ - ablascomplexsplitlength(a, n, &s1, &s2, _state); - if( optypea==0&&isupper ) - { - cmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - cmatrixgemm(s1, s2, k, ae_complex_from_d(alpha), a, ia, ja, 0, a, ia+s1, ja, 2, ae_complex_from_d(beta), c, ic, jc+s1, _state); - cmatrixsyrk(s2, k, alpha, a, ia+s1, ja, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - if( optypea==0&&!isupper ) - { - cmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - cmatrixgemm(s2, s1, k, ae_complex_from_d(alpha), a, ia+s1, ja, 0, a, ia, ja, 2, ae_complex_from_d(beta), c, ic+s1, jc, _state); - cmatrixsyrk(s2, k, alpha, a, ia+s1, ja, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - if( optypea!=0&&isupper ) - { - cmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - cmatrixgemm(s1, s2, k, ae_complex_from_d(alpha), a, ia, ja, 2, a, ia, ja+s1, 0, ae_complex_from_d(beta), c, ic, jc+s1, _state); - cmatrixsyrk(s2, k, alpha, a, ia, ja+s1, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - if( optypea!=0&&!isupper ) - { - cmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - cmatrixgemm(s2, s1, k, ae_complex_from_d(alpha), a, ia, ja+s1, 2, a, ia, ja, 0, ae_complex_from_d(beta), c, ic+s1, jc, _state); - cmatrixsyrk(s2, k, alpha, a, ia, ja+s1, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - } -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_cmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, ae_state *_state) -{ - cmatrixsyrk(n,k,alpha,a,ia,ja,optypea,beta,c,ic,jc,isupper, _state); -} - - -void rmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablasblocksize(a, _state); - - /* - * Use MKL or generic basecase code - */ - if( rmatrixsyrkmkl(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state) ) - { - return; - } - if( n<=bs&&k<=bs ) - { - ablas_rmatrixsyrk2(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - return; - } - - /* - * Recursive subdivision of the problem - */ - if( k>=n ) - { - - /* - * Split K - */ - ablassplitlength(a, k, &s1, &s2, _state); - if( optypea==0 ) - { - rmatrixsyrk(n, s1, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - rmatrixsyrk(n, s2, alpha, a, ia, ja+s1, optypea, 1.0, c, ic, jc, isupper, _state); - } - else - { - rmatrixsyrk(n, s1, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - rmatrixsyrk(n, s2, alpha, a, ia+s1, ja, optypea, 1.0, c, ic, jc, isupper, _state); - } - } - else - { - - /* - * Split N - */ - ablassplitlength(a, n, &s1, &s2, _state); - if( optypea==0&&isupper ) - { - rmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - rmatrixgemm(s1, s2, k, alpha, a, ia, ja, 0, a, ia+s1, ja, 1, beta, c, ic, jc+s1, _state); - rmatrixsyrk(s2, k, alpha, a, ia+s1, ja, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - if( optypea==0&&!isupper ) - { - rmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - rmatrixgemm(s2, s1, k, alpha, a, ia+s1, ja, 0, a, ia, ja, 1, beta, c, ic+s1, jc, _state); - rmatrixsyrk(s2, k, alpha, a, ia+s1, ja, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - if( optypea!=0&&isupper ) - { - rmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - rmatrixgemm(s1, s2, k, alpha, a, ia, ja, 1, a, ia, ja+s1, 0, beta, c, ic, jc+s1, _state); - rmatrixsyrk(s2, k, alpha, a, ia, ja+s1, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - if( optypea!=0&&!isupper ) - { - rmatrixsyrk(s1, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state); - rmatrixgemm(s2, s1, k, alpha, a, ia, ja+s1, 1, a, ia, ja, 0, beta, c, ic+s1, jc, _state); - rmatrixsyrk(s2, k, alpha, a, ia, ja+s1, optypea, beta, c, ic+s1, jc+s1, isupper, _state); - return; - } - } -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_rmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, ae_state *_state) -{ - rmatrixsyrk(n,k,alpha,a,ia,ja,optypea,beta,c,ic,jc,isupper, _state); -} - - -void cmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablascomplexblocksize(a, _state); - if( (m<=bs&&n<=bs)&&k<=bs ) - { - cmatrixgemmk(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - return; - } - - /* - * SMP support is turned on when M or N are larger than some boundary value. - * Magnitude of K is not taken into account because splitting on K does not - * allow us to spawn child tasks. - */ - - /* - * Recursive algorithm: parallel splitting on M/N - */ - if( m>=n&&m>=k ) - { - - /* - * A*B = (A1 A2)^T*B - */ - ablascomplexsplitlength(a, m, &s1, &s2, _state); - cmatrixgemm(s1, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - if( optypea==0 ) - { - cmatrixgemm(s2, n, k, alpha, a, ia+s1, ja, optypea, b, ib, jb, optypeb, beta, c, ic+s1, jc, _state); - } - else - { - cmatrixgemm(s2, n, k, alpha, a, ia, ja+s1, optypea, b, ib, jb, optypeb, beta, c, ic+s1, jc, _state); - } - return; - } - if( n>=m&&n>=k ) - { - - /* - * A*B = A*(B1 B2) - */ - ablascomplexsplitlength(a, n, &s1, &s2, _state); - if( optypeb==0 ) - { - cmatrixgemm(m, s1, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - cmatrixgemm(m, s2, k, alpha, a, ia, ja, optypea, b, ib, jb+s1, optypeb, beta, c, ic, jc+s1, _state); - } - else - { - cmatrixgemm(m, s1, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - cmatrixgemm(m, s2, k, alpha, a, ia, ja, optypea, b, ib+s1, jb, optypeb, beta, c, ic, jc+s1, _state); - } - return; - } - - /* - * Recursive algorithm: serial splitting on K - */ - - /* - * A*B = (A1 A2)*(B1 B2)^T - */ - ablascomplexsplitlength(a, k, &s1, &s2, _state); - if( optypea==0&&optypeb==0 ) - { - cmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - cmatrixgemm(m, n, s2, alpha, a, ia, ja+s1, optypea, b, ib+s1, jb, optypeb, ae_complex_from_d(1.0), c, ic, jc, _state); - } - if( optypea==0&&optypeb!=0 ) - { - cmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - cmatrixgemm(m, n, s2, alpha, a, ia, ja+s1, optypea, b, ib, jb+s1, optypeb, ae_complex_from_d(1.0), c, ic, jc, _state); - } - if( optypea!=0&&optypeb==0 ) - { - cmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - cmatrixgemm(m, n, s2, alpha, a, ia+s1, ja, optypea, b, ib+s1, jb, optypeb, ae_complex_from_d(1.0), c, ic, jc, _state); - } - if( optypea!=0&&optypeb!=0 ) - { - cmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - cmatrixgemm(m, n, s2, alpha, a, ia+s1, ja, optypea, b, ib, jb+s1, optypeb, ae_complex_from_d(1.0), c, ic, jc, _state); - } - return; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_cmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, ae_state *_state) -{ - cmatrixgemm(m,n,k,alpha,a,ia,ja,optypea,b,ib,jb,optypeb,beta,c,ic,jc, _state); -} - - -void rmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state) -{ - ae_int_t s1; - ae_int_t s2; - ae_int_t bs; - - - bs = ablasblocksize(a, _state); - - /* - * Check input sizes for correctness - */ - ae_assert(optypea==0||optypea==1, "RMatrixGEMM: incorrect OpTypeA (must be 0 or 1)", _state); - ae_assert(optypeb==0||optypeb==1, "RMatrixGEMM: incorrect OpTypeB (must be 0 or 1)", _state); - ae_assert(ic+m<=c->rows, "RMatrixGEMM: incorect size of output matrix C", _state); - ae_assert(jc+n<=c->cols, "RMatrixGEMM: incorect size of output matrix C", _state); - - /* - * Use MKL or ALGLIB basecase code - */ - if( rmatrixgemmmkl(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state) ) - { - return; - } - if( (m<=bs&&n<=bs)&&k<=bs ) - { - rmatrixgemmk(m, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - return; - } - - /* - * SMP support is turned on when M or N are larger than some boundary value. - * Magnitude of K is not taken into account because splitting on K does not - * allow us to spawn child tasks. - */ - - /* - * Recursive algorithm: split on M or N - */ - if( m>=n&&m>=k ) - { - - /* - * A*B = (A1 A2)^T*B - */ - ablassplitlength(a, m, &s1, &s2, _state); - if( optypea==0 ) - { - rmatrixgemm(s1, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(s2, n, k, alpha, a, ia+s1, ja, optypea, b, ib, jb, optypeb, beta, c, ic+s1, jc, _state); - } - else - { - rmatrixgemm(s1, n, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(s2, n, k, alpha, a, ia, ja+s1, optypea, b, ib, jb, optypeb, beta, c, ic+s1, jc, _state); - } - return; - } - if( n>=m&&n>=k ) - { - - /* - * A*B = A*(B1 B2) - */ - ablassplitlength(a, n, &s1, &s2, _state); - if( optypeb==0 ) - { - rmatrixgemm(m, s1, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(m, s2, k, alpha, a, ia, ja, optypea, b, ib, jb+s1, optypeb, beta, c, ic, jc+s1, _state); - } - else - { - rmatrixgemm(m, s1, k, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(m, s2, k, alpha, a, ia, ja, optypea, b, ib+s1, jb, optypeb, beta, c, ic, jc+s1, _state); - } - return; - } - - /* - * Recursive algorithm: split on K - */ - - /* - * A*B = (A1 A2)*(B1 B2)^T - */ - ablassplitlength(a, k, &s1, &s2, _state); - if( optypea==0&&optypeb==0 ) - { - rmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(m, n, s2, alpha, a, ia, ja+s1, optypea, b, ib+s1, jb, optypeb, 1.0, c, ic, jc, _state); - } - if( optypea==0&&optypeb!=0 ) - { - rmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(m, n, s2, alpha, a, ia, ja+s1, optypea, b, ib, jb+s1, optypeb, 1.0, c, ic, jc, _state); - } - if( optypea!=0&&optypeb==0 ) - { - rmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(m, n, s2, alpha, a, ia+s1, ja, optypea, b, ib+s1, jb, optypeb, 1.0, c, ic, jc, _state); - } - if( optypea!=0&&optypeb!=0 ) - { - rmatrixgemm(m, n, s1, alpha, a, ia, ja, optypea, b, ib, jb, optypeb, beta, c, ic, jc, _state); - rmatrixgemm(m, n, s2, alpha, a, ia+s1, ja, optypea, b, ib, jb+s1, optypeb, 1.0, c, ic, jc, _state); - } - return; -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_rmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, ae_state *_state) -{ - rmatrixgemm(m,n,k,alpha,a,ia,ja,optypea,b,ib,jb,optypeb,beta,c,ic,jc, _state); -} - - -/************************************************************************* -Complex ABLASSplitLength - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -static void ablas_ablasinternalsplitlength(ae_int_t n, - ae_int_t nb, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state) -{ - ae_int_t r; - - *n1 = 0; - *n2 = 0; - - if( n<=nb ) - { - - /* - * Block size, no further splitting - */ - *n1 = n; - *n2 = 0; - } - else - { - - /* - * Greater than block size - */ - if( n%nb!=0 ) - { - - /* - * Split remainder - */ - *n2 = n%nb; - *n1 = n-(*n2); - } - else - { - - /* - * Split on block boundaries - */ - *n2 = n/2; - *n1 = n-(*n2); - if( *n1%nb==0 ) - { - return; - } - r = nb-*n1%nb; - *n1 = *n1+r; - *n2 = *n2-r; - } - } -} - - -/************************************************************************* -Level 2 variant of CMatrixRightTRSM -*************************************************************************/ -static void ablas_cmatrixrighttrsm2(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_complex vc; - ae_complex vd; - - - - /* - * Special case - */ - if( n*m==0 ) - { - return; - } - - /* - * Try to call fast TRSM - */ - if( cmatrixrighttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state) ) - { - return; - } - - /* - * General case - */ - if( isupper ) - { - - /* - * Upper triangular matrix - */ - if( optype==0 ) - { - - /* - * X*A^(-1) - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = a->ptr.pp_complex[i1+j][j1+j]; - } - x->ptr.pp_complex[i2+i][j2+j] = ae_c_div(x->ptr.pp_complex[i2+i][j2+j],vd); - if( jptr.pp_complex[i2+i][j2+j]; - ae_v_csubc(&x->ptr.pp_complex[i2+i][j2+j+1], 1, &a->ptr.pp_complex[i1+j][j1+j+1], 1, "N", ae_v_len(j2+j+1,j2+n-1), vc); - } - } - } - return; - } - if( optype==1 ) - { - - /* - * X*A^(-T) - */ - for(i=0; i<=m-1; i++) - { - for(j=n-1; j>=0; j--) - { - vc = ae_complex_from_d(0); - vd = ae_complex_from_d(1); - if( jptr.pp_complex[i2+i][j2+j+1], 1, "N", &a->ptr.pp_complex[i1+j][j1+j+1], 1, "N", ae_v_len(j2+j+1,j2+n-1)); - } - if( !isunit ) - { - vd = a->ptr.pp_complex[i1+j][j1+j]; - } - x->ptr.pp_complex[i2+i][j2+j] = ae_c_div(ae_c_sub(x->ptr.pp_complex[i2+i][j2+j],vc),vd); - } - } - return; - } - if( optype==2 ) - { - - /* - * X*A^(-H) - */ - for(i=0; i<=m-1; i++) - { - for(j=n-1; j>=0; j--) - { - vc = ae_complex_from_d(0); - vd = ae_complex_from_d(1); - if( jptr.pp_complex[i2+i][j2+j+1], 1, "N", &a->ptr.pp_complex[i1+j][j1+j+1], 1, "Conj", ae_v_len(j2+j+1,j2+n-1)); - } - if( !isunit ) - { - vd = ae_c_conj(a->ptr.pp_complex[i1+j][j1+j], _state); - } - x->ptr.pp_complex[i2+i][j2+j] = ae_c_div(ae_c_sub(x->ptr.pp_complex[i2+i][j2+j],vc),vd); - } - } - return; - } - } - else - { - - /* - * Lower triangular matrix - */ - if( optype==0 ) - { - - /* - * X*A^(-1) - */ - for(i=0; i<=m-1; i++) - { - for(j=n-1; j>=0; j--) - { - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = a->ptr.pp_complex[i1+j][j1+j]; - } - x->ptr.pp_complex[i2+i][j2+j] = ae_c_div(x->ptr.pp_complex[i2+i][j2+j],vd); - if( j>0 ) - { - vc = x->ptr.pp_complex[i2+i][j2+j]; - ae_v_csubc(&x->ptr.pp_complex[i2+i][j2], 1, &a->ptr.pp_complex[i1+j][j1], 1, "N", ae_v_len(j2,j2+j-1), vc); - } - } - } - return; - } - if( optype==1 ) - { - - /* - * X*A^(-T) - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - vc = ae_complex_from_d(0); - vd = ae_complex_from_d(1); - if( j>0 ) - { - vc = ae_v_cdotproduct(&x->ptr.pp_complex[i2+i][j2], 1, "N", &a->ptr.pp_complex[i1+j][j1], 1, "N", ae_v_len(j2,j2+j-1)); - } - if( !isunit ) - { - vd = a->ptr.pp_complex[i1+j][j1+j]; - } - x->ptr.pp_complex[i2+i][j2+j] = ae_c_div(ae_c_sub(x->ptr.pp_complex[i2+i][j2+j],vc),vd); - } - } - return; - } - if( optype==2 ) - { - - /* - * X*A^(-H) - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - vc = ae_complex_from_d(0); - vd = ae_complex_from_d(1); - if( j>0 ) - { - vc = ae_v_cdotproduct(&x->ptr.pp_complex[i2+i][j2], 1, "N", &a->ptr.pp_complex[i1+j][j1], 1, "Conj", ae_v_len(j2,j2+j-1)); - } - if( !isunit ) - { - vd = ae_c_conj(a->ptr.pp_complex[i1+j][j1+j], _state); - } - x->ptr.pp_complex[i2+i][j2+j] = ae_c_div(ae_c_sub(x->ptr.pp_complex[i2+i][j2+j],vc),vd); - } - } - return; - } - } -} - - -/************************************************************************* -Level-2 subroutine -*************************************************************************/ -static void ablas_cmatrixlefttrsm2(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_complex vc; - ae_complex vd; - - - - /* - * Special case - */ - if( n*m==0 ) - { - return; - } - - /* - * Try to call fast TRSM - */ - if( cmatrixlefttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state) ) - { - return; - } - - /* - * General case - */ - if( isupper ) - { - - /* - * Upper triangular matrix - */ - if( optype==0 ) - { - - /* - * A^(-1)*X - */ - for(i=m-1; i>=0; i--) - { - for(j=i+1; j<=m-1; j++) - { - vc = a->ptr.pp_complex[i1+i][j1+j]; - ae_v_csubc(&x->ptr.pp_complex[i2+i][j2], 1, &x->ptr.pp_complex[i2+j][j2], 1, "N", ae_v_len(j2,j2+n-1), vc); - } - if( !isunit ) - { - vd = ae_c_d_div(1,a->ptr.pp_complex[i1+i][j1+i]); - ae_v_cmulc(&x->ptr.pp_complex[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - } - } - return; - } - if( optype==1 ) - { - - /* - * A^(-T)*X - */ - for(i=0; i<=m-1; i++) - { - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = ae_c_d_div(1,a->ptr.pp_complex[i1+i][j1+i]); - } - ae_v_cmulc(&x->ptr.pp_complex[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - for(j=i+1; j<=m-1; j++) - { - vc = a->ptr.pp_complex[i1+i][j1+j]; - ae_v_csubc(&x->ptr.pp_complex[i2+j][j2], 1, &x->ptr.pp_complex[i2+i][j2], 1, "N", ae_v_len(j2,j2+n-1), vc); - } - } - return; - } - if( optype==2 ) - { - - /* - * A^(-H)*X - */ - for(i=0; i<=m-1; i++) - { - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = ae_c_d_div(1,ae_c_conj(a->ptr.pp_complex[i1+i][j1+i], _state)); - } - ae_v_cmulc(&x->ptr.pp_complex[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - for(j=i+1; j<=m-1; j++) - { - vc = ae_c_conj(a->ptr.pp_complex[i1+i][j1+j], _state); - ae_v_csubc(&x->ptr.pp_complex[i2+j][j2], 1, &x->ptr.pp_complex[i2+i][j2], 1, "N", ae_v_len(j2,j2+n-1), vc); - } - } - return; - } - } - else - { - - /* - * Lower triangular matrix - */ - if( optype==0 ) - { - - /* - * A^(-1)*X - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=i-1; j++) - { - vc = a->ptr.pp_complex[i1+i][j1+j]; - ae_v_csubc(&x->ptr.pp_complex[i2+i][j2], 1, &x->ptr.pp_complex[i2+j][j2], 1, "N", ae_v_len(j2,j2+n-1), vc); - } - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = ae_c_d_div(1,a->ptr.pp_complex[i1+j][j1+j]); - } - ae_v_cmulc(&x->ptr.pp_complex[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - } - return; - } - if( optype==1 ) - { - - /* - * A^(-T)*X - */ - for(i=m-1; i>=0; i--) - { - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = ae_c_d_div(1,a->ptr.pp_complex[i1+i][j1+i]); - } - ae_v_cmulc(&x->ptr.pp_complex[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - for(j=i-1; j>=0; j--) - { - vc = a->ptr.pp_complex[i1+i][j1+j]; - ae_v_csubc(&x->ptr.pp_complex[i2+j][j2], 1, &x->ptr.pp_complex[i2+i][j2], 1, "N", ae_v_len(j2,j2+n-1), vc); - } - } - return; - } - if( optype==2 ) - { - - /* - * A^(-H)*X - */ - for(i=m-1; i>=0; i--) - { - if( isunit ) - { - vd = ae_complex_from_d(1); - } - else - { - vd = ae_c_d_div(1,ae_c_conj(a->ptr.pp_complex[i1+i][j1+i], _state)); - } - ae_v_cmulc(&x->ptr.pp_complex[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - for(j=i-1; j>=0; j--) - { - vc = ae_c_conj(a->ptr.pp_complex[i1+i][j1+j], _state); - ae_v_csubc(&x->ptr.pp_complex[i2+j][j2], 1, &x->ptr.pp_complex[i2+i][j2], 1, "N", ae_v_len(j2,j2+n-1), vc); - } - } - return; - } - } -} - - -/************************************************************************* -Level 2 subroutine - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -static void ablas_rmatrixrighttrsm2(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double vr; - double vd; - - - - /* - * Special case - */ - if( n*m==0 ) - { - return; - } - - /* - * Try to use "fast" code - */ - if( rmatrixrighttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state) ) - { - return; - } - - /* - * General case - */ - if( isupper ) - { - - /* - * Upper triangular matrix - */ - if( optype==0 ) - { - - /* - * X*A^(-1) - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( isunit ) - { - vd = 1; - } - else - { - vd = a->ptr.pp_double[i1+j][j1+j]; - } - x->ptr.pp_double[i2+i][j2+j] = x->ptr.pp_double[i2+i][j2+j]/vd; - if( jptr.pp_double[i2+i][j2+j]; - ae_v_subd(&x->ptr.pp_double[i2+i][j2+j+1], 1, &a->ptr.pp_double[i1+j][j1+j+1], 1, ae_v_len(j2+j+1,j2+n-1), vr); - } - } - } - return; - } - if( optype==1 ) - { - - /* - * X*A^(-T) - */ - for(i=0; i<=m-1; i++) - { - for(j=n-1; j>=0; j--) - { - vr = 0; - vd = 1; - if( jptr.pp_double[i2+i][j2+j+1], 1, &a->ptr.pp_double[i1+j][j1+j+1], 1, ae_v_len(j2+j+1,j2+n-1)); - } - if( !isunit ) - { - vd = a->ptr.pp_double[i1+j][j1+j]; - } - x->ptr.pp_double[i2+i][j2+j] = (x->ptr.pp_double[i2+i][j2+j]-vr)/vd; - } - } - return; - } - } - else - { - - /* - * Lower triangular matrix - */ - if( optype==0 ) - { - - /* - * X*A^(-1) - */ - for(i=0; i<=m-1; i++) - { - for(j=n-1; j>=0; j--) - { - if( isunit ) - { - vd = 1; - } - else - { - vd = a->ptr.pp_double[i1+j][j1+j]; - } - x->ptr.pp_double[i2+i][j2+j] = x->ptr.pp_double[i2+i][j2+j]/vd; - if( j>0 ) - { - vr = x->ptr.pp_double[i2+i][j2+j]; - ae_v_subd(&x->ptr.pp_double[i2+i][j2], 1, &a->ptr.pp_double[i1+j][j1], 1, ae_v_len(j2,j2+j-1), vr); - } - } - } - return; - } - if( optype==1 ) - { - - /* - * X*A^(-T) - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - vr = 0; - vd = 1; - if( j>0 ) - { - vr = ae_v_dotproduct(&x->ptr.pp_double[i2+i][j2], 1, &a->ptr.pp_double[i1+j][j1], 1, ae_v_len(j2,j2+j-1)); - } - if( !isunit ) - { - vd = a->ptr.pp_double[i1+j][j1+j]; - } - x->ptr.pp_double[i2+i][j2+j] = (x->ptr.pp_double[i2+i][j2+j]-vr)/vd; - } - } - return; - } - } -} - - -/************************************************************************* -Level 2 subroutine -*************************************************************************/ -static void ablas_rmatrixlefttrsm2(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double vr; - double vd; - - - - /* - * Special case - */ - if( n==0||m==0 ) - { - return; - } - - /* - * Try fast code - */ - if( rmatrixlefttrsmf(m, n, a, i1, j1, isupper, isunit, optype, x, i2, j2, _state) ) - { - return; - } - - /* - * General case - */ - if( isupper ) - { - - /* - * Upper triangular matrix - */ - if( optype==0 ) - { - - /* - * A^(-1)*X - */ - for(i=m-1; i>=0; i--) - { - for(j=i+1; j<=m-1; j++) - { - vr = a->ptr.pp_double[i1+i][j1+j]; - ae_v_subd(&x->ptr.pp_double[i2+i][j2], 1, &x->ptr.pp_double[i2+j][j2], 1, ae_v_len(j2,j2+n-1), vr); - } - if( !isunit ) - { - vd = 1/a->ptr.pp_double[i1+i][j1+i]; - ae_v_muld(&x->ptr.pp_double[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - } - } - return; - } - if( optype==1 ) - { - - /* - * A^(-T)*X - */ - for(i=0; i<=m-1; i++) - { - if( isunit ) - { - vd = 1; - } - else - { - vd = 1/a->ptr.pp_double[i1+i][j1+i]; - } - ae_v_muld(&x->ptr.pp_double[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - for(j=i+1; j<=m-1; j++) - { - vr = a->ptr.pp_double[i1+i][j1+j]; - ae_v_subd(&x->ptr.pp_double[i2+j][j2], 1, &x->ptr.pp_double[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vr); - } - } - return; - } - } - else - { - - /* - * Lower triangular matrix - */ - if( optype==0 ) - { - - /* - * A^(-1)*X - */ - for(i=0; i<=m-1; i++) - { - for(j=0; j<=i-1; j++) - { - vr = a->ptr.pp_double[i1+i][j1+j]; - ae_v_subd(&x->ptr.pp_double[i2+i][j2], 1, &x->ptr.pp_double[i2+j][j2], 1, ae_v_len(j2,j2+n-1), vr); - } - if( isunit ) - { - vd = 1; - } - else - { - vd = 1/a->ptr.pp_double[i1+j][j1+j]; - } - ae_v_muld(&x->ptr.pp_double[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - } - return; - } - if( optype==1 ) - { - - /* - * A^(-T)*X - */ - for(i=m-1; i>=0; i--) - { - if( isunit ) - { - vd = 1; - } - else - { - vd = 1/a->ptr.pp_double[i1+i][j1+i]; - } - ae_v_muld(&x->ptr.pp_double[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vd); - for(j=i-1; j>=0; j--) - { - vr = a->ptr.pp_double[i1+i][j1+j]; - ae_v_subd(&x->ptr.pp_double[i2+j][j2], 1, &x->ptr.pp_double[i2+i][j2], 1, ae_v_len(j2,j2+n-1), vr); - } - } - return; - } - } -} - - -/************************************************************************* -Level 2 subroutine -*************************************************************************/ -static void ablas_cmatrixsyrk2(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - ae_complex v; - - - - /* - * Fast exit (nothing to be done) - */ - if( (ae_fp_eq(alpha,0)||k==0)&&ae_fp_eq(beta,1) ) - { - return; - } - - /* - * Try to call fast SYRK - */ - if( cmatrixsyrkf(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state) ) - { - return; - } - - /* - * SYRK - */ - if( optypea==0 ) - { - - /* - * C=alpha*A*A^H+beta*C - */ - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - if( ae_fp_neq(alpha,0)&&k>0 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[ia+i][ja], 1, "N", &a->ptr.pp_complex[ia+j][ja], 1, "Conj", ae_v_len(ja,ja+k-1)); - } - else - { - v = ae_complex_from_d(0); - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_complex[ic+i][jc+j] = ae_c_mul_d(v,alpha); - } - else - { - c->ptr.pp_complex[ic+i][jc+j] = ae_c_add(ae_c_mul_d(c->ptr.pp_complex[ic+i][jc+j],beta),ae_c_mul_d(v,alpha)); - } - } - } - return; - } - else - { - - /* - * C=alpha*A^H*A+beta*C - */ - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - if( ae_fp_eq(beta,0) ) - { - for(j=j1; j<=j2; j++) - { - c->ptr.pp_complex[ic+i][jc+j] = ae_complex_from_d(0); - } - } - else - { - ae_v_cmuld(&c->ptr.pp_complex[ic+i][jc+j1], 1, ae_v_len(jc+j1,jc+j2), beta); - } - } - for(i=0; i<=k-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( isupper ) - { - j1 = j; - j2 = n-1; - } - else - { - j1 = 0; - j2 = j; - } - v = ae_c_mul_d(ae_c_conj(a->ptr.pp_complex[ia+i][ja+j], _state),alpha); - ae_v_caddc(&c->ptr.pp_complex[ic+j][jc+j1], 1, &a->ptr.pp_complex[ia+i][ja+j1], 1, "N", ae_v_len(jc+j1,jc+j2), v); - } - } - return; - } -} - - -/************************************************************************* -Level 2 subrotuine -*************************************************************************/ -static void ablas_rmatrixsyrk2(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - double v; - - - - /* - * Fast exit (nothing to be done) - */ - if( (ae_fp_eq(alpha,0)||k==0)&&ae_fp_eq(beta,1) ) - { - return; - } - - /* - * Try to call fast SYRK - */ - if( rmatrixsyrkf(n, k, alpha, a, ia, ja, optypea, beta, c, ic, jc, isupper, _state) ) - { - return; - } - - /* - * SYRK - */ - if( optypea==0 ) - { - - /* - * C=alpha*A*A^H+beta*C - */ - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - if( ae_fp_neq(alpha,0)&&k>0 ) - { - v = ae_v_dotproduct(&a->ptr.pp_double[ia+i][ja], 1, &a->ptr.pp_double[ia+j][ja], 1, ae_v_len(ja,ja+k-1)); - } - else - { - v = 0; - } - if( ae_fp_eq(beta,0) ) - { - c->ptr.pp_double[ic+i][jc+j] = alpha*v; - } - else - { - c->ptr.pp_double[ic+i][jc+j] = beta*c->ptr.pp_double[ic+i][jc+j]+alpha*v; - } - } - } - return; - } - else - { - - /* - * C=alpha*A^H*A+beta*C - */ - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - if( ae_fp_eq(beta,0) ) - { - for(j=j1; j<=j2; j++) - { - c->ptr.pp_double[ic+i][jc+j] = 0; - } - } - else - { - ae_v_muld(&c->ptr.pp_double[ic+i][jc+j1], 1, ae_v_len(jc+j1,jc+j2), beta); - } - } - for(i=0; i<=k-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( isupper ) - { - j1 = j; - j2 = n-1; - } - else - { - j1 = 0; - j2 = j; - } - v = alpha*a->ptr.pp_double[ia+i][ja+j]; - ae_v_addd(&c->ptr.pp_double[ic+j][jc+j1], 1, &a->ptr.pp_double[ia+i][ja+j1], 1, ae_v_len(jc+j1,jc+j2), v); - } - } - return; - } -} - - - - -/************************************************************************* -QR decomposition of a rectangular matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and R in compact form (see below). - Tau - array of scalar factors which are used to form - matrix Q. Array whose index ranges within [0.. Min(M-1,N-1)]. - -Matrix A is represented as A = QR, where Q is an orthogonal matrix of size -MxM, R - upper triangular (or upper trapezoid) matrix of size M x N. - -The elements of matrix R are located on and above the main diagonal of -matrix A. The elements which are located in Tau array and below the main -diagonal of matrix A are used to form matrix Q as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(0)*H(2)*...*H(k-1), - -where k = min(m,n), and each H(i) is in the form - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - real vector, -so that v(0:i-1) = 0, v(i) = 1, v(i+1:m-1) stored in A(i+1:m-1,i). - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqr(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t rowscount; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_REAL, _state, ae_true); - - if( m<=0||n<=0 ) - { - ae_frame_leave(_state); - return; - } - minmn = ae_minint(m, n, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(tau, minmn, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, m, ablasblocksize(a, _state), _state); - ae_matrix_set_length(&tmpt, ablasblocksize(a, _state), 2*ablasblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, 2*ablasblocksize(a, _state), n, _state); - - /* - * Blocked code - */ - blockstart = 0; - while(blockstart!=minmn) - { - - /* - * Determine block size - */ - blocksize = minmn-blockstart; - if( blocksize>ablasblocksize(a, _state) ) - { - blocksize = ablasblocksize(a, _state); - } - rowscount = m-blockstart; - - /* - * QR decomposition of submatrix. - * Matrix is copied to temporary storage to solve - * some TLB issues arising from non-contiguous memory - * access pattern. - */ - rmatrixcopy(rowscount, blocksize, a, blockstart, blockstart, &tmpa, 0, 0, _state); - rmatrixqrbasecase(&tmpa, rowscount, blocksize, &work, &t, &taubuf, _state); - rmatrixcopy(rowscount, blocksize, &tmpa, 0, 0, a, blockstart, blockstart, _state); - ae_v_move(&tau->ptr.p_double[blockstart], 1, &taubuf.ptr.p_double[0], 1, ae_v_len(blockstart,blockstart+blocksize-1)); - - /* - * Update the rest, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( blockstart+blocksize<=n-1 ) - { - if( n-blockstart-blocksize>=2*ablasblocksize(a, _state)||rowscount>=4*ablasblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_rmatrixblockreflector(&tmpa, &taubuf, ae_true, rowscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q'. - * - * Q = E + Y*T*Y' = E + TmpA*TmpT*TmpA' - * Q' = E + Y*T'*Y' = E + TmpA*TmpT'*TmpA' - */ - rmatrixgemm(blocksize, n-blockstart-blocksize, rowscount, 1.0, &tmpa, 0, 0, 1, a, blockstart, blockstart+blocksize, 0, 0.0, &tmpr, 0, 0, _state); - rmatrixgemm(blocksize, n-blockstart-blocksize, blocksize, 1.0, &tmpt, 0, 0, 1, &tmpr, 0, 0, 0, 0.0, &tmpr, blocksize, 0, _state); - rmatrixgemm(rowscount, n-blockstart-blocksize, blocksize, 1.0, &tmpa, 0, 0, 0, &tmpr, blocksize, 0, 0, 1.0, a, blockstart, blockstart+blocksize, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=0; i<=blocksize-1; i++) - { - ae_v_move(&t.ptr.p_double[1], 1, &tmpa.ptr.pp_double[i][i], tmpa.stride, ae_v_len(1,rowscount-i)); - t.ptr.p_double[1] = 1; - applyreflectionfromtheleft(a, taubuf.ptr.p_double[i], &t, blockstart+i, m-1, blockstart+blocksize, n-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart+blocksize; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -LQ decomposition of a rectangular matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices L and Q in compact form (see below) - Tau - array of scalar factors which are used to form - matrix Q. Array whose index ranges within [0..Min(M,N)-1]. - -Matrix A is represented as A = LQ, where Q is an orthogonal matrix of size -MxM, L - lower triangular (or lower trapezoid) matrix of size M x N. - -The elements of matrix L are located on and below the main diagonal of -matrix A. The elements which are located in Tau array and above the main -diagonal of matrix A are used to form matrix Q as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(k-1)*H(k-2)*...*H(1)*H(0), - -where k = min(m,n), and each H(i) is of the form - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - real vector, so that v(0:i-1)=0, -v(i) = 1, v(i+1:n-1) stored in A(i,i+1:n-1). - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlq(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t columnscount; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_REAL, _state, ae_true); - - if( m<=0||n<=0 ) - { - ae_frame_leave(_state); - return; - } - minmn = ae_minint(m, n, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(tau, minmn, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, ablasblocksize(a, _state), n, _state); - ae_matrix_set_length(&tmpt, ablasblocksize(a, _state), 2*ablasblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, m, 2*ablasblocksize(a, _state), _state); - - /* - * Blocked code - */ - blockstart = 0; - while(blockstart!=minmn) - { - - /* - * Determine block size - */ - blocksize = minmn-blockstart; - if( blocksize>ablasblocksize(a, _state) ) - { - blocksize = ablasblocksize(a, _state); - } - columnscount = n-blockstart; - - /* - * LQ decomposition of submatrix. - * Matrix is copied to temporary storage to solve - * some TLB issues arising from non-contiguous memory - * access pattern. - */ - rmatrixcopy(blocksize, columnscount, a, blockstart, blockstart, &tmpa, 0, 0, _state); - rmatrixlqbasecase(&tmpa, blocksize, columnscount, &work, &t, &taubuf, _state); - rmatrixcopy(blocksize, columnscount, &tmpa, 0, 0, a, blockstart, blockstart, _state); - ae_v_move(&tau->ptr.p_double[blockstart], 1, &taubuf.ptr.p_double[0], 1, ae_v_len(blockstart,blockstart+blocksize-1)); - - /* - * Update the rest, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( blockstart+blocksize<=m-1 ) - { - if( m-blockstart-blocksize>=2*ablasblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_rmatrixblockreflector(&tmpa, &taubuf, ae_false, columnscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q. - * - * Q = E + Y*T*Y' = E + TmpA'*TmpT*TmpA - */ - rmatrixgemm(m-blockstart-blocksize, blocksize, columnscount, 1.0, a, blockstart+blocksize, blockstart, 0, &tmpa, 0, 0, 1, 0.0, &tmpr, 0, 0, _state); - rmatrixgemm(m-blockstart-blocksize, blocksize, blocksize, 1.0, &tmpr, 0, 0, 0, &tmpt, 0, 0, 0, 0.0, &tmpr, 0, blocksize, _state); - rmatrixgemm(m-blockstart-blocksize, columnscount, blocksize, 1.0, &tmpr, 0, blocksize, 0, &tmpa, 0, 0, 0, 1.0, a, blockstart+blocksize, blockstart, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=0; i<=blocksize-1; i++) - { - ae_v_move(&t.ptr.p_double[1], 1, &tmpa.ptr.pp_double[i][i], 1, ae_v_len(1,columnscount-i)); - t.ptr.p_double[1] = 1; - applyreflectionfromtheright(a, taubuf.ptr.p_double[i], &t, blockstart+blocksize, m-1, blockstart+i, n-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart+blocksize; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -QR decomposition of a rectangular complex matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and R in compact form - Tau - array of scalar factors which are used to form matrix Q. Array - whose indexes range within [0.. Min(M,N)-1] - -Matrix A is represented as A = QR, where Q is an orthogonal matrix of size -MxM, R - upper triangular (or upper trapezoid) matrix of size MxN. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void cmatrixqr(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t rowscount; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_init(&work, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&t, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_COMPLEX, _state, ae_true); - - if( m<=0||n<=0 ) - { - ae_frame_leave(_state); - return; - } - minmn = ae_minint(m, n, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(tau, minmn, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, m, ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(&tmpt, ablascomplexblocksize(a, _state), ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, 2*ablascomplexblocksize(a, _state), n, _state); - - /* - * Blocked code - */ - blockstart = 0; - while(blockstart!=minmn) - { - - /* - * Determine block size - */ - blocksize = minmn-blockstart; - if( blocksize>ablascomplexblocksize(a, _state) ) - { - blocksize = ablascomplexblocksize(a, _state); - } - rowscount = m-blockstart; - - /* - * QR decomposition of submatrix. - * Matrix is copied to temporary storage to solve - * some TLB issues arising from non-contiguous memory - * access pattern. - */ - cmatrixcopy(rowscount, blocksize, a, blockstart, blockstart, &tmpa, 0, 0, _state); - ortfac_cmatrixqrbasecase(&tmpa, rowscount, blocksize, &work, &t, &taubuf, _state); - cmatrixcopy(rowscount, blocksize, &tmpa, 0, 0, a, blockstart, blockstart, _state); - ae_v_cmove(&tau->ptr.p_complex[blockstart], 1, &taubuf.ptr.p_complex[0], 1, "N", ae_v_len(blockstart,blockstart+blocksize-1)); - - /* - * Update the rest, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( blockstart+blocksize<=n-1 ) - { - if( n-blockstart-blocksize>=2*ablascomplexblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_cmatrixblockreflector(&tmpa, &taubuf, ae_true, rowscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q'. - * - * Q = E + Y*T*Y' = E + TmpA*TmpT*TmpA' - * Q' = E + Y*T'*Y' = E + TmpA*TmpT'*TmpA' - */ - cmatrixgemm(blocksize, n-blockstart-blocksize, rowscount, ae_complex_from_d(1.0), &tmpa, 0, 0, 2, a, blockstart, blockstart+blocksize, 0, ae_complex_from_d(0.0), &tmpr, 0, 0, _state); - cmatrixgemm(blocksize, n-blockstart-blocksize, blocksize, ae_complex_from_d(1.0), &tmpt, 0, 0, 2, &tmpr, 0, 0, 0, ae_complex_from_d(0.0), &tmpr, blocksize, 0, _state); - cmatrixgemm(rowscount, n-blockstart-blocksize, blocksize, ae_complex_from_d(1.0), &tmpa, 0, 0, 0, &tmpr, blocksize, 0, 0, ae_complex_from_d(1.0), a, blockstart, blockstart+blocksize, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=0; i<=blocksize-1; i++) - { - ae_v_cmove(&t.ptr.p_complex[1], 1, &tmpa.ptr.pp_complex[i][i], tmpa.stride, "N", ae_v_len(1,rowscount-i)); - t.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheleft(a, ae_c_conj(taubuf.ptr.p_complex[i], _state), &t, blockstart+i, m-1, blockstart+blocksize, n-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart+blocksize; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -LQ decomposition of a rectangular complex matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and L in compact form - Tau - array of scalar factors which are used to form matrix Q. Array - whose indexes range within [0.. Min(M,N)-1] - -Matrix A is represented as A = LQ, where Q is an orthogonal matrix of size -MxM, L - lower triangular (or lower trapezoid) matrix of size MxN. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void cmatrixlq(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t columnscount; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_init(&work, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&t, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_COMPLEX, _state, ae_true); - - if( m<=0||n<=0 ) - { - ae_frame_leave(_state); - return; - } - minmn = ae_minint(m, n, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(tau, minmn, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, ablascomplexblocksize(a, _state), n, _state); - ae_matrix_set_length(&tmpt, ablascomplexblocksize(a, _state), ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, m, 2*ablascomplexblocksize(a, _state), _state); - - /* - * Blocked code - */ - blockstart = 0; - while(blockstart!=minmn) - { - - /* - * Determine block size - */ - blocksize = minmn-blockstart; - if( blocksize>ablascomplexblocksize(a, _state) ) - { - blocksize = ablascomplexblocksize(a, _state); - } - columnscount = n-blockstart; - - /* - * LQ decomposition of submatrix. - * Matrix is copied to temporary storage to solve - * some TLB issues arising from non-contiguous memory - * access pattern. - */ - cmatrixcopy(blocksize, columnscount, a, blockstart, blockstart, &tmpa, 0, 0, _state); - ortfac_cmatrixlqbasecase(&tmpa, blocksize, columnscount, &work, &t, &taubuf, _state); - cmatrixcopy(blocksize, columnscount, &tmpa, 0, 0, a, blockstart, blockstart, _state); - ae_v_cmove(&tau->ptr.p_complex[blockstart], 1, &taubuf.ptr.p_complex[0], 1, "N", ae_v_len(blockstart,blockstart+blocksize-1)); - - /* - * Update the rest, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( blockstart+blocksize<=m-1 ) - { - if( m-blockstart-blocksize>=2*ablascomplexblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_cmatrixblockreflector(&tmpa, &taubuf, ae_false, columnscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q. - * - * Q = E + Y*T*Y' = E + TmpA'*TmpT*TmpA - */ - cmatrixgemm(m-blockstart-blocksize, blocksize, columnscount, ae_complex_from_d(1.0), a, blockstart+blocksize, blockstart, 0, &tmpa, 0, 0, 2, ae_complex_from_d(0.0), &tmpr, 0, 0, _state); - cmatrixgemm(m-blockstart-blocksize, blocksize, blocksize, ae_complex_from_d(1.0), &tmpr, 0, 0, 0, &tmpt, 0, 0, 0, ae_complex_from_d(0.0), &tmpr, 0, blocksize, _state); - cmatrixgemm(m-blockstart-blocksize, columnscount, blocksize, ae_complex_from_d(1.0), &tmpr, 0, blocksize, 0, &tmpa, 0, 0, 0, ae_complex_from_d(1.0), a, blockstart+blocksize, blockstart, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=0; i<=blocksize-1; i++) - { - ae_v_cmove(&t.ptr.p_complex[1], 1, &tmpa.ptr.pp_complex[i][i], 1, "Conj", ae_v_len(1,columnscount-i)); - t.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheright(a, taubuf.ptr.p_complex[i], &t, blockstart+blocksize, m-1, blockstart+i, n-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart+blocksize; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Partial unpacking of matrix Q from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of RMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of the RMatrixQR subroutine. - QColumns - required number of columns of matrix Q. M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array whose indexes range within [0..M-1, 0..QColumns-1]. - If QColumns=0, the array remains unchanged. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqrunpackq(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_int_t qcolumns, - /* Real */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_int_t refcnt; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t rowscount; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_REAL, _state, ae_true); - - ae_assert(qcolumns<=m, "UnpackQFromQR: QColumns>M!", _state); - if( (m<=0||n<=0)||qcolumns<=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - minmn = ae_minint(m, n, _state); - refcnt = ae_minint(minmn, qcolumns, _state); - ae_matrix_set_length(q, m, qcolumns, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=qcolumns-1; j++) - { - if( i==j ) - { - q->ptr.pp_double[i][j] = 1; - } - else - { - q->ptr.pp_double[i][j] = 0; - } - } - } - ae_vector_set_length(&work, ae_maxint(m, qcolumns, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, qcolumns, _state)+1, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, m, ablasblocksize(a, _state), _state); - ae_matrix_set_length(&tmpt, ablasblocksize(a, _state), 2*ablasblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, 2*ablasblocksize(a, _state), qcolumns, _state); - - /* - * Blocked code - */ - blockstart = ablasblocksize(a, _state)*(refcnt/ablasblocksize(a, _state)); - blocksize = refcnt-blockstart; - while(blockstart>=0) - { - rowscount = m-blockstart; - if( blocksize>0 ) - { - - /* - * Copy current block - */ - rmatrixcopy(rowscount, blocksize, a, blockstart, blockstart, &tmpa, 0, 0, _state); - ae_v_move(&taubuf.ptr.p_double[0], 1, &tau->ptr.p_double[blockstart], 1, ae_v_len(0,blocksize-1)); - - /* - * Update, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( qcolumns>=2*ablasblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_rmatrixblockreflector(&tmpa, &taubuf, ae_true, rowscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply matrix by Q. - * - * Q = E + Y*T*Y' = E + TmpA*TmpT*TmpA' - */ - rmatrixgemm(blocksize, qcolumns, rowscount, 1.0, &tmpa, 0, 0, 1, q, blockstart, 0, 0, 0.0, &tmpr, 0, 0, _state); - rmatrixgemm(blocksize, qcolumns, blocksize, 1.0, &tmpt, 0, 0, 0, &tmpr, 0, 0, 0, 0.0, &tmpr, blocksize, 0, _state); - rmatrixgemm(rowscount, qcolumns, blocksize, 1.0, &tmpa, 0, 0, 0, &tmpr, blocksize, 0, 0, 1.0, q, blockstart, 0, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=blocksize-1; i>=0; i--) - { - ae_v_move(&t.ptr.p_double[1], 1, &tmpa.ptr.pp_double[i][i], tmpa.stride, ae_v_len(1,rowscount-i)); - t.ptr.p_double[1] = 1; - applyreflectionfromtheleft(q, taubuf.ptr.p_double[i], &t, blockstart+i, m-1, 0, qcolumns-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart-ablasblocksize(a, _state); - blocksize = ablasblocksize(a, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking of matrix R from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of RMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - R - matrix R, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqrunpackr(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* r, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - ae_matrix_clear(r); - - if( m<=0||n<=0 ) - { - return; - } - k = ae_minint(m, n, _state); - ae_matrix_set_length(r, m, n, _state); - for(i=0; i<=n-1; i++) - { - r->ptr.pp_double[0][i] = 0; - } - for(i=1; i<=m-1; i++) - { - ae_v_move(&r->ptr.pp_double[i][0], 1, &r->ptr.pp_double[0][0], 1, ae_v_len(0,n-1)); - } - for(i=0; i<=k-1; i++) - { - ae_v_move(&r->ptr.pp_double[i][i], 1, &a->ptr.pp_double[i][i], 1, ae_v_len(i,n-1)); - } -} - - -/************************************************************************* -Partial unpacking of matrix Q from the LQ decomposition of a matrix A - -Input parameters: - A - matrices L and Q in compact form. - Output of RMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of the RMatrixLQ subroutine. - QRows - required number of rows in matrix Q. N>=QRows>=0. - -Output parameters: - Q - first QRows rows of matrix Q. Array whose indexes range - within [0..QRows-1, 0..N-1]. If QRows=0, the array remains - unchanged. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlqunpackq(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_int_t qrows, - /* Real */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_int_t refcnt; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t columnscount; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_REAL, _state, ae_true); - - ae_assert(qrows<=n, "RMatrixLQUnpackQ: QRows>N!", _state); - if( (m<=0||n<=0)||qrows<=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - minmn = ae_minint(m, n, _state); - refcnt = ae_minint(minmn, qrows, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, ablasblocksize(a, _state), n, _state); - ae_matrix_set_length(&tmpt, ablasblocksize(a, _state), 2*ablasblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, qrows, 2*ablasblocksize(a, _state), _state); - ae_matrix_set_length(q, qrows, n, _state); - for(i=0; i<=qrows-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - q->ptr.pp_double[i][j] = 1; - } - else - { - q->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * Blocked code - */ - blockstart = ablasblocksize(a, _state)*(refcnt/ablasblocksize(a, _state)); - blocksize = refcnt-blockstart; - while(blockstart>=0) - { - columnscount = n-blockstart; - if( blocksize>0 ) - { - - /* - * Copy submatrix - */ - rmatrixcopy(blocksize, columnscount, a, blockstart, blockstart, &tmpa, 0, 0, _state); - ae_v_move(&taubuf.ptr.p_double[0], 1, &tau->ptr.p_double[blockstart], 1, ae_v_len(0,blocksize-1)); - - /* - * Update matrix, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( qrows>=2*ablasblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_rmatrixblockreflector(&tmpa, &taubuf, ae_false, columnscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q'. - * - * Q' = E + Y*T'*Y' = E + TmpA'*TmpT'*TmpA - */ - rmatrixgemm(qrows, blocksize, columnscount, 1.0, q, 0, blockstart, 0, &tmpa, 0, 0, 1, 0.0, &tmpr, 0, 0, _state); - rmatrixgemm(qrows, blocksize, blocksize, 1.0, &tmpr, 0, 0, 0, &tmpt, 0, 0, 1, 0.0, &tmpr, 0, blocksize, _state); - rmatrixgemm(qrows, columnscount, blocksize, 1.0, &tmpr, 0, blocksize, 0, &tmpa, 0, 0, 0, 1.0, q, 0, blockstart, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=blocksize-1; i>=0; i--) - { - ae_v_move(&t.ptr.p_double[1], 1, &tmpa.ptr.pp_double[i][i], 1, ae_v_len(1,columnscount-i)); - t.ptr.p_double[1] = 1; - applyreflectionfromtheright(q, taubuf.ptr.p_double[i], &t, 0, qrows-1, blockstart+i, n-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart-ablasblocksize(a, _state); - blocksize = ablasblocksize(a, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking of matrix L from the LQ decomposition of a matrix A - -Input parameters: - A - matrices Q and L in compact form. - Output of RMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - L - matrix L, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlqunpackl(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* l, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - ae_matrix_clear(l); - - if( m<=0||n<=0 ) - { - return; - } - ae_matrix_set_length(l, m, n, _state); - for(i=0; i<=n-1; i++) - { - l->ptr.pp_double[0][i] = 0; - } - for(i=1; i<=m-1; i++) - { - ae_v_move(&l->ptr.pp_double[i][0], 1, &l->ptr.pp_double[0][0], 1, ae_v_len(0,n-1)); - } - for(i=0; i<=m-1; i++) - { - k = ae_minint(i, n-1, _state); - ae_v_move(&l->ptr.pp_double[i][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,k)); - } -} - - -/************************************************************************* -Partial unpacking of matrix Q from QR decomposition of a complex matrix A. - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixQR subroutine . - M - number of rows in matrix A. M>=0. - N - number of columns in matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of CMatrixQR subroutine . - QColumns - required number of columns in matrix Q. M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array whose index ranges within [0..M-1, 0..QColumns-1]. - If QColumns=0, array isn't changed. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixqrunpackq(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_int_t qcolumns, - /* Complex */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_int_t refcnt; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t rowscount; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&work, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&t, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(qcolumns<=m, "UnpackQFromQR: QColumns>M!", _state); - if( m<=0||n<=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - minmn = ae_minint(m, n, _state); - refcnt = ae_minint(minmn, qcolumns, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, m, ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(&tmpt, ablascomplexblocksize(a, _state), ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, 2*ablascomplexblocksize(a, _state), qcolumns, _state); - ae_matrix_set_length(q, m, qcolumns, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=qcolumns-1; j++) - { - if( i==j ) - { - q->ptr.pp_complex[i][j] = ae_complex_from_d(1); - } - else - { - q->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - } - - /* - * Blocked code - */ - blockstart = ablascomplexblocksize(a, _state)*(refcnt/ablascomplexblocksize(a, _state)); - blocksize = refcnt-blockstart; - while(blockstart>=0) - { - rowscount = m-blockstart; - if( blocksize>0 ) - { - - /* - * QR decomposition of submatrix. - * Matrix is copied to temporary storage to solve - * some TLB issues arising from non-contiguous memory - * access pattern. - */ - cmatrixcopy(rowscount, blocksize, a, blockstart, blockstart, &tmpa, 0, 0, _state); - ae_v_cmove(&taubuf.ptr.p_complex[0], 1, &tau->ptr.p_complex[blockstart], 1, "N", ae_v_len(0,blocksize-1)); - - /* - * Update matrix, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( qcolumns>=2*ablascomplexblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_cmatrixblockreflector(&tmpa, &taubuf, ae_true, rowscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q. - * - * Q = E + Y*T*Y' = E + TmpA*TmpT*TmpA' - */ - cmatrixgemm(blocksize, qcolumns, rowscount, ae_complex_from_d(1.0), &tmpa, 0, 0, 2, q, blockstart, 0, 0, ae_complex_from_d(0.0), &tmpr, 0, 0, _state); - cmatrixgemm(blocksize, qcolumns, blocksize, ae_complex_from_d(1.0), &tmpt, 0, 0, 0, &tmpr, 0, 0, 0, ae_complex_from_d(0.0), &tmpr, blocksize, 0, _state); - cmatrixgemm(rowscount, qcolumns, blocksize, ae_complex_from_d(1.0), &tmpa, 0, 0, 0, &tmpr, blocksize, 0, 0, ae_complex_from_d(1.0), q, blockstart, 0, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=blocksize-1; i>=0; i--) - { - ae_v_cmove(&t.ptr.p_complex[1], 1, &tmpa.ptr.pp_complex[i][i], tmpa.stride, "N", ae_v_len(1,rowscount-i)); - t.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheleft(q, taubuf.ptr.p_complex[i], &t, blockstart+i, m-1, 0, qcolumns-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart-ablascomplexblocksize(a, _state); - blocksize = ablascomplexblocksize(a, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking of matrix R from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - R - matrix R, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixqrunpackr(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* r, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - ae_matrix_clear(r); - - if( m<=0||n<=0 ) - { - return; - } - k = ae_minint(m, n, _state); - ae_matrix_set_length(r, m, n, _state); - for(i=0; i<=n-1; i++) - { - r->ptr.pp_complex[0][i] = ae_complex_from_d(0); - } - for(i=1; i<=m-1; i++) - { - ae_v_cmove(&r->ptr.pp_complex[i][0], 1, &r->ptr.pp_complex[0][0], 1, "N", ae_v_len(0,n-1)); - } - for(i=0; i<=k-1; i++) - { - ae_v_cmove(&r->ptr.pp_complex[i][i], 1, &a->ptr.pp_complex[i][i], 1, "N", ae_v_len(i,n-1)); - } -} - - -/************************************************************************* -Partial unpacking of matrix Q from LQ decomposition of a complex matrix A. - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixLQ subroutine . - M - number of rows in matrix A. M>=0. - N - number of columns in matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of CMatrixLQ subroutine . - QRows - required number of rows in matrix Q. N>=QColumns>=0. - -Output parameters: - Q - first QRows rows of matrix Q. - Array whose index ranges within [0..QRows-1, 0..N-1]. - If QRows=0, array isn't changed. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlqunpackq(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_int_t qrows, - /* Complex */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_vector taubuf; - ae_int_t minmn; - ae_int_t refcnt; - ae_matrix tmpa; - ae_matrix tmpt; - ae_matrix tmpr; - ae_int_t blockstart; - ae_int_t blocksize; - ae_int_t columnscount; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&work, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&t, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&taubuf, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpa, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpt, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&tmpr, 0, 0, DT_COMPLEX, _state, ae_true); - - if( m<=0||n<=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Init - */ - minmn = ae_minint(m, n, _state); - refcnt = ae_minint(minmn, qrows, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&t, ae_maxint(m, n, _state)+1, _state); - ae_vector_set_length(&taubuf, minmn, _state); - ae_matrix_set_length(&tmpa, ablascomplexblocksize(a, _state), n, _state); - ae_matrix_set_length(&tmpt, ablascomplexblocksize(a, _state), ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(&tmpr, qrows, 2*ablascomplexblocksize(a, _state), _state); - ae_matrix_set_length(q, qrows, n, _state); - for(i=0; i<=qrows-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - q->ptr.pp_complex[i][j] = ae_complex_from_d(1); - } - else - { - q->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - } - - /* - * Blocked code - */ - blockstart = ablascomplexblocksize(a, _state)*(refcnt/ablascomplexblocksize(a, _state)); - blocksize = refcnt-blockstart; - while(blockstart>=0) - { - columnscount = n-blockstart; - if( blocksize>0 ) - { - - /* - * LQ decomposition of submatrix. - * Matrix is copied to temporary storage to solve - * some TLB issues arising from non-contiguous memory - * access pattern. - */ - cmatrixcopy(blocksize, columnscount, a, blockstart, blockstart, &tmpa, 0, 0, _state); - ae_v_cmove(&taubuf.ptr.p_complex[0], 1, &tau->ptr.p_complex[blockstart], 1, "N", ae_v_len(0,blocksize-1)); - - /* - * Update matrix, choose between: - * a) Level 2 algorithm (when the rest of the matrix is small enough) - * b) blocked algorithm, see algorithm 5 from 'A storage efficient WY - * representation for products of Householder transformations', - * by R. Schreiber and C. Van Loan. - */ - if( qrows>=2*ablascomplexblocksize(a, _state) ) - { - - /* - * Prepare block reflector - */ - ortfac_cmatrixblockreflector(&tmpa, &taubuf, ae_false, columnscount, blocksize, &tmpt, &work, _state); - - /* - * Multiply the rest of A by Q'. - * - * Q' = E + Y*T'*Y' = E + TmpA'*TmpT'*TmpA - */ - cmatrixgemm(qrows, blocksize, columnscount, ae_complex_from_d(1.0), q, 0, blockstart, 0, &tmpa, 0, 0, 2, ae_complex_from_d(0.0), &tmpr, 0, 0, _state); - cmatrixgemm(qrows, blocksize, blocksize, ae_complex_from_d(1.0), &tmpr, 0, 0, 0, &tmpt, 0, 0, 2, ae_complex_from_d(0.0), &tmpr, 0, blocksize, _state); - cmatrixgemm(qrows, columnscount, blocksize, ae_complex_from_d(1.0), &tmpr, 0, blocksize, 0, &tmpa, 0, 0, 0, ae_complex_from_d(1.0), q, 0, blockstart, _state); - } - else - { - - /* - * Level 2 algorithm - */ - for(i=blocksize-1; i>=0; i--) - { - ae_v_cmove(&t.ptr.p_complex[1], 1, &tmpa.ptr.pp_complex[i][i], 1, "Conj", ae_v_len(1,columnscount-i)); - t.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheright(q, ae_c_conj(taubuf.ptr.p_complex[i], _state), &t, 0, qrows-1, blockstart+i, n-1, &work, _state); - } - } - } - - /* - * Advance - */ - blockstart = blockstart-ablascomplexblocksize(a, _state); - blocksize = ablascomplexblocksize(a, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking of matrix L from the LQ decomposition of a matrix A - -Input parameters: - A - matrices Q and L in compact form. - Output of CMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - L - matrix L, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlqunpackl(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* l, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - - ae_matrix_clear(l); - - if( m<=0||n<=0 ) - { - return; - } - ae_matrix_set_length(l, m, n, _state); - for(i=0; i<=n-1; i++) - { - l->ptr.pp_complex[0][i] = ae_complex_from_d(0); - } - for(i=1; i<=m-1; i++) - { - ae_v_cmove(&l->ptr.pp_complex[i][0], 1, &l->ptr.pp_complex[0][0], 1, "N", ae_v_len(0,n-1)); - } - for(i=0; i<=m-1; i++) - { - k = ae_minint(i, n-1, _state); - ae_v_cmove(&l->ptr.pp_complex[i][0], 1, &a->ptr.pp_complex[i][0], 1, "N", ae_v_len(0,k)); - } -} - - -/************************************************************************* -Base case for real QR - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -void rmatrixqrbasecase(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* work, - /* Real */ ae_vector* t, - /* Real */ ae_vector* tau, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t minmn; - double tmp; - - - minmn = ae_minint(m, n, _state); - - /* - * Test the input arguments - */ - k = minmn; - for(i=0; i<=k-1; i++) - { - - /* - * Generate elementary reflector H(i) to annihilate A(i+1:m,i) - */ - ae_v_move(&t->ptr.p_double[1], 1, &a->ptr.pp_double[i][i], a->stride, ae_v_len(1,m-i)); - generatereflection(t, m-i, &tmp, _state); - tau->ptr.p_double[i] = tmp; - ae_v_move(&a->ptr.pp_double[i][i], a->stride, &t->ptr.p_double[1], 1, ae_v_len(i,m-1)); - t->ptr.p_double[1] = 1; - if( iptr.p_double[i], t, i, m-1, i+1, n-1, work, _state); - } - } -} - - -/************************************************************************* -Base case for real LQ - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -void rmatrixlqbasecase(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* work, - /* Real */ ae_vector* t, - /* Real */ ae_vector* tau, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - double tmp; - - - k = ae_minint(m, n, _state); - for(i=0; i<=k-1; i++) - { - - /* - * Generate elementary reflector H(i) to annihilate A(i,i+1:n-1) - */ - ae_v_move(&t->ptr.p_double[1], 1, &a->ptr.pp_double[i][i], 1, ae_v_len(1,n-i)); - generatereflection(t, n-i, &tmp, _state); - tau->ptr.p_double[i] = tmp; - ae_v_move(&a->ptr.pp_double[i][i], 1, &t->ptr.p_double[1], 1, ae_v_len(i,n-1)); - t->ptr.p_double[1] = 1; - if( iptr.p_double[i], t, i+1, m-1, i, n-1, work, _state); - } - } -} - - -/************************************************************************* -Reduction of a rectangular matrix to bidiagonal form - -The algorithm reduces the rectangular matrix A to bidiagonal form by -orthogonal transformations P and Q: A = Q*B*P. - -Input parameters: - A - source matrix. array[0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q, B, P in compact form (see below). - TauQ - scalar factors which are used to form matrix Q. - TauP - scalar factors which are used to form matrix P. - -The main diagonal and one of the secondary diagonals of matrix A are -replaced with bidiagonal matrix B. Other elements contain elementary -reflections which form MxM matrix Q and NxN matrix P, respectively. - -If M>=N, B is the upper bidiagonal MxN matrix and is stored in the -corresponding elements of matrix A. Matrix Q is represented as a -product of elementary reflections Q = H(0)*H(1)*...*H(n-1), where -H(i) = 1-tau*v*v'. Here tau is a scalar which is stored in TauQ[i], and -vector v has the following structure: v(0:i-1)=0, v(i)=1, v(i+1:m-1) is -stored in elements A(i+1:m-1,i). Matrix P is as follows: P = -G(0)*G(1)*...*G(n-2), where G(i) = 1 - tau*u*u'. Tau is stored in TauP[i], -u(0:i)=0, u(i+1)=1, u(i+2:n-1) is stored in elements A(i,i+2:n-1). - -If M n): m=5, n=6 (m < n): - -( d e u1 u1 u1 ) ( d u1 u1 u1 u1 u1 ) -( v1 d e u2 u2 ) ( e d u2 u2 u2 u2 ) -( v1 v2 d e u3 ) ( v1 e d u3 u3 u3 ) -( v1 v2 v3 d e ) ( v1 v2 e d u4 u4 ) -( v1 v2 v3 v4 d ) ( v1 v2 v3 e d u5 ) -( v1 v2 v3 v4 v5 ) - -Here vi and ui are vectors which form H(i) and G(i), and d and e - -are the diagonal and off-diagonal elements of matrix B. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -void rmatrixbd(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tauq, - /* Real */ ae_vector* taup, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_vector t; - ae_int_t maxmn; - ae_int_t i; - double ltau; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tauq); - ae_vector_clear(taup); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - - /* - * Prepare - */ - if( n<=0||m<=0 ) - { - ae_frame_leave(_state); - return; - } - maxmn = ae_maxint(m, n, _state); - ae_vector_set_length(&work, maxmn+1, _state); - ae_vector_set_length(&t, maxmn+1, _state); - if( m>=n ) - { - ae_vector_set_length(tauq, n, _state); - ae_vector_set_length(taup, n, _state); - } - else - { - ae_vector_set_length(tauq, m, _state); - ae_vector_set_length(taup, m, _state); - } - if( m>=n ) - { - - /* - * Reduce to upper bidiagonal form - */ - for(i=0; i<=n-1; i++) - { - - /* - * Generate elementary reflector H(i) to annihilate A(i+1:m-1,i) - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[i][i], a->stride, ae_v_len(1,m-i)); - generatereflection(&t, m-i, <au, _state); - tauq->ptr.p_double[i] = ltau; - ae_v_move(&a->ptr.pp_double[i][i], a->stride, &t.ptr.p_double[1], 1, ae_v_len(i,m-1)); - t.ptr.p_double[1] = 1; - - /* - * Apply H(i) to A(i:m-1,i+1:n-1) from the left - */ - applyreflectionfromtheleft(a, ltau, &t, i, m-1, i+1, n-1, &work, _state); - if( iptr.pp_double[i][i+1], 1, ae_v_len(1,n-i-1)); - generatereflection(&t, n-1-i, <au, _state); - taup->ptr.p_double[i] = ltau; - ae_v_move(&a->ptr.pp_double[i][i+1], 1, &t.ptr.p_double[1], 1, ae_v_len(i+1,n-1)); - t.ptr.p_double[1] = 1; - - /* - * Apply G(i) to A(i+1:m-1,i+1:n-1) from the right - */ - applyreflectionfromtheright(a, ltau, &t, i+1, m-1, i+1, n-1, &work, _state); - } - else - { - taup->ptr.p_double[i] = 0; - } - } - } - else - { - - /* - * Reduce to lower bidiagonal form - */ - for(i=0; i<=m-1; i++) - { - - /* - * Generate elementary reflector G(i) to annihilate A(i,i+1:n-1) - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[i][i], 1, ae_v_len(1,n-i)); - generatereflection(&t, n-i, <au, _state); - taup->ptr.p_double[i] = ltau; - ae_v_move(&a->ptr.pp_double[i][i], 1, &t.ptr.p_double[1], 1, ae_v_len(i,n-1)); - t.ptr.p_double[1] = 1; - - /* - * Apply G(i) to A(i+1:m-1,i:n-1) from the right - */ - applyreflectionfromtheright(a, ltau, &t, i+1, m-1, i, n-1, &work, _state); - if( iptr.pp_double[i+1][i], a->stride, ae_v_len(1,m-1-i)); - generatereflection(&t, m-1-i, <au, _state); - tauq->ptr.p_double[i] = ltau; - ae_v_move(&a->ptr.pp_double[i+1][i], a->stride, &t.ptr.p_double[1], 1, ae_v_len(i+1,m-1)); - t.ptr.p_double[1] = 1; - - /* - * Apply H(i) to A(i+1:m-1,i+1:n-1) from the left - */ - applyreflectionfromtheleft(a, ltau, &t, i+1, m-1, i+1, n-1, &work, _state); - } - else - { - tauq->ptr.p_double[i] = 0; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking matrix Q which reduces a matrix to bidiagonal form. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUQ - scalar factors which are used to form Q. - Output of ToBidiagonal subroutine. - QColumns - required number of columns in matrix Q. - M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array[0..M-1, 0..QColumns-1] - If QColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackq(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tauq, - ae_int_t qcolumns, - /* Real */ ae_matrix* q, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(q); - - ae_assert(qcolumns<=m, "RMatrixBDUnpackQ: QColumns>M!", _state); - ae_assert(qcolumns>=0, "RMatrixBDUnpackQ: QColumns<0!", _state); - if( (m==0||n==0)||qcolumns==0 ) - { - return; - } - - /* - * prepare Q - */ - ae_matrix_set_length(q, m, qcolumns, _state); - for(i=0; i<=m-1; i++) - { - for(j=0; j<=qcolumns-1; j++) - { - if( i==j ) - { - q->ptr.pp_double[i][j] = 1; - } - else - { - q->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * Calculate - */ - rmatrixbdmultiplybyq(qp, m, n, tauq, q, m, qcolumns, ae_false, ae_false, _state); -} - - -/************************************************************************* -Multiplication by matrix Q which reduces matrix A to bidiagonal form. - -The algorithm allows pre- or post-multiply by Q or Q'. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUQ - scalar factors which are used to form Q. - Output of ToBidiagonal subroutine. - Z - multiplied matrix. - array[0..ZRows-1,0..ZColumns-1] - ZRows - number of rows in matrix Z. If FromTheRight=False, - ZRows=M, otherwise ZRows can be arbitrary. - ZColumns - number of columns in matrix Z. If FromTheRight=True, - ZColumns=M, otherwise ZColumns can be arbitrary. - FromTheRight - pre- or post-multiply. - DoTranspose - multiply by Q or Q'. - -Output parameters: - Z - product of Z and Q. - Array[0..ZRows-1,0..ZColumns-1] - If ZRows=0 or ZColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdmultiplybyq(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tauq, - /* Real */ ae_matrix* z, - ae_int_t zrows, - ae_int_t zcolumns, - ae_bool fromtheright, - ae_bool dotranspose, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t i1; - ae_int_t i2; - ae_int_t istep; - ae_vector v; - ae_vector work; - ae_int_t mx; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - if( ((m<=0||n<=0)||zrows<=0)||zcolumns<=0 ) - { - ae_frame_leave(_state); - return; - } - ae_assert((fromtheright&&zcolumns==m)||(!fromtheright&&zrows==m), "RMatrixBDMultiplyByQ: incorrect Z size!", _state); - - /* - * init - */ - mx = ae_maxint(m, n, _state); - mx = ae_maxint(mx, zrows, _state); - mx = ae_maxint(mx, zcolumns, _state); - ae_vector_set_length(&v, mx+1, _state); - ae_vector_set_length(&work, mx+1, _state); - if( m>=n ) - { - - /* - * setup - */ - if( fromtheright ) - { - i1 = 0; - i2 = n-1; - istep = 1; - } - else - { - i1 = n-1; - i2 = 0; - istep = -1; - } - if( dotranspose ) - { - i = i1; - i1 = i2; - i2 = i; - istep = -istep; - } - - /* - * Process - */ - i = i1; - do - { - ae_v_move(&v.ptr.p_double[1], 1, &qp->ptr.pp_double[i][i], qp->stride, ae_v_len(1,m-i)); - v.ptr.p_double[1] = 1; - if( fromtheright ) - { - applyreflectionfromtheright(z, tauq->ptr.p_double[i], &v, 0, zrows-1, i, m-1, &work, _state); - } - else - { - applyreflectionfromtheleft(z, tauq->ptr.p_double[i], &v, i, m-1, 0, zcolumns-1, &work, _state); - } - i = i+istep; - } - while(i!=i2+istep); - } - else - { - - /* - * setup - */ - if( fromtheright ) - { - i1 = 0; - i2 = m-2; - istep = 1; - } - else - { - i1 = m-2; - i2 = 0; - istep = -1; - } - if( dotranspose ) - { - i = i1; - i1 = i2; - i2 = i; - istep = -istep; - } - - /* - * Process - */ - if( m-1>0 ) - { - i = i1; - do - { - ae_v_move(&v.ptr.p_double[1], 1, &qp->ptr.pp_double[i+1][i], qp->stride, ae_v_len(1,m-i-1)); - v.ptr.p_double[1] = 1; - if( fromtheright ) - { - applyreflectionfromtheright(z, tauq->ptr.p_double[i], &v, 0, zrows-1, i+1, m-1, &work, _state); - } - else - { - applyreflectionfromtheleft(z, tauq->ptr.p_double[i], &v, i+1, m-1, 0, zcolumns-1, &work, _state); - } - i = i+istep; - } - while(i!=i2+istep); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking matrix P which reduces matrix A to bidiagonal form. -The subroutine returns transposed matrix P. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUP - scalar factors which are used to form P. - Output of ToBidiagonal subroutine. - PTRows - required number of rows of matrix P^T. N >= PTRows >= 0. - -Output parameters: - PT - first PTRows columns of matrix P^T - Array[0..PTRows-1, 0..N-1] - If PTRows=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackpt(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* taup, - ae_int_t ptrows, - /* Real */ ae_matrix* pt, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(pt); - - ae_assert(ptrows<=n, "RMatrixBDUnpackPT: PTRows>N!", _state); - ae_assert(ptrows>=0, "RMatrixBDUnpackPT: PTRows<0!", _state); - if( (m==0||n==0)||ptrows==0 ) - { - return; - } - - /* - * prepare PT - */ - ae_matrix_set_length(pt, ptrows, n, _state); - for(i=0; i<=ptrows-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - pt->ptr.pp_double[i][j] = 1; - } - else - { - pt->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * Calculate - */ - rmatrixbdmultiplybyp(qp, m, n, taup, pt, ptrows, n, ae_true, ae_true, _state); -} - - -/************************************************************************* -Multiplication by matrix P which reduces matrix A to bidiagonal form. - -The algorithm allows pre- or post-multiply by P or P'. - -Input parameters: - QP - matrices Q and P in compact form. - Output of RMatrixBD subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUP - scalar factors which are used to form P. - Output of RMatrixBD subroutine. - Z - multiplied matrix. - Array whose indexes range within [0..ZRows-1,0..ZColumns-1]. - ZRows - number of rows in matrix Z. If FromTheRight=False, - ZRows=N, otherwise ZRows can be arbitrary. - ZColumns - number of columns in matrix Z. If FromTheRight=True, - ZColumns=N, otherwise ZColumns can be arbitrary. - FromTheRight - pre- or post-multiply. - DoTranspose - multiply by P or P'. - -Output parameters: - Z - product of Z and P. - Array whose indexes range within [0..ZRows-1,0..ZColumns-1]. - If ZRows=0 or ZColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdmultiplybyp(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* taup, - /* Real */ ae_matrix* z, - ae_int_t zrows, - ae_int_t zcolumns, - ae_bool fromtheright, - ae_bool dotranspose, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector v; - ae_vector work; - ae_int_t mx; - ae_int_t i1; - ae_int_t i2; - ae_int_t istep; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - if( ((m<=0||n<=0)||zrows<=0)||zcolumns<=0 ) - { - ae_frame_leave(_state); - return; - } - ae_assert((fromtheright&&zcolumns==n)||(!fromtheright&&zrows==n), "RMatrixBDMultiplyByP: incorrect Z size!", _state); - - /* - * init - */ - mx = ae_maxint(m, n, _state); - mx = ae_maxint(mx, zrows, _state); - mx = ae_maxint(mx, zcolumns, _state); - ae_vector_set_length(&v, mx+1, _state); - ae_vector_set_length(&work, mx+1, _state); - if( m>=n ) - { - - /* - * setup - */ - if( fromtheright ) - { - i1 = n-2; - i2 = 0; - istep = -1; - } - else - { - i1 = 0; - i2 = n-2; - istep = 1; - } - if( !dotranspose ) - { - i = i1; - i1 = i2; - i2 = i; - istep = -istep; - } - - /* - * Process - */ - if( n-1>0 ) - { - i = i1; - do - { - ae_v_move(&v.ptr.p_double[1], 1, &qp->ptr.pp_double[i][i+1], 1, ae_v_len(1,n-1-i)); - v.ptr.p_double[1] = 1; - if( fromtheright ) - { - applyreflectionfromtheright(z, taup->ptr.p_double[i], &v, 0, zrows-1, i+1, n-1, &work, _state); - } - else - { - applyreflectionfromtheleft(z, taup->ptr.p_double[i], &v, i+1, n-1, 0, zcolumns-1, &work, _state); - } - i = i+istep; - } - while(i!=i2+istep); - } - } - else - { - - /* - * setup - */ - if( fromtheright ) - { - i1 = m-1; - i2 = 0; - istep = -1; - } - else - { - i1 = 0; - i2 = m-1; - istep = 1; - } - if( !dotranspose ) - { - i = i1; - i1 = i2; - i2 = i; - istep = -istep; - } - - /* - * Process - */ - i = i1; - do - { - ae_v_move(&v.ptr.p_double[1], 1, &qp->ptr.pp_double[i][i], 1, ae_v_len(1,n-i)); - v.ptr.p_double[1] = 1; - if( fromtheright ) - { - applyreflectionfromtheright(z, taup->ptr.p_double[i], &v, 0, zrows-1, i, n-1, &work, _state); - } - else - { - applyreflectionfromtheleft(z, taup->ptr.p_double[i], &v, i, n-1, 0, zcolumns-1, &work, _state); - } - i = i+istep; - } - while(i!=i2+istep); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking of the main and secondary diagonals of bidiagonal decomposition -of matrix A. - -Input parameters: - B - output of RMatrixBD subroutine. - M - number of rows in matrix B. - N - number of columns in matrix B. - -Output parameters: - IsUpper - True, if the matrix is upper bidiagonal. - otherwise IsUpper is False. - D - the main diagonal. - Array whose index ranges within [0..Min(M,N)-1]. - E - the secondary diagonal (upper or lower, depending on - the value of IsUpper). - Array index ranges within [0..Min(M,N)-1], the last - element is not used. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackdiagonals(/* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t n, - ae_bool* isupper, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_state *_state) -{ - ae_int_t i; - - *isupper = ae_false; - ae_vector_clear(d); - ae_vector_clear(e); - - *isupper = m>=n; - if( m<=0||n<=0 ) - { - return; - } - if( *isupper ) - { - ae_vector_set_length(d, n, _state); - ae_vector_set_length(e, n, _state); - for(i=0; i<=n-2; i++) - { - d->ptr.p_double[i] = b->ptr.pp_double[i][i]; - e->ptr.p_double[i] = b->ptr.pp_double[i][i+1]; - } - d->ptr.p_double[n-1] = b->ptr.pp_double[n-1][n-1]; - } - else - { - ae_vector_set_length(d, m, _state); - ae_vector_set_length(e, m, _state); - for(i=0; i<=m-2; i++) - { - d->ptr.p_double[i] = b->ptr.pp_double[i][i]; - e->ptr.p_double[i] = b->ptr.pp_double[i+1][i]; - } - d->ptr.p_double[m-1] = b->ptr.pp_double[m-1][m-1]; - } -} - - -/************************************************************************* -Reduction of a square matrix to upper Hessenberg form: Q'*A*Q = H, -where Q is an orthogonal matrix, H - Hessenberg matrix. - -Input parameters: - A - matrix A with elements [0..N-1, 0..N-1] - N - size of matrix A. - -Output parameters: - A - matrices Q and P in compact form (see below). - Tau - array of scalar factors which are used to form matrix Q. - Array whose index ranges within [0..N-2] - -Matrix H is located on the main diagonal, on the lower secondary diagonal -and above the main diagonal of matrix A. The elements which are used to -form matrix Q are situated in array Tau and below the lower secondary -diagonal of matrix A as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(0)*H(2)*...*H(n-2), - -where each H(i) is given by - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - is a real vector, -so that v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) stored in A(i+2:n-1,i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void rmatrixhessenberg(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - double v; - ae_vector t; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=0, "RMatrixHessenberg: incorrect N!", _state); - - /* - * Quick return if possible - */ - if( n<=1 ) - { - ae_frame_leave(_state); - return; - } - ae_vector_set_length(tau, n-2+1, _state); - ae_vector_set_length(&t, n+1, _state); - ae_vector_set_length(&work, n-1+1, _state); - for(i=0; i<=n-2; i++) - { - - /* - * Compute elementary reflector H(i) to annihilate A(i+2:ihi,i) - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(1,n-i-1)); - generatereflection(&t, n-i-1, &v, _state); - ae_v_move(&a->ptr.pp_double[i+1][i], a->stride, &t.ptr.p_double[1], 1, ae_v_len(i+1,n-1)); - tau->ptr.p_double[i] = v; - t.ptr.p_double[1] = 1; - - /* - * Apply H(i) to A(1:ihi,i+1:ihi) from the right - */ - applyreflectionfromtheright(a, v, &t, 0, n-1, i+1, n-1, &work, _state); - - /* - * Apply H(i) to A(i+1:ihi,i+1:n) from the left - */ - applyreflectionfromtheleft(a, v, &t, i+1, n-1, i+1, n-1, &work, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking matrix Q which reduces matrix A to upper Hessenberg form - -Input parameters: - A - output of RMatrixHessenberg subroutine. - N - size of matrix A. - Tau - scalar factors which are used to form Q. - Output of RMatrixHessenberg subroutine. - -Output parameters: - Q - matrix Q. - Array whose indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixhessenbergunpackq(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - /* Real */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector v; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - ae_matrix_set_length(q, n-1+1, n-1+1, _state); - ae_vector_set_length(&v, n-1+1, _state); - ae_vector_set_length(&work, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - q->ptr.pp_double[i][j] = 1; - } - else - { - q->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * unpack Q - */ - for(i=0; i<=n-2; i++) - { - - /* - * Apply H(i) - */ - ae_v_move(&v.ptr.p_double[1], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(1,n-i-1)); - v.ptr.p_double[1] = 1; - applyreflectionfromtheright(q, tau->ptr.p_double[i], &v, 0, n-1, i+1, n-1, &work, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking matrix H (the result of matrix A reduction to upper Hessenberg form) - -Input parameters: - A - output of RMatrixHessenberg subroutine. - N - size of matrix A. - -Output parameters: - H - matrix H. Array whose indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixhessenbergunpackh(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_matrix* h, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector v; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(h); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(h, n-1+1, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-2; j++) - { - h->ptr.pp_double[i][j] = 0; - } - j = ae_maxint(0, i-1, _state); - ae_v_move(&h->ptr.pp_double[i][j], 1, &a->ptr.pp_double[i][j], 1, ae_v_len(j,n-1)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Reduction of a symmetric matrix which is given by its higher or lower -triangular part to a tridiagonal matrix using orthogonal similarity -transformation: Q'*A*Q=T. - -Input parameters: - A - matrix to be transformed - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. If IsUpper = True, then matrix A is given - by its upper triangle, and the lower triangle is not used - and not modified by the algorithm, and vice versa - if IsUpper = False. - -Output parameters: - A - matrices T and Q in compact form (see lower) - Tau - array of factors which are forming matrices H(i) - array with elements [0..N-2]. - D - main diagonal of symmetric matrix T. - array with elements [0..N-1]. - E - secondary diagonal of symmetric matrix T. - array with elements [0..N-2]. - - - If IsUpper=True, the matrix Q is represented as a product of elementary - reflectors - - Q = H(n-2) . . . H(2) H(0). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a real scalar, and v is a real vector with - v(i+1:n-1) = 0, v(i) = 1, v(0:i-1) is stored on exit in - A(0:i-1,i+1), and tau in TAU(i). - - If IsUpper=False, the matrix Q is represented as a product of elementary - reflectors - - Q = H(0) H(2) . . . H(n-2). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a real scalar, and v is a real vector with - v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) is stored on exit in A(i+2:n-1,i), - and tau in TAU(i). - - The contents of A on exit are illustrated by the following examples - with n = 5: - - if UPLO = 'U': if UPLO = 'L': - - ( d e v1 v2 v3 ) ( d ) - ( d e v2 v3 ) ( e d ) - ( d e v3 ) ( v0 e d ) - ( d e ) ( v0 v1 e d ) - ( d ) ( v0 v1 v2 e d ) - - where d and e denote diagonal and off-diagonal elements of T, and vi - denotes an element of the vector defining H(i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void smatrixtd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tau, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - double alpha; - double taui; - double v; - ae_vector t; - ae_vector t2; - ae_vector t3; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_clear(d); - ae_vector_clear(e); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t3, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&t, n+1, _state); - ae_vector_set_length(&t2, n+1, _state); - ae_vector_set_length(&t3, n+1, _state); - if( n>1 ) - { - ae_vector_set_length(tau, n-2+1, _state); - } - ae_vector_set_length(d, n-1+1, _state); - if( n>1 ) - { - ae_vector_set_length(e, n-2+1, _state); - } - if( isupper ) - { - - /* - * Reduce the upper triangle of A - */ - for(i=n-2; i>=0; i--) - { - - /* - * Generate elementary reflector H() = E - tau * v * v' - */ - if( i>=1 ) - { - ae_v_move(&t.ptr.p_double[2], 1, &a->ptr.pp_double[0][i+1], a->stride, ae_v_len(2,i+1)); - } - t.ptr.p_double[1] = a->ptr.pp_double[i][i+1]; - generatereflection(&t, i+1, &taui, _state); - if( i>=1 ) - { - ae_v_move(&a->ptr.pp_double[0][i+1], a->stride, &t.ptr.p_double[2], 1, ae_v_len(0,i-1)); - } - a->ptr.pp_double[i][i+1] = t.ptr.p_double[1]; - e->ptr.p_double[i] = a->ptr.pp_double[i][i+1]; - if( ae_fp_neq(taui,0) ) - { - - /* - * Apply H from both sides to A - */ - a->ptr.pp_double[i][i+1] = 1; - - /* - * Compute x := tau * A * v storing x in TAU - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[0][i+1], a->stride, ae_v_len(1,i+1)); - symmetricmatrixvectormultiply(a, isupper, 0, i, &t, taui, &t3, _state); - ae_v_move(&tau->ptr.p_double[0], 1, &t3.ptr.p_double[1], 1, ae_v_len(0,i)); - - /* - * Compute w := x - 1/2 * tau * (x'*v) * v - */ - v = ae_v_dotproduct(&tau->ptr.p_double[0], 1, &a->ptr.pp_double[0][i+1], a->stride, ae_v_len(0,i)); - alpha = -0.5*taui*v; - ae_v_addd(&tau->ptr.p_double[0], 1, &a->ptr.pp_double[0][i+1], a->stride, ae_v_len(0,i), alpha); - - /* - * Apply the transformation as a rank-2 update: - * A := A - v * w' - w * v' - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[0][i+1], a->stride, ae_v_len(1,i+1)); - ae_v_move(&t3.ptr.p_double[1], 1, &tau->ptr.p_double[0], 1, ae_v_len(1,i+1)); - symmetricrank2update(a, isupper, 0, i, &t, &t3, &t2, -1, _state); - a->ptr.pp_double[i][i+1] = e->ptr.p_double[i]; - } - d->ptr.p_double[i+1] = a->ptr.pp_double[i+1][i+1]; - tau->ptr.p_double[i] = taui; - } - d->ptr.p_double[0] = a->ptr.pp_double[0][0]; - } - else - { - - /* - * Reduce the lower triangle of A - */ - for(i=0; i<=n-2; i++) - { - - /* - * Generate elementary reflector H = E - tau * v * v' - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(1,n-i-1)); - generatereflection(&t, n-i-1, &taui, _state); - ae_v_move(&a->ptr.pp_double[i+1][i], a->stride, &t.ptr.p_double[1], 1, ae_v_len(i+1,n-1)); - e->ptr.p_double[i] = a->ptr.pp_double[i+1][i]; - if( ae_fp_neq(taui,0) ) - { - - /* - * Apply H from both sides to A - */ - a->ptr.pp_double[i+1][i] = 1; - - /* - * Compute x := tau * A * v storing y in TAU - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(1,n-i-1)); - symmetricmatrixvectormultiply(a, isupper, i+1, n-1, &t, taui, &t2, _state); - ae_v_move(&tau->ptr.p_double[i], 1, &t2.ptr.p_double[1], 1, ae_v_len(i,n-2)); - - /* - * Compute w := x - 1/2 * tau * (x'*v) * v - */ - v = ae_v_dotproduct(&tau->ptr.p_double[i], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(i,n-2)); - alpha = -0.5*taui*v; - ae_v_addd(&tau->ptr.p_double[i], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(i,n-2), alpha); - - /* - * Apply the transformation as a rank-2 update: - * A := A - v * w' - w * v' - * - */ - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(1,n-i-1)); - ae_v_move(&t2.ptr.p_double[1], 1, &tau->ptr.p_double[i], 1, ae_v_len(1,n-i-1)); - symmetricrank2update(a, isupper, i+1, n-1, &t, &t2, &t3, -1, _state); - a->ptr.pp_double[i+1][i] = e->ptr.p_double[i]; - } - d->ptr.p_double[i] = a->ptr.pp_double[i][i]; - tau->ptr.p_double[i] = taui; - } - d->ptr.p_double[n-1] = a->ptr.pp_double[n-1][n-1]; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking matrix Q which reduces symmetric matrix to a tridiagonal -form. - -Input parameters: - A - the result of a SMatrixTD subroutine - N - size of matrix A. - IsUpper - storage format (a parameter of SMatrixTD subroutine) - Tau - the result of a SMatrixTD subroutine - -Output parameters: - Q - transformation matrix. - array with elements [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void smatrixtdunpackq(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tau, - /* Real */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector v; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - ae_matrix_set_length(q, n-1+1, n-1+1, _state); - ae_vector_set_length(&v, n+1, _state); - ae_vector_set_length(&work, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - q->ptr.pp_double[i][j] = 1; - } - else - { - q->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * unpack Q - */ - if( isupper ) - { - for(i=0; i<=n-2; i++) - { - - /* - * Apply H(i) - */ - ae_v_move(&v.ptr.p_double[1], 1, &a->ptr.pp_double[0][i+1], a->stride, ae_v_len(1,i+1)); - v.ptr.p_double[i+1] = 1; - applyreflectionfromtheleft(q, tau->ptr.p_double[i], &v, 0, i, 0, n-1, &work, _state); - } - } - else - { - for(i=n-2; i>=0; i--) - { - - /* - * Apply H(i) - */ - ae_v_move(&v.ptr.p_double[1], 1, &a->ptr.pp_double[i+1][i], a->stride, ae_v_len(1,n-i-1)); - v.ptr.p_double[1] = 1; - applyreflectionfromtheleft(q, tau->ptr.p_double[i], &v, i+1, n-1, 0, n-1, &work, _state); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Reduction of a Hermitian matrix which is given by its higher or lower -triangular part to a real tridiagonal matrix using unitary similarity -transformation: Q'*A*Q = T. - -Input parameters: - A - matrix to be transformed - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. If IsUpper = True, then matrix A is given - by its upper triangle, and the lower triangle is not used - and not modified by the algorithm, and vice versa - if IsUpper = False. - -Output parameters: - A - matrices T and Q in compact form (see lower) - Tau - array of factors which are forming matrices H(i) - array with elements [0..N-2]. - D - main diagonal of real symmetric matrix T. - array with elements [0..N-1]. - E - secondary diagonal of real symmetric matrix T. - array with elements [0..N-2]. - - - If IsUpper=True, the matrix Q is represented as a product of elementary - reflectors - - Q = H(n-2) . . . H(2) H(0). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a complex scalar, and v is a complex vector with - v(i+1:n-1) = 0, v(i) = 1, v(0:i-1) is stored on exit in - A(0:i-1,i+1), and tau in TAU(i). - - If IsUpper=False, the matrix Q is represented as a product of elementary - reflectors - - Q = H(0) H(2) . . . H(n-2). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a complex scalar, and v is a complex vector with - v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) is stored on exit in A(i+2:n-1,i), - and tau in TAU(i). - - The contents of A on exit are illustrated by the following examples - with n = 5: - - if UPLO = 'U': if UPLO = 'L': - - ( d e v1 v2 v3 ) ( d ) - ( d e v2 v3 ) ( e d ) - ( d e v3 ) ( v0 e d ) - ( d e ) ( v0 v1 e d ) - ( d ) ( v0 v1 v2 e d ) - -where d and e denote diagonal and off-diagonal elements of T, and vi -denotes an element of the vector defining H(i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void hmatrixtd(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tau, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_complex alpha; - ae_complex taui; - ae_complex v; - ae_vector t; - ae_vector t2; - ae_vector t3; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_clear(d); - ae_vector_clear(e); - ae_vector_init(&t, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&t2, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&t3, 0, DT_COMPLEX, _state, ae_true); - - if( n<=0 ) - { - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - ae_assert(ae_fp_eq(a->ptr.pp_complex[i][i].y,0), "Assertion failed", _state); - } - if( n>1 ) - { - ae_vector_set_length(tau, n-2+1, _state); - ae_vector_set_length(e, n-2+1, _state); - } - ae_vector_set_length(d, n-1+1, _state); - ae_vector_set_length(&t, n-1+1, _state); - ae_vector_set_length(&t2, n-1+1, _state); - ae_vector_set_length(&t3, n-1+1, _state); - if( isupper ) - { - - /* - * Reduce the upper triangle of A - */ - a->ptr.pp_complex[n-1][n-1] = ae_complex_from_d(a->ptr.pp_complex[n-1][n-1].x); - for(i=n-2; i>=0; i--) - { - - /* - * Generate elementary reflector H = I+1 - tau * v * v' - */ - alpha = a->ptr.pp_complex[i][i+1]; - t.ptr.p_complex[1] = alpha; - if( i>=1 ) - { - ae_v_cmove(&t.ptr.p_complex[2], 1, &a->ptr.pp_complex[0][i+1], a->stride, "N", ae_v_len(2,i+1)); - } - complexgeneratereflection(&t, i+1, &taui, _state); - if( i>=1 ) - { - ae_v_cmove(&a->ptr.pp_complex[0][i+1], a->stride, &t.ptr.p_complex[2], 1, "N", ae_v_len(0,i-1)); - } - alpha = t.ptr.p_complex[1]; - e->ptr.p_double[i] = alpha.x; - if( ae_c_neq_d(taui,0) ) - { - - /* - * Apply H(I+1) from both sides to A - */ - a->ptr.pp_complex[i][i+1] = ae_complex_from_d(1); - - /* - * Compute x := tau * A * v storing x in TAU - */ - ae_v_cmove(&t.ptr.p_complex[1], 1, &a->ptr.pp_complex[0][i+1], a->stride, "N", ae_v_len(1,i+1)); - hermitianmatrixvectormultiply(a, isupper, 0, i, &t, taui, &t2, _state); - ae_v_cmove(&tau->ptr.p_complex[0], 1, &t2.ptr.p_complex[1], 1, "N", ae_v_len(0,i)); - - /* - * Compute w := x - 1/2 * tau * (x'*v) * v - */ - v = ae_v_cdotproduct(&tau->ptr.p_complex[0], 1, "Conj", &a->ptr.pp_complex[0][i+1], a->stride, "N", ae_v_len(0,i)); - alpha = ae_c_neg(ae_c_mul(ae_c_mul_d(taui,0.5),v)); - ae_v_caddc(&tau->ptr.p_complex[0], 1, &a->ptr.pp_complex[0][i+1], a->stride, "N", ae_v_len(0,i), alpha); - - /* - * Apply the transformation as a rank-2 update: - * A := A - v * w' - w * v' - */ - ae_v_cmove(&t.ptr.p_complex[1], 1, &a->ptr.pp_complex[0][i+1], a->stride, "N", ae_v_len(1,i+1)); - ae_v_cmove(&t3.ptr.p_complex[1], 1, &tau->ptr.p_complex[0], 1, "N", ae_v_len(1,i+1)); - hermitianrank2update(a, isupper, 0, i, &t, &t3, &t2, ae_complex_from_d(-1), _state); - } - else - { - a->ptr.pp_complex[i][i] = ae_complex_from_d(a->ptr.pp_complex[i][i].x); - } - a->ptr.pp_complex[i][i+1] = ae_complex_from_d(e->ptr.p_double[i]); - d->ptr.p_double[i+1] = a->ptr.pp_complex[i+1][i+1].x; - tau->ptr.p_complex[i] = taui; - } - d->ptr.p_double[0] = a->ptr.pp_complex[0][0].x; - } - else - { - - /* - * Reduce the lower triangle of A - */ - a->ptr.pp_complex[0][0] = ae_complex_from_d(a->ptr.pp_complex[0][0].x); - for(i=0; i<=n-2; i++) - { - - /* - * Generate elementary reflector H = I - tau * v * v' - */ - ae_v_cmove(&t.ptr.p_complex[1], 1, &a->ptr.pp_complex[i+1][i], a->stride, "N", ae_v_len(1,n-i-1)); - complexgeneratereflection(&t, n-i-1, &taui, _state); - ae_v_cmove(&a->ptr.pp_complex[i+1][i], a->stride, &t.ptr.p_complex[1], 1, "N", ae_v_len(i+1,n-1)); - e->ptr.p_double[i] = a->ptr.pp_complex[i+1][i].x; - if( ae_c_neq_d(taui,0) ) - { - - /* - * Apply H(i) from both sides to A(i+1:n,i+1:n) - */ - a->ptr.pp_complex[i+1][i] = ae_complex_from_d(1); - - /* - * Compute x := tau * A * v storing y in TAU - */ - ae_v_cmove(&t.ptr.p_complex[1], 1, &a->ptr.pp_complex[i+1][i], a->stride, "N", ae_v_len(1,n-i-1)); - hermitianmatrixvectormultiply(a, isupper, i+1, n-1, &t, taui, &t2, _state); - ae_v_cmove(&tau->ptr.p_complex[i], 1, &t2.ptr.p_complex[1], 1, "N", ae_v_len(i,n-2)); - - /* - * Compute w := x - 1/2 * tau * (x'*v) * v - */ - v = ae_v_cdotproduct(&tau->ptr.p_complex[i], 1, "Conj", &a->ptr.pp_complex[i+1][i], a->stride, "N", ae_v_len(i,n-2)); - alpha = ae_c_neg(ae_c_mul(ae_c_mul_d(taui,0.5),v)); - ae_v_caddc(&tau->ptr.p_complex[i], 1, &a->ptr.pp_complex[i+1][i], a->stride, "N", ae_v_len(i,n-2), alpha); - - /* - * Apply the transformation as a rank-2 update: - * A := A - v * w' - w * v' - */ - ae_v_cmove(&t.ptr.p_complex[1], 1, &a->ptr.pp_complex[i+1][i], a->stride, "N", ae_v_len(1,n-i-1)); - ae_v_cmove(&t2.ptr.p_complex[1], 1, &tau->ptr.p_complex[i], 1, "N", ae_v_len(1,n-i-1)); - hermitianrank2update(a, isupper, i+1, n-1, &t, &t2, &t3, ae_complex_from_d(-1), _state); - } - else - { - a->ptr.pp_complex[i+1][i+1] = ae_complex_from_d(a->ptr.pp_complex[i+1][i+1].x); - } - a->ptr.pp_complex[i+1][i] = ae_complex_from_d(e->ptr.p_double[i]); - d->ptr.p_double[i] = a->ptr.pp_complex[i][i].x; - tau->ptr.p_complex[i] = taui; - } - d->ptr.p_double[n-1] = a->ptr.pp_complex[n-1][n-1].x; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Unpacking matrix Q which reduces a Hermitian matrix to a real tridiagonal -form. - -Input parameters: - A - the result of a HMatrixTD subroutine - N - size of matrix A. - IsUpper - storage format (a parameter of HMatrixTD subroutine) - Tau - the result of a HMatrixTD subroutine - -Output parameters: - Q - transformation matrix. - array with elements [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void hmatrixtdunpackq(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tau, - /* Complex */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector v; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&v, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&work, 0, DT_COMPLEX, _state, ae_true); - - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - ae_matrix_set_length(q, n-1+1, n-1+1, _state); - ae_vector_set_length(&v, n+1, _state); - ae_vector_set_length(&work, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - q->ptr.pp_complex[i][j] = ae_complex_from_d(1); - } - else - { - q->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - } - - /* - * unpack Q - */ - if( isupper ) - { - for(i=0; i<=n-2; i++) - { - - /* - * Apply H(i) - */ - ae_v_cmove(&v.ptr.p_complex[1], 1, &a->ptr.pp_complex[0][i+1], a->stride, "N", ae_v_len(1,i+1)); - v.ptr.p_complex[i+1] = ae_complex_from_d(1); - complexapplyreflectionfromtheleft(q, tau->ptr.p_complex[i], &v, 0, i, 0, n-1, &work, _state); - } - } - else - { - for(i=n-2; i>=0; i--) - { - - /* - * Apply H(i) - */ - ae_v_cmove(&v.ptr.p_complex[1], 1, &a->ptr.pp_complex[i+1][i], a->stride, "N", ae_v_len(1,n-i-1)); - v.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheleft(q, tau->ptr.p_complex[i], &v, i+1, n-1, 0, n-1, &work, _state); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Base case for complex QR - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -static void ortfac_cmatrixqrbasecase(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* work, - /* Complex */ ae_vector* t, - /* Complex */ ae_vector* tau, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_int_t mmi; - ae_int_t minmn; - ae_complex tmp; - - - minmn = ae_minint(m, n, _state); - if( minmn<=0 ) - { - return; - } - - /* - * Test the input arguments - */ - k = ae_minint(m, n, _state); - for(i=0; i<=k-1; i++) - { - - /* - * Generate elementary reflector H(i) to annihilate A(i+1:m,i) - */ - mmi = m-i; - ae_v_cmove(&t->ptr.p_complex[1], 1, &a->ptr.pp_complex[i][i], a->stride, "N", ae_v_len(1,mmi)); - complexgeneratereflection(t, mmi, &tmp, _state); - tau->ptr.p_complex[i] = tmp; - ae_v_cmove(&a->ptr.pp_complex[i][i], a->stride, &t->ptr.p_complex[1], 1, "N", ae_v_len(i,m-1)); - t->ptr.p_complex[1] = ae_complex_from_d(1); - if( iptr.p_complex[i], _state), t, i, m-1, i+1, n-1, work, _state); - } - } -} - - -/************************************************************************* -Base case for complex LQ - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -static void ortfac_cmatrixlqbasecase(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* work, - /* Complex */ ae_vector* t, - /* Complex */ ae_vector* tau, - ae_state *_state) -{ - ae_int_t i; - ae_int_t minmn; - ae_complex tmp; - - - minmn = ae_minint(m, n, _state); - if( minmn<=0 ) - { - return; - } - - /* - * Test the input arguments - */ - for(i=0; i<=minmn-1; i++) - { - - /* - * Generate elementary reflector H(i) - * - * NOTE: ComplexGenerateReflection() generates left reflector, - * i.e. H which reduces x by applyiong from the left, but we - * need RIGHT reflector. So we replace H=E-tau*v*v' by H^H, - * which changes v to conj(v). - */ - ae_v_cmove(&t->ptr.p_complex[1], 1, &a->ptr.pp_complex[i][i], 1, "Conj", ae_v_len(1,n-i)); - complexgeneratereflection(t, n-i, &tmp, _state); - tau->ptr.p_complex[i] = tmp; - ae_v_cmove(&a->ptr.pp_complex[i][i], 1, &t->ptr.p_complex[1], 1, "Conj", ae_v_len(i,n-1)); - t->ptr.p_complex[1] = ae_complex_from_d(1); - if( iptr.p_complex[i], t, i+1, m-1, i, n-1, work, _state); - } - } -} - - -/************************************************************************* -Generate block reflector: -* fill unused parts of reflectors matrix by zeros -* fill diagonal of reflectors matrix by ones -* generate triangular factor T - -PARAMETERS: - A - either LengthA*BlockSize (if ColumnwiseA) or - BlockSize*LengthA (if not ColumnwiseA) matrix of - elementary reflectors. - Modified on exit. - Tau - scalar factors - ColumnwiseA - reflectors are stored in rows or in columns - LengthA - length of largest reflector - BlockSize - number of reflectors - T - array[BlockSize,2*BlockSize]. Left BlockSize*BlockSize - submatrix stores triangular factor on exit. - WORK - array[BlockSize] - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -static void ortfac_rmatrixblockreflector(/* Real */ ae_matrix* a, - /* Real */ ae_vector* tau, - ae_bool columnwisea, - ae_int_t lengtha, - ae_int_t blocksize, - /* Real */ ae_matrix* t, - /* Real */ ae_vector* work, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k; - double v; - - - - /* - * fill beginning of new column with zeros, - * load 1.0 in the first non-zero element - */ - for(k=0; k<=blocksize-1; k++) - { - if( columnwisea ) - { - for(i=0; i<=k-1; i++) - { - a->ptr.pp_double[i][k] = 0; - } - } - else - { - for(i=0; i<=k-1; i++) - { - a->ptr.pp_double[k][i] = 0; - } - } - a->ptr.pp_double[k][k] = 1; - } - - /* - * Calculate Gram matrix of A - */ - for(i=0; i<=blocksize-1; i++) - { - for(j=0; j<=blocksize-1; j++) - { - t->ptr.pp_double[i][blocksize+j] = 0; - } - } - for(k=0; k<=lengtha-1; k++) - { - for(j=1; j<=blocksize-1; j++) - { - if( columnwisea ) - { - v = a->ptr.pp_double[k][j]; - if( ae_fp_neq(v,0) ) - { - ae_v_addd(&t->ptr.pp_double[j][blocksize], 1, &a->ptr.pp_double[k][0], 1, ae_v_len(blocksize,blocksize+j-1), v); - } - } - else - { - v = a->ptr.pp_double[j][k]; - if( ae_fp_neq(v,0) ) - { - ae_v_addd(&t->ptr.pp_double[j][blocksize], 1, &a->ptr.pp_double[0][k], a->stride, ae_v_len(blocksize,blocksize+j-1), v); - } - } - } - } - - /* - * Prepare Y (stored in TmpA) and T (stored in TmpT) - */ - for(k=0; k<=blocksize-1; k++) - { - - /* - * fill non-zero part of T, use pre-calculated Gram matrix - */ - ae_v_move(&work->ptr.p_double[0], 1, &t->ptr.pp_double[k][blocksize], 1, ae_v_len(0,k-1)); - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&t->ptr.pp_double[i][i], 1, &work->ptr.p_double[i], 1, ae_v_len(i,k-1)); - t->ptr.pp_double[i][k] = -tau->ptr.p_double[k]*v; - } - t->ptr.pp_double[k][k] = -tau->ptr.p_double[k]; - - /* - * Rest of T is filled by zeros - */ - for(i=k+1; i<=blocksize-1; i++) - { - t->ptr.pp_double[i][k] = 0; - } - } -} - - -/************************************************************************* -Generate block reflector (complex): -* fill unused parts of reflectors matrix by zeros -* fill diagonal of reflectors matrix by ones -* generate triangular factor T - - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -static void ortfac_cmatrixblockreflector(/* Complex */ ae_matrix* a, - /* Complex */ ae_vector* tau, - ae_bool columnwisea, - ae_int_t lengtha, - ae_int_t blocksize, - /* Complex */ ae_matrix* t, - /* Complex */ ae_vector* work, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - ae_complex v; - - - - /* - * Prepare Y (stored in TmpA) and T (stored in TmpT) - */ - for(k=0; k<=blocksize-1; k++) - { - - /* - * fill beginning of new column with zeros, - * load 1.0 in the first non-zero element - */ - if( columnwisea ) - { - for(i=0; i<=k-1; i++) - { - a->ptr.pp_complex[i][k] = ae_complex_from_d(0); - } - } - else - { - for(i=0; i<=k-1; i++) - { - a->ptr.pp_complex[k][i] = ae_complex_from_d(0); - } - } - a->ptr.pp_complex[k][k] = ae_complex_from_d(1); - - /* - * fill non-zero part of T, - */ - for(i=0; i<=k-1; i++) - { - if( columnwisea ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[k][i], a->stride, "Conj", &a->ptr.pp_complex[k][k], a->stride, "N", ae_v_len(k,lengtha-1)); - } - else - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[i][k], 1, "N", &a->ptr.pp_complex[k][k], 1, "Conj", ae_v_len(k,lengtha-1)); - } - work->ptr.p_complex[i] = v; - } - for(i=0; i<=k-1; i++) - { - v = ae_v_cdotproduct(&t->ptr.pp_complex[i][i], 1, "N", &work->ptr.p_complex[i], 1, "N", ae_v_len(i,k-1)); - t->ptr.pp_complex[i][k] = ae_c_neg(ae_c_mul(tau->ptr.p_complex[k],v)); - } - t->ptr.pp_complex[k][k] = ae_c_neg(tau->ptr.p_complex[k]); - - /* - * Rest of T is filled by zeros - */ - for(i=k+1; i<=blocksize-1; i++) - { - t->ptr.pp_complex[i][k] = ae_complex_from_d(0); - } - } -} - - - - -/************************************************************************* -Singular value decomposition of a bidiagonal matrix (extended algorithm) - -The algorithm performs the singular value decomposition of a bidiagonal -matrix B (upper or lower) representing it as B = Q*S*P^T, where Q and P - -orthogonal matrices, S - diagonal matrix with non-negative elements on the -main diagonal, in descending order. - -The algorithm finds singular values. In addition, the algorithm can -calculate matrices Q and P (more precisely, not the matrices, but their -product with given matrices U and VT - U*Q and (P^T)*VT)). Of course, -matrices U and VT can be of any type, including identity. Furthermore, the -algorithm can calculate Q'*C (this product is calculated more effectively -than U*Q, because this calculation operates with rows instead of matrix -columns). - -The feature of the algorithm is its ability to find all singular values -including those which are arbitrarily close to 0 with relative accuracy -close to machine precision. If the parameter IsFractionalAccuracyRequired -is set to True, all singular values will have high relative accuracy close -to machine precision. If the parameter is set to False, only the biggest -singular value will have relative accuracy close to machine precision. -The absolute error of other singular values is equal to the absolute error -of the biggest singular value. - -Input parameters: - D - main diagonal of matrix B. - Array whose index ranges within [0..N-1]. - E - superdiagonal (or subdiagonal) of matrix B. - Array whose index ranges within [0..N-2]. - N - size of matrix B. - IsUpper - True, if the matrix is upper bidiagonal. - IsFractionalAccuracyRequired - - THIS PARAMETER IS IGNORED SINCE ALGLIB 3.5.0 - SINGULAR VALUES ARE ALWAYS SEARCHED WITH HIGH ACCURACY. - U - matrix to be multiplied by Q. - Array whose indexes range within [0..NRU-1, 0..N-1]. - The matrix can be bigger, in that case only the submatrix - [0..NRU-1, 0..N-1] will be multiplied by Q. - NRU - number of rows in matrix U. - C - matrix to be multiplied by Q'. - Array whose indexes range within [0..N-1, 0..NCC-1]. - The matrix can be bigger, in that case only the submatrix - [0..N-1, 0..NCC-1] will be multiplied by Q'. - NCC - number of columns in matrix C. - VT - matrix to be multiplied by P^T. - Array whose indexes range within [0..N-1, 0..NCVT-1]. - The matrix can be bigger, in that case only the submatrix - [0..N-1, 0..NCVT-1] will be multiplied by P^T. - NCVT - number of columns in matrix VT. - -Output parameters: - D - singular values of matrix B in descending order. - U - if NRU>0, contains matrix U*Q. - VT - if NCVT>0, contains matrix (P^T)*VT. - C - if NCC>0, contains matrix Q'*C. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - -Additional information: - The type of convergence is controlled by the internal parameter TOL. - If the parameter is greater than 0, the singular values will have - relative accuracy TOL. If TOL<0, the singular values will have - absolute accuracy ABS(TOL)*norm(B). - By default, |TOL| falls within the range of 10*Epsilon and 100*Epsilon, - where Epsilon is the machine precision. It is not recommended to use - TOL less than 10*Epsilon since this will considerably slow down the - algorithm and may not lead to error decreasing. -History: - * 31 March, 2007. - changed MAXITR from 6 to 12. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1999. -*************************************************************************/ -ae_bool rmatrixbdsvd(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_bool isupper, - ae_bool isfractionalaccuracyrequired, - /* Real */ ae_matrix* u, - ae_int_t nru, - /* Real */ ae_matrix* c, - ae_int_t ncc, - /* Real */ ae_matrix* vt, - ae_int_t ncvt, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _e; - ae_vector d1; - ae_vector e1; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - ae_vector_init(&d1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e1, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&d1, n+1, _state); - ae_v_move(&d1.ptr.p_double[1], 1, &d->ptr.p_double[0], 1, ae_v_len(1,n)); - if( n>1 ) - { - ae_vector_set_length(&e1, n-1+1, _state); - ae_v_move(&e1.ptr.p_double[1], 1, &e->ptr.p_double[0], 1, ae_v_len(1,n-1)); - } - result = bdsvd_bidiagonalsvddecompositioninternal(&d1, &e1, n, isupper, isfractionalaccuracyrequired, u, 0, nru, c, 0, ncc, vt, 0, ncvt, _state); - ae_v_move(&d->ptr.p_double[0], 1, &d1.ptr.p_double[1], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); - return result; -} - - -ae_bool bidiagonalsvddecomposition(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_bool isupper, - ae_bool isfractionalaccuracyrequired, - /* Real */ ae_matrix* u, - ae_int_t nru, - /* Real */ ae_matrix* c, - ae_int_t ncc, - /* Real */ ae_matrix* vt, - ae_int_t ncvt, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _e; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - - result = bdsvd_bidiagonalsvddecompositioninternal(d, e, n, isupper, isfractionalaccuracyrequired, u, 1, nru, c, 1, ncc, vt, 1, ncvt, _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Internal working subroutine for bidiagonal decomposition -*************************************************************************/ -static ae_bool bdsvd_bidiagonalsvddecompositioninternal(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_bool isupper, - ae_bool isfractionalaccuracyrequired, - /* Real */ ae_matrix* u, - ae_int_t ustart, - ae_int_t nru, - /* Real */ ae_matrix* c, - ae_int_t cstart, - ae_int_t ncc, - /* Real */ ae_matrix* vt, - ae_int_t vstart, - ae_int_t ncvt, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _e; - ae_int_t i; - ae_int_t idir; - ae_int_t isub; - ae_int_t iter; - ae_int_t j; - ae_int_t ll; - ae_int_t lll; - ae_int_t m; - ae_int_t maxit; - ae_int_t oldll; - ae_int_t oldm; - double abse; - double abss; - double cosl; - double cosr; - double cs; - double eps; - double f; - double g; - double h; - double mu; - double oldcs; - double oldsn; - double r; - double shift; - double sigmn; - double sigmx; - double sinl; - double sinr; - double sll; - double smax; - double smin; - double sminl; - double sminoa; - double sn; - double thresh; - double tol; - double tolmul; - double unfl; - ae_vector work0; - ae_vector work1; - ae_vector work2; - ae_vector work3; - ae_int_t maxitr; - ae_bool matrixsplitflag; - ae_bool iterflag; - ae_vector utemp; - ae_vector vttemp; - ae_vector ctemp; - ae_vector etemp; - ae_bool fwddir; - double tmp; - ae_int_t mm1; - ae_int_t mm0; - ae_bool bchangedir; - ae_int_t uend; - ae_int_t cend; - ae_int_t vend; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - ae_vector_init(&work0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&utemp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&vttemp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ctemp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&etemp, 0, DT_REAL, _state, ae_true); - - result = ae_true; - if( n==0 ) - { - ae_frame_leave(_state); - return result; - } - if( n==1 ) - { - if( ae_fp_less(d->ptr.p_double[1],0) ) - { - d->ptr.p_double[1] = -d->ptr.p_double[1]; - if( ncvt>0 ) - { - ae_v_muld(&vt->ptr.pp_double[vstart][vstart], 1, ae_v_len(vstart,vstart+ncvt-1), -1); - } - } - ae_frame_leave(_state); - return result; - } - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - ll = 0; - oldsn = 0; - - /* - * init - */ - ae_vector_set_length(&work0, n-1+1, _state); - ae_vector_set_length(&work1, n-1+1, _state); - ae_vector_set_length(&work2, n-1+1, _state); - ae_vector_set_length(&work3, n-1+1, _state); - uend = ustart+ae_maxint(nru-1, 0, _state); - vend = vstart+ae_maxint(ncvt-1, 0, _state); - cend = cstart+ae_maxint(ncc-1, 0, _state); - ae_vector_set_length(&utemp, uend+1, _state); - ae_vector_set_length(&vttemp, vend+1, _state); - ae_vector_set_length(&ctemp, cend+1, _state); - maxitr = 12; - fwddir = ae_true; - - /* - * resize E from N-1 to N - */ - ae_vector_set_length(&etemp, n+1, _state); - for(i=1; i<=n-1; i++) - { - etemp.ptr.p_double[i] = e->ptr.p_double[i]; - } - ae_vector_set_length(e, n+1, _state); - for(i=1; i<=n-1; i++) - { - e->ptr.p_double[i] = etemp.ptr.p_double[i]; - } - e->ptr.p_double[n] = 0; - idir = 0; - - /* - * Get machine constants - */ - eps = ae_machineepsilon; - unfl = ae_minrealnumber; - - /* - * If matrix lower bidiagonal, rotate to be upper bidiagonal - * by applying Givens rotations on the left - */ - if( !isupper ) - { - for(i=1; i<=n-1; i++) - { - generaterotation(d->ptr.p_double[i], e->ptr.p_double[i], &cs, &sn, &r, _state); - d->ptr.p_double[i] = r; - e->ptr.p_double[i] = sn*d->ptr.p_double[i+1]; - d->ptr.p_double[i+1] = cs*d->ptr.p_double[i+1]; - work0.ptr.p_double[i] = cs; - work1.ptr.p_double[i] = sn; - } - - /* - * Update singular vectors if desired - */ - if( nru>0 ) - { - applyrotationsfromtheright(fwddir, ustart, uend, 1+ustart-1, n+ustart-1, &work0, &work1, u, &utemp, _state); - } - if( ncc>0 ) - { - applyrotationsfromtheleft(fwddir, 1+cstart-1, n+cstart-1, cstart, cend, &work0, &work1, c, &ctemp, _state); - } - } - - /* - * Compute singular values to relative accuracy TOL - * (By setting TOL to be negative, algorithm will compute - * singular values to absolute accuracy ABS(TOL)*norm(input matrix)) - */ - tolmul = ae_maxreal(10, ae_minreal(100, ae_pow(eps, -0.125, _state), _state), _state); - tol = tolmul*eps; - - /* - * Compute approximate maximum, minimum singular values - */ - smax = 0; - for(i=1; i<=n; i++) - { - smax = ae_maxreal(smax, ae_fabs(d->ptr.p_double[i], _state), _state); - } - for(i=1; i<=n-1; i++) - { - smax = ae_maxreal(smax, ae_fabs(e->ptr.p_double[i], _state), _state); - } - sminl = 0; - if( ae_fp_greater_eq(tol,0) ) - { - - /* - * Relative accuracy desired - */ - sminoa = ae_fabs(d->ptr.p_double[1], _state); - if( ae_fp_neq(sminoa,0) ) - { - mu = sminoa; - for(i=2; i<=n; i++) - { - mu = ae_fabs(d->ptr.p_double[i], _state)*(mu/(mu+ae_fabs(e->ptr.p_double[i-1], _state))); - sminoa = ae_minreal(sminoa, mu, _state); - if( ae_fp_eq(sminoa,0) ) - { - break; - } - } - } - sminoa = sminoa/ae_sqrt(n, _state); - thresh = ae_maxreal(tol*sminoa, maxitr*n*n*unfl, _state); - } - else - { - - /* - * Absolute accuracy desired - */ - thresh = ae_maxreal(ae_fabs(tol, _state)*smax, maxitr*n*n*unfl, _state); - } - - /* - * Prepare for main iteration loop for the singular values - * (MAXIT is the maximum number of passes through the inner - * loop permitted before nonconvergence signalled.) - */ - maxit = maxitr*n*n; - iter = 0; - oldll = -1; - oldm = -1; - - /* - * M points to last element of unconverged part of matrix - */ - m = n; - - /* - * Begin main iteration loop - */ - for(;;) - { - - /* - * Check for convergence or exceeding iteration count - */ - if( m<=1 ) - { - break; - } - if( iter>maxit ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Find diagonal block of matrix to work on - */ - if( ae_fp_less(tol,0)&&ae_fp_less_eq(ae_fabs(d->ptr.p_double[m], _state),thresh) ) - { - d->ptr.p_double[m] = 0; - } - smax = ae_fabs(d->ptr.p_double[m], _state); - smin = smax; - matrixsplitflag = ae_false; - for(lll=1; lll<=m-1; lll++) - { - ll = m-lll; - abss = ae_fabs(d->ptr.p_double[ll], _state); - abse = ae_fabs(e->ptr.p_double[ll], _state); - if( ae_fp_less(tol,0)&&ae_fp_less_eq(abss,thresh) ) - { - d->ptr.p_double[ll] = 0; - } - if( ae_fp_less_eq(abse,thresh) ) - { - matrixsplitflag = ae_true; - break; - } - smin = ae_minreal(smin, abss, _state); - smax = ae_maxreal(smax, ae_maxreal(abss, abse, _state), _state); - } - if( !matrixsplitflag ) - { - ll = 0; - } - else - { - - /* - * Matrix splits since E(LL) = 0 - */ - e->ptr.p_double[ll] = 0; - if( ll==m-1 ) - { - - /* - * Convergence of bottom singular value, return to top of loop - */ - m = m-1; - continue; - } - } - ll = ll+1; - - /* - * E(LL) through E(M-1) are nonzero, E(LL-1) is zero - */ - if( ll==m-1 ) - { - - /* - * 2 by 2 block, handle separately - */ - bdsvd_svdv2x2(d->ptr.p_double[m-1], e->ptr.p_double[m-1], d->ptr.p_double[m], &sigmn, &sigmx, &sinr, &cosr, &sinl, &cosl, _state); - d->ptr.p_double[m-1] = sigmx; - e->ptr.p_double[m-1] = 0; - d->ptr.p_double[m] = sigmn; - - /* - * Compute singular vectors, if desired - */ - if( ncvt>0 ) - { - mm0 = m+(vstart-1); - mm1 = m-1+(vstart-1); - ae_v_moved(&vttemp.ptr.p_double[vstart], 1, &vt->ptr.pp_double[mm1][vstart], 1, ae_v_len(vstart,vend), cosr); - ae_v_addd(&vttemp.ptr.p_double[vstart], 1, &vt->ptr.pp_double[mm0][vstart], 1, ae_v_len(vstart,vend), sinr); - ae_v_muld(&vt->ptr.pp_double[mm0][vstart], 1, ae_v_len(vstart,vend), cosr); - ae_v_subd(&vt->ptr.pp_double[mm0][vstart], 1, &vt->ptr.pp_double[mm1][vstart], 1, ae_v_len(vstart,vend), sinr); - ae_v_move(&vt->ptr.pp_double[mm1][vstart], 1, &vttemp.ptr.p_double[vstart], 1, ae_v_len(vstart,vend)); - } - if( nru>0 ) - { - mm0 = m+ustart-1; - mm1 = m-1+ustart-1; - ae_v_moved(&utemp.ptr.p_double[ustart], 1, &u->ptr.pp_double[ustart][mm1], u->stride, ae_v_len(ustart,uend), cosl); - ae_v_addd(&utemp.ptr.p_double[ustart], 1, &u->ptr.pp_double[ustart][mm0], u->stride, ae_v_len(ustart,uend), sinl); - ae_v_muld(&u->ptr.pp_double[ustart][mm0], u->stride, ae_v_len(ustart,uend), cosl); - ae_v_subd(&u->ptr.pp_double[ustart][mm0], u->stride, &u->ptr.pp_double[ustart][mm1], u->stride, ae_v_len(ustart,uend), sinl); - ae_v_move(&u->ptr.pp_double[ustart][mm1], u->stride, &utemp.ptr.p_double[ustart], 1, ae_v_len(ustart,uend)); - } - if( ncc>0 ) - { - mm0 = m+cstart-1; - mm1 = m-1+cstart-1; - ae_v_moved(&ctemp.ptr.p_double[cstart], 1, &c->ptr.pp_double[mm1][cstart], 1, ae_v_len(cstart,cend), cosl); - ae_v_addd(&ctemp.ptr.p_double[cstart], 1, &c->ptr.pp_double[mm0][cstart], 1, ae_v_len(cstart,cend), sinl); - ae_v_muld(&c->ptr.pp_double[mm0][cstart], 1, ae_v_len(cstart,cend), cosl); - ae_v_subd(&c->ptr.pp_double[mm0][cstart], 1, &c->ptr.pp_double[mm1][cstart], 1, ae_v_len(cstart,cend), sinl); - ae_v_move(&c->ptr.pp_double[mm1][cstart], 1, &ctemp.ptr.p_double[cstart], 1, ae_v_len(cstart,cend)); - } - m = m-2; - continue; - } - - /* - * If working on new submatrix, choose shift direction - * (from larger end diagonal element towards smaller) - * - * Previously was - * "if (LL>OLDM) or (M - * Very strange that LAPACK still contains it. - */ - bchangedir = ae_false; - if( idir==1&&ae_fp_less(ae_fabs(d->ptr.p_double[ll], _state),1.0E-3*ae_fabs(d->ptr.p_double[m], _state)) ) - { - bchangedir = ae_true; - } - if( idir==2&&ae_fp_less(ae_fabs(d->ptr.p_double[m], _state),1.0E-3*ae_fabs(d->ptr.p_double[ll], _state)) ) - { - bchangedir = ae_true; - } - if( (ll!=oldll||m!=oldm)||bchangedir ) - { - if( ae_fp_greater_eq(ae_fabs(d->ptr.p_double[ll], _state),ae_fabs(d->ptr.p_double[m], _state)) ) - { - - /* - * Chase bulge from top (big end) to bottom (small end) - */ - idir = 1; - } - else - { - - /* - * Chase bulge from bottom (big end) to top (small end) - */ - idir = 2; - } - } - - /* - * Apply convergence tests - */ - if( idir==1 ) - { - - /* - * Run convergence test in forward direction - * First apply standard test to bottom of matrix - */ - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[m-1], _state),ae_fabs(tol, _state)*ae_fabs(d->ptr.p_double[m], _state))||(ae_fp_less(tol,0)&&ae_fp_less_eq(ae_fabs(e->ptr.p_double[m-1], _state),thresh)) ) - { - e->ptr.p_double[m-1] = 0; - continue; - } - if( ae_fp_greater_eq(tol,0) ) - { - - /* - * If relative accuracy desired, - * apply convergence criterion forward - */ - mu = ae_fabs(d->ptr.p_double[ll], _state); - sminl = mu; - iterflag = ae_false; - for(lll=ll; lll<=m-1; lll++) - { - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[lll], _state),tol*mu) ) - { - e->ptr.p_double[lll] = 0; - iterflag = ae_true; - break; - } - mu = ae_fabs(d->ptr.p_double[lll+1], _state)*(mu/(mu+ae_fabs(e->ptr.p_double[lll], _state))); - sminl = ae_minreal(sminl, mu, _state); - } - if( iterflag ) - { - continue; - } - } - } - else - { - - /* - * Run convergence test in backward direction - * First apply standard test to top of matrix - */ - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[ll], _state),ae_fabs(tol, _state)*ae_fabs(d->ptr.p_double[ll], _state))||(ae_fp_less(tol,0)&&ae_fp_less_eq(ae_fabs(e->ptr.p_double[ll], _state),thresh)) ) - { - e->ptr.p_double[ll] = 0; - continue; - } - if( ae_fp_greater_eq(tol,0) ) - { - - /* - * If relative accuracy desired, - * apply convergence criterion backward - */ - mu = ae_fabs(d->ptr.p_double[m], _state); - sminl = mu; - iterflag = ae_false; - for(lll=m-1; lll>=ll; lll--) - { - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[lll], _state),tol*mu) ) - { - e->ptr.p_double[lll] = 0; - iterflag = ae_true; - break; - } - mu = ae_fabs(d->ptr.p_double[lll], _state)*(mu/(mu+ae_fabs(e->ptr.p_double[lll], _state))); - sminl = ae_minreal(sminl, mu, _state); - } - if( iterflag ) - { - continue; - } - } - } - oldll = ll; - oldm = m; - - /* - * Compute shift. First, test if shifting would ruin relative - * accuracy, and if so set the shift to zero. - */ - if( ae_fp_greater_eq(tol,0)&&ae_fp_less_eq(n*tol*(sminl/smax),ae_maxreal(eps, 0.01*tol, _state)) ) - { - - /* - * Use a zero shift to avoid loss of relative accuracy - */ - shift = 0; - } - else - { - - /* - * Compute the shift from 2-by-2 block at end of matrix - */ - if( idir==1 ) - { - sll = ae_fabs(d->ptr.p_double[ll], _state); - bdsvd_svd2x2(d->ptr.p_double[m-1], e->ptr.p_double[m-1], d->ptr.p_double[m], &shift, &r, _state); - } - else - { - sll = ae_fabs(d->ptr.p_double[m], _state); - bdsvd_svd2x2(d->ptr.p_double[ll], e->ptr.p_double[ll], d->ptr.p_double[ll+1], &shift, &r, _state); - } - - /* - * Test if shift negligible, and if so set to zero - */ - if( ae_fp_greater(sll,0) ) - { - if( ae_fp_less(ae_sqr(shift/sll, _state),eps) ) - { - shift = 0; - } - } - } - - /* - * Increment iteration count - */ - iter = iter+m-ll; - - /* - * If SHIFT = 0, do simplified QR iteration - */ - if( ae_fp_eq(shift,0) ) - { - if( idir==1 ) - { - - /* - * Chase bulge from top to bottom - * Save cosines and sines for later singular vector updates - */ - cs = 1; - oldcs = 1; - for(i=ll; i<=m-1; i++) - { - generaterotation(d->ptr.p_double[i]*cs, e->ptr.p_double[i], &cs, &sn, &r, _state); - if( i>ll ) - { - e->ptr.p_double[i-1] = oldsn*r; - } - generaterotation(oldcs*r, d->ptr.p_double[i+1]*sn, &oldcs, &oldsn, &tmp, _state); - d->ptr.p_double[i] = tmp; - work0.ptr.p_double[i-ll+1] = cs; - work1.ptr.p_double[i-ll+1] = sn; - work2.ptr.p_double[i-ll+1] = oldcs; - work3.ptr.p_double[i-ll+1] = oldsn; - } - h = d->ptr.p_double[m]*cs; - d->ptr.p_double[m] = h*oldcs; - e->ptr.p_double[m-1] = h*oldsn; - - /* - * Update singular vectors - */ - if( ncvt>0 ) - { - applyrotationsfromtheleft(fwddir, ll+vstart-1, m+vstart-1, vstart, vend, &work0, &work1, vt, &vttemp, _state); - } - if( nru>0 ) - { - applyrotationsfromtheright(fwddir, ustart, uend, ll+ustart-1, m+ustart-1, &work2, &work3, u, &utemp, _state); - } - if( ncc>0 ) - { - applyrotationsfromtheleft(fwddir, ll+cstart-1, m+cstart-1, cstart, cend, &work2, &work3, c, &ctemp, _state); - } - - /* - * Test convergence - */ - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[m-1], _state),thresh) ) - { - e->ptr.p_double[m-1] = 0; - } - } - else - { - - /* - * Chase bulge from bottom to top - * Save cosines and sines for later singular vector updates - */ - cs = 1; - oldcs = 1; - for(i=m; i>=ll+1; i--) - { - generaterotation(d->ptr.p_double[i]*cs, e->ptr.p_double[i-1], &cs, &sn, &r, _state); - if( iptr.p_double[i] = oldsn*r; - } - generaterotation(oldcs*r, d->ptr.p_double[i-1]*sn, &oldcs, &oldsn, &tmp, _state); - d->ptr.p_double[i] = tmp; - work0.ptr.p_double[i-ll] = cs; - work1.ptr.p_double[i-ll] = -sn; - work2.ptr.p_double[i-ll] = oldcs; - work3.ptr.p_double[i-ll] = -oldsn; - } - h = d->ptr.p_double[ll]*cs; - d->ptr.p_double[ll] = h*oldcs; - e->ptr.p_double[ll] = h*oldsn; - - /* - * Update singular vectors - */ - if( ncvt>0 ) - { - applyrotationsfromtheleft(!fwddir, ll+vstart-1, m+vstart-1, vstart, vend, &work2, &work3, vt, &vttemp, _state); - } - if( nru>0 ) - { - applyrotationsfromtheright(!fwddir, ustart, uend, ll+ustart-1, m+ustart-1, &work0, &work1, u, &utemp, _state); - } - if( ncc>0 ) - { - applyrotationsfromtheleft(!fwddir, ll+cstart-1, m+cstart-1, cstart, cend, &work0, &work1, c, &ctemp, _state); - } - - /* - * Test convergence - */ - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[ll], _state),thresh) ) - { - e->ptr.p_double[ll] = 0; - } - } - } - else - { - - /* - * Use nonzero shift - */ - if( idir==1 ) - { - - /* - * Chase bulge from top to bottom - * Save cosines and sines for later singular vector updates - */ - f = (ae_fabs(d->ptr.p_double[ll], _state)-shift)*(bdsvd_extsignbdsqr(1, d->ptr.p_double[ll], _state)+shift/d->ptr.p_double[ll]); - g = e->ptr.p_double[ll]; - for(i=ll; i<=m-1; i++) - { - generaterotation(f, g, &cosr, &sinr, &r, _state); - if( i>ll ) - { - e->ptr.p_double[i-1] = r; - } - f = cosr*d->ptr.p_double[i]+sinr*e->ptr.p_double[i]; - e->ptr.p_double[i] = cosr*e->ptr.p_double[i]-sinr*d->ptr.p_double[i]; - g = sinr*d->ptr.p_double[i+1]; - d->ptr.p_double[i+1] = cosr*d->ptr.p_double[i+1]; - generaterotation(f, g, &cosl, &sinl, &r, _state); - d->ptr.p_double[i] = r; - f = cosl*e->ptr.p_double[i]+sinl*d->ptr.p_double[i+1]; - d->ptr.p_double[i+1] = cosl*d->ptr.p_double[i+1]-sinl*e->ptr.p_double[i]; - if( iptr.p_double[i+1]; - e->ptr.p_double[i+1] = cosl*e->ptr.p_double[i+1]; - } - work0.ptr.p_double[i-ll+1] = cosr; - work1.ptr.p_double[i-ll+1] = sinr; - work2.ptr.p_double[i-ll+1] = cosl; - work3.ptr.p_double[i-ll+1] = sinl; - } - e->ptr.p_double[m-1] = f; - - /* - * Update singular vectors - */ - if( ncvt>0 ) - { - applyrotationsfromtheleft(fwddir, ll+vstart-1, m+vstart-1, vstart, vend, &work0, &work1, vt, &vttemp, _state); - } - if( nru>0 ) - { - applyrotationsfromtheright(fwddir, ustart, uend, ll+ustart-1, m+ustart-1, &work2, &work3, u, &utemp, _state); - } - if( ncc>0 ) - { - applyrotationsfromtheleft(fwddir, ll+cstart-1, m+cstart-1, cstart, cend, &work2, &work3, c, &ctemp, _state); - } - - /* - * Test convergence - */ - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[m-1], _state),thresh) ) - { - e->ptr.p_double[m-1] = 0; - } - } - else - { - - /* - * Chase bulge from bottom to top - * Save cosines and sines for later singular vector updates - */ - f = (ae_fabs(d->ptr.p_double[m], _state)-shift)*(bdsvd_extsignbdsqr(1, d->ptr.p_double[m], _state)+shift/d->ptr.p_double[m]); - g = e->ptr.p_double[m-1]; - for(i=m; i>=ll+1; i--) - { - generaterotation(f, g, &cosr, &sinr, &r, _state); - if( iptr.p_double[i] = r; - } - f = cosr*d->ptr.p_double[i]+sinr*e->ptr.p_double[i-1]; - e->ptr.p_double[i-1] = cosr*e->ptr.p_double[i-1]-sinr*d->ptr.p_double[i]; - g = sinr*d->ptr.p_double[i-1]; - d->ptr.p_double[i-1] = cosr*d->ptr.p_double[i-1]; - generaterotation(f, g, &cosl, &sinl, &r, _state); - d->ptr.p_double[i] = r; - f = cosl*e->ptr.p_double[i-1]+sinl*d->ptr.p_double[i-1]; - d->ptr.p_double[i-1] = cosl*d->ptr.p_double[i-1]-sinl*e->ptr.p_double[i-1]; - if( i>ll+1 ) - { - g = sinl*e->ptr.p_double[i-2]; - e->ptr.p_double[i-2] = cosl*e->ptr.p_double[i-2]; - } - work0.ptr.p_double[i-ll] = cosr; - work1.ptr.p_double[i-ll] = -sinr; - work2.ptr.p_double[i-ll] = cosl; - work3.ptr.p_double[i-ll] = -sinl; - } - e->ptr.p_double[ll] = f; - - /* - * Test convergence - */ - if( ae_fp_less_eq(ae_fabs(e->ptr.p_double[ll], _state),thresh) ) - { - e->ptr.p_double[ll] = 0; - } - - /* - * Update singular vectors if desired - */ - if( ncvt>0 ) - { - applyrotationsfromtheleft(!fwddir, ll+vstart-1, m+vstart-1, vstart, vend, &work2, &work3, vt, &vttemp, _state); - } - if( nru>0 ) - { - applyrotationsfromtheright(!fwddir, ustart, uend, ll+ustart-1, m+ustart-1, &work0, &work1, u, &utemp, _state); - } - if( ncc>0 ) - { - applyrotationsfromtheleft(!fwddir, ll+cstart-1, m+cstart-1, cstart, cend, &work0, &work1, c, &ctemp, _state); - } - } - } - - /* - * QR iteration finished, go back and check convergence - */ - continue; - } - - /* - * All singular values converged, so make them positive - */ - for(i=1; i<=n; i++) - { - if( ae_fp_less(d->ptr.p_double[i],0) ) - { - d->ptr.p_double[i] = -d->ptr.p_double[i]; - - /* - * Change sign of singular vectors, if desired - */ - if( ncvt>0 ) - { - ae_v_muld(&vt->ptr.pp_double[i+vstart-1][vstart], 1, ae_v_len(vstart,vend), -1); - } - } - } - - /* - * Sort the singular values into decreasing order (insertion sort on - * singular values, but only one transposition per singular vector) - */ - for(i=1; i<=n-1; i++) - { - - /* - * Scan for smallest D(I) - */ - isub = 1; - smin = d->ptr.p_double[1]; - for(j=2; j<=n+1-i; j++) - { - if( ae_fp_less_eq(d->ptr.p_double[j],smin) ) - { - isub = j; - smin = d->ptr.p_double[j]; - } - } - if( isub!=n+1-i ) - { - - /* - * Swap singular values and vectors - */ - d->ptr.p_double[isub] = d->ptr.p_double[n+1-i]; - d->ptr.p_double[n+1-i] = smin; - if( ncvt>0 ) - { - j = n+1-i; - ae_v_move(&vttemp.ptr.p_double[vstart], 1, &vt->ptr.pp_double[isub+vstart-1][vstart], 1, ae_v_len(vstart,vend)); - ae_v_move(&vt->ptr.pp_double[isub+vstart-1][vstart], 1, &vt->ptr.pp_double[j+vstart-1][vstart], 1, ae_v_len(vstart,vend)); - ae_v_move(&vt->ptr.pp_double[j+vstart-1][vstart], 1, &vttemp.ptr.p_double[vstart], 1, ae_v_len(vstart,vend)); - } - if( nru>0 ) - { - j = n+1-i; - ae_v_move(&utemp.ptr.p_double[ustart], 1, &u->ptr.pp_double[ustart][isub+ustart-1], u->stride, ae_v_len(ustart,uend)); - ae_v_move(&u->ptr.pp_double[ustart][isub+ustart-1], u->stride, &u->ptr.pp_double[ustart][j+ustart-1], u->stride, ae_v_len(ustart,uend)); - ae_v_move(&u->ptr.pp_double[ustart][j+ustart-1], u->stride, &utemp.ptr.p_double[ustart], 1, ae_v_len(ustart,uend)); - } - if( ncc>0 ) - { - j = n+1-i; - ae_v_move(&ctemp.ptr.p_double[cstart], 1, &c->ptr.pp_double[isub+cstart-1][cstart], 1, ae_v_len(cstart,cend)); - ae_v_move(&c->ptr.pp_double[isub+cstart-1][cstart], 1, &c->ptr.pp_double[j+cstart-1][cstart], 1, ae_v_len(cstart,cend)); - ae_v_move(&c->ptr.pp_double[j+cstart-1][cstart], 1, &ctemp.ptr.p_double[cstart], 1, ae_v_len(cstart,cend)); - } - } - } - ae_frame_leave(_state); - return result; -} - - -static double bdsvd_extsignbdsqr(double a, double b, ae_state *_state) -{ - double result; - - - if( ae_fp_greater_eq(b,0) ) - { - result = ae_fabs(a, _state); - } - else - { - result = -ae_fabs(a, _state); - } - return result; -} - - -static void bdsvd_svd2x2(double f, - double g, - double h, - double* ssmin, - double* ssmax, - ae_state *_state) -{ - double aas; - double at; - double au; - double c; - double fa; - double fhmn; - double fhmx; - double ga; - double ha; - - *ssmin = 0; - *ssmax = 0; - - fa = ae_fabs(f, _state); - ga = ae_fabs(g, _state); - ha = ae_fabs(h, _state); - fhmn = ae_minreal(fa, ha, _state); - fhmx = ae_maxreal(fa, ha, _state); - if( ae_fp_eq(fhmn,0) ) - { - *ssmin = 0; - if( ae_fp_eq(fhmx,0) ) - { - *ssmax = ga; - } - else - { - *ssmax = ae_maxreal(fhmx, ga, _state)*ae_sqrt(1+ae_sqr(ae_minreal(fhmx, ga, _state)/ae_maxreal(fhmx, ga, _state), _state), _state); - } - } - else - { - if( ae_fp_less(ga,fhmx) ) - { - aas = 1+fhmn/fhmx; - at = (fhmx-fhmn)/fhmx; - au = ae_sqr(ga/fhmx, _state); - c = 2/(ae_sqrt(aas*aas+au, _state)+ae_sqrt(at*at+au, _state)); - *ssmin = fhmn*c; - *ssmax = fhmx/c; - } - else - { - au = fhmx/ga; - if( ae_fp_eq(au,0) ) - { - - /* - * Avoid possible harmful underflow if exponent range - * asymmetric (true SSMIN may not underflow even if - * AU underflows) - */ - *ssmin = fhmn*fhmx/ga; - *ssmax = ga; - } - else - { - aas = 1+fhmn/fhmx; - at = (fhmx-fhmn)/fhmx; - c = 1/(ae_sqrt(1+ae_sqr(aas*au, _state), _state)+ae_sqrt(1+ae_sqr(at*au, _state), _state)); - *ssmin = fhmn*c*au; - *ssmin = *ssmin+(*ssmin); - *ssmax = ga/(c+c); - } - } - } -} - - -static void bdsvd_svdv2x2(double f, - double g, - double h, - double* ssmin, - double* ssmax, - double* snr, - double* csr, - double* snl, - double* csl, - ae_state *_state) -{ - ae_bool gasmal; - ae_bool swp; - ae_int_t pmax; - double a; - double clt; - double crt; - double d; - double fa; - double ft; - double ga; - double gt; - double ha; - double ht; - double l; - double m; - double mm; - double r; - double s; - double slt; - double srt; - double t; - double temp; - double tsign; - double tt; - double v; - - *ssmin = 0; - *ssmax = 0; - *snr = 0; - *csr = 0; - *snl = 0; - *csl = 0; - - ft = f; - fa = ae_fabs(ft, _state); - ht = h; - ha = ae_fabs(h, _state); - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - clt = 0; - crt = 0; - slt = 0; - srt = 0; - tsign = 0; - - /* - * PMAX points to the maximum absolute element of matrix - * PMAX = 1 if F largest in absolute values - * PMAX = 2 if G largest in absolute values - * PMAX = 3 if H largest in absolute values - */ - pmax = 1; - swp = ae_fp_greater(ha,fa); - if( swp ) - { - - /* - * Now FA .ge. HA - */ - pmax = 3; - temp = ft; - ft = ht; - ht = temp; - temp = fa; - fa = ha; - ha = temp; - } - gt = g; - ga = ae_fabs(gt, _state); - if( ae_fp_eq(ga,0) ) - { - - /* - * Diagonal matrix - */ - *ssmin = ha; - *ssmax = fa; - clt = 1; - crt = 1; - slt = 0; - srt = 0; - } - else - { - gasmal = ae_true; - if( ae_fp_greater(ga,fa) ) - { - pmax = 2; - if( ae_fp_less(fa/ga,ae_machineepsilon) ) - { - - /* - * Case of very large GA - */ - gasmal = ae_false; - *ssmax = ga; - if( ae_fp_greater(ha,1) ) - { - v = ga/ha; - *ssmin = fa/v; - } - else - { - v = fa/ga; - *ssmin = v*ha; - } - clt = 1; - slt = ht/gt; - srt = 1; - crt = ft/gt; - } - } - if( gasmal ) - { - - /* - * Normal case - */ - d = fa-ha; - if( ae_fp_eq(d,fa) ) - { - l = 1; - } - else - { - l = d/fa; - } - m = gt/ft; - t = 2-l; - mm = m*m; - tt = t*t; - s = ae_sqrt(tt+mm, _state); - if( ae_fp_eq(l,0) ) - { - r = ae_fabs(m, _state); - } - else - { - r = ae_sqrt(l*l+mm, _state); - } - a = 0.5*(s+r); - *ssmin = ha/a; - *ssmax = fa*a; - if( ae_fp_eq(mm,0) ) - { - - /* - * Note that M is very tiny - */ - if( ae_fp_eq(l,0) ) - { - t = bdsvd_extsignbdsqr(2, ft, _state)*bdsvd_extsignbdsqr(1, gt, _state); - } - else - { - t = gt/bdsvd_extsignbdsqr(d, ft, _state)+m/t; - } - } - else - { - t = (m/(s+t)+m/(r+l))*(1+a); - } - l = ae_sqrt(t*t+4, _state); - crt = 2/l; - srt = t/l; - clt = (crt+srt*m)/a; - v = ht/ft; - slt = v*srt/a; - } - } - if( swp ) - { - *csl = srt; - *snl = crt; - *csr = slt; - *snr = clt; - } - else - { - *csl = clt; - *snl = slt; - *csr = crt; - *snr = srt; - } - - /* - * Correct signs of SSMAX and SSMIN - */ - if( pmax==1 ) - { - tsign = bdsvd_extsignbdsqr(1, *csr, _state)*bdsvd_extsignbdsqr(1, *csl, _state)*bdsvd_extsignbdsqr(1, f, _state); - } - if( pmax==2 ) - { - tsign = bdsvd_extsignbdsqr(1, *snr, _state)*bdsvd_extsignbdsqr(1, *csl, _state)*bdsvd_extsignbdsqr(1, g, _state); - } - if( pmax==3 ) - { - tsign = bdsvd_extsignbdsqr(1, *snr, _state)*bdsvd_extsignbdsqr(1, *snl, _state)*bdsvd_extsignbdsqr(1, h, _state); - } - *ssmax = bdsvd_extsignbdsqr(*ssmax, tsign, _state); - *ssmin = bdsvd_extsignbdsqr(*ssmin, tsign*bdsvd_extsignbdsqr(1, f, _state)*bdsvd_extsignbdsqr(1, h, _state), _state); -} - - - - -/************************************************************************* -Singular value decomposition of a rectangular matrix. - -The algorithm calculates the singular value decomposition of a matrix of -size MxN: A = U * S * V^T - -The algorithm finds the singular values and, optionally, matrices U and V^T. -The algorithm can find both first min(M,N) columns of matrix U and rows of -matrix V^T (singular vectors), and matrices U and V^T wholly (of sizes MxM -and NxN respectively). - -Take into account that the subroutine does not return matrix V but V^T. - -Input parameters: - A - matrix to be decomposed. - Array whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - UNeeded - 0, 1 or 2. See the description of the parameter U. - VTNeeded - 0, 1 or 2. See the description of the parameter VT. - AdditionalMemory - - If the parameter: - * equals 0, the algorithm doesn’t use additional - memory (lower requirements, lower performance). - * equals 1, the algorithm uses additional - memory of size min(M,N)*min(M,N) of real numbers. - It often speeds up the algorithm. - * equals 2, the algorithm uses additional - memory of size M*min(M,N) of real numbers. - It allows to get a maximum performance. - The recommended value of the parameter is 2. - -Output parameters: - W - contains singular values in descending order. - U - if UNeeded=0, U isn't changed, the left singular vectors - are not calculated. - if Uneeded=1, U contains left singular vectors (first - min(M,N) columns of matrix U). Array whose indexes range - within [0..M-1, 0..Min(M,N)-1]. - if UNeeded=2, U contains matrix U wholly. Array whose - indexes range within [0..M-1, 0..M-1]. - VT - if VTNeeded=0, VT isn’t changed, the right singular vectors - are not calculated. - if VTNeeded=1, VT contains right singular vectors (first - min(M,N) rows of matrix V^T). Array whose indexes range - within [0..min(M,N)-1, 0..N-1]. - if VTNeeded=2, VT contains matrix V^T wholly. Array whose - indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -ae_bool rmatrixsvd(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_int_t uneeded, - ae_int_t vtneeded, - ae_int_t additionalmemory, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* u, - /* Real */ ae_matrix* vt, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector tauq; - ae_vector taup; - ae_vector tau; - ae_vector e; - ae_vector work; - ae_matrix t2; - ae_bool isupper; - ae_int_t minmn; - ae_int_t ncu; - ae_int_t nrvt; - ae_int_t nru; - ae_int_t ncvt; - ae_int_t i; - ae_int_t j; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(w); - ae_matrix_clear(u); - ae_matrix_clear(vt); - ae_vector_init(&tauq, 0, DT_REAL, _state, ae_true); - ae_vector_init(&taup, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&t2, 0, 0, DT_REAL, _state, ae_true); - - result = ae_true; - if( m==0||n==0 ) - { - ae_frame_leave(_state); - return result; - } - ae_assert(uneeded>=0&&uneeded<=2, "SVDDecomposition: wrong parameters!", _state); - ae_assert(vtneeded>=0&&vtneeded<=2, "SVDDecomposition: wrong parameters!", _state); - ae_assert(additionalmemory>=0&&additionalmemory<=2, "SVDDecomposition: wrong parameters!", _state); - - /* - * initialize - */ - minmn = ae_minint(m, n, _state); - ae_vector_set_length(w, minmn+1, _state); - ncu = 0; - nru = 0; - if( uneeded==1 ) - { - nru = m; - ncu = minmn; - ae_matrix_set_length(u, nru-1+1, ncu-1+1, _state); - } - if( uneeded==2 ) - { - nru = m; - ncu = m; - ae_matrix_set_length(u, nru-1+1, ncu-1+1, _state); - } - nrvt = 0; - ncvt = 0; - if( vtneeded==1 ) - { - nrvt = minmn; - ncvt = n; - ae_matrix_set_length(vt, nrvt-1+1, ncvt-1+1, _state); - } - if( vtneeded==2 ) - { - nrvt = n; - ncvt = n; - ae_matrix_set_length(vt, nrvt-1+1, ncvt-1+1, _state); - } - - /* - * M much larger than N - * Use bidiagonal reduction with QR-decomposition - */ - if( ae_fp_greater(m,1.6*n) ) - { - if( uneeded==0 ) - { - - /* - * No left singular vectors to be computed - */ - rmatrixqr(a, m, n, &tau, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - rmatrixbd(a, n, n, &tauq, &taup, _state); - rmatrixbdunpackpt(a, n, n, &taup, nrvt, vt, _state); - rmatrixbdunpackdiagonals(a, n, n, &isupper, w, &e, _state); - result = rmatrixbdsvd(w, &e, n, isupper, ae_false, u, 0, a, 0, vt, ncvt, _state); - ae_frame_leave(_state); - return result; - } - else - { - - /* - * Left singular vectors (may be full matrix U) to be computed - */ - rmatrixqr(a, m, n, &tau, _state); - rmatrixqrunpackq(a, m, n, &tau, ncu, u, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - rmatrixbd(a, n, n, &tauq, &taup, _state); - rmatrixbdunpackpt(a, n, n, &taup, nrvt, vt, _state); - rmatrixbdunpackdiagonals(a, n, n, &isupper, w, &e, _state); - if( additionalmemory<1 ) - { - - /* - * No additional memory can be used - */ - rmatrixbdmultiplybyq(a, n, n, &tauq, u, m, n, ae_true, ae_false, _state); - result = rmatrixbdsvd(w, &e, n, isupper, ae_false, u, m, a, 0, vt, ncvt, _state); - } - else - { - - /* - * Large U. Transforming intermediate matrix T2 - */ - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - rmatrixbdunpackq(a, n, n, &tauq, n, &t2, _state); - copymatrix(u, 0, m-1, 0, n-1, a, 0, m-1, 0, n-1, _state); - inplacetranspose(&t2, 0, n-1, 0, n-1, &work, _state); - result = rmatrixbdsvd(w, &e, n, isupper, ae_false, u, 0, &t2, n, vt, ncvt, _state); - matrixmatrixmultiply(a, 0, m-1, 0, n-1, ae_false, &t2, 0, n-1, 0, n-1, ae_true, 1.0, u, 0, m-1, 0, n-1, 0.0, &work, _state); - } - ae_frame_leave(_state); - return result; - } - } - - /* - * N much larger than M - * Use bidiagonal reduction with LQ-decomposition - */ - if( ae_fp_greater(n,1.6*m) ) - { - if( vtneeded==0 ) - { - - /* - * No right singular vectors to be computed - */ - rmatrixlq(a, m, n, &tau, _state); - for(i=0; i<=m-1; i++) - { - for(j=i+1; j<=m-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - rmatrixbd(a, m, m, &tauq, &taup, _state); - rmatrixbdunpackq(a, m, m, &tauq, ncu, u, _state); - rmatrixbdunpackdiagonals(a, m, m, &isupper, w, &e, _state); - ae_vector_set_length(&work, m+1, _state); - inplacetranspose(u, 0, nru-1, 0, ncu-1, &work, _state); - result = rmatrixbdsvd(w, &e, m, isupper, ae_false, a, 0, u, nru, vt, 0, _state); - inplacetranspose(u, 0, nru-1, 0, ncu-1, &work, _state); - ae_frame_leave(_state); - return result; - } - else - { - - /* - * Right singular vectors (may be full matrix VT) to be computed - */ - rmatrixlq(a, m, n, &tau, _state); - rmatrixlqunpackq(a, m, n, &tau, nrvt, vt, _state); - for(i=0; i<=m-1; i++) - { - for(j=i+1; j<=m-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - rmatrixbd(a, m, m, &tauq, &taup, _state); - rmatrixbdunpackq(a, m, m, &tauq, ncu, u, _state); - rmatrixbdunpackdiagonals(a, m, m, &isupper, w, &e, _state); - ae_vector_set_length(&work, ae_maxint(m, n, _state)+1, _state); - inplacetranspose(u, 0, nru-1, 0, ncu-1, &work, _state); - if( additionalmemory<1 ) - { - - /* - * No additional memory available - */ - rmatrixbdmultiplybyp(a, m, m, &taup, vt, m, n, ae_false, ae_true, _state); - result = rmatrixbdsvd(w, &e, m, isupper, ae_false, a, 0, u, nru, vt, n, _state); - } - else - { - - /* - * Large VT. Transforming intermediate matrix T2 - */ - rmatrixbdunpackpt(a, m, m, &taup, m, &t2, _state); - result = rmatrixbdsvd(w, &e, m, isupper, ae_false, a, 0, u, nru, &t2, m, _state); - copymatrix(vt, 0, m-1, 0, n-1, a, 0, m-1, 0, n-1, _state); - matrixmatrixmultiply(&t2, 0, m-1, 0, m-1, ae_false, a, 0, m-1, 0, n-1, ae_false, 1.0, vt, 0, m-1, 0, n-1, 0.0, &work, _state); - } - inplacetranspose(u, 0, nru-1, 0, ncu-1, &work, _state); - ae_frame_leave(_state); - return result; - } - } - - /* - * M<=N - * We can use inplace transposition of U to get rid of columnwise operations - */ - if( m<=n ) - { - rmatrixbd(a, m, n, &tauq, &taup, _state); - rmatrixbdunpackq(a, m, n, &tauq, ncu, u, _state); - rmatrixbdunpackpt(a, m, n, &taup, nrvt, vt, _state); - rmatrixbdunpackdiagonals(a, m, n, &isupper, w, &e, _state); - ae_vector_set_length(&work, m+1, _state); - inplacetranspose(u, 0, nru-1, 0, ncu-1, &work, _state); - result = rmatrixbdsvd(w, &e, minmn, isupper, ae_false, a, 0, u, nru, vt, ncvt, _state); - inplacetranspose(u, 0, nru-1, 0, ncu-1, &work, _state); - ae_frame_leave(_state); - return result; - } - - /* - * Simple bidiagonal reduction - */ - rmatrixbd(a, m, n, &tauq, &taup, _state); - rmatrixbdunpackq(a, m, n, &tauq, ncu, u, _state); - rmatrixbdunpackpt(a, m, n, &taup, nrvt, vt, _state); - rmatrixbdunpackdiagonals(a, m, n, &isupper, w, &e, _state); - if( additionalmemory<2||uneeded==0 ) - { - - /* - * We cant use additional memory or there is no need in such operations - */ - result = rmatrixbdsvd(w, &e, minmn, isupper, ae_false, u, nru, a, 0, vt, ncvt, _state); - } - else - { - - /* - * We can use additional memory - */ - ae_matrix_set_length(&t2, minmn-1+1, m-1+1, _state); - copyandtranspose(u, 0, m-1, 0, minmn-1, &t2, 0, minmn-1, 0, m-1, _state); - result = rmatrixbdsvd(w, &e, minmn, isupper, ae_false, u, 0, &t2, m, vt, ncvt, _state); - copyandtranspose(&t2, 0, minmn-1, 0, m-1, u, 0, m-1, 0, minmn-1, _state); - } - ae_frame_leave(_state); - return result; -} - - - - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a symmetric matrix - -The algorithm finds eigen pairs of a symmetric matrix by reducing it to -tridiagonal form and using the QL/QR algorithm. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpper - storage format. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -ae_bool smatrixevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - /* Real */ ae_vector* d, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector tau; - ae_vector e; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(d); - ae_matrix_clear(z); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded==0||zneeded==1, "SMatrixEVD: incorrect ZNeeded", _state); - smatrixtd(a, n, isupper, &tau, d, &e, _state); - if( zneeded==1 ) - { - smatrixtdunpackq(a, n, isupper, &tau, z, _state); - } - result = smatrixtdevd(d, &e, n, zneeded, z, _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Subroutine for finding the eigenvalues (and eigenvectors) of a symmetric -matrix in a given half open interval (A, B] by using a bisection and -inverse iteration - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. Array [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - B1, B2 - half open interval (B1, B2] to search eigenvalues in. - -Output parameters: - M - number of eigenvalues found in a given half-interval (M>=0). - W - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..M-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if successful. M contains the number of eigenvalues in the given - half-interval (could be equal to 0), W contains the eigenvalues, - Z contains the eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned, - M is equal to 0. - - -- ALGLIB -- - Copyright 07.01.2006 by Bochkanov Sergey -*************************************************************************/ -ae_bool smatrixevdr(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - double b1, - double b2, - ae_int_t* m, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector tau; - ae_vector e; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - *m = 0; - ae_vector_clear(w); - ae_matrix_clear(z); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded==0||zneeded==1, "SMatrixTDEVDR: incorrect ZNeeded", _state); - smatrixtd(a, n, isupper, &tau, w, &e, _state); - if( zneeded==1 ) - { - smatrixtdunpackq(a, n, isupper, &tau, z, _state); - } - result = smatrixtdevdr(w, &e, n, zneeded, b1, b2, m, z, _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Subroutine for finding the eigenvalues and eigenvectors of a symmetric -matrix with given indexes by using bisection and inverse iteration methods. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - -Output parameters: - W - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..I2-I1]. - In that case, the eigenvectors are stored in the matrix columns. - -Result: - True, if successful. W contains the eigenvalues, Z contains the - eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned. - - -- ALGLIB -- - Copyright 07.01.2006 by Bochkanov Sergey -*************************************************************************/ -ae_bool smatrixevdi(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector tau; - ae_vector e; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(w); - ae_matrix_clear(z); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded==0||zneeded==1, "SMatrixEVDI: incorrect ZNeeded", _state); - smatrixtd(a, n, isupper, &tau, w, &e, _state); - if( zneeded==1 ) - { - smatrixtdunpackq(a, n, isupper, &tau, z, _state); - } - result = smatrixtdevdi(w, &e, n, zneeded, i1, i2, z, _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a Hermitian matrix - -The algorithm finds eigen pairs of a Hermitian matrix by reducing it to -real tridiagonal form and using the QL/QR algorithm. - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - -Note: - eigenvectors of Hermitian matrix are defined up to multiplication by - a complex number L, such that |L|=1. - - -- ALGLIB -- - Copyright 2005, 23 March 2007 by Bochkanov Sergey -*************************************************************************/ -ae_bool hmatrixevd(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - /* Real */ ae_vector* d, - /* Complex */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector tau; - ae_vector e; - ae_vector work; - ae_matrix t; - ae_matrix q; - ae_int_t i; - ae_int_t k; - double v; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(d); - ae_matrix_clear(z); - ae_vector_init(&tau, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&t, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&q, 0, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(zneeded==0||zneeded==1, "HermitianEVD: incorrect ZNeeded", _state); - - /* - * Reduce to tridiagonal form - */ - hmatrixtd(a, n, isupper, &tau, d, &e, _state); - if( zneeded==1 ) - { - hmatrixtdunpackq(a, n, isupper, &tau, &q, _state); - zneeded = 2; - } - - /* - * TDEVD - */ - result = smatrixtdevd(d, &e, n, zneeded, &t, _state); - - /* - * Eigenvectors are needed - * Calculate Z = Q*T = Re(Q)*T + i*Im(Q)*T - */ - if( result&&zneeded!=0 ) - { - ae_vector_set_length(&work, n-1+1, _state); - ae_matrix_set_length(z, n-1+1, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - - /* - * Calculate real part - */ - for(k=0; k<=n-1; k++) - { - work.ptr.p_double[k] = 0; - } - for(k=0; k<=n-1; k++) - { - v = q.ptr.pp_complex[i][k].x; - ae_v_addd(&work.ptr.p_double[0], 1, &t.ptr.pp_double[k][0], 1, ae_v_len(0,n-1), v); - } - for(k=0; k<=n-1; k++) - { - z->ptr.pp_complex[i][k].x = work.ptr.p_double[k]; - } - - /* - * Calculate imaginary part - */ - for(k=0; k<=n-1; k++) - { - work.ptr.p_double[k] = 0; - } - for(k=0; k<=n-1; k++) - { - v = q.ptr.pp_complex[i][k].y; - ae_v_addd(&work.ptr.p_double[0], 1, &t.ptr.pp_double[k][0], 1, ae_v_len(0,n-1), v); - } - for(k=0; k<=n-1; k++) - { - z->ptr.pp_complex[i][k].y = work.ptr.p_double[k]; - } - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Subroutine for finding the eigenvalues (and eigenvectors) of a Hermitian -matrix in a given half-interval (A, B] by using a bisection and inverse -iteration - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. Array whose indexes range within - [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - B1, B2 - half-interval (B1, B2] to search eigenvalues in. - -Output parameters: - M - number of eigenvalues found in a given half-interval, M>=0 - W - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..M-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if successful. M contains the number of eigenvalues in the given - half-interval (could be equal to 0), W contains the eigenvalues, - Z contains the eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration - subroutine wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned, M is - equal to 0. - -Note: - eigen vectors of Hermitian matrix are defined up to multiplication by - a complex number L, such as |L|=1. - - -- ALGLIB -- - Copyright 07.01.2006, 24.03.2007 by Bochkanov Sergey. -*************************************************************************/ -ae_bool hmatrixevdr(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - double b1, - double b2, - ae_int_t* m, - /* Real */ ae_vector* w, - /* Complex */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_matrix q; - ae_matrix t; - ae_vector tau; - ae_vector e; - ae_vector work; - ae_int_t i; - ae_int_t k; - double v; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - *m = 0; - ae_vector_clear(w); - ae_matrix_clear(z); - ae_matrix_init(&q, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&t, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tau, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded==0||zneeded==1, "HermitianEigenValuesAndVectorsInInterval: incorrect ZNeeded", _state); - - /* - * Reduce to tridiagonal form - */ - hmatrixtd(a, n, isupper, &tau, w, &e, _state); - if( zneeded==1 ) - { - hmatrixtdunpackq(a, n, isupper, &tau, &q, _state); - zneeded = 2; - } - - /* - * Bisection and inverse iteration - */ - result = smatrixtdevdr(w, &e, n, zneeded, b1, b2, m, &t, _state); - - /* - * Eigenvectors are needed - * Calculate Z = Q*T = Re(Q)*T + i*Im(Q)*T - */ - if( (result&&zneeded!=0)&&*m!=0 ) - { - ae_vector_set_length(&work, *m-1+1, _state); - ae_matrix_set_length(z, n-1+1, *m-1+1, _state); - for(i=0; i<=n-1; i++) - { - - /* - * Calculate real part - */ - for(k=0; k<=*m-1; k++) - { - work.ptr.p_double[k] = 0; - } - for(k=0; k<=n-1; k++) - { - v = q.ptr.pp_complex[i][k].x; - ae_v_addd(&work.ptr.p_double[0], 1, &t.ptr.pp_double[k][0], 1, ae_v_len(0,*m-1), v); - } - for(k=0; k<=*m-1; k++) - { - z->ptr.pp_complex[i][k].x = work.ptr.p_double[k]; - } - - /* - * Calculate imaginary part - */ - for(k=0; k<=*m-1; k++) - { - work.ptr.p_double[k] = 0; - } - for(k=0; k<=n-1; k++) - { - v = q.ptr.pp_complex[i][k].y; - ae_v_addd(&work.ptr.p_double[0], 1, &t.ptr.pp_double[k][0], 1, ae_v_len(0,*m-1), v); - } - for(k=0; k<=*m-1; k++) - { - z->ptr.pp_complex[i][k].y = work.ptr.p_double[k]; - } - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Subroutine for finding the eigenvalues and eigenvectors of a Hermitian -matrix with given indexes by using bisection and inverse iteration methods - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - -Output parameters: - W - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..I2-I1]. - In that case, the eigenvectors are stored in the matrix - columns. - -Result: - True, if successful. W contains the eigenvalues, Z contains the - eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration - subroutine wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned. - -Note: - eigen vectors of Hermitian matrix are defined up to multiplication by - a complex number L, such as |L|=1. - - -- ALGLIB -- - Copyright 07.01.2006, 24.03.2007 by Bochkanov Sergey. -*************************************************************************/ -ae_bool hmatrixevdi(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* w, - /* Complex */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_matrix q; - ae_matrix t; - ae_vector tau; - ae_vector e; - ae_vector work; - ae_int_t i; - ae_int_t k; - double v; - ae_int_t m; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(w); - ae_matrix_clear(z); - ae_matrix_init(&q, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&t, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tau, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&e, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded==0||zneeded==1, "HermitianEigenValuesAndVectorsByIndexes: incorrect ZNeeded", _state); - - /* - * Reduce to tridiagonal form - */ - hmatrixtd(a, n, isupper, &tau, w, &e, _state); - if( zneeded==1 ) - { - hmatrixtdunpackq(a, n, isupper, &tau, &q, _state); - zneeded = 2; - } - - /* - * Bisection and inverse iteration - */ - result = smatrixtdevdi(w, &e, n, zneeded, i1, i2, &t, _state); - - /* - * Eigenvectors are needed - * Calculate Z = Q*T = Re(Q)*T + i*Im(Q)*T - */ - m = i2-i1+1; - if( result&&zneeded!=0 ) - { - ae_vector_set_length(&work, m-1+1, _state); - ae_matrix_set_length(z, n-1+1, m-1+1, _state); - for(i=0; i<=n-1; i++) - { - - /* - * Calculate real part - */ - for(k=0; k<=m-1; k++) - { - work.ptr.p_double[k] = 0; - } - for(k=0; k<=n-1; k++) - { - v = q.ptr.pp_complex[i][k].x; - ae_v_addd(&work.ptr.p_double[0], 1, &t.ptr.pp_double[k][0], 1, ae_v_len(0,m-1), v); - } - for(k=0; k<=m-1; k++) - { - z->ptr.pp_complex[i][k].x = work.ptr.p_double[k]; - } - - /* - * Calculate imaginary part - */ - for(k=0; k<=m-1; k++) - { - work.ptr.p_double[k] = 0; - } - for(k=0; k<=n-1; k++) - { - v = q.ptr.pp_complex[i][k].y; - ae_v_addd(&work.ptr.p_double[0], 1, &t.ptr.pp_double[k][0], 1, ae_v_len(0,m-1), v); - } - for(k=0; k<=m-1; k++) - { - z->ptr.pp_complex[i][k].y = work.ptr.p_double[k]; - } - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a tridiagonal symmetric matrix - -The algorithm finds the eigen pairs of a tridiagonal symmetric matrix by -using an QL/QR algorithm with implicit shifts. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix - are multiplied by the square matrix Z. It is used if the - tridiagonal matrix is obtained by the similarity - transformation of a symmetric matrix; - * 2, the eigenvectors of a tridiagonal matrix replace the - square matrix Z; - * 3, matrix Z contains the first row of the eigenvectors - matrix. - Z - if ZNeeded=1, Z contains the square matrix by which the - eigenvectors are multiplied. - Array whose indexes range within [0..N-1, 0..N-1]. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the product of a given matrix (from the left) - and the eigenvectors matrix (from the right); - * 2, Z contains the eigenvectors. - * 3, Z contains the first row of the eigenvectors matrix. - If ZNeeded<3, Z is the array whose indexes range within [0..N-1, 0..N-1]. - In that case, the eigenvectors are stored in the matrix columns. - If ZNeeded=3, Z is the array whose indexes range within [0..0, 0..N-1]. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -ae_bool smatrixtdevd(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _e; - ae_vector d1; - ae_vector e1; - ae_matrix z1; - ae_int_t i; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - ae_vector_init(&d1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e1, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z1, 0, 0, DT_REAL, _state, ae_true); - - - /* - * Prepare 1-based task - */ - ae_vector_set_length(&d1, n+1, _state); - ae_vector_set_length(&e1, n+1, _state); - ae_v_move(&d1.ptr.p_double[1], 1, &d->ptr.p_double[0], 1, ae_v_len(1,n)); - if( n>1 ) - { - ae_v_move(&e1.ptr.p_double[1], 1, &e->ptr.p_double[0], 1, ae_v_len(1,n-1)); - } - if( zneeded==1 ) - { - ae_matrix_set_length(&z1, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - ae_v_move(&z1.ptr.pp_double[i][1], 1, &z->ptr.pp_double[i-1][0], 1, ae_v_len(1,n)); - } - } - - /* - * Solve 1-based task - */ - result = evd_tridiagonalevd(&d1, &e1, n, zneeded, &z1, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - - /* - * Convert back to 0-based result - */ - ae_v_move(&d->ptr.p_double[0], 1, &d1.ptr.p_double[1], 1, ae_v_len(0,n-1)); - if( zneeded!=0 ) - { - if( zneeded==1 ) - { - for(i=1; i<=n; i++) - { - ae_v_move(&z->ptr.pp_double[i-1][0], 1, &z1.ptr.pp_double[i][1], 1, ae_v_len(0,n-1)); - } - ae_frame_leave(_state); - return result; - } - if( zneeded==2 ) - { - ae_matrix_set_length(z, n-1+1, n-1+1, _state); - for(i=1; i<=n; i++) - { - ae_v_move(&z->ptr.pp_double[i-1][0], 1, &z1.ptr.pp_double[i][1], 1, ae_v_len(0,n-1)); - } - ae_frame_leave(_state); - return result; - } - if( zneeded==3 ) - { - ae_matrix_set_length(z, 0+1, n-1+1, _state); - ae_v_move(&z->ptr.pp_double[0][0], 1, &z1.ptr.pp_double[1][1], 1, ae_v_len(0,n-1)); - ae_frame_leave(_state); - return result; - } - ae_assert(ae_false, "SMatrixTDEVD: Incorrect ZNeeded!", _state); - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Subroutine for finding the tridiagonal matrix eigenvalues/vectors in a -given half-interval (A, B] by using bisection and inverse iteration. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix, N>=0. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix are multiplied - by the square matrix Z. It is used if the tridiagonal - matrix is obtained by the similarity transformation - of a symmetric matrix. - * 2, the eigenvectors of a tridiagonal matrix replace matrix Z. - A, B - half-interval (A, B] to search eigenvalues in. - Z - if ZNeeded is equal to: - * 0, Z isn't used and remains unchanged; - * 1, Z contains the square matrix (array whose indexes range - within [0..N-1, 0..N-1]) which reduces the given symmetric - matrix to tridiagonal form; - * 2, Z isn't used (but changed on the exit). - -Output parameters: - D - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - M - number of eigenvalues found in the given half-interval (M>=0). - Z - if ZNeeded is equal to: - * 0, doesn't contain any information; - * 1, contains the product of a given NxN matrix Z (from the - left) and NxM matrix of the eigenvectors found (from the - right). Array whose indexes range within [0..N-1, 0..M-1]. - * 2, contains the matrix of the eigenvectors found. - Array whose indexes range within [0..N-1, 0..M-1]. - -Result: - - True, if successful. In that case, M contains the number of eigenvalues - in the given half-interval (could be equal to 0), D contains the eigenvalues, - Z contains the eigenvectors (if needed). - It should be noted that the subroutine changes the size of arrays D and Z. - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. In that case, - the eigenvalues and eigenvectors are not returned, M is equal to 0. - - -- ALGLIB -- - Copyright 31.03.2008 by Bochkanov Sergey -*************************************************************************/ -ae_bool smatrixtdevdr(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - double a, - double b, - ae_int_t* m, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t errorcode; - ae_int_t nsplit; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t cr; - ae_vector iblock; - ae_vector isplit; - ae_vector ifail; - ae_vector d1; - ae_vector e1; - ae_vector w; - ae_matrix z2; - ae_matrix z3; - double v; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - *m = 0; - ae_vector_init(&iblock, 0, DT_INT, _state, ae_true); - ae_vector_init(&isplit, 0, DT_INT, _state, ae_true); - ae_vector_init(&ifail, 0, DT_INT, _state, ae_true); - ae_vector_init(&d1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z2, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z3, 0, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded>=0&&zneeded<=2, "SMatrixTDEVDR: incorrect ZNeeded!", _state); - - /* - * Special cases - */ - if( ae_fp_less_eq(b,a) ) - { - *m = 0; - result = ae_true; - ae_frame_leave(_state); - return result; - } - if( n<=0 ) - { - *m = 0; - result = ae_true; - ae_frame_leave(_state); - return result; - } - - /* - * Copy D,E to D1, E1 - */ - ae_vector_set_length(&d1, n+1, _state); - ae_v_move(&d1.ptr.p_double[1], 1, &d->ptr.p_double[0], 1, ae_v_len(1,n)); - if( n>1 ) - { - ae_vector_set_length(&e1, n-1+1, _state); - ae_v_move(&e1.ptr.p_double[1], 1, &e->ptr.p_double[0], 1, ae_v_len(1,n-1)); - } - - /* - * No eigen vectors - */ - if( zneeded==0 ) - { - result = evd_internalbisectioneigenvalues(&d1, &e1, n, 2, 1, a, b, 0, 0, -1, &w, m, &nsplit, &iblock, &isplit, &errorcode, _state); - if( !result||*m==0 ) - { - *m = 0; - ae_frame_leave(_state); - return result; - } - ae_vector_set_length(d, *m-1+1, _state); - ae_v_move(&d->ptr.p_double[0], 1, &w.ptr.p_double[1], 1, ae_v_len(0,*m-1)); - ae_frame_leave(_state); - return result; - } - - /* - * Eigen vectors are multiplied by Z - */ - if( zneeded==1 ) - { - - /* - * Find eigen pairs - */ - result = evd_internalbisectioneigenvalues(&d1, &e1, n, 2, 2, a, b, 0, 0, -1, &w, m, &nsplit, &iblock, &isplit, &errorcode, _state); - if( !result||*m==0 ) - { - *m = 0; - ae_frame_leave(_state); - return result; - } - evd_internaldstein(n, &d1, &e1, *m, &w, &iblock, &isplit, &z2, &ifail, &cr, _state); - if( cr!=0 ) - { - *m = 0; - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Sort eigen values and vectors - */ - for(i=1; i<=*m; i++) - { - k = i; - for(j=i; j<=*m; j++) - { - if( ae_fp_less(w.ptr.p_double[j],w.ptr.p_double[k]) ) - { - k = j; - } - } - v = w.ptr.p_double[i]; - w.ptr.p_double[i] = w.ptr.p_double[k]; - w.ptr.p_double[k] = v; - for(j=1; j<=n; j++) - { - v = z2.ptr.pp_double[j][i]; - z2.ptr.pp_double[j][i] = z2.ptr.pp_double[j][k]; - z2.ptr.pp_double[j][k] = v; - } - } - - /* - * Transform Z2 and overwrite Z - */ - ae_matrix_set_length(&z3, *m+1, n+1, _state); - for(i=1; i<=*m; i++) - { - ae_v_move(&z3.ptr.pp_double[i][1], 1, &z2.ptr.pp_double[1][i], z2.stride, ae_v_len(1,n)); - } - for(i=1; i<=n; i++) - { - for(j=1; j<=*m; j++) - { - v = ae_v_dotproduct(&z->ptr.pp_double[i-1][0], 1, &z3.ptr.pp_double[j][1], 1, ae_v_len(0,n-1)); - z2.ptr.pp_double[i][j] = v; - } - } - ae_matrix_set_length(z, n-1+1, *m-1+1, _state); - for(i=1; i<=*m; i++) - { - ae_v_move(&z->ptr.pp_double[0][i-1], z->stride, &z2.ptr.pp_double[1][i], z2.stride, ae_v_len(0,n-1)); - } - - /* - * Store W - */ - ae_vector_set_length(d, *m-1+1, _state); - for(i=1; i<=*m; i++) - { - d->ptr.p_double[i-1] = w.ptr.p_double[i]; - } - ae_frame_leave(_state); - return result; - } - - /* - * Eigen vectors are stored in Z - */ - if( zneeded==2 ) - { - - /* - * Find eigen pairs - */ - result = evd_internalbisectioneigenvalues(&d1, &e1, n, 2, 2, a, b, 0, 0, -1, &w, m, &nsplit, &iblock, &isplit, &errorcode, _state); - if( !result||*m==0 ) - { - *m = 0; - ae_frame_leave(_state); - return result; - } - evd_internaldstein(n, &d1, &e1, *m, &w, &iblock, &isplit, &z2, &ifail, &cr, _state); - if( cr!=0 ) - { - *m = 0; - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Sort eigen values and vectors - */ - for(i=1; i<=*m; i++) - { - k = i; - for(j=i; j<=*m; j++) - { - if( ae_fp_less(w.ptr.p_double[j],w.ptr.p_double[k]) ) - { - k = j; - } - } - v = w.ptr.p_double[i]; - w.ptr.p_double[i] = w.ptr.p_double[k]; - w.ptr.p_double[k] = v; - for(j=1; j<=n; j++) - { - v = z2.ptr.pp_double[j][i]; - z2.ptr.pp_double[j][i] = z2.ptr.pp_double[j][k]; - z2.ptr.pp_double[j][k] = v; - } - } - - /* - * Store W - */ - ae_vector_set_length(d, *m-1+1, _state); - for(i=1; i<=*m; i++) - { - d->ptr.p_double[i-1] = w.ptr.p_double[i]; - } - ae_matrix_set_length(z, n-1+1, *m-1+1, _state); - for(i=1; i<=*m; i++) - { - ae_v_move(&z->ptr.pp_double[0][i-1], z->stride, &z2.ptr.pp_double[1][i], z2.stride, ae_v_len(0,n-1)); - } - ae_frame_leave(_state); - return result; - } - result = ae_false; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Subroutine for finding tridiagonal matrix eigenvalues/vectors with given -indexes (in ascending order) by using the bisection and inverse iteraion. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix. N>=0. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix are multiplied - by the square matrix Z. It is used if the - tridiagonal matrix is obtained by the similarity transformation - of a symmetric matrix. - * 2, the eigenvectors of a tridiagonal matrix replace - matrix Z. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - Z - if ZNeeded is equal to: - * 0, Z isn't used and remains unchanged; - * 1, Z contains the square matrix (array whose indexes range within [0..N-1, 0..N-1]) - which reduces the given symmetric matrix to tridiagonal form; - * 2, Z isn't used (but changed on the exit). - -Output parameters: - D - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, doesn't contain any information; - * 1, contains the product of a given NxN matrix Z (from the left) and - Nx(I2-I1) matrix of the eigenvectors found (from the right). - Array whose indexes range within [0..N-1, 0..I2-I1]. - * 2, contains the matrix of the eigenvalues found. - Array whose indexes range within [0..N-1, 0..I2-I1]. - - -Result: - - True, if successful. In that case, D contains the eigenvalues, - Z contains the eigenvectors (if needed). - It should be noted that the subroutine changes the size of arrays D and Z. - - False, if the bisection method subroutine wasn't able to find the eigenvalues - in the given interval or if the inverse iteration subroutine wasn't able - to find all the corresponding eigenvectors. In that case, the eigenvalues - and eigenvectors are not returned. - - -- ALGLIB -- - Copyright 25.12.2005 by Bochkanov Sergey -*************************************************************************/ -ae_bool smatrixtdevdi(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t errorcode; - ae_int_t nsplit; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t m; - ae_int_t cr; - ae_vector iblock; - ae_vector isplit; - ae_vector ifail; - ae_vector w; - ae_vector d1; - ae_vector e1; - ae_matrix z2; - ae_matrix z3; - double v; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&iblock, 0, DT_INT, _state, ae_true); - ae_vector_init(&isplit, 0, DT_INT, _state, ae_true); - ae_vector_init(&ifail, 0, DT_INT, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&d1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&e1, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z2, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&z3, 0, 0, DT_REAL, _state, ae_true); - - ae_assert((0<=i1&&i1<=i2)&&i2ptr.p_double[0], 1, ae_v_len(1,n)); - if( n>1 ) - { - ae_vector_set_length(&e1, n-1+1, _state); - ae_v_move(&e1.ptr.p_double[1], 1, &e->ptr.p_double[0], 1, ae_v_len(1,n-1)); - } - - /* - * No eigen vectors - */ - if( zneeded==0 ) - { - result = evd_internalbisectioneigenvalues(&d1, &e1, n, 3, 1, 0, 0, i1+1, i2+1, -1, &w, &m, &nsplit, &iblock, &isplit, &errorcode, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - if( m!=i2-i1+1 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - ae_vector_set_length(d, m-1+1, _state); - for(i=1; i<=m; i++) - { - d->ptr.p_double[i-1] = w.ptr.p_double[i]; - } - ae_frame_leave(_state); - return result; - } - - /* - * Eigen vectors are multiplied by Z - */ - if( zneeded==1 ) - { - - /* - * Find eigen pairs - */ - result = evd_internalbisectioneigenvalues(&d1, &e1, n, 3, 2, 0, 0, i1+1, i2+1, -1, &w, &m, &nsplit, &iblock, &isplit, &errorcode, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - if( m!=i2-i1+1 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - evd_internaldstein(n, &d1, &e1, m, &w, &iblock, &isplit, &z2, &ifail, &cr, _state); - if( cr!=0 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Sort eigen values and vectors - */ - for(i=1; i<=m; i++) - { - k = i; - for(j=i; j<=m; j++) - { - if( ae_fp_less(w.ptr.p_double[j],w.ptr.p_double[k]) ) - { - k = j; - } - } - v = w.ptr.p_double[i]; - w.ptr.p_double[i] = w.ptr.p_double[k]; - w.ptr.p_double[k] = v; - for(j=1; j<=n; j++) - { - v = z2.ptr.pp_double[j][i]; - z2.ptr.pp_double[j][i] = z2.ptr.pp_double[j][k]; - z2.ptr.pp_double[j][k] = v; - } - } - - /* - * Transform Z2 and overwrite Z - */ - ae_matrix_set_length(&z3, m+1, n+1, _state); - for(i=1; i<=m; i++) - { - ae_v_move(&z3.ptr.pp_double[i][1], 1, &z2.ptr.pp_double[1][i], z2.stride, ae_v_len(1,n)); - } - for(i=1; i<=n; i++) - { - for(j=1; j<=m; j++) - { - v = ae_v_dotproduct(&z->ptr.pp_double[i-1][0], 1, &z3.ptr.pp_double[j][1], 1, ae_v_len(0,n-1)); - z2.ptr.pp_double[i][j] = v; - } - } - ae_matrix_set_length(z, n-1+1, m-1+1, _state); - for(i=1; i<=m; i++) - { - ae_v_move(&z->ptr.pp_double[0][i-1], z->stride, &z2.ptr.pp_double[1][i], z2.stride, ae_v_len(0,n-1)); - } - - /* - * Store W - */ - ae_vector_set_length(d, m-1+1, _state); - for(i=1; i<=m; i++) - { - d->ptr.p_double[i-1] = w.ptr.p_double[i]; - } - ae_frame_leave(_state); - return result; - } - - /* - * Eigen vectors are stored in Z - */ - if( zneeded==2 ) - { - - /* - * Find eigen pairs - */ - result = evd_internalbisectioneigenvalues(&d1, &e1, n, 3, 2, 0, 0, i1+1, i2+1, -1, &w, &m, &nsplit, &iblock, &isplit, &errorcode, _state); - if( !result ) - { - ae_frame_leave(_state); - return result; - } - if( m!=i2-i1+1 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - evd_internaldstein(n, &d1, &e1, m, &w, &iblock, &isplit, &z2, &ifail, &cr, _state); - if( cr!=0 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Sort eigen values and vectors - */ - for(i=1; i<=m; i++) - { - k = i; - for(j=i; j<=m; j++) - { - if( ae_fp_less(w.ptr.p_double[j],w.ptr.p_double[k]) ) - { - k = j; - } - } - v = w.ptr.p_double[i]; - w.ptr.p_double[i] = w.ptr.p_double[k]; - w.ptr.p_double[k] = v; - for(j=1; j<=n; j++) - { - v = z2.ptr.pp_double[j][i]; - z2.ptr.pp_double[j][i] = z2.ptr.pp_double[j][k]; - z2.ptr.pp_double[j][k] = v; - } - } - - /* - * Store Z - */ - ae_matrix_set_length(z, n-1+1, m-1+1, _state); - for(i=1; i<=m; i++) - { - ae_v_move(&z->ptr.pp_double[0][i-1], z->stride, &z2.ptr.pp_double[1][i], z2.stride, ae_v_len(0,n-1)); - } - - /* - * Store W - */ - ae_vector_set_length(d, m-1+1, _state); - for(i=1; i<=m; i++) - { - d->ptr.p_double[i-1] = w.ptr.p_double[i]; - } - ae_frame_leave(_state); - return result; - } - result = ae_false; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Finding eigenvalues and eigenvectors of a general matrix - -The algorithm finds eigenvalues and eigenvectors of a general matrix by -using the QR algorithm with multiple shifts. The algorithm can find -eigenvalues and both left and right eigenvectors. - -The right eigenvector is a vector x such that A*x = w*x, and the left -eigenvector is a vector y such that y'*A = w*y' (here y' implies a complex -conjugate transposition of vector y). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - VNeeded - flag controlling whether eigenvectors are needed or not. - If VNeeded is equal to: - * 0, eigenvectors are not returned; - * 1, right eigenvectors are returned; - * 2, left eigenvectors are returned; - * 3, both left and right eigenvectors are returned. - -Output parameters: - WR - real parts of eigenvalues. - Array whose index ranges within [0..N-1]. - WR - imaginary parts of eigenvalues. - Array whose index ranges within [0..N-1]. - VL, VR - arrays of left and right eigenvectors (if they are needed). - If WI[i]=0, the respective eigenvalue is a real number, - and it corresponds to the column number I of matrices VL/VR. - If WI[i]>0, we have a pair of complex conjugate numbers with - positive and negative imaginary parts: - the first eigenvalue WR[i] + sqrt(-1)*WI[i]; - the second eigenvalue WR[i+1] + sqrt(-1)*WI[i+1]; - WI[i]>0 - WI[i+1] = -WI[i] < 0 - In that case, the eigenvector corresponding to the first - eigenvalue is located in i and i+1 columns of matrices - VL/VR (the column number i contains the real part, and the - column number i+1 contains the imaginary part), and the vector - corresponding to the second eigenvalue is a complex conjugate to - the first vector. - Arrays whose indexes range within [0..N-1, 0..N-1]. - -Result: - True, if the algorithm has converged. - False, if the algorithm has not converged. - -Note 1: - Some users may ask the following question: what if WI[N-1]>0? - WI[N] must contain an eigenvalue which is complex conjugate to the - N-th eigenvalue, but the array has only size N? - The answer is as follows: such a situation cannot occur because the - algorithm finds a pairs of eigenvalues, therefore, if WI[i]>0, I is - strictly less than N-1. - -Note 2: - The algorithm performance depends on the value of the internal parameter - NS of the InternalSchurDecomposition subroutine which defines the number - of shifts in the QR algorithm (similarly to the block width in block-matrix - algorithms of linear algebra). If you require maximum performance - on your machine, it is recommended to adjust this parameter manually. - - -See also the InternalTREVC subroutine. - -The algorithm is based on the LAPACK 3.0 library. -*************************************************************************/ -ae_bool rmatrixevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t vneeded, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - /* Real */ ae_matrix* vl, - /* Real */ ae_matrix* vr, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_matrix a1; - ae_matrix vl1; - ae_matrix vr1; - ae_vector wr1; - ae_vector wi1; - ae_int_t i; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(wr); - ae_vector_clear(wi); - ae_matrix_clear(vl); - ae_matrix_clear(vr); - ae_matrix_init(&a1, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vl1, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vr1, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wr1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wi1, 0, DT_REAL, _state, ae_true); - - ae_assert(vneeded>=0&&vneeded<=3, "RMatrixEVD: incorrect VNeeded!", _state); - ae_matrix_set_length(&a1, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - ae_v_move(&a1.ptr.pp_double[i][1], 1, &a->ptr.pp_double[i-1][0], 1, ae_v_len(1,n)); - } - result = evd_nonsymmetricevd(&a1, n, vneeded, &wr1, &wi1, &vl1, &vr1, _state); - if( result ) - { - ae_vector_set_length(wr, n-1+1, _state); - ae_vector_set_length(wi, n-1+1, _state); - ae_v_move(&wr->ptr.p_double[0], 1, &wr1.ptr.p_double[1], 1, ae_v_len(0,n-1)); - ae_v_move(&wi->ptr.p_double[0], 1, &wi1.ptr.p_double[1], 1, ae_v_len(0,n-1)); - if( vneeded==2||vneeded==3 ) - { - ae_matrix_set_length(vl, n-1+1, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - ae_v_move(&vl->ptr.pp_double[i][0], 1, &vl1.ptr.pp_double[i+1][1], 1, ae_v_len(0,n-1)); - } - } - if( vneeded==1||vneeded==3 ) - { - ae_matrix_set_length(vr, n-1+1, n-1+1, _state); - for(i=0; i<=n-1; i++) - { - ae_v_move(&vr->ptr.pp_double[i][0], 1, &vr1.ptr.pp_double[i+1][1], 1, ae_v_len(0,n-1)); - } - } - } - ae_frame_leave(_state); - return result; -} - - -static ae_bool evd_tridiagonalevd(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - /* Real */ ae_matrix* z, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _e; - ae_int_t maxit; - ae_int_t i; - ae_int_t ii; - ae_int_t iscale; - ae_int_t j; - ae_int_t jtot; - ae_int_t k; - ae_int_t t; - ae_int_t l; - ae_int_t l1; - ae_int_t lend; - ae_int_t lendm1; - ae_int_t lendp1; - ae_int_t lendsv; - ae_int_t lm1; - ae_int_t lsv; - ae_int_t m; - ae_int_t mm1; - ae_int_t nm1; - ae_int_t nmaxit; - ae_int_t tmpint; - double anorm; - double b; - double c; - double eps; - double eps2; - double f; - double g; - double p; - double r; - double rt1; - double rt2; - double s; - double safmax; - double safmin; - double ssfmax; - double ssfmin; - double tst; - double tmp; - ae_vector work1; - ae_vector work2; - ae_vector workc; - ae_vector works; - ae_vector wtemp; - ae_bool gotoflag; - ae_int_t zrows; - ae_bool wastranspose; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - ae_vector_init(&work1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&workc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&works, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wtemp, 0, DT_REAL, _state, ae_true); - - ae_assert(zneeded>=0&&zneeded<=3, "TridiagonalEVD: Incorrent ZNeeded", _state); - - /* - * Quick return if possible - */ - if( zneeded<0||zneeded>3 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - result = ae_true; - if( n==0 ) - { - ae_frame_leave(_state); - return result; - } - if( n==1 ) - { - if( zneeded==2||zneeded==3 ) - { - ae_matrix_set_length(z, 1+1, 1+1, _state); - z->ptr.pp_double[1][1] = 1; - } - ae_frame_leave(_state); - return result; - } - maxit = 30; - - /* - * Initialize arrays - */ - ae_vector_set_length(&wtemp, n+1, _state); - ae_vector_set_length(&work1, n-1+1, _state); - ae_vector_set_length(&work2, n-1+1, _state); - ae_vector_set_length(&workc, n+1, _state); - ae_vector_set_length(&works, n+1, _state); - - /* - * Determine the unit roundoff and over/underflow thresholds. - */ - eps = ae_machineepsilon; - eps2 = ae_sqr(eps, _state); - safmin = ae_minrealnumber; - safmax = ae_maxrealnumber; - ssfmax = ae_sqrt(safmax, _state)/3; - ssfmin = ae_sqrt(safmin, _state)/eps2; - - /* - * Prepare Z - * - * Here we are using transposition to get rid of column operations - * - */ - wastranspose = ae_false; - zrows = 0; - if( zneeded==1 ) - { - zrows = n; - } - if( zneeded==2 ) - { - zrows = n; - } - if( zneeded==3 ) - { - zrows = 1; - } - if( zneeded==1 ) - { - wastranspose = ae_true; - inplacetranspose(z, 1, n, 1, n, &wtemp, _state); - } - if( zneeded==2 ) - { - wastranspose = ae_true; - ae_matrix_set_length(z, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - for(j=1; j<=n; j++) - { - if( i==j ) - { - z->ptr.pp_double[i][j] = 1; - } - else - { - z->ptr.pp_double[i][j] = 0; - } - } - } - } - if( zneeded==3 ) - { - wastranspose = ae_false; - ae_matrix_set_length(z, 1+1, n+1, _state); - for(j=1; j<=n; j++) - { - if( j==1 ) - { - z->ptr.pp_double[1][j] = 1; - } - else - { - z->ptr.pp_double[1][j] = 0; - } - } - } - nmaxit = n*maxit; - jtot = 0; - - /* - * Determine where the matrix splits and choose QL or QR iteration - * for each block, according to whether top or bottom diagonal - * element is smaller. - */ - l1 = 1; - nm1 = n-1; - for(;;) - { - if( l1>n ) - { - break; - } - if( l1>1 ) - { - e->ptr.p_double[l1-1] = 0; - } - gotoflag = ae_false; - m = l1; - if( l1<=nm1 ) - { - for(m=l1; m<=nm1; m++) - { - tst = ae_fabs(e->ptr.p_double[m], _state); - if( ae_fp_eq(tst,0) ) - { - gotoflag = ae_true; - break; - } - if( ae_fp_less_eq(tst,ae_sqrt(ae_fabs(d->ptr.p_double[m], _state), _state)*ae_sqrt(ae_fabs(d->ptr.p_double[m+1], _state), _state)*eps) ) - { - e->ptr.p_double[m] = 0; - gotoflag = ae_true; - break; - } - } - } - if( !gotoflag ) - { - m = n; - } - - /* - * label 30: - */ - l = l1; - lsv = l; - lend = m; - lendsv = lend; - l1 = m+1; - if( lend==l ) - { - continue; - } - - /* - * Scale submatrix in rows and columns L to LEND - */ - if( l==lend ) - { - anorm = ae_fabs(d->ptr.p_double[l], _state); - } - else - { - anorm = ae_maxreal(ae_fabs(d->ptr.p_double[l], _state)+ae_fabs(e->ptr.p_double[l], _state), ae_fabs(e->ptr.p_double[lend-1], _state)+ae_fabs(d->ptr.p_double[lend], _state), _state); - for(i=l+1; i<=lend-1; i++) - { - anorm = ae_maxreal(anorm, ae_fabs(d->ptr.p_double[i], _state)+ae_fabs(e->ptr.p_double[i], _state)+ae_fabs(e->ptr.p_double[i-1], _state), _state); - } - } - iscale = 0; - if( ae_fp_eq(anorm,0) ) - { - continue; - } - if( ae_fp_greater(anorm,ssfmax) ) - { - iscale = 1; - tmp = ssfmax/anorm; - tmpint = lend-1; - ae_v_muld(&d->ptr.p_double[l], 1, ae_v_len(l,lend), tmp); - ae_v_muld(&e->ptr.p_double[l], 1, ae_v_len(l,tmpint), tmp); - } - if( ae_fp_less(anorm,ssfmin) ) - { - iscale = 2; - tmp = ssfmin/anorm; - tmpint = lend-1; - ae_v_muld(&d->ptr.p_double[l], 1, ae_v_len(l,lend), tmp); - ae_v_muld(&e->ptr.p_double[l], 1, ae_v_len(l,tmpint), tmp); - } - - /* - * Choose between QL and QR iteration - */ - if( ae_fp_less(ae_fabs(d->ptr.p_double[lend], _state),ae_fabs(d->ptr.p_double[l], _state)) ) - { - lend = lsv; - l = lendsv; - } - if( lend>l ) - { - - /* - * QL Iteration - * - * Look for small subdiagonal element. - */ - for(;;) - { - gotoflag = ae_false; - if( l!=lend ) - { - lendm1 = lend-1; - for(m=l; m<=lendm1; m++) - { - tst = ae_sqr(ae_fabs(e->ptr.p_double[m], _state), _state); - if( ae_fp_less_eq(tst,eps2*ae_fabs(d->ptr.p_double[m], _state)*ae_fabs(d->ptr.p_double[m+1], _state)+safmin) ) - { - gotoflag = ae_true; - break; - } - } - } - if( !gotoflag ) - { - m = lend; - } - if( mptr.p_double[m] = 0; - } - p = d->ptr.p_double[l]; - if( m!=l ) - { - - /* - * If remaining matrix is 2-by-2, use DLAE2 or SLAEV2 - * to compute its eigensystem. - */ - if( m==l+1 ) - { - if( zneeded>0 ) - { - evd_tdevdev2(d->ptr.p_double[l], e->ptr.p_double[l], d->ptr.p_double[l+1], &rt1, &rt2, &c, &s, _state); - work1.ptr.p_double[l] = c; - work2.ptr.p_double[l] = s; - workc.ptr.p_double[1] = work1.ptr.p_double[l]; - works.ptr.p_double[1] = work2.ptr.p_double[l]; - if( !wastranspose ) - { - applyrotationsfromtheright(ae_false, 1, zrows, l, l+1, &workc, &works, z, &wtemp, _state); - } - else - { - applyrotationsfromtheleft(ae_false, l, l+1, 1, zrows, &workc, &works, z, &wtemp, _state); - } - } - else - { - evd_tdevde2(d->ptr.p_double[l], e->ptr.p_double[l], d->ptr.p_double[l+1], &rt1, &rt2, _state); - } - d->ptr.p_double[l] = rt1; - d->ptr.p_double[l+1] = rt2; - e->ptr.p_double[l] = 0; - l = l+2; - if( l<=lend ) - { - continue; - } - - /* - * GOTO 140 - */ - break; - } - if( jtot==nmaxit ) - { - - /* - * GOTO 140 - */ - break; - } - jtot = jtot+1; - - /* - * Form shift. - */ - g = (d->ptr.p_double[l+1]-p)/(2*e->ptr.p_double[l]); - r = evd_tdevdpythag(g, 1, _state); - g = d->ptr.p_double[m]-p+e->ptr.p_double[l]/(g+evd_tdevdextsign(r, g, _state)); - s = 1; - c = 1; - p = 0; - - /* - * Inner loop - */ - mm1 = m-1; - for(i=mm1; i>=l; i--) - { - f = s*e->ptr.p_double[i]; - b = c*e->ptr.p_double[i]; - generaterotation(g, f, &c, &s, &r, _state); - if( i!=m-1 ) - { - e->ptr.p_double[i+1] = r; - } - g = d->ptr.p_double[i+1]-p; - r = (d->ptr.p_double[i]-g)*s+2*c*b; - p = s*r; - d->ptr.p_double[i+1] = g+p; - g = c*r-b; - - /* - * If eigenvectors are desired, then save rotations. - */ - if( zneeded>0 ) - { - work1.ptr.p_double[i] = c; - work2.ptr.p_double[i] = -s; - } - } - - /* - * If eigenvectors are desired, then apply saved rotations. - */ - if( zneeded>0 ) - { - for(i=l; i<=m-1; i++) - { - workc.ptr.p_double[i-l+1] = work1.ptr.p_double[i]; - works.ptr.p_double[i-l+1] = work2.ptr.p_double[i]; - } - if( !wastranspose ) - { - applyrotationsfromtheright(ae_false, 1, zrows, l, m, &workc, &works, z, &wtemp, _state); - } - else - { - applyrotationsfromtheleft(ae_false, l, m, 1, zrows, &workc, &works, z, &wtemp, _state); - } - } - d->ptr.p_double[l] = d->ptr.p_double[l]-p; - e->ptr.p_double[l] = g; - continue; - } - - /* - * Eigenvalue found. - */ - d->ptr.p_double[l] = p; - l = l+1; - if( l<=lend ) - { - continue; - } - break; - } - } - else - { - - /* - * QR Iteration - * - * Look for small superdiagonal element. - */ - for(;;) - { - gotoflag = ae_false; - if( l!=lend ) - { - lendp1 = lend+1; - for(m=l; m>=lendp1; m--) - { - tst = ae_sqr(ae_fabs(e->ptr.p_double[m-1], _state), _state); - if( ae_fp_less_eq(tst,eps2*ae_fabs(d->ptr.p_double[m], _state)*ae_fabs(d->ptr.p_double[m-1], _state)+safmin) ) - { - gotoflag = ae_true; - break; - } - } - } - if( !gotoflag ) - { - m = lend; - } - if( m>lend ) - { - e->ptr.p_double[m-1] = 0; - } - p = d->ptr.p_double[l]; - if( m!=l ) - { - - /* - * If remaining matrix is 2-by-2, use DLAE2 or SLAEV2 - * to compute its eigensystem. - */ - if( m==l-1 ) - { - if( zneeded>0 ) - { - evd_tdevdev2(d->ptr.p_double[l-1], e->ptr.p_double[l-1], d->ptr.p_double[l], &rt1, &rt2, &c, &s, _state); - work1.ptr.p_double[m] = c; - work2.ptr.p_double[m] = s; - workc.ptr.p_double[1] = c; - works.ptr.p_double[1] = s; - if( !wastranspose ) - { - applyrotationsfromtheright(ae_true, 1, zrows, l-1, l, &workc, &works, z, &wtemp, _state); - } - else - { - applyrotationsfromtheleft(ae_true, l-1, l, 1, zrows, &workc, &works, z, &wtemp, _state); - } - } - else - { - evd_tdevde2(d->ptr.p_double[l-1], e->ptr.p_double[l-1], d->ptr.p_double[l], &rt1, &rt2, _state); - } - d->ptr.p_double[l-1] = rt1; - d->ptr.p_double[l] = rt2; - e->ptr.p_double[l-1] = 0; - l = l-2; - if( l>=lend ) - { - continue; - } - break; - } - if( jtot==nmaxit ) - { - break; - } - jtot = jtot+1; - - /* - * Form shift. - */ - g = (d->ptr.p_double[l-1]-p)/(2*e->ptr.p_double[l-1]); - r = evd_tdevdpythag(g, 1, _state); - g = d->ptr.p_double[m]-p+e->ptr.p_double[l-1]/(g+evd_tdevdextsign(r, g, _state)); - s = 1; - c = 1; - p = 0; - - /* - * Inner loop - */ - lm1 = l-1; - for(i=m; i<=lm1; i++) - { - f = s*e->ptr.p_double[i]; - b = c*e->ptr.p_double[i]; - generaterotation(g, f, &c, &s, &r, _state); - if( i!=m ) - { - e->ptr.p_double[i-1] = r; - } - g = d->ptr.p_double[i]-p; - r = (d->ptr.p_double[i+1]-g)*s+2*c*b; - p = s*r; - d->ptr.p_double[i] = g+p; - g = c*r-b; - - /* - * If eigenvectors are desired, then save rotations. - */ - if( zneeded>0 ) - { - work1.ptr.p_double[i] = c; - work2.ptr.p_double[i] = s; - } - } - - /* - * If eigenvectors are desired, then apply saved rotations. - */ - if( zneeded>0 ) - { - for(i=m; i<=l-1; i++) - { - workc.ptr.p_double[i-m+1] = work1.ptr.p_double[i]; - works.ptr.p_double[i-m+1] = work2.ptr.p_double[i]; - } - if( !wastranspose ) - { - applyrotationsfromtheright(ae_true, 1, zrows, m, l, &workc, &works, z, &wtemp, _state); - } - else - { - applyrotationsfromtheleft(ae_true, m, l, 1, zrows, &workc, &works, z, &wtemp, _state); - } - } - d->ptr.p_double[l] = d->ptr.p_double[l]-p; - e->ptr.p_double[lm1] = g; - continue; - } - - /* - * Eigenvalue found. - */ - d->ptr.p_double[l] = p; - l = l-1; - if( l>=lend ) - { - continue; - } - break; - } - } - - /* - * Undo scaling if necessary - */ - if( iscale==1 ) - { - tmp = anorm/ssfmax; - tmpint = lendsv-1; - ae_v_muld(&d->ptr.p_double[lsv], 1, ae_v_len(lsv,lendsv), tmp); - ae_v_muld(&e->ptr.p_double[lsv], 1, ae_v_len(lsv,tmpint), tmp); - } - if( iscale==2 ) - { - tmp = anorm/ssfmin; - tmpint = lendsv-1; - ae_v_muld(&d->ptr.p_double[lsv], 1, ae_v_len(lsv,lendsv), tmp); - ae_v_muld(&e->ptr.p_double[lsv], 1, ae_v_len(lsv,tmpint), tmp); - } - - /* - * Check for no convergence to an eigenvalue after a total - * of N*MAXIT iterations. - */ - if( jtot>=nmaxit ) - { - result = ae_false; - if( wastranspose ) - { - inplacetranspose(z, 1, n, 1, n, &wtemp, _state); - } - ae_frame_leave(_state); - return result; - } - } - - /* - * Order eigenvalues and eigenvectors. - */ - if( zneeded==0 ) - { - - /* - * Sort - */ - if( n==1 ) - { - ae_frame_leave(_state); - return result; - } - if( n==2 ) - { - if( ae_fp_greater(d->ptr.p_double[1],d->ptr.p_double[2]) ) - { - tmp = d->ptr.p_double[1]; - d->ptr.p_double[1] = d->ptr.p_double[2]; - d->ptr.p_double[2] = tmp; - } - ae_frame_leave(_state); - return result; - } - i = 2; - do - { - t = i; - while(t!=1) - { - k = t/2; - if( ae_fp_greater_eq(d->ptr.p_double[k],d->ptr.p_double[t]) ) - { - t = 1; - } - else - { - tmp = d->ptr.p_double[k]; - d->ptr.p_double[k] = d->ptr.p_double[t]; - d->ptr.p_double[t] = tmp; - t = k; - } - } - i = i+1; - } - while(i<=n); - i = n-1; - do - { - tmp = d->ptr.p_double[i+1]; - d->ptr.p_double[i+1] = d->ptr.p_double[1]; - d->ptr.p_double[1] = tmp; - t = 1; - while(t!=0) - { - k = 2*t; - if( k>i ) - { - t = 0; - } - else - { - if( kptr.p_double[k+1],d->ptr.p_double[k]) ) - { - k = k+1; - } - } - if( ae_fp_greater_eq(d->ptr.p_double[t],d->ptr.p_double[k]) ) - { - t = 0; - } - else - { - tmp = d->ptr.p_double[k]; - d->ptr.p_double[k] = d->ptr.p_double[t]; - d->ptr.p_double[t] = tmp; - t = k; - } - } - } - i = i-1; - } - while(i>=1); - } - else - { - - /* - * Use Selection Sort to minimize swaps of eigenvectors - */ - for(ii=2; ii<=n; ii++) - { - i = ii-1; - k = i; - p = d->ptr.p_double[i]; - for(j=ii; j<=n; j++) - { - if( ae_fp_less(d->ptr.p_double[j],p) ) - { - k = j; - p = d->ptr.p_double[j]; - } - } - if( k!=i ) - { - d->ptr.p_double[k] = d->ptr.p_double[i]; - d->ptr.p_double[i] = p; - if( wastranspose ) - { - ae_v_move(&wtemp.ptr.p_double[1], 1, &z->ptr.pp_double[i][1], 1, ae_v_len(1,n)); - ae_v_move(&z->ptr.pp_double[i][1], 1, &z->ptr.pp_double[k][1], 1, ae_v_len(1,n)); - ae_v_move(&z->ptr.pp_double[k][1], 1, &wtemp.ptr.p_double[1], 1, ae_v_len(1,n)); - } - else - { - ae_v_move(&wtemp.ptr.p_double[1], 1, &z->ptr.pp_double[1][i], z->stride, ae_v_len(1,zrows)); - ae_v_move(&z->ptr.pp_double[1][i], z->stride, &z->ptr.pp_double[1][k], z->stride, ae_v_len(1,zrows)); - ae_v_move(&z->ptr.pp_double[1][k], z->stride, &wtemp.ptr.p_double[1], 1, ae_v_len(1,zrows)); - } - } - } - if( wastranspose ) - { - inplacetranspose(z, 1, n, 1, n, &wtemp, _state); - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -DLAE2 computes the eigenvalues of a 2-by-2 symmetric matrix - [ A B ] - [ B C ]. -On return, RT1 is the eigenvalue of larger absolute value, and RT2 -is the eigenvalue of smaller absolute value. - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -static void evd_tdevde2(double a, - double b, - double c, - double* rt1, - double* rt2, - ae_state *_state) -{ - double ab; - double acmn; - double acmx; - double adf; - double df; - double rt; - double sm; - double tb; - - *rt1 = 0; - *rt2 = 0; - - sm = a+c; - df = a-c; - adf = ae_fabs(df, _state); - tb = b+b; - ab = ae_fabs(tb, _state); - if( ae_fp_greater(ae_fabs(a, _state),ae_fabs(c, _state)) ) - { - acmx = a; - acmn = c; - } - else - { - acmx = c; - acmn = a; - } - if( ae_fp_greater(adf,ab) ) - { - rt = adf*ae_sqrt(1+ae_sqr(ab/adf, _state), _state); - } - else - { - if( ae_fp_less(adf,ab) ) - { - rt = ab*ae_sqrt(1+ae_sqr(adf/ab, _state), _state); - } - else - { - - /* - * Includes case AB=ADF=0 - */ - rt = ab*ae_sqrt(2, _state); - } - } - if( ae_fp_less(sm,0) ) - { - *rt1 = 0.5*(sm-rt); - - /* - * Order of execution important. - * To get fully accurate smaller eigenvalue, - * next line needs to be executed in higher precision. - */ - *rt2 = acmx/(*rt1)*acmn-b/(*rt1)*b; - } - else - { - if( ae_fp_greater(sm,0) ) - { - *rt1 = 0.5*(sm+rt); - - /* - * Order of execution important. - * To get fully accurate smaller eigenvalue, - * next line needs to be executed in higher precision. - */ - *rt2 = acmx/(*rt1)*acmn-b/(*rt1)*b; - } - else - { - - /* - * Includes case RT1 = RT2 = 0 - */ - *rt1 = 0.5*rt; - *rt2 = -0.5*rt; - } - } -} - - -/************************************************************************* -DLAEV2 computes the eigendecomposition of a 2-by-2 symmetric matrix - - [ A B ] - [ B C ]. - -On return, RT1 is the eigenvalue of larger absolute value, RT2 is the -eigenvalue of smaller absolute value, and (CS1,SN1) is the unit right -eigenvector for RT1, giving the decomposition - - [ CS1 SN1 ] [ A B ] [ CS1 -SN1 ] = [ RT1 0 ] - [-SN1 CS1 ] [ B C ] [ SN1 CS1 ] [ 0 RT2 ]. - - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -static void evd_tdevdev2(double a, - double b, - double c, - double* rt1, - double* rt2, - double* cs1, - double* sn1, - ae_state *_state) -{ - ae_int_t sgn1; - ae_int_t sgn2; - double ab; - double acmn; - double acmx; - double acs; - double adf; - double cs; - double ct; - double df; - double rt; - double sm; - double tb; - double tn; - - *rt1 = 0; - *rt2 = 0; - *cs1 = 0; - *sn1 = 0; - - - /* - * Compute the eigenvalues - */ - sm = a+c; - df = a-c; - adf = ae_fabs(df, _state); - tb = b+b; - ab = ae_fabs(tb, _state); - if( ae_fp_greater(ae_fabs(a, _state),ae_fabs(c, _state)) ) - { - acmx = a; - acmn = c; - } - else - { - acmx = c; - acmn = a; - } - if( ae_fp_greater(adf,ab) ) - { - rt = adf*ae_sqrt(1+ae_sqr(ab/adf, _state), _state); - } - else - { - if( ae_fp_less(adf,ab) ) - { - rt = ab*ae_sqrt(1+ae_sqr(adf/ab, _state), _state); - } - else - { - - /* - * Includes case AB=ADF=0 - */ - rt = ab*ae_sqrt(2, _state); - } - } - if( ae_fp_less(sm,0) ) - { - *rt1 = 0.5*(sm-rt); - sgn1 = -1; - - /* - * Order of execution important. - * To get fully accurate smaller eigenvalue, - * next line needs to be executed in higher precision. - */ - *rt2 = acmx/(*rt1)*acmn-b/(*rt1)*b; - } - else - { - if( ae_fp_greater(sm,0) ) - { - *rt1 = 0.5*(sm+rt); - sgn1 = 1; - - /* - * Order of execution important. - * To get fully accurate smaller eigenvalue, - * next line needs to be executed in higher precision. - */ - *rt2 = acmx/(*rt1)*acmn-b/(*rt1)*b; - } - else - { - - /* - * Includes case RT1 = RT2 = 0 - */ - *rt1 = 0.5*rt; - *rt2 = -0.5*rt; - sgn1 = 1; - } - } - - /* - * Compute the eigenvector - */ - if( ae_fp_greater_eq(df,0) ) - { - cs = df+rt; - sgn2 = 1; - } - else - { - cs = df-rt; - sgn2 = -1; - } - acs = ae_fabs(cs, _state); - if( ae_fp_greater(acs,ab) ) - { - ct = -tb/cs; - *sn1 = 1/ae_sqrt(1+ct*ct, _state); - *cs1 = ct*(*sn1); - } - else - { - if( ae_fp_eq(ab,0) ) - { - *cs1 = 1; - *sn1 = 0; - } - else - { - tn = -cs/tb; - *cs1 = 1/ae_sqrt(1+tn*tn, _state); - *sn1 = tn*(*cs1); - } - } - if( sgn1==sgn2 ) - { - tn = *cs1; - *cs1 = -*sn1; - *sn1 = tn; - } -} - - -/************************************************************************* -Internal routine -*************************************************************************/ -static double evd_tdevdpythag(double a, double b, ae_state *_state) -{ - double result; - - - if( ae_fp_less(ae_fabs(a, _state),ae_fabs(b, _state)) ) - { - result = ae_fabs(b, _state)*ae_sqrt(1+ae_sqr(a/b, _state), _state); - } - else - { - result = ae_fabs(a, _state)*ae_sqrt(1+ae_sqr(b/a, _state), _state); - } - return result; -} - - -/************************************************************************* -Internal routine -*************************************************************************/ -static double evd_tdevdextsign(double a, double b, ae_state *_state) -{ - double result; - - - if( ae_fp_greater_eq(b,0) ) - { - result = ae_fabs(a, _state); - } - else - { - result = -ae_fabs(a, _state); - } - return result; -} - - -static ae_bool evd_internalbisectioneigenvalues(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t irange, - ae_int_t iorder, - double vl, - double vu, - ae_int_t il, - ae_int_t iu, - double abstol, - /* Real */ ae_vector* w, - ae_int_t* m, - ae_int_t* nsplit, - /* Integer */ ae_vector* iblock, - /* Integer */ ae_vector* isplit, - ae_int_t* errorcode, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _d; - ae_vector _e; - double fudge; - double relfac; - ae_bool ncnvrg; - ae_bool toofew; - ae_int_t ib; - ae_int_t ibegin; - ae_int_t idiscl; - ae_int_t idiscu; - ae_int_t ie; - ae_int_t iend; - ae_int_t iinfo; - ae_int_t im; - ae_int_t iin; - ae_int_t ioff; - ae_int_t iout; - ae_int_t itmax; - ae_int_t iw; - ae_int_t iwoff; - ae_int_t j; - ae_int_t itmp1; - ae_int_t jb; - ae_int_t jdisc; - ae_int_t je; - ae_int_t nwl; - ae_int_t nwu; - double atoli; - double bnorm; - double gl; - double gu; - double pivmin; - double rtoli; - double safemn; - double tmp1; - double tmp2; - double tnorm; - double ulp; - double wkill; - double wl; - double wlu; - double wu; - double wul; - double scalefactor; - double t; - ae_vector idumma; - ae_vector work; - ae_vector iwork; - ae_vector ia1s2; - ae_vector ra1s2; - ae_matrix ra1s2x2; - ae_matrix ia1s2x2; - ae_vector ra1siin; - ae_vector ra2siin; - ae_vector ra3siin; - ae_vector ra4siin; - ae_matrix ra1siinx2; - ae_matrix ia1siinx2; - ae_vector iworkspace; - ae_vector rworkspace; - ae_int_t tmpi; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_d, d, _state, ae_true); - d = &_d; - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - ae_vector_clear(w); - *m = 0; - *nsplit = 0; - ae_vector_clear(iblock); - ae_vector_clear(isplit); - *errorcode = 0; - ae_vector_init(&idumma, 0, DT_INT, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&iwork, 0, DT_INT, _state, ae_true); - ae_vector_init(&ia1s2, 0, DT_INT, _state, ae_true); - ae_vector_init(&ra1s2, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ra1s2x2, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ia1s2x2, 0, 0, DT_INT, _state, ae_true); - ae_vector_init(&ra1siin, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ra2siin, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ra3siin, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ra4siin, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ra1siinx2, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ia1siinx2, 0, 0, DT_INT, _state, ae_true); - ae_vector_init(&iworkspace, 0, DT_INT, _state, ae_true); - ae_vector_init(&rworkspace, 0, DT_REAL, _state, ae_true); - - - /* - * Quick return if possible - */ - *m = 0; - if( n==0 ) - { - result = ae_true; - ae_frame_leave(_state); - return result; - } - - /* - * Get machine constants - * NB is the minimum vector length for vector bisection, or 0 - * if only scalar is to be done. - */ - fudge = 2; - relfac = 2; - safemn = ae_minrealnumber; - ulp = 2*ae_machineepsilon; - rtoli = ulp*relfac; - ae_vector_set_length(&idumma, 1+1, _state); - ae_vector_set_length(&work, 4*n+1, _state); - ae_vector_set_length(&iwork, 3*n+1, _state); - ae_vector_set_length(w, n+1, _state); - ae_vector_set_length(iblock, n+1, _state); - ae_vector_set_length(isplit, n+1, _state); - ae_vector_set_length(&ia1s2, 2+1, _state); - ae_vector_set_length(&ra1s2, 2+1, _state); - ae_matrix_set_length(&ra1s2x2, 2+1, 2+1, _state); - ae_matrix_set_length(&ia1s2x2, 2+1, 2+1, _state); - ae_vector_set_length(&ra1siin, n+1, _state); - ae_vector_set_length(&ra2siin, n+1, _state); - ae_vector_set_length(&ra3siin, n+1, _state); - ae_vector_set_length(&ra4siin, n+1, _state); - ae_matrix_set_length(&ra1siinx2, n+1, 2+1, _state); - ae_matrix_set_length(&ia1siinx2, n+1, 2+1, _state); - ae_vector_set_length(&iworkspace, n+1, _state); - ae_vector_set_length(&rworkspace, n+1, _state); - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - wlu = 0; - wul = 0; - - /* - * Check for Errors - */ - result = ae_false; - *errorcode = 0; - if( irange<=0||irange>=4 ) - { - *errorcode = -4; - } - if( iorder<=0||iorder>=3 ) - { - *errorcode = -5; - } - if( n<0 ) - { - *errorcode = -3; - } - if( irange==2&&ae_fp_greater_eq(vl,vu) ) - { - *errorcode = -6; - } - if( irange==3&&(il<1||il>ae_maxint(1, n, _state)) ) - { - *errorcode = -8; - } - if( irange==3&&(iun) ) - { - *errorcode = -9; - } - if( *errorcode!=0 ) - { - ae_frame_leave(_state); - return result; - } - - /* - * Initialize error flags - */ - ncnvrg = ae_false; - toofew = ae_false; - - /* - * Simplifications: - */ - if( (irange==3&&il==1)&&iu==n ) - { - irange = 1; - } - - /* - * Special Case when N=1 - */ - if( n==1 ) - { - *nsplit = 1; - isplit->ptr.p_int[1] = 1; - if( irange==2&&(ae_fp_greater_eq(vl,d->ptr.p_double[1])||ae_fp_less(vu,d->ptr.p_double[1])) ) - { - *m = 0; - } - else - { - w->ptr.p_double[1] = d->ptr.p_double[1]; - iblock->ptr.p_int[1] = 1; - *m = 1; - } - result = ae_true; - ae_frame_leave(_state); - return result; - } - - /* - * Scaling - */ - t = ae_fabs(d->ptr.p_double[n], _state); - for(j=1; j<=n-1; j++) - { - t = ae_maxreal(t, ae_fabs(d->ptr.p_double[j], _state), _state); - t = ae_maxreal(t, ae_fabs(e->ptr.p_double[j], _state), _state); - } - scalefactor = 1; - if( ae_fp_neq(t,0) ) - { - if( ae_fp_greater(t,ae_sqrt(ae_sqrt(ae_minrealnumber, _state), _state)*ae_sqrt(ae_maxrealnumber, _state)) ) - { - scalefactor = t; - } - if( ae_fp_less(t,ae_sqrt(ae_sqrt(ae_maxrealnumber, _state), _state)*ae_sqrt(ae_minrealnumber, _state)) ) - { - scalefactor = t; - } - for(j=1; j<=n-1; j++) - { - d->ptr.p_double[j] = d->ptr.p_double[j]/scalefactor; - e->ptr.p_double[j] = e->ptr.p_double[j]/scalefactor; - } - d->ptr.p_double[n] = d->ptr.p_double[n]/scalefactor; - } - - /* - * Compute Splitting Points - */ - *nsplit = 1; - work.ptr.p_double[n] = 0; - pivmin = 1; - for(j=2; j<=n; j++) - { - tmp1 = ae_sqr(e->ptr.p_double[j-1], _state); - if( ae_fp_greater(ae_fabs(d->ptr.p_double[j]*d->ptr.p_double[j-1], _state)*ae_sqr(ulp, _state)+safemn,tmp1) ) - { - isplit->ptr.p_int[*nsplit] = j-1; - *nsplit = *nsplit+1; - work.ptr.p_double[j-1] = 0; - } - else - { - work.ptr.p_double[j-1] = tmp1; - pivmin = ae_maxreal(pivmin, tmp1, _state); - } - } - isplit->ptr.p_int[*nsplit] = n; - pivmin = pivmin*safemn; - - /* - * Compute Interval and ATOLI - */ - if( irange==3 ) - { - - /* - * RANGE='I': Compute the interval containing eigenvalues - * IL through IU. - * - * Compute Gershgorin interval for entire (split) matrix - * and use it as the initial interval - */ - gu = d->ptr.p_double[1]; - gl = d->ptr.p_double[1]; - tmp1 = 0; - for(j=1; j<=n-1; j++) - { - tmp2 = ae_sqrt(work.ptr.p_double[j], _state); - gu = ae_maxreal(gu, d->ptr.p_double[j]+tmp1+tmp2, _state); - gl = ae_minreal(gl, d->ptr.p_double[j]-tmp1-tmp2, _state); - tmp1 = tmp2; - } - gu = ae_maxreal(gu, d->ptr.p_double[n]+tmp1, _state); - gl = ae_minreal(gl, d->ptr.p_double[n]-tmp1, _state); - tnorm = ae_maxreal(ae_fabs(gl, _state), ae_fabs(gu, _state), _state); - gl = gl-fudge*tnorm*ulp*n-fudge*2*pivmin; - gu = gu+fudge*tnorm*ulp*n+fudge*pivmin; - - /* - * Compute Iteration parameters - */ - itmax = ae_iceil((ae_log(tnorm+pivmin, _state)-ae_log(pivmin, _state))/ae_log(2, _state), _state)+2; - if( ae_fp_less_eq(abstol,0) ) - { - atoli = ulp*tnorm; - } - else - { - atoli = abstol; - } - work.ptr.p_double[n+1] = gl; - work.ptr.p_double[n+2] = gl; - work.ptr.p_double[n+3] = gu; - work.ptr.p_double[n+4] = gu; - work.ptr.p_double[n+5] = gl; - work.ptr.p_double[n+6] = gu; - iwork.ptr.p_int[1] = -1; - iwork.ptr.p_int[2] = -1; - iwork.ptr.p_int[3] = n+1; - iwork.ptr.p_int[4] = n+1; - iwork.ptr.p_int[5] = il-1; - iwork.ptr.p_int[6] = iu; - - /* - * Calling DLAEBZ - * - * DLAEBZ( 3, ITMAX, N, 2, 2, NB, ATOLI, RTOLI, PIVMIN, D, E, - * WORK, IWORK( 5 ), WORK( N+1 ), WORK( N+5 ), IOUT, - * IWORK, W, IBLOCK, IINFO ) - */ - ia1s2.ptr.p_int[1] = iwork.ptr.p_int[5]; - ia1s2.ptr.p_int[2] = iwork.ptr.p_int[6]; - ra1s2.ptr.p_double[1] = work.ptr.p_double[n+5]; - ra1s2.ptr.p_double[2] = work.ptr.p_double[n+6]; - ra1s2x2.ptr.pp_double[1][1] = work.ptr.p_double[n+1]; - ra1s2x2.ptr.pp_double[2][1] = work.ptr.p_double[n+2]; - ra1s2x2.ptr.pp_double[1][2] = work.ptr.p_double[n+3]; - ra1s2x2.ptr.pp_double[2][2] = work.ptr.p_double[n+4]; - ia1s2x2.ptr.pp_int[1][1] = iwork.ptr.p_int[1]; - ia1s2x2.ptr.pp_int[2][1] = iwork.ptr.p_int[2]; - ia1s2x2.ptr.pp_int[1][2] = iwork.ptr.p_int[3]; - ia1s2x2.ptr.pp_int[2][2] = iwork.ptr.p_int[4]; - evd_internaldlaebz(3, itmax, n, 2, 2, atoli, rtoli, pivmin, d, e, &work, &ia1s2, &ra1s2x2, &ra1s2, &iout, &ia1s2x2, w, iblock, &iinfo, _state); - iwork.ptr.p_int[5] = ia1s2.ptr.p_int[1]; - iwork.ptr.p_int[6] = ia1s2.ptr.p_int[2]; - work.ptr.p_double[n+5] = ra1s2.ptr.p_double[1]; - work.ptr.p_double[n+6] = ra1s2.ptr.p_double[2]; - work.ptr.p_double[n+1] = ra1s2x2.ptr.pp_double[1][1]; - work.ptr.p_double[n+2] = ra1s2x2.ptr.pp_double[2][1]; - work.ptr.p_double[n+3] = ra1s2x2.ptr.pp_double[1][2]; - work.ptr.p_double[n+4] = ra1s2x2.ptr.pp_double[2][2]; - iwork.ptr.p_int[1] = ia1s2x2.ptr.pp_int[1][1]; - iwork.ptr.p_int[2] = ia1s2x2.ptr.pp_int[2][1]; - iwork.ptr.p_int[3] = ia1s2x2.ptr.pp_int[1][2]; - iwork.ptr.p_int[4] = ia1s2x2.ptr.pp_int[2][2]; - if( iwork.ptr.p_int[6]==iu ) - { - wl = work.ptr.p_double[n+1]; - wlu = work.ptr.p_double[n+3]; - nwl = iwork.ptr.p_int[1]; - wu = work.ptr.p_double[n+4]; - wul = work.ptr.p_double[n+2]; - nwu = iwork.ptr.p_int[4]; - } - else - { - wl = work.ptr.p_double[n+2]; - wlu = work.ptr.p_double[n+4]; - nwl = iwork.ptr.p_int[2]; - wu = work.ptr.p_double[n+3]; - wul = work.ptr.p_double[n+1]; - nwu = iwork.ptr.p_int[3]; - } - if( ((nwl<0||nwl>=n)||nwu<1)||nwu>n ) - { - *errorcode = 4; - result = ae_false; - ae_frame_leave(_state); - return result; - } - } - else - { - - /* - * RANGE='A' or 'V' -- Set ATOLI - */ - tnorm = ae_maxreal(ae_fabs(d->ptr.p_double[1], _state)+ae_fabs(e->ptr.p_double[1], _state), ae_fabs(d->ptr.p_double[n], _state)+ae_fabs(e->ptr.p_double[n-1], _state), _state); - for(j=2; j<=n-1; j++) - { - tnorm = ae_maxreal(tnorm, ae_fabs(d->ptr.p_double[j], _state)+ae_fabs(e->ptr.p_double[j-1], _state)+ae_fabs(e->ptr.p_double[j], _state), _state); - } - if( ae_fp_less_eq(abstol,0) ) - { - atoli = ulp*tnorm; - } - else - { - atoli = abstol; - } - if( irange==2 ) - { - wl = vl; - wu = vu; - } - else - { - wl = 0; - wu = 0; - } - } - - /* - * Find Eigenvalues -- Loop Over Blocks and recompute NWL and NWU. - * NWL accumulates the number of eigenvalues .le. WL, - * NWU accumulates the number of eigenvalues .le. WU - */ - *m = 0; - iend = 0; - *errorcode = 0; - nwl = 0; - nwu = 0; - for(jb=1; jb<=*nsplit; jb++) - { - ioff = iend; - ibegin = ioff+1; - iend = isplit->ptr.p_int[jb]; - iin = iend-ioff; - if( iin==1 ) - { - - /* - * Special Case -- IIN=1 - */ - if( irange==1||ae_fp_greater_eq(wl,d->ptr.p_double[ibegin]-pivmin) ) - { - nwl = nwl+1; - } - if( irange==1||ae_fp_greater_eq(wu,d->ptr.p_double[ibegin]-pivmin) ) - { - nwu = nwu+1; - } - if( irange==1||(ae_fp_less(wl,d->ptr.p_double[ibegin]-pivmin)&&ae_fp_greater_eq(wu,d->ptr.p_double[ibegin]-pivmin)) ) - { - *m = *m+1; - w->ptr.p_double[*m] = d->ptr.p_double[ibegin]; - iblock->ptr.p_int[*m] = jb; - } - } - else - { - - /* - * General Case -- IIN > 1 - * - * Compute Gershgorin Interval - * and use it as the initial interval - */ - gu = d->ptr.p_double[ibegin]; - gl = d->ptr.p_double[ibegin]; - tmp1 = 0; - for(j=ibegin; j<=iend-1; j++) - { - tmp2 = ae_fabs(e->ptr.p_double[j], _state); - gu = ae_maxreal(gu, d->ptr.p_double[j]+tmp1+tmp2, _state); - gl = ae_minreal(gl, d->ptr.p_double[j]-tmp1-tmp2, _state); - tmp1 = tmp2; - } - gu = ae_maxreal(gu, d->ptr.p_double[iend]+tmp1, _state); - gl = ae_minreal(gl, d->ptr.p_double[iend]-tmp1, _state); - bnorm = ae_maxreal(ae_fabs(gl, _state), ae_fabs(gu, _state), _state); - gl = gl-fudge*bnorm*ulp*iin-fudge*pivmin; - gu = gu+fudge*bnorm*ulp*iin+fudge*pivmin; - - /* - * Compute ATOLI for the current submatrix - */ - if( ae_fp_less_eq(abstol,0) ) - { - atoli = ulp*ae_maxreal(ae_fabs(gl, _state), ae_fabs(gu, _state), _state); - } - else - { - atoli = abstol; - } - if( irange>1 ) - { - if( ae_fp_less(gu,wl) ) - { - nwl = nwl+iin; - nwu = nwu+iin; - continue; - } - gl = ae_maxreal(gl, wl, _state); - gu = ae_minreal(gu, wu, _state); - if( ae_fp_greater_eq(gl,gu) ) - { - continue; - } - } - - /* - * Set Up Initial Interval - */ - work.ptr.p_double[n+1] = gl; - work.ptr.p_double[n+iin+1] = gu; - - /* - * Calling DLAEBZ - * - * CALL DLAEBZ( 1, 0, IN, IN, 1, NB, ATOLI, RTOLI, PIVMIN, - * D( IBEGIN ), E( IBEGIN ), WORK( IBEGIN ), - * IDUMMA, WORK( N+1 ), WORK( N+2*IN+1 ), IM, - * IWORK, W( M+1 ), IBLOCK( M+1 ), IINFO ) - */ - for(tmpi=1; tmpi<=iin; tmpi++) - { - ra1siin.ptr.p_double[tmpi] = d->ptr.p_double[ibegin-1+tmpi]; - if( ibegin-1+tmpiptr.p_double[ibegin-1+tmpi]; - } - ra3siin.ptr.p_double[tmpi] = work.ptr.p_double[ibegin-1+tmpi]; - ra1siinx2.ptr.pp_double[tmpi][1] = work.ptr.p_double[n+tmpi]; - ra1siinx2.ptr.pp_double[tmpi][2] = work.ptr.p_double[n+tmpi+iin]; - ra4siin.ptr.p_double[tmpi] = work.ptr.p_double[n+2*iin+tmpi]; - rworkspace.ptr.p_double[tmpi] = w->ptr.p_double[*m+tmpi]; - iworkspace.ptr.p_int[tmpi] = iblock->ptr.p_int[*m+tmpi]; - ia1siinx2.ptr.pp_int[tmpi][1] = iwork.ptr.p_int[tmpi]; - ia1siinx2.ptr.pp_int[tmpi][2] = iwork.ptr.p_int[tmpi+iin]; - } - evd_internaldlaebz(1, 0, iin, iin, 1, atoli, rtoli, pivmin, &ra1siin, &ra2siin, &ra3siin, &idumma, &ra1siinx2, &ra4siin, &im, &ia1siinx2, &rworkspace, &iworkspace, &iinfo, _state); - for(tmpi=1; tmpi<=iin; tmpi++) - { - work.ptr.p_double[n+tmpi] = ra1siinx2.ptr.pp_double[tmpi][1]; - work.ptr.p_double[n+tmpi+iin] = ra1siinx2.ptr.pp_double[tmpi][2]; - work.ptr.p_double[n+2*iin+tmpi] = ra4siin.ptr.p_double[tmpi]; - w->ptr.p_double[*m+tmpi] = rworkspace.ptr.p_double[tmpi]; - iblock->ptr.p_int[*m+tmpi] = iworkspace.ptr.p_int[tmpi]; - iwork.ptr.p_int[tmpi] = ia1siinx2.ptr.pp_int[tmpi][1]; - iwork.ptr.p_int[tmpi+iin] = ia1siinx2.ptr.pp_int[tmpi][2]; - } - nwl = nwl+iwork.ptr.p_int[1]; - nwu = nwu+iwork.ptr.p_int[iin+1]; - iwoff = *m-iwork.ptr.p_int[1]; - - /* - * Compute Eigenvalues - */ - itmax = ae_iceil((ae_log(gu-gl+pivmin, _state)-ae_log(pivmin, _state))/ae_log(2, _state), _state)+2; - - /* - * Calling DLAEBZ - * - *CALL DLAEBZ( 2, ITMAX, IN, IN, 1, NB, ATOLI, RTOLI, PIVMIN, - * D( IBEGIN ), E( IBEGIN ), WORK( IBEGIN ), - * IDUMMA, WORK( N+1 ), WORK( N+2*IN+1 ), IOUT, - * IWORK, W( M+1 ), IBLOCK( M+1 ), IINFO ) - */ - for(tmpi=1; tmpi<=iin; tmpi++) - { - ra1siin.ptr.p_double[tmpi] = d->ptr.p_double[ibegin-1+tmpi]; - if( ibegin-1+tmpiptr.p_double[ibegin-1+tmpi]; - } - ra3siin.ptr.p_double[tmpi] = work.ptr.p_double[ibegin-1+tmpi]; - ra1siinx2.ptr.pp_double[tmpi][1] = work.ptr.p_double[n+tmpi]; - ra1siinx2.ptr.pp_double[tmpi][2] = work.ptr.p_double[n+tmpi+iin]; - ra4siin.ptr.p_double[tmpi] = work.ptr.p_double[n+2*iin+tmpi]; - rworkspace.ptr.p_double[tmpi] = w->ptr.p_double[*m+tmpi]; - iworkspace.ptr.p_int[tmpi] = iblock->ptr.p_int[*m+tmpi]; - ia1siinx2.ptr.pp_int[tmpi][1] = iwork.ptr.p_int[tmpi]; - ia1siinx2.ptr.pp_int[tmpi][2] = iwork.ptr.p_int[tmpi+iin]; - } - evd_internaldlaebz(2, itmax, iin, iin, 1, atoli, rtoli, pivmin, &ra1siin, &ra2siin, &ra3siin, &idumma, &ra1siinx2, &ra4siin, &iout, &ia1siinx2, &rworkspace, &iworkspace, &iinfo, _state); - for(tmpi=1; tmpi<=iin; tmpi++) - { - work.ptr.p_double[n+tmpi] = ra1siinx2.ptr.pp_double[tmpi][1]; - work.ptr.p_double[n+tmpi+iin] = ra1siinx2.ptr.pp_double[tmpi][2]; - work.ptr.p_double[n+2*iin+tmpi] = ra4siin.ptr.p_double[tmpi]; - w->ptr.p_double[*m+tmpi] = rworkspace.ptr.p_double[tmpi]; - iblock->ptr.p_int[*m+tmpi] = iworkspace.ptr.p_int[tmpi]; - iwork.ptr.p_int[tmpi] = ia1siinx2.ptr.pp_int[tmpi][1]; - iwork.ptr.p_int[tmpi+iin] = ia1siinx2.ptr.pp_int[tmpi][2]; - } - - /* - * Copy Eigenvalues Into W and IBLOCK - * Use -JB for block number for unconverged eigenvalues. - */ - for(j=1; j<=iout; j++) - { - tmp1 = 0.5*(work.ptr.p_double[j+n]+work.ptr.p_double[j+iin+n]); - - /* - * Flag non-convergence. - */ - if( j>iout-iinfo ) - { - ncnvrg = ae_true; - ib = -jb; - } - else - { - ib = jb; - } - for(je=iwork.ptr.p_int[j]+1+iwoff; je<=iwork.ptr.p_int[j+iin]+iwoff; je++) - { - w->ptr.p_double[je] = tmp1; - iblock->ptr.p_int[je] = ib; - } - } - *m = *m+im; - } - } - - /* - * If RANGE='I', then (WL,WU) contains eigenvalues NWL+1,...,NWU - * If NWL+1 < IL or NWU > IU, discard extra eigenvalues. - */ - if( irange==3 ) - { - im = 0; - idiscl = il-1-nwl; - idiscu = nwu-iu; - if( idiscl>0||idiscu>0 ) - { - for(je=1; je<=*m; je++) - { - if( ae_fp_less_eq(w->ptr.p_double[je],wlu)&&idiscl>0 ) - { - idiscl = idiscl-1; - } - else - { - if( ae_fp_greater_eq(w->ptr.p_double[je],wul)&&idiscu>0 ) - { - idiscu = idiscu-1; - } - else - { - im = im+1; - w->ptr.p_double[im] = w->ptr.p_double[je]; - iblock->ptr.p_int[im] = iblock->ptr.p_int[je]; - } - } - } - *m = im; - } - if( idiscl>0||idiscu>0 ) - { - - /* - * Code to deal with effects of bad arithmetic: - * Some low eigenvalues to be discarded are not in (WL,WLU], - * or high eigenvalues to be discarded are not in (WUL,WU] - * so just kill off the smallest IDISCL/largest IDISCU - * eigenvalues, by simply finding the smallest/largest - * eigenvalue(s). - * - * (If N(w) is monotone non-decreasing, this should never - * happen.) - */ - if( idiscl>0 ) - { - wkill = wu; - for(jdisc=1; jdisc<=idiscl; jdisc++) - { - iw = 0; - for(je=1; je<=*m; je++) - { - if( iblock->ptr.p_int[je]!=0&&(ae_fp_less(w->ptr.p_double[je],wkill)||iw==0) ) - { - iw = je; - wkill = w->ptr.p_double[je]; - } - } - iblock->ptr.p_int[iw] = 0; - } - } - if( idiscu>0 ) - { - wkill = wl; - for(jdisc=1; jdisc<=idiscu; jdisc++) - { - iw = 0; - for(je=1; je<=*m; je++) - { - if( iblock->ptr.p_int[je]!=0&&(ae_fp_greater(w->ptr.p_double[je],wkill)||iw==0) ) - { - iw = je; - wkill = w->ptr.p_double[je]; - } - } - iblock->ptr.p_int[iw] = 0; - } - } - im = 0; - for(je=1; je<=*m; je++) - { - if( iblock->ptr.p_int[je]!=0 ) - { - im = im+1; - w->ptr.p_double[im] = w->ptr.p_double[je]; - iblock->ptr.p_int[im] = iblock->ptr.p_int[je]; - } - } - *m = im; - } - if( idiscl<0||idiscu<0 ) - { - toofew = ae_true; - } - } - - /* - * If ORDER='B', do nothing -- the eigenvalues are already sorted - * by block. - * If ORDER='E', sort the eigenvalues from smallest to largest - */ - if( iorder==1&&*nsplit>1 ) - { - for(je=1; je<=*m-1; je++) - { - ie = 0; - tmp1 = w->ptr.p_double[je]; - for(j=je+1; j<=*m; j++) - { - if( ae_fp_less(w->ptr.p_double[j],tmp1) ) - { - ie = j; - tmp1 = w->ptr.p_double[j]; - } - } - if( ie!=0 ) - { - itmp1 = iblock->ptr.p_int[ie]; - w->ptr.p_double[ie] = w->ptr.p_double[je]; - iblock->ptr.p_int[ie] = iblock->ptr.p_int[je]; - w->ptr.p_double[je] = tmp1; - iblock->ptr.p_int[je] = itmp1; - } - } - } - for(j=1; j<=*m; j++) - { - w->ptr.p_double[j] = w->ptr.p_double[j]*scalefactor; - } - *errorcode = 0; - if( ncnvrg ) - { - *errorcode = *errorcode+1; - } - if( toofew ) - { - *errorcode = *errorcode+2; - } - result = *errorcode==0; - ae_frame_leave(_state); - return result; -} - - -static void evd_internaldstein(ae_int_t n, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t m, - /* Real */ ae_vector* w, - /* Integer */ ae_vector* iblock, - /* Integer */ ae_vector* isplit, - /* Real */ ae_matrix* z, - /* Integer */ ae_vector* ifail, - ae_int_t* info, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _e; - ae_vector _w; - ae_int_t maxits; - ae_int_t extra; - ae_int_t b1; - ae_int_t blksiz; - ae_int_t bn; - ae_int_t gpind; - ae_int_t i; - ae_int_t iinfo; - ae_int_t its; - ae_int_t j; - ae_int_t j1; - ae_int_t jblk; - ae_int_t jmax; - ae_int_t nblk; - ae_int_t nrmchk; - double dtpcrt; - double eps; - double eps1; - double nrm; - double onenrm; - double ortol; - double pertol; - double scl; - double sep; - double tol; - double xj; - double xjm; - double ztr; - ae_vector work1; - ae_vector work2; - ae_vector work3; - ae_vector work4; - ae_vector work5; - ae_vector iwork; - ae_bool tmpcriterion; - ae_int_t ti; - ae_int_t i1; - ae_int_t i2; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_e, e, _state, ae_true); - e = &_e; - ae_vector_init_copy(&_w, w, _state, ae_true); - w = &_w; - ae_matrix_clear(z); - ae_vector_clear(ifail); - *info = 0; - ae_vector_init(&work1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work3, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work4, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work5, 0, DT_REAL, _state, ae_true); - ae_vector_init(&iwork, 0, DT_INT, _state, ae_true); - - maxits = 5; - extra = 2; - ae_vector_set_length(&work1, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(&work2, ae_maxint(n-1, 1, _state)+1, _state); - ae_vector_set_length(&work3, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(&work4, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(&work5, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(&iwork, ae_maxint(n, 1, _state)+1, _state); - ae_vector_set_length(ifail, ae_maxint(m, 1, _state)+1, _state); - ae_matrix_set_length(z, ae_maxint(n, 1, _state)+1, ae_maxint(m, 1, _state)+1, _state); - - /* - * these initializers are not really necessary, - * but without them compiler complains about uninitialized locals - */ - gpind = 0; - onenrm = 0; - ortol = 0; - dtpcrt = 0; - xjm = 0; - - /* - * Test the input parameters. - */ - *info = 0; - for(i=1; i<=m; i++) - { - ifail->ptr.p_int[i] = 0; - } - if( n<0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( m<0||m>n ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - for(j=2; j<=m; j++) - { - if( iblock->ptr.p_int[j]ptr.p_int[j-1] ) - { - *info = -6; - break; - } - if( iblock->ptr.p_int[j]==iblock->ptr.p_int[j-1]&&ae_fp_less(w->ptr.p_double[j],w->ptr.p_double[j-1]) ) - { - *info = -5; - break; - } - } - if( *info!=0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Quick return if possible - */ - if( n==0||m==0 ) - { - ae_frame_leave(_state); - return; - } - if( n==1 ) - { - z->ptr.pp_double[1][1] = 1; - ae_frame_leave(_state); - return; - } - - /* - * Some preparations - */ - ti = n-1; - ae_v_move(&work1.ptr.p_double[1], 1, &e->ptr.p_double[1], 1, ae_v_len(1,ti)); - ae_vector_set_length(e, n+1, _state); - ae_v_move(&e->ptr.p_double[1], 1, &work1.ptr.p_double[1], 1, ae_v_len(1,ti)); - ae_v_move(&work1.ptr.p_double[1], 1, &w->ptr.p_double[1], 1, ae_v_len(1,m)); - ae_vector_set_length(w, n+1, _state); - ae_v_move(&w->ptr.p_double[1], 1, &work1.ptr.p_double[1], 1, ae_v_len(1,m)); - - /* - * Get machine constants. - */ - eps = ae_machineepsilon; - - /* - * Compute eigenvectors of matrix blocks. - */ - j1 = 1; - for(nblk=1; nblk<=iblock->ptr.p_int[m]; nblk++) - { - - /* - * Find starting and ending indices of block nblk. - */ - if( nblk==1 ) - { - b1 = 1; - } - else - { - b1 = isplit->ptr.p_int[nblk-1]+1; - } - bn = isplit->ptr.p_int[nblk]; - blksiz = bn-b1+1; - if( blksiz!=1 ) - { - - /* - * Compute reorthogonalization criterion and stopping criterion. - */ - gpind = b1; - onenrm = ae_fabs(d->ptr.p_double[b1], _state)+ae_fabs(e->ptr.p_double[b1], _state); - onenrm = ae_maxreal(onenrm, ae_fabs(d->ptr.p_double[bn], _state)+ae_fabs(e->ptr.p_double[bn-1], _state), _state); - for(i=b1+1; i<=bn-1; i++) - { - onenrm = ae_maxreal(onenrm, ae_fabs(d->ptr.p_double[i], _state)+ae_fabs(e->ptr.p_double[i-1], _state)+ae_fabs(e->ptr.p_double[i], _state), _state); - } - ortol = 0.001*onenrm; - dtpcrt = ae_sqrt(0.1/blksiz, _state); - } - - /* - * Loop through eigenvalues of block nblk. - */ - jblk = 0; - for(j=j1; j<=m; j++) - { - if( iblock->ptr.p_int[j]!=nblk ) - { - j1 = j; - break; - } - jblk = jblk+1; - xj = w->ptr.p_double[j]; - if( blksiz==1 ) - { - - /* - * Skip all the work if the block size is one. - */ - work1.ptr.p_double[1] = 1; - } - else - { - - /* - * If eigenvalues j and j-1 are too close, add a relatively - * small perturbation. - */ - if( jblk>1 ) - { - eps1 = ae_fabs(eps*xj, _state); - pertol = 10*eps1; - sep = xj-xjm; - if( ae_fp_less(sep,pertol) ) - { - xj = xjm+pertol; - } - } - its = 0; - nrmchk = 0; - - /* - * Get random starting vector. - */ - for(ti=1; ti<=blksiz; ti++) - { - work1.ptr.p_double[ti] = 2*ae_randomreal(_state)-1; - } - - /* - * Copy the matrix T so it won't be destroyed in factorization. - */ - for(ti=1; ti<=blksiz-1; ti++) - { - work2.ptr.p_double[ti] = e->ptr.p_double[b1+ti-1]; - work3.ptr.p_double[ti] = e->ptr.p_double[b1+ti-1]; - work4.ptr.p_double[ti] = d->ptr.p_double[b1+ti-1]; - } - work4.ptr.p_double[blksiz] = d->ptr.p_double[b1+blksiz-1]; - - /* - * Compute LU factors with partial pivoting ( PT = LU ) - */ - tol = 0; - evd_tdininternaldlagtf(blksiz, &work4, xj, &work2, &work3, tol, &work5, &iwork, &iinfo, _state); - - /* - * Update iteration count. - */ - do - { - its = its+1; - if( its>maxits ) - { - - /* - * If stopping criterion was not satisfied, update info and - * store eigenvector number in array ifail. - */ - *info = *info+1; - ifail->ptr.p_int[*info] = j; - break; - } - - /* - * Normalize and scale the righthand side vector Pb. - */ - v = 0; - for(ti=1; ti<=blksiz; ti++) - { - v = v+ae_fabs(work1.ptr.p_double[ti], _state); - } - scl = blksiz*onenrm*ae_maxreal(eps, ae_fabs(work4.ptr.p_double[blksiz], _state), _state)/v; - ae_v_muld(&work1.ptr.p_double[1], 1, ae_v_len(1,blksiz), scl); - - /* - * Solve the system LU = Pb. - */ - evd_tdininternaldlagts(blksiz, &work4, &work2, &work3, &work5, &iwork, &work1, &tol, &iinfo, _state); - - /* - * Reorthogonalize by modified Gram-Schmidt if eigenvalues are - * close enough. - */ - if( jblk!=1 ) - { - if( ae_fp_greater(ae_fabs(xj-xjm, _state),ortol) ) - { - gpind = j; - } - if( gpind!=j ) - { - for(i=gpind; i<=j-1; i++) - { - i1 = b1; - i2 = b1+blksiz-1; - ztr = ae_v_dotproduct(&work1.ptr.p_double[1], 1, &z->ptr.pp_double[i1][i], z->stride, ae_v_len(1,blksiz)); - ae_v_subd(&work1.ptr.p_double[1], 1, &z->ptr.pp_double[i1][i], z->stride, ae_v_len(1,blksiz), ztr); - touchint(&i2, _state); - } - } - } - - /* - * Check the infinity norm of the iterate. - */ - jmax = vectoridxabsmax(&work1, 1, blksiz, _state); - nrm = ae_fabs(work1.ptr.p_double[jmax], _state); - - /* - * Continue for additional iterations after norm reaches - * stopping criterion. - */ - tmpcriterion = ae_false; - if( ae_fp_less(nrm,dtpcrt) ) - { - tmpcriterion = ae_true; - } - else - { - nrmchk = nrmchk+1; - if( nrmchkptr.pp_double[i][j] = 0; - } - for(i=1; i<=blksiz; i++) - { - z->ptr.pp_double[b1+i-1][j] = work1.ptr.p_double[i]; - } - - /* - * Save the shift to check eigenvalue spacing at next - * iteration. - */ - xjm = xj; - } - } - ae_frame_leave(_state); -} - - -static void evd_tdininternaldlagtf(ae_int_t n, - /* Real */ ae_vector* a, - double lambdav, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - double tol, - /* Real */ ae_vector* d, - /* Integer */ ae_vector* iin, - ae_int_t* info, - ae_state *_state) -{ - ae_int_t k; - double eps; - double mult; - double piv1; - double piv2; - double scale1; - double scale2; - double temp; - double tl; - - *info = 0; - - *info = 0; - if( n<0 ) - { - *info = -1; - return; - } - if( n==0 ) - { - return; - } - a->ptr.p_double[1] = a->ptr.p_double[1]-lambdav; - iin->ptr.p_int[n] = 0; - if( n==1 ) - { - if( ae_fp_eq(a->ptr.p_double[1],0) ) - { - iin->ptr.p_int[1] = 1; - } - return; - } - eps = ae_machineepsilon; - tl = ae_maxreal(tol, eps, _state); - scale1 = ae_fabs(a->ptr.p_double[1], _state)+ae_fabs(b->ptr.p_double[1], _state); - for(k=1; k<=n-1; k++) - { - a->ptr.p_double[k+1] = a->ptr.p_double[k+1]-lambdav; - scale2 = ae_fabs(c->ptr.p_double[k], _state)+ae_fabs(a->ptr.p_double[k+1], _state); - if( kptr.p_double[k+1], _state); - } - if( ae_fp_eq(a->ptr.p_double[k],0) ) - { - piv1 = 0; - } - else - { - piv1 = ae_fabs(a->ptr.p_double[k], _state)/scale1; - } - if( ae_fp_eq(c->ptr.p_double[k],0) ) - { - iin->ptr.p_int[k] = 0; - piv2 = 0; - scale1 = scale2; - if( kptr.p_double[k] = 0; - } - } - else - { - piv2 = ae_fabs(c->ptr.p_double[k], _state)/scale2; - if( ae_fp_less_eq(piv2,piv1) ) - { - iin->ptr.p_int[k] = 0; - scale1 = scale2; - c->ptr.p_double[k] = c->ptr.p_double[k]/a->ptr.p_double[k]; - a->ptr.p_double[k+1] = a->ptr.p_double[k+1]-c->ptr.p_double[k]*b->ptr.p_double[k]; - if( kptr.p_double[k] = 0; - } - } - else - { - iin->ptr.p_int[k] = 1; - mult = a->ptr.p_double[k]/c->ptr.p_double[k]; - a->ptr.p_double[k] = c->ptr.p_double[k]; - temp = a->ptr.p_double[k+1]; - a->ptr.p_double[k+1] = b->ptr.p_double[k]-mult*temp; - if( kptr.p_double[k] = b->ptr.p_double[k+1]; - b->ptr.p_double[k+1] = -mult*d->ptr.p_double[k]; - } - b->ptr.p_double[k] = temp; - c->ptr.p_double[k] = mult; - } - } - if( ae_fp_less_eq(ae_maxreal(piv1, piv2, _state),tl)&&iin->ptr.p_int[n]==0 ) - { - iin->ptr.p_int[n] = k; - } - } - if( ae_fp_less_eq(ae_fabs(a->ptr.p_double[n], _state),scale1*tl)&&iin->ptr.p_int[n]==0 ) - { - iin->ptr.p_int[n] = n; - } -} - - -static void evd_tdininternaldlagts(ae_int_t n, - /* Real */ ae_vector* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* c, - /* Real */ ae_vector* d, - /* Integer */ ae_vector* iin, - /* Real */ ae_vector* y, - double* tol, - ae_int_t* info, - ae_state *_state) -{ - ae_int_t k; - double absak; - double ak; - double bignum; - double eps; - double pert; - double sfmin; - double temp; - - *info = 0; - - *info = 0; - if( n<0 ) - { - *info = -1; - return; - } - if( n==0 ) - { - return; - } - eps = ae_machineepsilon; - sfmin = ae_minrealnumber; - bignum = 1/sfmin; - if( ae_fp_less_eq(*tol,0) ) - { - *tol = ae_fabs(a->ptr.p_double[1], _state); - if( n>1 ) - { - *tol = ae_maxreal(*tol, ae_maxreal(ae_fabs(a->ptr.p_double[2], _state), ae_fabs(b->ptr.p_double[1], _state), _state), _state); - } - for(k=3; k<=n; k++) - { - *tol = ae_maxreal(*tol, ae_maxreal(ae_fabs(a->ptr.p_double[k], _state), ae_maxreal(ae_fabs(b->ptr.p_double[k-1], _state), ae_fabs(d->ptr.p_double[k-2], _state), _state), _state), _state); - } - *tol = *tol*eps; - if( ae_fp_eq(*tol,0) ) - { - *tol = eps; - } - } - for(k=2; k<=n; k++) - { - if( iin->ptr.p_int[k-1]==0 ) - { - y->ptr.p_double[k] = y->ptr.p_double[k]-c->ptr.p_double[k-1]*y->ptr.p_double[k-1]; - } - else - { - temp = y->ptr.p_double[k-1]; - y->ptr.p_double[k-1] = y->ptr.p_double[k]; - y->ptr.p_double[k] = temp-c->ptr.p_double[k-1]*y->ptr.p_double[k]; - } - } - for(k=n; k>=1; k--) - { - if( k<=n-2 ) - { - temp = y->ptr.p_double[k]-b->ptr.p_double[k]*y->ptr.p_double[k+1]-d->ptr.p_double[k]*y->ptr.p_double[k+2]; - } - else - { - if( k==n-1 ) - { - temp = y->ptr.p_double[k]-b->ptr.p_double[k]*y->ptr.p_double[k+1]; - } - else - { - temp = y->ptr.p_double[k]; - } - } - ak = a->ptr.p_double[k]; - pert = ae_fabs(*tol, _state); - if( ae_fp_less(ak,0) ) - { - pert = -pert; - } - for(;;) - { - absak = ae_fabs(ak, _state); - if( ae_fp_less(absak,1) ) - { - if( ae_fp_less(absak,sfmin) ) - { - if( ae_fp_eq(absak,0)||ae_fp_greater(ae_fabs(temp, _state)*sfmin,absak) ) - { - ak = ak+pert; - pert = 2*pert; - continue; - } - else - { - temp = temp*bignum; - ak = ak*bignum; - } - } - else - { - if( ae_fp_greater(ae_fabs(temp, _state),absak*bignum) ) - { - ak = ak+pert; - pert = 2*pert; - continue; - } - } - } - break; - } - y->ptr.p_double[k] = temp/ak; - } -} - - -static void evd_internaldlaebz(ae_int_t ijob, - ae_int_t nitmax, - ae_int_t n, - ae_int_t mmax, - ae_int_t minp, - double abstol, - double reltol, - double pivmin, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - /* Real */ ae_vector* e2, - /* Integer */ ae_vector* nval, - /* Real */ ae_matrix* ab, - /* Real */ ae_vector* c, - ae_int_t* mout, - /* Integer */ ae_matrix* nab, - /* Real */ ae_vector* work, - /* Integer */ ae_vector* iwork, - ae_int_t* info, - ae_state *_state) -{ - ae_int_t itmp1; - ae_int_t itmp2; - ae_int_t j; - ae_int_t ji; - ae_int_t jit; - ae_int_t jp; - ae_int_t kf; - ae_int_t kfnew; - ae_int_t kl; - ae_int_t klnew; - double tmp1; - double tmp2; - - *mout = 0; - *info = 0; - - *info = 0; - if( ijob<1||ijob>3 ) - { - *info = -1; - return; - } - - /* - * Initialize NAB - */ - if( ijob==1 ) - { - - /* - * Compute the number of eigenvalues in the initial intervals. - */ - *mout = 0; - - /* - *DIR$ NOVECTOR - */ - for(ji=1; ji<=minp; ji++) - { - for(jp=1; jp<=2; jp++) - { - tmp1 = d->ptr.p_double[1]-ab->ptr.pp_double[ji][jp]; - if( ae_fp_less(ae_fabs(tmp1, _state),pivmin) ) - { - tmp1 = -pivmin; - } - nab->ptr.pp_int[ji][jp] = 0; - if( ae_fp_less_eq(tmp1,0) ) - { - nab->ptr.pp_int[ji][jp] = 1; - } - for(j=2; j<=n; j++) - { - tmp1 = d->ptr.p_double[j]-e2->ptr.p_double[j-1]/tmp1-ab->ptr.pp_double[ji][jp]; - if( ae_fp_less(ae_fabs(tmp1, _state),pivmin) ) - { - tmp1 = -pivmin; - } - if( ae_fp_less_eq(tmp1,0) ) - { - nab->ptr.pp_int[ji][jp] = nab->ptr.pp_int[ji][jp]+1; - } - } - } - *mout = *mout+nab->ptr.pp_int[ji][2]-nab->ptr.pp_int[ji][1]; - } - return; - } - - /* - * Initialize for loop - * - * KF and KL have the following meaning: - * Intervals 1,...,KF-1 have converged. - * Intervals KF,...,KL still need to be refined. - */ - kf = 1; - kl = minp; - - /* - * If IJOB=2, initialize C. - * If IJOB=3, use the user-supplied starting point. - */ - if( ijob==2 ) - { - for(ji=1; ji<=minp; ji++) - { - c->ptr.p_double[ji] = 0.5*(ab->ptr.pp_double[ji][1]+ab->ptr.pp_double[ji][2]); - } - } - - /* - * Iteration loop - */ - for(jit=1; jit<=nitmax; jit++) - { - - /* - * Loop over intervals - * - * - * Serial Version of the loop - */ - klnew = kl; - for(ji=kf; ji<=kl; ji++) - { - - /* - * Compute N(w), the number of eigenvalues less than w - */ - tmp1 = c->ptr.p_double[ji]; - tmp2 = d->ptr.p_double[1]-tmp1; - itmp1 = 0; - if( ae_fp_less_eq(tmp2,pivmin) ) - { - itmp1 = 1; - tmp2 = ae_minreal(tmp2, -pivmin, _state); - } - - /* - * A series of compiler directives to defeat vectorization - * for the next loop - * - **$PL$ CMCHAR=' ' - *CDIR$ NEXTSCALAR - *C$DIR SCALAR - *CDIR$ NEXT SCALAR - *CVD$L NOVECTOR - *CDEC$ NOVECTOR - *CVD$ NOVECTOR - **VDIR NOVECTOR - **VOCL LOOP,SCALAR - *CIBM PREFER SCALAR - **$PL$ CMCHAR='*' - */ - for(j=2; j<=n; j++) - { - tmp2 = d->ptr.p_double[j]-e2->ptr.p_double[j-1]/tmp2-tmp1; - if( ae_fp_less_eq(tmp2,pivmin) ) - { - itmp1 = itmp1+1; - tmp2 = ae_minreal(tmp2, -pivmin, _state); - } - } - if( ijob<=2 ) - { - - /* - * IJOB=2: Choose all intervals containing eigenvalues. - * - * Insure that N(w) is monotone - */ - itmp1 = ae_minint(nab->ptr.pp_int[ji][2], ae_maxint(nab->ptr.pp_int[ji][1], itmp1, _state), _state); - - /* - * Update the Queue -- add intervals if both halves - * contain eigenvalues. - */ - if( itmp1==nab->ptr.pp_int[ji][2] ) - { - - /* - * No eigenvalue in the upper interval: - * just use the lower interval. - */ - ab->ptr.pp_double[ji][2] = tmp1; - } - else - { - if( itmp1==nab->ptr.pp_int[ji][1] ) - { - - /* - * No eigenvalue in the lower interval: - * just use the upper interval. - */ - ab->ptr.pp_double[ji][1] = tmp1; - } - else - { - if( klnewptr.pp_double[klnew][2] = ab->ptr.pp_double[ji][2]; - nab->ptr.pp_int[klnew][2] = nab->ptr.pp_int[ji][2]; - ab->ptr.pp_double[klnew][1] = tmp1; - nab->ptr.pp_int[klnew][1] = itmp1; - ab->ptr.pp_double[ji][2] = tmp1; - nab->ptr.pp_int[ji][2] = itmp1; - } - else - { - *info = mmax+1; - return; - } - } - } - } - else - { - - /* - * IJOB=3: Binary search. Keep only the interval - * containing w s.t. N(w) = NVAL - */ - if( itmp1<=nval->ptr.p_int[ji] ) - { - ab->ptr.pp_double[ji][1] = tmp1; - nab->ptr.pp_int[ji][1] = itmp1; - } - if( itmp1>=nval->ptr.p_int[ji] ) - { - ab->ptr.pp_double[ji][2] = tmp1; - nab->ptr.pp_int[ji][2] = itmp1; - } - } - } - kl = klnew; - - /* - * Check for convergence - */ - kfnew = kf; - for(ji=kf; ji<=kl; ji++) - { - tmp1 = ae_fabs(ab->ptr.pp_double[ji][2]-ab->ptr.pp_double[ji][1], _state); - tmp2 = ae_maxreal(ae_fabs(ab->ptr.pp_double[ji][2], _state), ae_fabs(ab->ptr.pp_double[ji][1], _state), _state); - if( ae_fp_less(tmp1,ae_maxreal(abstol, ae_maxreal(pivmin, reltol*tmp2, _state), _state))||nab->ptr.pp_int[ji][1]>=nab->ptr.pp_int[ji][2] ) - { - - /* - * Converged -- Swap with position KFNEW, - * then increment KFNEW - */ - if( ji>kfnew ) - { - tmp1 = ab->ptr.pp_double[ji][1]; - tmp2 = ab->ptr.pp_double[ji][2]; - itmp1 = nab->ptr.pp_int[ji][1]; - itmp2 = nab->ptr.pp_int[ji][2]; - ab->ptr.pp_double[ji][1] = ab->ptr.pp_double[kfnew][1]; - ab->ptr.pp_double[ji][2] = ab->ptr.pp_double[kfnew][2]; - nab->ptr.pp_int[ji][1] = nab->ptr.pp_int[kfnew][1]; - nab->ptr.pp_int[ji][2] = nab->ptr.pp_int[kfnew][2]; - ab->ptr.pp_double[kfnew][1] = tmp1; - ab->ptr.pp_double[kfnew][2] = tmp2; - nab->ptr.pp_int[kfnew][1] = itmp1; - nab->ptr.pp_int[kfnew][2] = itmp2; - if( ijob==3 ) - { - itmp1 = nval->ptr.p_int[ji]; - nval->ptr.p_int[ji] = nval->ptr.p_int[kfnew]; - nval->ptr.p_int[kfnew] = itmp1; - } - } - kfnew = kfnew+1; - } - } - kf = kfnew; - - /* - * Choose Midpoints - */ - for(ji=kf; ji<=kl; ji++) - { - c->ptr.p_double[ji] = 0.5*(ab->ptr.pp_double[ji][1]+ab->ptr.pp_double[ji][2]); - } - - /* - * If no more intervals to refine, quit. - */ - if( kf>kl ) - { - break; - } - } - - /* - * Converged - */ - *info = ae_maxint(kl+1-kf, 0, _state); - *mout = kl; -} - - -/************************************************************************* -Internal subroutine - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - June 30, 1999 -*************************************************************************/ -static void evd_internaltrevc(/* Real */ ae_matrix* t, - ae_int_t n, - ae_int_t side, - ae_int_t howmny, - /* Boolean */ ae_vector* vselect, - /* Real */ ae_matrix* vl, - /* Real */ ae_matrix* vr, - ae_int_t* m, - ae_int_t* info, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _vselect; - ae_bool allv; - ae_bool bothv; - ae_bool leftv; - ae_bool over; - ae_bool pair; - ae_bool rightv; - ae_bool somev; - ae_int_t i; - ae_int_t ierr; - ae_int_t ii; - ae_int_t ip; - ae_int_t iis; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - ae_int_t jnxt; - ae_int_t k; - ae_int_t ki; - ae_int_t n2; - double beta; - double bignum; - double emax; - double rec; - double remax; - double scl; - double smin; - double smlnum; - double ulp; - double unfl; - double vcrit; - double vmax; - double wi; - double wr; - double xnorm; - ae_matrix x; - ae_vector work; - ae_vector temp; - ae_matrix temp11; - ae_matrix temp22; - ae_matrix temp11b; - ae_matrix temp21b; - ae_matrix temp12b; - ae_matrix temp22b; - ae_bool skipflag; - ae_int_t k1; - ae_int_t k2; - ae_int_t k3; - ae_int_t k4; - double vt; - ae_vector rswap4; - ae_vector zswap4; - ae_matrix ipivot44; - ae_vector civ4; - ae_vector crv4; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_vselect, vselect, _state, ae_true); - vselect = &_vselect; - *m = 0; - *info = 0; - ae_matrix_init(&x, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - ae_vector_init(&temp, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&temp11, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&temp22, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&temp11b, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&temp21b, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&temp12b, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&temp22b, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&rswap4, 0, DT_BOOL, _state, ae_true); - ae_vector_init(&zswap4, 0, DT_BOOL, _state, ae_true); - ae_matrix_init(&ipivot44, 0, 0, DT_INT, _state, ae_true); - ae_vector_init(&civ4, 0, DT_REAL, _state, ae_true); - ae_vector_init(&crv4, 0, DT_REAL, _state, ae_true); - - ae_matrix_set_length(&x, 2+1, 2+1, _state); - ae_matrix_set_length(&temp11, 1+1, 1+1, _state); - ae_matrix_set_length(&temp11b, 1+1, 1+1, _state); - ae_matrix_set_length(&temp21b, 2+1, 1+1, _state); - ae_matrix_set_length(&temp12b, 1+1, 2+1, _state); - ae_matrix_set_length(&temp22b, 2+1, 2+1, _state); - ae_matrix_set_length(&temp22, 2+1, 2+1, _state); - ae_vector_set_length(&work, 3*n+1, _state); - ae_vector_set_length(&temp, n+1, _state); - ae_vector_set_length(&rswap4, 4+1, _state); - ae_vector_set_length(&zswap4, 4+1, _state); - ae_matrix_set_length(&ipivot44, 4+1, 4+1, _state); - ae_vector_set_length(&civ4, 4+1, _state); - ae_vector_set_length(&crv4, 4+1, _state); - if( howmny!=1 ) - { - if( side==1||side==3 ) - { - ae_matrix_set_length(vr, n+1, n+1, _state); - } - if( side==2||side==3 ) - { - ae_matrix_set_length(vl, n+1, n+1, _state); - } - } - - /* - * Decode and test the input parameters - */ - bothv = side==3; - rightv = side==1||bothv; - leftv = side==2||bothv; - allv = howmny==2; - over = howmny==1; - somev = howmny==3; - *info = 0; - if( n<0 ) - { - *info = -2; - ae_frame_leave(_state); - return; - } - if( !rightv&&!leftv ) - { - *info = -3; - ae_frame_leave(_state); - return; - } - if( (!allv&&!over)&&!somev ) - { - *info = -4; - ae_frame_leave(_state); - return; - } - - /* - * Set M to the number of columns required to store the selected - * eigenvectors, standardize the array SELECT if necessary, and - * test MM. - */ - if( somev ) - { - *m = 0; - pair = ae_false; - for(j=1; j<=n; j++) - { - if( pair ) - { - pair = ae_false; - vselect->ptr.p_bool[j] = ae_false; - } - else - { - if( jptr.pp_double[j+1][j],0) ) - { - if( vselect->ptr.p_bool[j] ) - { - *m = *m+1; - } - } - else - { - pair = ae_true; - if( vselect->ptr.p_bool[j]||vselect->ptr.p_bool[j+1] ) - { - vselect->ptr.p_bool[j] = ae_true; - *m = *m+2; - } - } - } - else - { - if( vselect->ptr.p_bool[n] ) - { - *m = *m+1; - } - } - } - } - } - else - { - *m = n; - } - - /* - * Quick return if possible. - */ - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Set the constants to control overflow. - */ - unfl = ae_minrealnumber; - ulp = ae_machineepsilon; - smlnum = unfl*(n/ulp); - bignum = (1-ulp)/smlnum; - - /* - * Compute 1-norm of each column of strictly upper triangular - * part of T to control overflow in triangular solver. - */ - work.ptr.p_double[1] = 0; - for(j=2; j<=n; j++) - { - work.ptr.p_double[j] = 0; - for(i=1; i<=j-1; i++) - { - work.ptr.p_double[j] = work.ptr.p_double[j]+ae_fabs(t->ptr.pp_double[i][j], _state); - } - } - - /* - * Index IP is used to specify the real or complex eigenvalue: - * IP = 0, real eigenvalue, - * 1, first of conjugate complex pair: (wr,wi) - * -1, second of conjugate complex pair: (wr,wi) - */ - n2 = 2*n; - if( rightv ) - { - - /* - * Compute right eigenvectors. - */ - ip = 0; - iis = *m; - for(ki=n; ki>=1; ki--) - { - skipflag = ae_false; - if( ip==1 ) - { - skipflag = ae_true; - } - else - { - if( ki!=1 ) - { - if( ae_fp_neq(t->ptr.pp_double[ki][ki-1],0) ) - { - ip = -1; - } - } - if( somev ) - { - if( ip==0 ) - { - if( !vselect->ptr.p_bool[ki] ) - { - skipflag = ae_true; - } - } - else - { - if( !vselect->ptr.p_bool[ki-1] ) - { - skipflag = ae_true; - } - } - } - } - if( !skipflag ) - { - - /* - * Compute the KI-th eigenvalue (WR,WI). - */ - wr = t->ptr.pp_double[ki][ki]; - wi = 0; - if( ip!=0 ) - { - wi = ae_sqrt(ae_fabs(t->ptr.pp_double[ki][ki-1], _state), _state)*ae_sqrt(ae_fabs(t->ptr.pp_double[ki-1][ki], _state), _state); - } - smin = ae_maxreal(ulp*(ae_fabs(wr, _state)+ae_fabs(wi, _state)), smlnum, _state); - if( ip==0 ) - { - - /* - * Real right eigenvector - */ - work.ptr.p_double[ki+n] = 1; - - /* - * Form right-hand side - */ - for(k=1; k<=ki-1; k++) - { - work.ptr.p_double[k+n] = -t->ptr.pp_double[k][ki]; - } - - /* - * Solve the upper quasi-triangular system: - * (T(1:KI-1,1:KI-1) - WR)*X = SCALE*WORK. - */ - jnxt = ki-1; - for(j=ki-1; j>=1; j--) - { - if( j>jnxt ) - { - continue; - } - j1 = j; - j2 = j; - jnxt = j-1; - if( j>1 ) - { - if( ae_fp_neq(t->ptr.pp_double[j][j-1],0) ) - { - j1 = j-1; - jnxt = j-2; - } - } - if( j1==j2 ) - { - - /* - * 1-by-1 diagonal block - */ - temp11.ptr.pp_double[1][1] = t->ptr.pp_double[j][j]; - temp11b.ptr.pp_double[1][1] = work.ptr.p_double[j+n]; - evd_internalhsevdlaln2(ae_false, 1, 1, smin, 1, &temp11, 1.0, 1.0, &temp11b, wr, 0.0, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale X(1,1) to avoid overflow when updating - * the right-hand side. - */ - if( ae_fp_greater(xnorm,1) ) - { - if( ae_fp_greater(work.ptr.p_double[j],bignum/xnorm) ) - { - x.ptr.pp_double[1][1] = x.ptr.pp_double[1][1]/xnorm; - scl = scl/xnorm; - } - } - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - k1 = n+1; - k2 = n+ki; - ae_v_muld(&work.ptr.p_double[k1], 1, ae_v_len(k1,k2), scl); - } - work.ptr.p_double[j+n] = x.ptr.pp_double[1][1]; - - /* - * Update right-hand side - */ - k1 = 1+n; - k2 = j-1+n; - k3 = j-1; - vt = -x.ptr.pp_double[1][1]; - ae_v_addd(&work.ptr.p_double[k1], 1, &t->ptr.pp_double[1][j], t->stride, ae_v_len(k1,k2), vt); - } - else - { - - /* - * 2-by-2 diagonal block - */ - temp22.ptr.pp_double[1][1] = t->ptr.pp_double[j-1][j-1]; - temp22.ptr.pp_double[1][2] = t->ptr.pp_double[j-1][j]; - temp22.ptr.pp_double[2][1] = t->ptr.pp_double[j][j-1]; - temp22.ptr.pp_double[2][2] = t->ptr.pp_double[j][j]; - temp21b.ptr.pp_double[1][1] = work.ptr.p_double[j-1+n]; - temp21b.ptr.pp_double[2][1] = work.ptr.p_double[j+n]; - evd_internalhsevdlaln2(ae_false, 2, 1, smin, 1.0, &temp22, 1.0, 1.0, &temp21b, wr, 0, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale X(1,1) and X(2,1) to avoid overflow when - * updating the right-hand side. - */ - if( ae_fp_greater(xnorm,1) ) - { - beta = ae_maxreal(work.ptr.p_double[j-1], work.ptr.p_double[j], _state); - if( ae_fp_greater(beta,bignum/xnorm) ) - { - x.ptr.pp_double[1][1] = x.ptr.pp_double[1][1]/xnorm; - x.ptr.pp_double[2][1] = x.ptr.pp_double[2][1]/xnorm; - scl = scl/xnorm; - } - } - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - k1 = 1+n; - k2 = ki+n; - ae_v_muld(&work.ptr.p_double[k1], 1, ae_v_len(k1,k2), scl); - } - work.ptr.p_double[j-1+n] = x.ptr.pp_double[1][1]; - work.ptr.p_double[j+n] = x.ptr.pp_double[2][1]; - - /* - * Update right-hand side - */ - k1 = 1+n; - k2 = j-2+n; - k3 = j-2; - k4 = j-1; - vt = -x.ptr.pp_double[1][1]; - ae_v_addd(&work.ptr.p_double[k1], 1, &t->ptr.pp_double[1][k4], t->stride, ae_v_len(k1,k2), vt); - vt = -x.ptr.pp_double[2][1]; - ae_v_addd(&work.ptr.p_double[k1], 1, &t->ptr.pp_double[1][j], t->stride, ae_v_len(k1,k2), vt); - } - } - - /* - * Copy the vector x or Q*x to VR and normalize. - */ - if( !over ) - { - k1 = 1+n; - k2 = ki+n; - ae_v_move(&vr->ptr.pp_double[1][iis], vr->stride, &work.ptr.p_double[k1], 1, ae_v_len(1,ki)); - ii = columnidxabsmax(vr, 1, ki, iis, _state); - remax = 1/ae_fabs(vr->ptr.pp_double[ii][iis], _state); - ae_v_muld(&vr->ptr.pp_double[1][iis], vr->stride, ae_v_len(1,ki), remax); - for(k=ki+1; k<=n; k++) - { - vr->ptr.pp_double[k][iis] = 0; - } - } - else - { - if( ki>1 ) - { - ae_v_move(&temp.ptr.p_double[1], 1, &vr->ptr.pp_double[1][ki], vr->stride, ae_v_len(1,n)); - matrixvectormultiply(vr, 1, n, 1, ki-1, ae_false, &work, 1+n, ki-1+n, 1.0, &temp, 1, n, work.ptr.p_double[ki+n], _state); - ae_v_move(&vr->ptr.pp_double[1][ki], vr->stride, &temp.ptr.p_double[1], 1, ae_v_len(1,n)); - } - ii = columnidxabsmax(vr, 1, n, ki, _state); - remax = 1/ae_fabs(vr->ptr.pp_double[ii][ki], _state); - ae_v_muld(&vr->ptr.pp_double[1][ki], vr->stride, ae_v_len(1,n), remax); - } - } - else - { - - /* - * Complex right eigenvector. - * - * Initial solve - * [ (T(KI-1,KI-1) T(KI-1,KI) ) - (WR + I* WI)]*X = 0. - * [ (T(KI,KI-1) T(KI,KI) ) ] - */ - if( ae_fp_greater_eq(ae_fabs(t->ptr.pp_double[ki-1][ki], _state),ae_fabs(t->ptr.pp_double[ki][ki-1], _state)) ) - { - work.ptr.p_double[ki-1+n] = 1; - work.ptr.p_double[ki+n2] = wi/t->ptr.pp_double[ki-1][ki]; - } - else - { - work.ptr.p_double[ki-1+n] = -wi/t->ptr.pp_double[ki][ki-1]; - work.ptr.p_double[ki+n2] = 1; - } - work.ptr.p_double[ki+n] = 0; - work.ptr.p_double[ki-1+n2] = 0; - - /* - * Form right-hand side - */ - for(k=1; k<=ki-2; k++) - { - work.ptr.p_double[k+n] = -work.ptr.p_double[ki-1+n]*t->ptr.pp_double[k][ki-1]; - work.ptr.p_double[k+n2] = -work.ptr.p_double[ki+n2]*t->ptr.pp_double[k][ki]; - } - - /* - * Solve upper quasi-triangular system: - * (T(1:KI-2,1:KI-2) - (WR+i*WI))*X = SCALE*(WORK+i*WORK2) - */ - jnxt = ki-2; - for(j=ki-2; j>=1; j--) - { - if( j>jnxt ) - { - continue; - } - j1 = j; - j2 = j; - jnxt = j-1; - if( j>1 ) - { - if( ae_fp_neq(t->ptr.pp_double[j][j-1],0) ) - { - j1 = j-1; - jnxt = j-2; - } - } - if( j1==j2 ) - { - - /* - * 1-by-1 diagonal block - */ - temp11.ptr.pp_double[1][1] = t->ptr.pp_double[j][j]; - temp12b.ptr.pp_double[1][1] = work.ptr.p_double[j+n]; - temp12b.ptr.pp_double[1][2] = work.ptr.p_double[j+n+n]; - evd_internalhsevdlaln2(ae_false, 1, 2, smin, 1.0, &temp11, 1.0, 1.0, &temp12b, wr, wi, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale X(1,1) and X(1,2) to avoid overflow when - * updating the right-hand side. - */ - if( ae_fp_greater(xnorm,1) ) - { - if( ae_fp_greater(work.ptr.p_double[j],bignum/xnorm) ) - { - x.ptr.pp_double[1][1] = x.ptr.pp_double[1][1]/xnorm; - x.ptr.pp_double[1][2] = x.ptr.pp_double[1][2]/xnorm; - scl = scl/xnorm; - } - } - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - k1 = 1+n; - k2 = ki+n; - ae_v_muld(&work.ptr.p_double[k1], 1, ae_v_len(k1,k2), scl); - k1 = 1+n2; - k2 = ki+n2; - ae_v_muld(&work.ptr.p_double[k1], 1, ae_v_len(k1,k2), scl); - } - work.ptr.p_double[j+n] = x.ptr.pp_double[1][1]; - work.ptr.p_double[j+n2] = x.ptr.pp_double[1][2]; - - /* - * Update the right-hand side - */ - k1 = 1+n; - k2 = j-1+n; - k3 = 1; - k4 = j-1; - vt = -x.ptr.pp_double[1][1]; - ae_v_addd(&work.ptr.p_double[k1], 1, &t->ptr.pp_double[k3][j], t->stride, ae_v_len(k1,k2), vt); - k1 = 1+n2; - k2 = j-1+n2; - k3 = 1; - k4 = j-1; - vt = -x.ptr.pp_double[1][2]; - ae_v_addd(&work.ptr.p_double[k1], 1, &t->ptr.pp_double[k3][j], t->stride, ae_v_len(k1,k2), vt); - } - else - { - - /* - * 2-by-2 diagonal block - */ - temp22.ptr.pp_double[1][1] = t->ptr.pp_double[j-1][j-1]; - temp22.ptr.pp_double[1][2] = t->ptr.pp_double[j-1][j]; - temp22.ptr.pp_double[2][1] = t->ptr.pp_double[j][j-1]; - temp22.ptr.pp_double[2][2] = t->ptr.pp_double[j][j]; - temp22b.ptr.pp_double[1][1] = work.ptr.p_double[j-1+n]; - temp22b.ptr.pp_double[1][2] = work.ptr.p_double[j-1+n+n]; - temp22b.ptr.pp_double[2][1] = work.ptr.p_double[j+n]; - temp22b.ptr.pp_double[2][2] = work.ptr.p_double[j+n+n]; - evd_internalhsevdlaln2(ae_false, 2, 2, smin, 1.0, &temp22, 1.0, 1.0, &temp22b, wr, wi, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale X to avoid overflow when updating - * the right-hand side. - */ - if( ae_fp_greater(xnorm,1) ) - { - beta = ae_maxreal(work.ptr.p_double[j-1], work.ptr.p_double[j], _state); - if( ae_fp_greater(beta,bignum/xnorm) ) - { - rec = 1/xnorm; - x.ptr.pp_double[1][1] = x.ptr.pp_double[1][1]*rec; - x.ptr.pp_double[1][2] = x.ptr.pp_double[1][2]*rec; - x.ptr.pp_double[2][1] = x.ptr.pp_double[2][1]*rec; - x.ptr.pp_double[2][2] = x.ptr.pp_double[2][2]*rec; - scl = scl*rec; - } - } - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - ae_v_muld(&work.ptr.p_double[1+n], 1, ae_v_len(1+n,ki+n), scl); - ae_v_muld(&work.ptr.p_double[1+n2], 1, ae_v_len(1+n2,ki+n2), scl); - } - work.ptr.p_double[j-1+n] = x.ptr.pp_double[1][1]; - work.ptr.p_double[j+n] = x.ptr.pp_double[2][1]; - work.ptr.p_double[j-1+n2] = x.ptr.pp_double[1][2]; - work.ptr.p_double[j+n2] = x.ptr.pp_double[2][2]; - - /* - * Update the right-hand side - */ - vt = -x.ptr.pp_double[1][1]; - ae_v_addd(&work.ptr.p_double[n+1], 1, &t->ptr.pp_double[1][j-1], t->stride, ae_v_len(n+1,n+j-2), vt); - vt = -x.ptr.pp_double[2][1]; - ae_v_addd(&work.ptr.p_double[n+1], 1, &t->ptr.pp_double[1][j], t->stride, ae_v_len(n+1,n+j-2), vt); - vt = -x.ptr.pp_double[1][2]; - ae_v_addd(&work.ptr.p_double[n2+1], 1, &t->ptr.pp_double[1][j-1], t->stride, ae_v_len(n2+1,n2+j-2), vt); - vt = -x.ptr.pp_double[2][2]; - ae_v_addd(&work.ptr.p_double[n2+1], 1, &t->ptr.pp_double[1][j], t->stride, ae_v_len(n2+1,n2+j-2), vt); - } - } - - /* - * Copy the vector x or Q*x to VR and normalize. - */ - if( !over ) - { - ae_v_move(&vr->ptr.pp_double[1][iis-1], vr->stride, &work.ptr.p_double[n+1], 1, ae_v_len(1,ki)); - ae_v_move(&vr->ptr.pp_double[1][iis], vr->stride, &work.ptr.p_double[n2+1], 1, ae_v_len(1,ki)); - emax = 0; - for(k=1; k<=ki; k++) - { - emax = ae_maxreal(emax, ae_fabs(vr->ptr.pp_double[k][iis-1], _state)+ae_fabs(vr->ptr.pp_double[k][iis], _state), _state); - } - remax = 1/emax; - ae_v_muld(&vr->ptr.pp_double[1][iis-1], vr->stride, ae_v_len(1,ki), remax); - ae_v_muld(&vr->ptr.pp_double[1][iis], vr->stride, ae_v_len(1,ki), remax); - for(k=ki+1; k<=n; k++) - { - vr->ptr.pp_double[k][iis-1] = 0; - vr->ptr.pp_double[k][iis] = 0; - } - } - else - { - if( ki>2 ) - { - ae_v_move(&temp.ptr.p_double[1], 1, &vr->ptr.pp_double[1][ki-1], vr->stride, ae_v_len(1,n)); - matrixvectormultiply(vr, 1, n, 1, ki-2, ae_false, &work, 1+n, ki-2+n, 1.0, &temp, 1, n, work.ptr.p_double[ki-1+n], _state); - ae_v_move(&vr->ptr.pp_double[1][ki-1], vr->stride, &temp.ptr.p_double[1], 1, ae_v_len(1,n)); - ae_v_move(&temp.ptr.p_double[1], 1, &vr->ptr.pp_double[1][ki], vr->stride, ae_v_len(1,n)); - matrixvectormultiply(vr, 1, n, 1, ki-2, ae_false, &work, 1+n2, ki-2+n2, 1.0, &temp, 1, n, work.ptr.p_double[ki+n2], _state); - ae_v_move(&vr->ptr.pp_double[1][ki], vr->stride, &temp.ptr.p_double[1], 1, ae_v_len(1,n)); - } - else - { - vt = work.ptr.p_double[ki-1+n]; - ae_v_muld(&vr->ptr.pp_double[1][ki-1], vr->stride, ae_v_len(1,n), vt); - vt = work.ptr.p_double[ki+n2]; - ae_v_muld(&vr->ptr.pp_double[1][ki], vr->stride, ae_v_len(1,n), vt); - } - emax = 0; - for(k=1; k<=n; k++) - { - emax = ae_maxreal(emax, ae_fabs(vr->ptr.pp_double[k][ki-1], _state)+ae_fabs(vr->ptr.pp_double[k][ki], _state), _state); - } - remax = 1/emax; - ae_v_muld(&vr->ptr.pp_double[1][ki-1], vr->stride, ae_v_len(1,n), remax); - ae_v_muld(&vr->ptr.pp_double[1][ki], vr->stride, ae_v_len(1,n), remax); - } - } - iis = iis-1; - if( ip!=0 ) - { - iis = iis-1; - } - } - if( ip==1 ) - { - ip = 0; - } - if( ip==-1 ) - { - ip = 1; - } - } - } - if( leftv ) - { - - /* - * Compute left eigenvectors. - */ - ip = 0; - iis = 1; - for(ki=1; ki<=n; ki++) - { - skipflag = ae_false; - if( ip==-1 ) - { - skipflag = ae_true; - } - else - { - if( ki!=n ) - { - if( ae_fp_neq(t->ptr.pp_double[ki+1][ki],0) ) - { - ip = 1; - } - } - if( somev ) - { - if( !vselect->ptr.p_bool[ki] ) - { - skipflag = ae_true; - } - } - } - if( !skipflag ) - { - - /* - * Compute the KI-th eigenvalue (WR,WI). - */ - wr = t->ptr.pp_double[ki][ki]; - wi = 0; - if( ip!=0 ) - { - wi = ae_sqrt(ae_fabs(t->ptr.pp_double[ki][ki+1], _state), _state)*ae_sqrt(ae_fabs(t->ptr.pp_double[ki+1][ki], _state), _state); - } - smin = ae_maxreal(ulp*(ae_fabs(wr, _state)+ae_fabs(wi, _state)), smlnum, _state); - if( ip==0 ) - { - - /* - * Real left eigenvector. - */ - work.ptr.p_double[ki+n] = 1; - - /* - * Form right-hand side - */ - for(k=ki+1; k<=n; k++) - { - work.ptr.p_double[k+n] = -t->ptr.pp_double[ki][k]; - } - - /* - * Solve the quasi-triangular system: - * (T(KI+1:N,KI+1:N) - WR)'*X = SCALE*WORK - */ - vmax = 1; - vcrit = bignum; - jnxt = ki+1; - for(j=ki+1; j<=n; j++) - { - if( jptr.pp_double[j+1][j],0) ) - { - j2 = j+1; - jnxt = j+2; - } - } - if( j1==j2 ) - { - - /* - * 1-by-1 diagonal block - * - * Scale if necessary to avoid overflow when forming - * the right-hand side. - */ - if( ae_fp_greater(work.ptr.p_double[j],vcrit) ) - { - rec = 1/vmax; - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), rec); - vmax = 1; - vcrit = bignum; - } - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+1][j], t->stride, &work.ptr.p_double[ki+1+n], 1, ae_v_len(ki+1,j-1)); - work.ptr.p_double[j+n] = work.ptr.p_double[j+n]-vt; - - /* - * Solve (T(J,J)-WR)'*X = WORK - */ - temp11.ptr.pp_double[1][1] = t->ptr.pp_double[j][j]; - temp11b.ptr.pp_double[1][1] = work.ptr.p_double[j+n]; - evd_internalhsevdlaln2(ae_false, 1, 1, smin, 1.0, &temp11, 1.0, 1.0, &temp11b, wr, 0, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), scl); - } - work.ptr.p_double[j+n] = x.ptr.pp_double[1][1]; - vmax = ae_maxreal(ae_fabs(work.ptr.p_double[j+n], _state), vmax, _state); - vcrit = bignum/vmax; - } - else - { - - /* - * 2-by-2 diagonal block - * - * Scale if necessary to avoid overflow when forming - * the right-hand side. - */ - beta = ae_maxreal(work.ptr.p_double[j], work.ptr.p_double[j+1], _state); - if( ae_fp_greater(beta,vcrit) ) - { - rec = 1/vmax; - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), rec); - vmax = 1; - vcrit = bignum; - } - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+1][j], t->stride, &work.ptr.p_double[ki+1+n], 1, ae_v_len(ki+1,j-1)); - work.ptr.p_double[j+n] = work.ptr.p_double[j+n]-vt; - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+1][j+1], t->stride, &work.ptr.p_double[ki+1+n], 1, ae_v_len(ki+1,j-1)); - work.ptr.p_double[j+1+n] = work.ptr.p_double[j+1+n]-vt; - - /* - * Solve - * [T(J,J)-WR T(J,J+1) ]'* X = SCALE*( WORK1 ) - * [T(J+1,J) T(J+1,J+1)-WR] ( WORK2 ) - */ - temp22.ptr.pp_double[1][1] = t->ptr.pp_double[j][j]; - temp22.ptr.pp_double[1][2] = t->ptr.pp_double[j][j+1]; - temp22.ptr.pp_double[2][1] = t->ptr.pp_double[j+1][j]; - temp22.ptr.pp_double[2][2] = t->ptr.pp_double[j+1][j+1]; - temp21b.ptr.pp_double[1][1] = work.ptr.p_double[j+n]; - temp21b.ptr.pp_double[2][1] = work.ptr.p_double[j+1+n]; - evd_internalhsevdlaln2(ae_true, 2, 1, smin, 1.0, &temp22, 1.0, 1.0, &temp21b, wr, 0, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), scl); - } - work.ptr.p_double[j+n] = x.ptr.pp_double[1][1]; - work.ptr.p_double[j+1+n] = x.ptr.pp_double[2][1]; - vmax = ae_maxreal(ae_fabs(work.ptr.p_double[j+n], _state), ae_maxreal(ae_fabs(work.ptr.p_double[j+1+n], _state), vmax, _state), _state); - vcrit = bignum/vmax; - } - } - - /* - * Copy the vector x or Q*x to VL and normalize. - */ - if( !over ) - { - ae_v_move(&vl->ptr.pp_double[ki][iis], vl->stride, &work.ptr.p_double[ki+n], 1, ae_v_len(ki,n)); - ii = columnidxabsmax(vl, ki, n, iis, _state); - remax = 1/ae_fabs(vl->ptr.pp_double[ii][iis], _state); - ae_v_muld(&vl->ptr.pp_double[ki][iis], vl->stride, ae_v_len(ki,n), remax); - for(k=1; k<=ki-1; k++) - { - vl->ptr.pp_double[k][iis] = 0; - } - } - else - { - if( kiptr.pp_double[1][ki], vl->stride, ae_v_len(1,n)); - matrixvectormultiply(vl, 1, n, ki+1, n, ae_false, &work, ki+1+n, n+n, 1.0, &temp, 1, n, work.ptr.p_double[ki+n], _state); - ae_v_move(&vl->ptr.pp_double[1][ki], vl->stride, &temp.ptr.p_double[1], 1, ae_v_len(1,n)); - } - ii = columnidxabsmax(vl, 1, n, ki, _state); - remax = 1/ae_fabs(vl->ptr.pp_double[ii][ki], _state); - ae_v_muld(&vl->ptr.pp_double[1][ki], vl->stride, ae_v_len(1,n), remax); - } - } - else - { - - /* - * Complex left eigenvector. - * - * Initial solve: - * ((T(KI,KI) T(KI,KI+1) )' - (WR - I* WI))*X = 0. - * ((T(KI+1,KI) T(KI+1,KI+1)) ) - */ - if( ae_fp_greater_eq(ae_fabs(t->ptr.pp_double[ki][ki+1], _state),ae_fabs(t->ptr.pp_double[ki+1][ki], _state)) ) - { - work.ptr.p_double[ki+n] = wi/t->ptr.pp_double[ki][ki+1]; - work.ptr.p_double[ki+1+n2] = 1; - } - else - { - work.ptr.p_double[ki+n] = 1; - work.ptr.p_double[ki+1+n2] = -wi/t->ptr.pp_double[ki+1][ki]; - } - work.ptr.p_double[ki+1+n] = 0; - work.ptr.p_double[ki+n2] = 0; - - /* - * Form right-hand side - */ - for(k=ki+2; k<=n; k++) - { - work.ptr.p_double[k+n] = -work.ptr.p_double[ki+n]*t->ptr.pp_double[ki][k]; - work.ptr.p_double[k+n2] = -work.ptr.p_double[ki+1+n2]*t->ptr.pp_double[ki+1][k]; - } - - /* - * Solve complex quasi-triangular system: - * ( T(KI+2,N:KI+2,N) - (WR-i*WI) )*X = WORK1+i*WORK2 - */ - vmax = 1; - vcrit = bignum; - jnxt = ki+2; - for(j=ki+2; j<=n; j++) - { - if( jptr.pp_double[j+1][j],0) ) - { - j2 = j+1; - jnxt = j+2; - } - } - if( j1==j2 ) - { - - /* - * 1-by-1 diagonal block - * - * Scale if necessary to avoid overflow when - * forming the right-hand side elements. - */ - if( ae_fp_greater(work.ptr.p_double[j],vcrit) ) - { - rec = 1/vmax; - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), rec); - ae_v_muld(&work.ptr.p_double[ki+n2], 1, ae_v_len(ki+n2,n+n2), rec); - vmax = 1; - vcrit = bignum; - } - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+2][j], t->stride, &work.ptr.p_double[ki+2+n], 1, ae_v_len(ki+2,j-1)); - work.ptr.p_double[j+n] = work.ptr.p_double[j+n]-vt; - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+2][j], t->stride, &work.ptr.p_double[ki+2+n2], 1, ae_v_len(ki+2,j-1)); - work.ptr.p_double[j+n2] = work.ptr.p_double[j+n2]-vt; - - /* - * Solve (T(J,J)-(WR-i*WI))*(X11+i*X12)= WK+I*WK2 - */ - temp11.ptr.pp_double[1][1] = t->ptr.pp_double[j][j]; - temp12b.ptr.pp_double[1][1] = work.ptr.p_double[j+n]; - temp12b.ptr.pp_double[1][2] = work.ptr.p_double[j+n+n]; - evd_internalhsevdlaln2(ae_false, 1, 2, smin, 1.0, &temp11, 1.0, 1.0, &temp12b, wr, -wi, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), scl); - ae_v_muld(&work.ptr.p_double[ki+n2], 1, ae_v_len(ki+n2,n+n2), scl); - } - work.ptr.p_double[j+n] = x.ptr.pp_double[1][1]; - work.ptr.p_double[j+n2] = x.ptr.pp_double[1][2]; - vmax = ae_maxreal(ae_fabs(work.ptr.p_double[j+n], _state), ae_maxreal(ae_fabs(work.ptr.p_double[j+n2], _state), vmax, _state), _state); - vcrit = bignum/vmax; - } - else - { - - /* - * 2-by-2 diagonal block - * - * Scale if necessary to avoid overflow when forming - * the right-hand side elements. - */ - beta = ae_maxreal(work.ptr.p_double[j], work.ptr.p_double[j+1], _state); - if( ae_fp_greater(beta,vcrit) ) - { - rec = 1/vmax; - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), rec); - ae_v_muld(&work.ptr.p_double[ki+n2], 1, ae_v_len(ki+n2,n+n2), rec); - vmax = 1; - vcrit = bignum; - } - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+2][j], t->stride, &work.ptr.p_double[ki+2+n], 1, ae_v_len(ki+2,j-1)); - work.ptr.p_double[j+n] = work.ptr.p_double[j+n]-vt; - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+2][j], t->stride, &work.ptr.p_double[ki+2+n2], 1, ae_v_len(ki+2,j-1)); - work.ptr.p_double[j+n2] = work.ptr.p_double[j+n2]-vt; - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+2][j+1], t->stride, &work.ptr.p_double[ki+2+n], 1, ae_v_len(ki+2,j-1)); - work.ptr.p_double[j+1+n] = work.ptr.p_double[j+1+n]-vt; - vt = ae_v_dotproduct(&t->ptr.pp_double[ki+2][j+1], t->stride, &work.ptr.p_double[ki+2+n2], 1, ae_v_len(ki+2,j-1)); - work.ptr.p_double[j+1+n2] = work.ptr.p_double[j+1+n2]-vt; - - /* - * Solve 2-by-2 complex linear equation - * ([T(j,j) T(j,j+1) ]'-(wr-i*wi)*I)*X = SCALE*B - * ([T(j+1,j) T(j+1,j+1)] ) - */ - temp22.ptr.pp_double[1][1] = t->ptr.pp_double[j][j]; - temp22.ptr.pp_double[1][2] = t->ptr.pp_double[j][j+1]; - temp22.ptr.pp_double[2][1] = t->ptr.pp_double[j+1][j]; - temp22.ptr.pp_double[2][2] = t->ptr.pp_double[j+1][j+1]; - temp22b.ptr.pp_double[1][1] = work.ptr.p_double[j+n]; - temp22b.ptr.pp_double[1][2] = work.ptr.p_double[j+n+n]; - temp22b.ptr.pp_double[2][1] = work.ptr.p_double[j+1+n]; - temp22b.ptr.pp_double[2][2] = work.ptr.p_double[j+1+n+n]; - evd_internalhsevdlaln2(ae_true, 2, 2, smin, 1.0, &temp22, 1.0, 1.0, &temp22b, wr, -wi, &rswap4, &zswap4, &ipivot44, &civ4, &crv4, &x, &scl, &xnorm, &ierr, _state); - - /* - * Scale if necessary - */ - if( ae_fp_neq(scl,1) ) - { - ae_v_muld(&work.ptr.p_double[ki+n], 1, ae_v_len(ki+n,n+n), scl); - ae_v_muld(&work.ptr.p_double[ki+n2], 1, ae_v_len(ki+n2,n+n2), scl); - } - work.ptr.p_double[j+n] = x.ptr.pp_double[1][1]; - work.ptr.p_double[j+n2] = x.ptr.pp_double[1][2]; - work.ptr.p_double[j+1+n] = x.ptr.pp_double[2][1]; - work.ptr.p_double[j+1+n2] = x.ptr.pp_double[2][2]; - vmax = ae_maxreal(ae_fabs(x.ptr.pp_double[1][1], _state), vmax, _state); - vmax = ae_maxreal(ae_fabs(x.ptr.pp_double[1][2], _state), vmax, _state); - vmax = ae_maxreal(ae_fabs(x.ptr.pp_double[2][1], _state), vmax, _state); - vmax = ae_maxreal(ae_fabs(x.ptr.pp_double[2][2], _state), vmax, _state); - vcrit = bignum/vmax; - } - } - - /* - * Copy the vector x or Q*x to VL and normalize. - */ - if( !over ) - { - ae_v_move(&vl->ptr.pp_double[ki][iis], vl->stride, &work.ptr.p_double[ki+n], 1, ae_v_len(ki,n)); - ae_v_move(&vl->ptr.pp_double[ki][iis+1], vl->stride, &work.ptr.p_double[ki+n2], 1, ae_v_len(ki,n)); - emax = 0; - for(k=ki; k<=n; k++) - { - emax = ae_maxreal(emax, ae_fabs(vl->ptr.pp_double[k][iis], _state)+ae_fabs(vl->ptr.pp_double[k][iis+1], _state), _state); - } - remax = 1/emax; - ae_v_muld(&vl->ptr.pp_double[ki][iis], vl->stride, ae_v_len(ki,n), remax); - ae_v_muld(&vl->ptr.pp_double[ki][iis+1], vl->stride, ae_v_len(ki,n), remax); - for(k=1; k<=ki-1; k++) - { - vl->ptr.pp_double[k][iis] = 0; - vl->ptr.pp_double[k][iis+1] = 0; - } - } - else - { - if( kiptr.pp_double[1][ki], vl->stride, ae_v_len(1,n)); - matrixvectormultiply(vl, 1, n, ki+2, n, ae_false, &work, ki+2+n, n+n, 1.0, &temp, 1, n, work.ptr.p_double[ki+n], _state); - ae_v_move(&vl->ptr.pp_double[1][ki], vl->stride, &temp.ptr.p_double[1], 1, ae_v_len(1,n)); - ae_v_move(&temp.ptr.p_double[1], 1, &vl->ptr.pp_double[1][ki+1], vl->stride, ae_v_len(1,n)); - matrixvectormultiply(vl, 1, n, ki+2, n, ae_false, &work, ki+2+n2, n+n2, 1.0, &temp, 1, n, work.ptr.p_double[ki+1+n2], _state); - ae_v_move(&vl->ptr.pp_double[1][ki+1], vl->stride, &temp.ptr.p_double[1], 1, ae_v_len(1,n)); - } - else - { - vt = work.ptr.p_double[ki+n]; - ae_v_muld(&vl->ptr.pp_double[1][ki], vl->stride, ae_v_len(1,n), vt); - vt = work.ptr.p_double[ki+1+n2]; - ae_v_muld(&vl->ptr.pp_double[1][ki+1], vl->stride, ae_v_len(1,n), vt); - } - emax = 0; - for(k=1; k<=n; k++) - { - emax = ae_maxreal(emax, ae_fabs(vl->ptr.pp_double[k][ki], _state)+ae_fabs(vl->ptr.pp_double[k][ki+1], _state), _state); - } - remax = 1/emax; - ae_v_muld(&vl->ptr.pp_double[1][ki], vl->stride, ae_v_len(1,n), remax); - ae_v_muld(&vl->ptr.pp_double[1][ki+1], vl->stride, ae_v_len(1,n), remax); - } - } - iis = iis+1; - if( ip!=0 ) - { - iis = iis+1; - } - } - if( ip==-1 ) - { - ip = 0; - } - if( ip==1 ) - { - ip = -1; - } - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -DLALN2 solves a system of the form (ca A - w D ) X = s B -or (ca A' - w D) X = s B with possible scaling ("s") and -perturbation of A. (A' means A-transpose.) - -A is an NA x NA real matrix, ca is a real scalar, D is an NA x NA -real diagonal matrix, w is a real or complex value, and X and B are -NA x 1 matrices -- real if w is real, complex if w is complex. NA -may be 1 or 2. - -If w is complex, X and B are represented as NA x 2 matrices, -the first column of each being the real part and the second -being the imaginary part. - -"s" is a scaling factor (.LE. 1), computed by DLALN2, which is -so chosen that X can be computed without overflow. X is further -scaled if necessary to assure that norm(ca A - w D)*norm(X) is less -than overflow. - -If both singular values of (ca A - w D) are less than SMIN, -SMIN*identity will be used instead of (ca A - w D). If only one -singular value is less than SMIN, one element of (ca A - w D) will be -perturbed enough to make the smallest singular value roughly SMIN. -If both singular values are at least SMIN, (ca A - w D) will not be -perturbed. In any case, the perturbation will be at most some small -multiple of max( SMIN, ulp*norm(ca A - w D) ). The singular values -are computed by infinity-norm approximations, and thus will only be -correct to a factor of 2 or so. - -Note: all input quantities are assumed to be smaller than overflow -by a reasonable factor. (See BIGNUM.) - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -static void evd_internalhsevdlaln2(ae_bool ltrans, - ae_int_t na, - ae_int_t nw, - double smin, - double ca, - /* Real */ ae_matrix* a, - double d1, - double d2, - /* Real */ ae_matrix* b, - double wr, - double wi, - /* Boolean */ ae_vector* rswap4, - /* Boolean */ ae_vector* zswap4, - /* Integer */ ae_matrix* ipivot44, - /* Real */ ae_vector* civ4, - /* Real */ ae_vector* crv4, - /* Real */ ae_matrix* x, - double* scl, - double* xnorm, - ae_int_t* info, - ae_state *_state) -{ - ae_int_t icmax; - ae_int_t j; - double bbnd; - double bi1; - double bi2; - double bignum; - double bnorm; - double br1; - double br2; - double ci21; - double ci22; - double cmax; - double cnorm; - double cr21; - double cr22; - double csi; - double csr; - double li21; - double lr21; - double smini; - double smlnum; - double temp; - double u22abs; - double ui11; - double ui11r; - double ui12; - double ui12s; - double ui22; - double ur11; - double ur11r; - double ur12; - double ur12s; - double ur22; - double xi1; - double xi2; - double xr1; - double xr2; - double tmp1; - double tmp2; - - *scl = 0; - *xnorm = 0; - *info = 0; - - zswap4->ptr.p_bool[1] = ae_false; - zswap4->ptr.p_bool[2] = ae_false; - zswap4->ptr.p_bool[3] = ae_true; - zswap4->ptr.p_bool[4] = ae_true; - rswap4->ptr.p_bool[1] = ae_false; - rswap4->ptr.p_bool[2] = ae_true; - rswap4->ptr.p_bool[3] = ae_false; - rswap4->ptr.p_bool[4] = ae_true; - ipivot44->ptr.pp_int[1][1] = 1; - ipivot44->ptr.pp_int[2][1] = 2; - ipivot44->ptr.pp_int[3][1] = 3; - ipivot44->ptr.pp_int[4][1] = 4; - ipivot44->ptr.pp_int[1][2] = 2; - ipivot44->ptr.pp_int[2][2] = 1; - ipivot44->ptr.pp_int[3][2] = 4; - ipivot44->ptr.pp_int[4][2] = 3; - ipivot44->ptr.pp_int[1][3] = 3; - ipivot44->ptr.pp_int[2][3] = 4; - ipivot44->ptr.pp_int[3][3] = 1; - ipivot44->ptr.pp_int[4][3] = 2; - ipivot44->ptr.pp_int[1][4] = 4; - ipivot44->ptr.pp_int[2][4] = 3; - ipivot44->ptr.pp_int[3][4] = 2; - ipivot44->ptr.pp_int[4][4] = 1; - smlnum = 2*ae_minrealnumber; - bignum = 1/smlnum; - smini = ae_maxreal(smin, smlnum, _state); - - /* - * Don't check for input errors - */ - *info = 0; - - /* - * Standard Initializations - */ - *scl = 1; - if( na==1 ) - { - - /* - * 1 x 1 (i.e., scalar) system C X = B - */ - if( nw==1 ) - { - - /* - * Real 1x1 system. - * - * C = ca A - w D - */ - csr = ca*a->ptr.pp_double[1][1]-wr*d1; - cnorm = ae_fabs(csr, _state); - - /* - * If | C | < SMINI, use C = SMINI - */ - if( ae_fp_less(cnorm,smini) ) - { - csr = smini; - cnorm = smini; - *info = 1; - } - - /* - * Check scaling for X = B / C - */ - bnorm = ae_fabs(b->ptr.pp_double[1][1], _state); - if( ae_fp_less(cnorm,1)&&ae_fp_greater(bnorm,1) ) - { - if( ae_fp_greater(bnorm,bignum*cnorm) ) - { - *scl = 1/bnorm; - } - } - - /* - * Compute X - */ - x->ptr.pp_double[1][1] = b->ptr.pp_double[1][1]*(*scl)/csr; - *xnorm = ae_fabs(x->ptr.pp_double[1][1], _state); - } - else - { - - /* - * Complex 1x1 system (w is complex) - * - * C = ca A - w D - */ - csr = ca*a->ptr.pp_double[1][1]-wr*d1; - csi = -wi*d1; - cnorm = ae_fabs(csr, _state)+ae_fabs(csi, _state); - - /* - * If | C | < SMINI, use C = SMINI - */ - if( ae_fp_less(cnorm,smini) ) - { - csr = smini; - csi = 0; - cnorm = smini; - *info = 1; - } - - /* - * Check scaling for X = B / C - */ - bnorm = ae_fabs(b->ptr.pp_double[1][1], _state)+ae_fabs(b->ptr.pp_double[1][2], _state); - if( ae_fp_less(cnorm,1)&&ae_fp_greater(bnorm,1) ) - { - if( ae_fp_greater(bnorm,bignum*cnorm) ) - { - *scl = 1/bnorm; - } - } - - /* - * Compute X - */ - evd_internalhsevdladiv(*scl*b->ptr.pp_double[1][1], *scl*b->ptr.pp_double[1][2], csr, csi, &tmp1, &tmp2, _state); - x->ptr.pp_double[1][1] = tmp1; - x->ptr.pp_double[1][2] = tmp2; - *xnorm = ae_fabs(x->ptr.pp_double[1][1], _state)+ae_fabs(x->ptr.pp_double[1][2], _state); - } - } - else - { - - /* - * 2x2 System - * - * Compute the real part of C = ca A - w D (or ca A' - w D ) - */ - crv4->ptr.p_double[1+0] = ca*a->ptr.pp_double[1][1]-wr*d1; - crv4->ptr.p_double[2+2] = ca*a->ptr.pp_double[2][2]-wr*d2; - if( ltrans ) - { - crv4->ptr.p_double[1+2] = ca*a->ptr.pp_double[2][1]; - crv4->ptr.p_double[2+0] = ca*a->ptr.pp_double[1][2]; - } - else - { - crv4->ptr.p_double[2+0] = ca*a->ptr.pp_double[2][1]; - crv4->ptr.p_double[1+2] = ca*a->ptr.pp_double[1][2]; - } - if( nw==1 ) - { - - /* - * Real 2x2 system (w is real) - * - * Find the largest element in C - */ - cmax = 0; - icmax = 0; - for(j=1; j<=4; j++) - { - if( ae_fp_greater(ae_fabs(crv4->ptr.p_double[j], _state),cmax) ) - { - cmax = ae_fabs(crv4->ptr.p_double[j], _state); - icmax = j; - } - } - - /* - * If norm(C) < SMINI, use SMINI*identity. - */ - if( ae_fp_less(cmax,smini) ) - { - bnorm = ae_maxreal(ae_fabs(b->ptr.pp_double[1][1], _state), ae_fabs(b->ptr.pp_double[2][1], _state), _state); - if( ae_fp_less(smini,1)&&ae_fp_greater(bnorm,1) ) - { - if( ae_fp_greater(bnorm,bignum*smini) ) - { - *scl = 1/bnorm; - } - } - temp = *scl/smini; - x->ptr.pp_double[1][1] = temp*b->ptr.pp_double[1][1]; - x->ptr.pp_double[2][1] = temp*b->ptr.pp_double[2][1]; - *xnorm = temp*bnorm; - *info = 1; - return; - } - - /* - * Gaussian elimination with complete pivoting. - */ - ur11 = crv4->ptr.p_double[icmax]; - cr21 = crv4->ptr.p_double[ipivot44->ptr.pp_int[2][icmax]]; - ur12 = crv4->ptr.p_double[ipivot44->ptr.pp_int[3][icmax]]; - cr22 = crv4->ptr.p_double[ipivot44->ptr.pp_int[4][icmax]]; - ur11r = 1/ur11; - lr21 = ur11r*cr21; - ur22 = cr22-ur12*lr21; - - /* - * If smaller pivot < SMINI, use SMINI - */ - if( ae_fp_less(ae_fabs(ur22, _state),smini) ) - { - ur22 = smini; - *info = 1; - } - if( rswap4->ptr.p_bool[icmax] ) - { - br1 = b->ptr.pp_double[2][1]; - br2 = b->ptr.pp_double[1][1]; - } - else - { - br1 = b->ptr.pp_double[1][1]; - br2 = b->ptr.pp_double[2][1]; - } - br2 = br2-lr21*br1; - bbnd = ae_maxreal(ae_fabs(br1*(ur22*ur11r), _state), ae_fabs(br2, _state), _state); - if( ae_fp_greater(bbnd,1)&&ae_fp_less(ae_fabs(ur22, _state),1) ) - { - if( ae_fp_greater_eq(bbnd,bignum*ae_fabs(ur22, _state)) ) - { - *scl = 1/bbnd; - } - } - xr2 = br2*(*scl)/ur22; - xr1 = *scl*br1*ur11r-xr2*(ur11r*ur12); - if( zswap4->ptr.p_bool[icmax] ) - { - x->ptr.pp_double[1][1] = xr2; - x->ptr.pp_double[2][1] = xr1; - } - else - { - x->ptr.pp_double[1][1] = xr1; - x->ptr.pp_double[2][1] = xr2; - } - *xnorm = ae_maxreal(ae_fabs(xr1, _state), ae_fabs(xr2, _state), _state); - - /* - * Further scaling if norm(A) norm(X) > overflow - */ - if( ae_fp_greater(*xnorm,1)&&ae_fp_greater(cmax,1) ) - { - if( ae_fp_greater(*xnorm,bignum/cmax) ) - { - temp = cmax/bignum; - x->ptr.pp_double[1][1] = temp*x->ptr.pp_double[1][1]; - x->ptr.pp_double[2][1] = temp*x->ptr.pp_double[2][1]; - *xnorm = temp*(*xnorm); - *scl = temp*(*scl); - } - } - } - else - { - - /* - * Complex 2x2 system (w is complex) - * - * Find the largest element in C - */ - civ4->ptr.p_double[1+0] = -wi*d1; - civ4->ptr.p_double[2+0] = 0; - civ4->ptr.p_double[1+2] = 0; - civ4->ptr.p_double[2+2] = -wi*d2; - cmax = 0; - icmax = 0; - for(j=1; j<=4; j++) - { - if( ae_fp_greater(ae_fabs(crv4->ptr.p_double[j], _state)+ae_fabs(civ4->ptr.p_double[j], _state),cmax) ) - { - cmax = ae_fabs(crv4->ptr.p_double[j], _state)+ae_fabs(civ4->ptr.p_double[j], _state); - icmax = j; - } - } - - /* - * If norm(C) < SMINI, use SMINI*identity. - */ - if( ae_fp_less(cmax,smini) ) - { - bnorm = ae_maxreal(ae_fabs(b->ptr.pp_double[1][1], _state)+ae_fabs(b->ptr.pp_double[1][2], _state), ae_fabs(b->ptr.pp_double[2][1], _state)+ae_fabs(b->ptr.pp_double[2][2], _state), _state); - if( ae_fp_less(smini,1)&&ae_fp_greater(bnorm,1) ) - { - if( ae_fp_greater(bnorm,bignum*smini) ) - { - *scl = 1/bnorm; - } - } - temp = *scl/smini; - x->ptr.pp_double[1][1] = temp*b->ptr.pp_double[1][1]; - x->ptr.pp_double[2][1] = temp*b->ptr.pp_double[2][1]; - x->ptr.pp_double[1][2] = temp*b->ptr.pp_double[1][2]; - x->ptr.pp_double[2][2] = temp*b->ptr.pp_double[2][2]; - *xnorm = temp*bnorm; - *info = 1; - return; - } - - /* - * Gaussian elimination with complete pivoting. - */ - ur11 = crv4->ptr.p_double[icmax]; - ui11 = civ4->ptr.p_double[icmax]; - cr21 = crv4->ptr.p_double[ipivot44->ptr.pp_int[2][icmax]]; - ci21 = civ4->ptr.p_double[ipivot44->ptr.pp_int[2][icmax]]; - ur12 = crv4->ptr.p_double[ipivot44->ptr.pp_int[3][icmax]]; - ui12 = civ4->ptr.p_double[ipivot44->ptr.pp_int[3][icmax]]; - cr22 = crv4->ptr.p_double[ipivot44->ptr.pp_int[4][icmax]]; - ci22 = civ4->ptr.p_double[ipivot44->ptr.pp_int[4][icmax]]; - if( icmax==1||icmax==4 ) - { - - /* - * Code when off-diagonals of pivoted C are real - */ - if( ae_fp_greater(ae_fabs(ur11, _state),ae_fabs(ui11, _state)) ) - { - temp = ui11/ur11; - ur11r = 1/(ur11*(1+ae_sqr(temp, _state))); - ui11r = -temp*ur11r; - } - else - { - temp = ur11/ui11; - ui11r = -1/(ui11*(1+ae_sqr(temp, _state))); - ur11r = -temp*ui11r; - } - lr21 = cr21*ur11r; - li21 = cr21*ui11r; - ur12s = ur12*ur11r; - ui12s = ur12*ui11r; - ur22 = cr22-ur12*lr21; - ui22 = ci22-ur12*li21; - } - else - { - - /* - * Code when diagonals of pivoted C are real - */ - ur11r = 1/ur11; - ui11r = 0; - lr21 = cr21*ur11r; - li21 = ci21*ur11r; - ur12s = ur12*ur11r; - ui12s = ui12*ur11r; - ur22 = cr22-ur12*lr21+ui12*li21; - ui22 = -ur12*li21-ui12*lr21; - } - u22abs = ae_fabs(ur22, _state)+ae_fabs(ui22, _state); - - /* - * If smaller pivot < SMINI, use SMINI - */ - if( ae_fp_less(u22abs,smini) ) - { - ur22 = smini; - ui22 = 0; - *info = 1; - } - if( rswap4->ptr.p_bool[icmax] ) - { - br2 = b->ptr.pp_double[1][1]; - br1 = b->ptr.pp_double[2][1]; - bi2 = b->ptr.pp_double[1][2]; - bi1 = b->ptr.pp_double[2][2]; - } - else - { - br1 = b->ptr.pp_double[1][1]; - br2 = b->ptr.pp_double[2][1]; - bi1 = b->ptr.pp_double[1][2]; - bi2 = b->ptr.pp_double[2][2]; - } - br2 = br2-lr21*br1+li21*bi1; - bi2 = bi2-li21*br1-lr21*bi1; - bbnd = ae_maxreal((ae_fabs(br1, _state)+ae_fabs(bi1, _state))*(u22abs*(ae_fabs(ur11r, _state)+ae_fabs(ui11r, _state))), ae_fabs(br2, _state)+ae_fabs(bi2, _state), _state); - if( ae_fp_greater(bbnd,1)&&ae_fp_less(u22abs,1) ) - { - if( ae_fp_greater_eq(bbnd,bignum*u22abs) ) - { - *scl = 1/bbnd; - br1 = *scl*br1; - bi1 = *scl*bi1; - br2 = *scl*br2; - bi2 = *scl*bi2; - } - } - evd_internalhsevdladiv(br2, bi2, ur22, ui22, &xr2, &xi2, _state); - xr1 = ur11r*br1-ui11r*bi1-ur12s*xr2+ui12s*xi2; - xi1 = ui11r*br1+ur11r*bi1-ui12s*xr2-ur12s*xi2; - if( zswap4->ptr.p_bool[icmax] ) - { - x->ptr.pp_double[1][1] = xr2; - x->ptr.pp_double[2][1] = xr1; - x->ptr.pp_double[1][2] = xi2; - x->ptr.pp_double[2][2] = xi1; - } - else - { - x->ptr.pp_double[1][1] = xr1; - x->ptr.pp_double[2][1] = xr2; - x->ptr.pp_double[1][2] = xi1; - x->ptr.pp_double[2][2] = xi2; - } - *xnorm = ae_maxreal(ae_fabs(xr1, _state)+ae_fabs(xi1, _state), ae_fabs(xr2, _state)+ae_fabs(xi2, _state), _state); - - /* - * Further scaling if norm(A) norm(X) > overflow - */ - if( ae_fp_greater(*xnorm,1)&&ae_fp_greater(cmax,1) ) - { - if( ae_fp_greater(*xnorm,bignum/cmax) ) - { - temp = cmax/bignum; - x->ptr.pp_double[1][1] = temp*x->ptr.pp_double[1][1]; - x->ptr.pp_double[2][1] = temp*x->ptr.pp_double[2][1]; - x->ptr.pp_double[1][2] = temp*x->ptr.pp_double[1][2]; - x->ptr.pp_double[2][2] = temp*x->ptr.pp_double[2][2]; - *xnorm = temp*(*xnorm); - *scl = temp*(*scl); - } - } - } - } -} - - -/************************************************************************* -performs complex division in real arithmetic - - a + i*b - p + i*q = --------- - c + i*d - -The algorithm is due to Robert L. Smith and can be found -in D. Knuth, The art of Computer Programming, Vol.2, p.195 - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -static void evd_internalhsevdladiv(double a, - double b, - double c, - double d, - double* p, - double* q, - ae_state *_state) -{ - double e; - double f; - - *p = 0; - *q = 0; - - if( ae_fp_less(ae_fabs(d, _state),ae_fabs(c, _state)) ) - { - e = d/c; - f = c+d*e; - *p = (a+b*e)/f; - *q = (b-a*e)/f; - } - else - { - e = c/d; - f = d+c*e; - *p = (b+a*e)/f; - *q = (-a+b*e)/f; - } -} - - -static ae_bool evd_nonsymmetricevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t vneeded, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - /* Real */ ae_matrix* vl, - /* Real */ ae_matrix* vr, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_matrix s; - ae_vector tau; - ae_vector sel; - ae_int_t i; - ae_int_t info; - ae_int_t m; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_clear(wr); - ae_vector_clear(wi); - ae_matrix_clear(vl); - ae_matrix_clear(vr); - ae_matrix_init(&s, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sel, 0, DT_BOOL, _state, ae_true); - - ae_assert(vneeded>=0&&vneeded<=3, "NonSymmetricEVD: incorrect VNeeded!", _state); - if( vneeded==0 ) - { - - /* - * Eigen values only - */ - evd_toupperhessenberg(a, n, &tau, _state); - internalschurdecomposition(a, n, 0, 0, wr, wi, &s, &info, _state); - result = info==0; - ae_frame_leave(_state); - return result; - } - - /* - * Eigen values and vectors - */ - evd_toupperhessenberg(a, n, &tau, _state); - evd_unpackqfromupperhessenberg(a, n, &tau, &s, _state); - internalschurdecomposition(a, n, 1, 1, wr, wi, &s, &info, _state); - result = info==0; - if( !result ) - { - ae_frame_leave(_state); - return result; - } - if( vneeded==1||vneeded==3 ) - { - ae_matrix_set_length(vr, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - ae_v_move(&vr->ptr.pp_double[i][1], 1, &s.ptr.pp_double[i][1], 1, ae_v_len(1,n)); - } - } - if( vneeded==2||vneeded==3 ) - { - ae_matrix_set_length(vl, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - ae_v_move(&vl->ptr.pp_double[i][1], 1, &s.ptr.pp_double[i][1], 1, ae_v_len(1,n)); - } - } - evd_internaltrevc(a, n, vneeded, 1, &sel, vl, vr, &m, &info, _state); - result = info==0; - ae_frame_leave(_state); - return result; -} - - -static void evd_toupperhessenberg(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t ip1; - ae_int_t nmi; - double v; - ae_vector t; - ae_vector work; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(tau); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=0, "ToUpperHessenberg: incorrect N!", _state); - - /* - * Quick return if possible - */ - if( n<=1 ) - { - ae_frame_leave(_state); - return; - } - ae_vector_set_length(tau, n-1+1, _state); - ae_vector_set_length(&t, n+1, _state); - ae_vector_set_length(&work, n+1, _state); - for(i=1; i<=n-1; i++) - { - - /* - * Compute elementary reflector H(i) to annihilate A(i+2:ihi,i) - */ - ip1 = i+1; - nmi = n-i; - ae_v_move(&t.ptr.p_double[1], 1, &a->ptr.pp_double[ip1][i], a->stride, ae_v_len(1,nmi)); - generatereflection(&t, nmi, &v, _state); - ae_v_move(&a->ptr.pp_double[ip1][i], a->stride, &t.ptr.p_double[1], 1, ae_v_len(ip1,n)); - tau->ptr.p_double[i] = v; - t.ptr.p_double[1] = 1; - - /* - * Apply H(i) to A(1:ihi,i+1:ihi) from the right - */ - applyreflectionfromtheright(a, v, &t, 1, n, i+1, n, &work, _state); - - /* - * Apply H(i) to A(i+1:ihi,i+1:n) from the left - */ - applyreflectionfromtheleft(a, v, &t, i+1, n, i+1, n, &work, _state); - } - ae_frame_leave(_state); -} - - -static void evd_unpackqfromupperhessenberg(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - /* Real */ ae_matrix* q, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector v; - ae_vector work; - ae_int_t ip1; - ae_int_t nmi; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(q); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - if( n==0 ) - { - ae_frame_leave(_state); - return; - } - - /* - * init - */ - ae_matrix_set_length(q, n+1, n+1, _state); - ae_vector_set_length(&v, n+1, _state); - ae_vector_set_length(&work, n+1, _state); - for(i=1; i<=n; i++) - { - for(j=1; j<=n; j++) - { - if( i==j ) - { - q->ptr.pp_double[i][j] = 1; - } - else - { - q->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * unpack Q - */ - for(i=1; i<=n-1; i++) - { - - /* - * Apply H(i) - */ - ip1 = i+1; - nmi = n-i; - ae_v_move(&v.ptr.p_double[1], 1, &a->ptr.pp_double[ip1][i], a->stride, ae_v_len(1,nmi)); - v.ptr.p_double[1] = 1; - applyreflectionfromtheright(q, tau->ptr.p_double[i], &v, 1, n, i+1, n, &work, _state); - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -Generation of a random uniformly distributed (Haar) orthogonal matrix - -INPUT PARAMETERS: - N - matrix size, N>=1 - -OUTPUT PARAMETERS: - A - orthogonal NxN matrix, array[0..N-1,0..N-1] - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonal(ae_int_t n, - /* Real */ ae_matrix* a, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(a); - - ae_assert(n>=1, "RMatrixRndOrthogonal: N<1!", _state); - ae_matrix_set_length(a, n, n, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - a->ptr.pp_double[i][j] = 1; - } - else - { - a->ptr.pp_double[i][j] = 0; - } - } - } - rmatrixrndorthogonalfromtheright(a, n, n, _state); -} - - -/************************************************************************* -Generation of random NxN matrix with given condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndcond(ae_int_t n, - double c, - /* Real */ ae_matrix* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double l1; - double l2; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(a); - _hqrndstate_init(&rs, _state, ae_true); - - ae_assert(n>=1&&ae_fp_greater_eq(c,1), "RMatrixRndCond: N<1 or C<1!", _state); - ae_matrix_set_length(a, n, n, _state); - if( n==1 ) - { - - /* - * special case - */ - a->ptr.pp_double[0][0] = 2*ae_randominteger(2, _state)-1; - ae_frame_leave(_state); - return; - } - hqrndrandomize(&rs, _state); - l1 = 0; - l2 = ae_log(1/c, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - a->ptr.pp_double[0][0] = ae_exp(l1, _state); - for(i=1; i<=n-2; i++) - { - a->ptr.pp_double[i][i] = ae_exp(hqrnduniformr(&rs, _state)*(l2-l1)+l1, _state); - } - a->ptr.pp_double[n-1][n-1] = ae_exp(l2, _state); - rmatrixrndorthogonalfromtheleft(a, n, n, _state); - rmatrixrndorthogonalfromtheright(a, n, n, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Generation of a random Haar distributed orthogonal complex matrix - -INPUT PARAMETERS: - N - matrix size, N>=1 - -OUTPUT PARAMETERS: - A - orthogonal NxN matrix, array[0..N-1,0..N-1] - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonal(ae_int_t n, - /* Complex */ ae_matrix* a, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - ae_matrix_clear(a); - - ae_assert(n>=1, "CMatrixRndOrthogonal: N<1!", _state); - ae_matrix_set_length(a, n, n, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - if( i==j ) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(1); - } - else - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - } - cmatrixrndorthogonalfromtheright(a, n, n, _state); -} - - -/************************************************************************* -Generation of random NxN complex matrix with given condition number C and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndcond(ae_int_t n, - double c, - /* Complex */ ae_matrix* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double l1; - double l2; - hqrndstate state; - ae_complex v; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(a); - _hqrndstate_init(&state, _state, ae_true); - - ae_assert(n>=1&&ae_fp_greater_eq(c,1), "CMatrixRndCond: N<1 or C<1!", _state); - ae_matrix_set_length(a, n, n, _state); - if( n==1 ) - { - - /* - * special case - */ - hqrndrandomize(&state, _state); - hqrndunit2(&state, &v.x, &v.y, _state); - a->ptr.pp_complex[0][0] = v; - ae_frame_leave(_state); - return; - } - hqrndrandomize(&state, _state); - l1 = 0; - l2 = ae_log(1/c, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - a->ptr.pp_complex[0][0] = ae_complex_from_d(ae_exp(l1, _state)); - for(i=1; i<=n-2; i++) - { - a->ptr.pp_complex[i][i] = ae_complex_from_d(ae_exp(hqrnduniformr(&state, _state)*(l2-l1)+l1, _state)); - } - a->ptr.pp_complex[n-1][n-1] = ae_complex_from_d(ae_exp(l2, _state)); - cmatrixrndorthogonalfromtheleft(a, n, n, _state); - cmatrixrndorthogonalfromtheright(a, n, n, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Generation of random NxN symmetric matrix with given condition number and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void smatrixrndcond(ae_int_t n, - double c, - /* Real */ ae_matrix* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double l1; - double l2; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(a); - _hqrndstate_init(&rs, _state, ae_true); - - ae_assert(n>=1&&ae_fp_greater_eq(c,1), "SMatrixRndCond: N<1 or C<1!", _state); - ae_matrix_set_length(a, n, n, _state); - if( n==1 ) - { - - /* - * special case - */ - a->ptr.pp_double[0][0] = 2*ae_randominteger(2, _state)-1; - ae_frame_leave(_state); - return; - } - - /* - * Prepare matrix - */ - hqrndrandomize(&rs, _state); - l1 = 0; - l2 = ae_log(1/c, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - a->ptr.pp_double[0][0] = ae_exp(l1, _state); - for(i=1; i<=n-2; i++) - { - a->ptr.pp_double[i][i] = (2*hqrnduniformi(&rs, 2, _state)-1)*ae_exp(hqrnduniformr(&rs, _state)*(l2-l1)+l1, _state); - } - a->ptr.pp_double[n-1][n-1] = ae_exp(l2, _state); - - /* - * Multiply - */ - smatrixrndmultiply(a, n, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Generation of random NxN symmetric positive definite matrix with given -condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random SPD matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixrndcond(ae_int_t n, - double c, - /* Real */ ae_matrix* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double l1; - double l2; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(a); - _hqrndstate_init(&rs, _state, ae_true); - - - /* - * Special cases - */ - if( n<=0||ae_fp_less(c,1) ) - { - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(a, n, n, _state); - if( n==1 ) - { - a->ptr.pp_double[0][0] = 1; - ae_frame_leave(_state); - return; - } - - /* - * Prepare matrix - */ - hqrndrandomize(&rs, _state); - l1 = 0; - l2 = ae_log(1/c, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - a->ptr.pp_double[0][0] = ae_exp(l1, _state); - for(i=1; i<=n-2; i++) - { - a->ptr.pp_double[i][i] = ae_exp(hqrnduniformr(&rs, _state)*(l2-l1)+l1, _state); - } - a->ptr.pp_double[n-1][n-1] = ae_exp(l2, _state); - - /* - * Multiply - */ - smatrixrndmultiply(a, n, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Generation of random NxN Hermitian matrix with given condition number and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hmatrixrndcond(ae_int_t n, - double c, - /* Complex */ ae_matrix* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double l1; - double l2; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(a); - _hqrndstate_init(&rs, _state, ae_true); - - ae_assert(n>=1&&ae_fp_greater_eq(c,1), "HMatrixRndCond: N<1 or C<1!", _state); - ae_matrix_set_length(a, n, n, _state); - if( n==1 ) - { - - /* - * special case - */ - a->ptr.pp_complex[0][0] = ae_complex_from_d(2*ae_randominteger(2, _state)-1); - ae_frame_leave(_state); - return; - } - - /* - * Prepare matrix - */ - hqrndrandomize(&rs, _state); - l1 = 0; - l2 = ae_log(1/c, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - a->ptr.pp_complex[0][0] = ae_complex_from_d(ae_exp(l1, _state)); - for(i=1; i<=n-2; i++) - { - a->ptr.pp_complex[i][i] = ae_complex_from_d((2*hqrnduniformi(&rs, 2, _state)-1)*ae_exp(hqrnduniformr(&rs, _state)*(l2-l1)+l1, _state)); - } - a->ptr.pp_complex[n-1][n-1] = ae_complex_from_d(ae_exp(l2, _state)); - - /* - * Multiply - */ - hmatrixrndmultiply(a, n, _state); - - /* - * post-process to ensure that matrix diagonal is real - */ - for(i=0; i<=n-1; i++) - { - a->ptr.pp_complex[i][i].y = 0; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Generation of random NxN Hermitian positive definite matrix with given -condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random HPD matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixrndcond(ae_int_t n, - double c, - /* Complex */ ae_matrix* a, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double l1; - double l2; - hqrndstate rs; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(a); - _hqrndstate_init(&rs, _state, ae_true); - - - /* - * Special cases - */ - if( n<=0||ae_fp_less(c,1) ) - { - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(a, n, n, _state); - if( n==1 ) - { - a->ptr.pp_complex[0][0] = ae_complex_from_d(1); - ae_frame_leave(_state); - return; - } - - /* - * Prepare matrix - */ - hqrndrandomize(&rs, _state); - l1 = 0; - l2 = ae_log(1/c, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - a->ptr.pp_complex[0][0] = ae_complex_from_d(ae_exp(l1, _state)); - for(i=1; i<=n-2; i++) - { - a->ptr.pp_complex[i][i] = ae_complex_from_d(ae_exp(hqrnduniformr(&rs, _state)*(l2-l1)+l1, _state)); - } - a->ptr.pp_complex[n-1][n-1] = ae_complex_from_d(ae_exp(l2, _state)); - - /* - * Multiply - */ - hmatrixrndmultiply(a, n, _state); - - /* - * post-process to ensure that matrix diagonal is real - */ - for(i=0; i<=n-1; i++) - { - a->ptr.pp_complex[i][i].y = 0; - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Multiplication of MxN matrix by NxN random Haar distributed orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonalfromtheright(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - double tau; - double lambdav; - ae_int_t s; - ae_int_t i; - double u1; - double u2; - ae_vector w; - ae_vector v; - hqrndstate state; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - _hqrndstate_init(&state, _state, ae_true); - - ae_assert(n>=1&&m>=1, "RMatrixRndOrthogonalFromTheRight: N<1 or M<1!", _state); - if( n==1 ) - { - - /* - * Special case - */ - tau = 2*ae_randominteger(2, _state)-1; - for(i=0; i<=m-1; i++) - { - a->ptr.pp_double[i][0] = a->ptr.pp_double[i][0]*tau; - } - ae_frame_leave(_state); - return; - } - - /* - * General case. - * First pass. - */ - ae_vector_set_length(&w, m, _state); - ae_vector_set_length(&v, n+1, _state); - hqrndrandomize(&state, _state); - for(s=2; s<=n; s++) - { - - /* - * Prepare random normal v - */ - do - { - i = 1; - while(i<=s) - { - hqrndnormal2(&state, &u1, &u2, _state); - v.ptr.p_double[i] = u1; - if( i+1<=s ) - { - v.ptr.p_double[i+1] = u2; - } - i = i+2; - } - lambdav = ae_v_dotproduct(&v.ptr.p_double[1], 1, &v.ptr.p_double[1], 1, ae_v_len(1,s)); - } - while(ae_fp_eq(lambdav,0)); - - /* - * Prepare and apply reflection - */ - generatereflection(&v, s, &tau, _state); - v.ptr.p_double[1] = 1; - applyreflectionfromtheright(a, tau, &v, 0, m-1, n-s, n-1, &w, _state); - } - - /* - * Second pass. - */ - for(i=0; i<=n-1; i++) - { - tau = 2*hqrnduniformi(&state, 2, _state)-1; - ae_v_muld(&a->ptr.pp_double[0][i], a->stride, ae_v_len(0,m-1), tau); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Multiplication of MxN matrix by MxM random Haar distributed orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - Q*A, where Q is random MxM orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonalfromtheleft(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - double tau; - double lambdav; - ae_int_t s; - ae_int_t i; - ae_int_t j; - double u1; - double u2; - ae_vector w; - ae_vector v; - hqrndstate state; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - _hqrndstate_init(&state, _state, ae_true); - - ae_assert(n>=1&&m>=1, "RMatrixRndOrthogonalFromTheRight: N<1 or M<1!", _state); - if( m==1 ) - { - - /* - * special case - */ - tau = 2*ae_randominteger(2, _state)-1; - for(j=0; j<=n-1; j++) - { - a->ptr.pp_double[0][j] = a->ptr.pp_double[0][j]*tau; - } - ae_frame_leave(_state); - return; - } - - /* - * General case. - * First pass. - */ - ae_vector_set_length(&w, n, _state); - ae_vector_set_length(&v, m+1, _state); - hqrndrandomize(&state, _state); - for(s=2; s<=m; s++) - { - - /* - * Prepare random normal v - */ - do - { - i = 1; - while(i<=s) - { - hqrndnormal2(&state, &u1, &u2, _state); - v.ptr.p_double[i] = u1; - if( i+1<=s ) - { - v.ptr.p_double[i+1] = u2; - } - i = i+2; - } - lambdav = ae_v_dotproduct(&v.ptr.p_double[1], 1, &v.ptr.p_double[1], 1, ae_v_len(1,s)); - } - while(ae_fp_eq(lambdav,0)); - - /* - * Prepare and apply reflection - */ - generatereflection(&v, s, &tau, _state); - v.ptr.p_double[1] = 1; - applyreflectionfromtheleft(a, tau, &v, m-s, m-1, 0, n-1, &w, _state); - } - - /* - * Second pass. - */ - for(i=0; i<=m-1; i++) - { - tau = 2*hqrnduniformi(&state, 2, _state)-1; - ae_v_muld(&a->ptr.pp_double[i][0], 1, ae_v_len(0,n-1), tau); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Multiplication of MxN complex matrix by NxN random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonalfromtheright(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_complex lambdav; - ae_complex tau; - ae_int_t s; - ae_int_t i; - ae_vector w; - ae_vector v; - hqrndstate state; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&w, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&v, 0, DT_COMPLEX, _state, ae_true); - _hqrndstate_init(&state, _state, ae_true); - - ae_assert(n>=1&&m>=1, "CMatrixRndOrthogonalFromTheRight: N<1 or M<1!", _state); - if( n==1 ) - { - - /* - * Special case - */ - hqrndrandomize(&state, _state); - hqrndunit2(&state, &tau.x, &tau.y, _state); - for(i=0; i<=m-1; i++) - { - a->ptr.pp_complex[i][0] = ae_c_mul(a->ptr.pp_complex[i][0],tau); - } - ae_frame_leave(_state); - return; - } - - /* - * General case. - * First pass. - */ - ae_vector_set_length(&w, m, _state); - ae_vector_set_length(&v, n+1, _state); - hqrndrandomize(&state, _state); - for(s=2; s<=n; s++) - { - - /* - * Prepare random normal v - */ - do - { - for(i=1; i<=s; i++) - { - hqrndnormal2(&state, &tau.x, &tau.y, _state); - v.ptr.p_complex[i] = tau; - } - lambdav = ae_v_cdotproduct(&v.ptr.p_complex[1], 1, "N", &v.ptr.p_complex[1], 1, "Conj", ae_v_len(1,s)); - } - while(ae_c_eq_d(lambdav,0)); - - /* - * Prepare and apply reflection - */ - complexgeneratereflection(&v, s, &tau, _state); - v.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheright(a, tau, &v, 0, m-1, n-s, n-1, &w, _state); - } - - /* - * Second pass. - */ - for(i=0; i<=n-1; i++) - { - hqrndunit2(&state, &tau.x, &tau.y, _state); - ae_v_cmulc(&a->ptr.pp_complex[0][i], a->stride, ae_v_len(0,m-1), tau); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Multiplication of MxN complex matrix by MxM random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - Q*A, where Q is random MxM orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonalfromtheleft(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_complex tau; - ae_complex lambdav; - ae_int_t s; - ae_int_t i; - ae_int_t j; - ae_vector w; - ae_vector v; - hqrndstate state; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&w, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&v, 0, DT_COMPLEX, _state, ae_true); - _hqrndstate_init(&state, _state, ae_true); - - ae_assert(n>=1&&m>=1, "CMatrixRndOrthogonalFromTheRight: N<1 or M<1!", _state); - if( m==1 ) - { - - /* - * special case - */ - hqrndrandomize(&state, _state); - hqrndunit2(&state, &tau.x, &tau.y, _state); - for(j=0; j<=n-1; j++) - { - a->ptr.pp_complex[0][j] = ae_c_mul(a->ptr.pp_complex[0][j],tau); - } - ae_frame_leave(_state); - return; - } - - /* - * General case. - * First pass. - */ - ae_vector_set_length(&w, n, _state); - ae_vector_set_length(&v, m+1, _state); - hqrndrandomize(&state, _state); - for(s=2; s<=m; s++) - { - - /* - * Prepare random normal v - */ - do - { - for(i=1; i<=s; i++) - { - hqrndnormal2(&state, &tau.x, &tau.y, _state); - v.ptr.p_complex[i] = tau; - } - lambdav = ae_v_cdotproduct(&v.ptr.p_complex[1], 1, "N", &v.ptr.p_complex[1], 1, "Conj", ae_v_len(1,s)); - } - while(ae_c_eq_d(lambdav,0)); - - /* - * Prepare and apply reflection - */ - complexgeneratereflection(&v, s, &tau, _state); - v.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheleft(a, tau, &v, m-s, m-1, 0, n-1, &w, _state); - } - - /* - * Second pass. - */ - for(i=0; i<=m-1; i++) - { - hqrndunit2(&state, &tau.x, &tau.y, _state); - ae_v_cmulc(&a->ptr.pp_complex[i][0], 1, ae_v_len(0,n-1), tau); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Symmetric multiplication of NxN matrix by random Haar distributed -orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..N-1, 0..N-1] - N - matrix size - -OUTPUT PARAMETERS: - A - Q'*A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void smatrixrndmultiply(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - double tau; - double lambdav; - ae_int_t s; - ae_int_t i; - double u1; - double u2; - ae_vector w; - ae_vector v; - hqrndstate state; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&v, 0, DT_REAL, _state, ae_true); - _hqrndstate_init(&state, _state, ae_true); - - - /* - * General case. - */ - ae_vector_set_length(&w, n, _state); - ae_vector_set_length(&v, n+1, _state); - hqrndrandomize(&state, _state); - for(s=2; s<=n; s++) - { - - /* - * Prepare random normal v - */ - do - { - i = 1; - while(i<=s) - { - hqrndnormal2(&state, &u1, &u2, _state); - v.ptr.p_double[i] = u1; - if( i+1<=s ) - { - v.ptr.p_double[i+1] = u2; - } - i = i+2; - } - lambdav = ae_v_dotproduct(&v.ptr.p_double[1], 1, &v.ptr.p_double[1], 1, ae_v_len(1,s)); - } - while(ae_fp_eq(lambdav,0)); - - /* - * Prepare and apply reflection - */ - generatereflection(&v, s, &tau, _state); - v.ptr.p_double[1] = 1; - applyreflectionfromtheright(a, tau, &v, 0, n-1, n-s, n-1, &w, _state); - applyreflectionfromtheleft(a, tau, &v, n-s, n-1, 0, n-1, &w, _state); - } - - /* - * Second pass. - */ - for(i=0; i<=n-1; i++) - { - tau = 2*hqrnduniformi(&state, 2, _state)-1; - ae_v_muld(&a->ptr.pp_double[0][i], a->stride, ae_v_len(0,n-1), tau); - ae_v_muld(&a->ptr.pp_double[i][0], 1, ae_v_len(0,n-1), tau); - } - - /* - * Copy upper triangle to lower - */ - for(i=0; i<=n-2; i++) - { - ae_v_move(&a->ptr.pp_double[i+1][i], a->stride, &a->ptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1)); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Hermitian multiplication of NxN matrix by random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..N-1, 0..N-1] - N - matrix size - -OUTPUT PARAMETERS: - A - Q^H*A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hmatrixrndmultiply(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_complex tau; - ae_complex lambdav; - ae_int_t s; - ae_int_t i; - ae_vector w; - ae_vector v; - hqrndstate state; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&w, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&v, 0, DT_COMPLEX, _state, ae_true); - _hqrndstate_init(&state, _state, ae_true); - - - /* - * General case. - */ - ae_vector_set_length(&w, n, _state); - ae_vector_set_length(&v, n+1, _state); - hqrndrandomize(&state, _state); - for(s=2; s<=n; s++) - { - - /* - * Prepare random normal v - */ - do - { - for(i=1; i<=s; i++) - { - hqrndnormal2(&state, &tau.x, &tau.y, _state); - v.ptr.p_complex[i] = tau; - } - lambdav = ae_v_cdotproduct(&v.ptr.p_complex[1], 1, "N", &v.ptr.p_complex[1], 1, "Conj", ae_v_len(1,s)); - } - while(ae_c_eq_d(lambdav,0)); - - /* - * Prepare and apply reflection - */ - complexgeneratereflection(&v, s, &tau, _state); - v.ptr.p_complex[1] = ae_complex_from_d(1); - complexapplyreflectionfromtheright(a, tau, &v, 0, n-1, n-s, n-1, &w, _state); - complexapplyreflectionfromtheleft(a, ae_c_conj(tau, _state), &v, n-s, n-1, 0, n-1, &w, _state); - } - - /* - * Second pass. - */ - for(i=0; i<=n-1; i++) - { - hqrndunit2(&state, &tau.x, &tau.y, _state); - ae_v_cmulc(&a->ptr.pp_complex[0][i], a->stride, ae_v_len(0,n-1), tau); - tau = ae_c_conj(tau, _state); - ae_v_cmulc(&a->ptr.pp_complex[i][0], 1, ae_v_len(0,n-1), tau); - } - - /* - * Change all values from lower triangle by complex-conjugate values - * from upper one - */ - for(i=0; i<=n-2; i++) - { - ae_v_cmove(&a->ptr.pp_complex[i+1][i], a->stride, &a->ptr.pp_complex[i][i+1], 1, "N", ae_v_len(i+1,n-1)); - } - for(s=0; s<=n-2; s++) - { - for(i=s+1; i<=n-1; i++) - { - a->ptr.pp_complex[i][s].y = -a->ptr.pp_complex[i][s].y; - } - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -LU decomposition of a general real matrix with row pivoting - -A is represented as A = P*L*U, where: -* L is lower unitriangular matrix -* U is upper triangular matrix -* P = P0*P1*...*PK, K=min(M,N)-1, - Pi - permutation matrix for I and Pivots[I] - -This is cache-oblivous implementation of LU decomposition. -It is optimized for square matrices. As for rectangular matrices: -* best case - M>>N -* worst case - N>>M, small M, large N, matrix does not fit in CPU cache - -INPUT PARAMETERS: - A - array[0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - - -OUTPUT PARAMETERS: - A - matrices L and U in compact form: - * L is stored under main diagonal - * U is stored on and above main diagonal - Pivots - permutation matrix in compact form. - array[0..Min(M-1,N-1)]. - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlu(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state) -{ - - ae_vector_clear(pivots); - - ae_assert(m>0, "RMatrixLU: incorrect M!", _state); - ae_assert(n>0, "RMatrixLU: incorrect N!", _state); - rmatrixplu(a, m, n, pivots, _state); -} - - -/************************************************************************* -LU decomposition of a general complex matrix with row pivoting - -A is represented as A = P*L*U, where: -* L is lower unitriangular matrix -* U is upper triangular matrix -* P = P0*P1*...*PK, K=min(M,N)-1, - Pi - permutation matrix for I and Pivots[I] - -This is cache-oblivous implementation of LU decomposition. It is optimized -for square matrices. As for rectangular matrices: -* best case - M>>N -* worst case - N>>M, small M, large N, matrix does not fit in CPU cache - -INPUT PARAMETERS: - A - array[0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - - -OUTPUT PARAMETERS: - A - matrices L and U in compact form: - * L is stored under main diagonal - * U is stored on and above main diagonal - Pivots - permutation matrix in compact form. - array[0..Min(M-1,N-1)]. - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlu(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state) -{ - - ae_vector_clear(pivots); - - ae_assert(m>0, "CMatrixLU: incorrect M!", _state); - ae_assert(n>0, "CMatrixLU: incorrect N!", _state); - cmatrixplu(a, m, n, pivots, _state); -} - - -/************************************************************************* -Cache-oblivious Cholesky decomposition - -The algorithm computes Cholesky decomposition of a Hermitian positive- -definite matrix. The result of an algorithm is a representation of A as -A=U'*U or A=L*L' (here X' detones conj(X^T)). - -INPUT PARAMETERS: - A - upper or lower triangle of a factorized matrix. - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - if IsUpper=True, then A contains an upper triangle of - a symmetric matrix, otherwise A contains a lower one. - -OUTPUT PARAMETERS: - A - the result of factorization. If IsUpper=True, then - the upper triangle contains matrix U, so that A = U'*U, - and the elements below the main diagonal are not modified. - Similarly, if IsUpper = False. - -RESULT: - If the matrix is positive-definite, the function returns True. - Otherwise, the function returns False. Contents of A is not determined - in such case. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -ae_bool hpdmatrixcholesky(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmp; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tmp, 0, DT_COMPLEX, _state, ae_true); - - if( n<1 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - result = trfac_hpdmatrixcholeskyrec(a, 0, n, isupper, &tmp, _state); - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Cache-oblivious Cholesky decomposition - -The algorithm computes Cholesky decomposition of a symmetric positive- -definite matrix. The result of an algorithm is a representation of A as -A=U^T*U or A=L*L^T - -INPUT PARAMETERS: - A - upper or lower triangle of a factorized matrix. - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - if IsUpper=True, then A contains an upper triangle of - a symmetric matrix, otherwise A contains a lower one. - -OUTPUT PARAMETERS: - A - the result of factorization. If IsUpper=True, then - the upper triangle contains matrix U, so that A = U^T*U, - and the elements below the main diagonal are not modified. - Similarly, if IsUpper = False. - -RESULT: - If the matrix is positive-definite, the function returns True. - Otherwise, the function returns False. Contents of A is not determined - in such case. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -ae_bool spdmatrixcholesky(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmp; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - if( n<1 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - result = spdmatrixcholeskyrec(a, 0, n, isupper, &tmp, _state); - ae_frame_leave(_state); - return result; -} - - -void rmatrixlup(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmp; - ae_int_t i; - ae_int_t j; - double mx; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(pivots); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - - /* - * Internal LU decomposition subroutine. - * Never call it directly. - */ - ae_assert(m>0, "RMatrixLUP: incorrect M!", _state); - ae_assert(n>0, "RMatrixLUP: incorrect N!", _state); - - /* - * Scale matrix to avoid overflows, - * decompose it, then scale back. - */ - mx = 0; - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - mx = ae_maxreal(mx, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_neq(mx,0) ) - { - v = 1/mx; - for(i=0; i<=m-1; i++) - { - ae_v_muld(&a->ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - } - } - ae_vector_set_length(pivots, ae_minint(m, n, _state), _state); - ae_vector_set_length(&tmp, 2*ae_maxint(m, n, _state), _state); - trfac_rmatrixluprec(a, 0, m, n, pivots, &tmp, _state); - if( ae_fp_neq(mx,0) ) - { - v = mx; - for(i=0; i<=m-1; i++) - { - ae_v_muld(&a->ptr.pp_double[i][0], 1, ae_v_len(0,ae_minint(i, n-1, _state)), v); - } - } - ae_frame_leave(_state); -} - - -void cmatrixlup(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmp; - ae_int_t i; - ae_int_t j; - double mx; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(pivots); - ae_vector_init(&tmp, 0, DT_COMPLEX, _state, ae_true); - - - /* - * Internal LU decomposition subroutine. - * Never call it directly. - */ - ae_assert(m>0, "CMatrixLUP: incorrect M!", _state); - ae_assert(n>0, "CMatrixLUP: incorrect N!", _state); - - /* - * Scale matrix to avoid overflows, - * decompose it, then scale back. - */ - mx = 0; - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - mx = ae_maxreal(mx, ae_c_abs(a->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_neq(mx,0) ) - { - v = 1/mx; - for(i=0; i<=m-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[i][0], 1, ae_v_len(0,n-1), v); - } - } - ae_vector_set_length(pivots, ae_minint(m, n, _state), _state); - ae_vector_set_length(&tmp, 2*ae_maxint(m, n, _state), _state); - trfac_cmatrixluprec(a, 0, m, n, pivots, &tmp, _state); - if( ae_fp_neq(mx,0) ) - { - v = mx; - for(i=0; i<=m-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[i][0], 1, ae_v_len(0,ae_minint(i, n-1, _state)), v); - } - } - ae_frame_leave(_state); -} - - -void rmatrixplu(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmp; - ae_int_t i; - ae_int_t j; - double mx; - double v; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(pivots); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - - /* - * Internal LU decomposition subroutine. - * Never call it directly. - */ - ae_assert(m>0, "RMatrixPLU: incorrect M!", _state); - ae_assert(n>0, "RMatrixPLU: incorrect N!", _state); - ae_vector_set_length(&tmp, 2*ae_maxint(m, n, _state), _state); - ae_vector_set_length(pivots, ae_minint(m, n, _state), _state); - - /* - * Scale matrix to avoid overflows, - * decompose it, then scale back. - */ - mx = 0; - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - mx = ae_maxreal(mx, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_neq(mx,0) ) - { - v = 1/mx; - for(i=0; i<=m-1; i++) - { - ae_v_muld(&a->ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - } - } - trfac_rmatrixplurec(a, 0, m, n, pivots, &tmp, _state); - if( ae_fp_neq(mx,0) ) - { - v = mx; - for(i=0; i<=ae_minint(m, n, _state)-1; i++) - { - ae_v_muld(&a->ptr.pp_double[i][i], 1, ae_v_len(i,n-1), v); - } - } - ae_frame_leave(_state); -} - - -void cmatrixplu(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tmp; - ae_int_t i; - ae_int_t j; - double mx; - ae_complex v; - - ae_frame_make(_state, &_frame_block); - ae_vector_clear(pivots); - ae_vector_init(&tmp, 0, DT_COMPLEX, _state, ae_true); - - - /* - * Internal LU decomposition subroutine. - * Never call it directly. - */ - ae_assert(m>0, "CMatrixPLU: incorrect M!", _state); - ae_assert(n>0, "CMatrixPLU: incorrect N!", _state); - ae_vector_set_length(&tmp, 2*ae_maxint(m, n, _state), _state); - ae_vector_set_length(pivots, ae_minint(m, n, _state), _state); - - /* - * Scale matrix to avoid overflows, - * decompose it, then scale back. - */ - mx = 0; - for(i=0; i<=m-1; i++) - { - for(j=0; j<=n-1; j++) - { - mx = ae_maxreal(mx, ae_c_abs(a->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_neq(mx,0) ) - { - v = ae_complex_from_d(1/mx); - for(i=0; i<=m-1; i++) - { - ae_v_cmulc(&a->ptr.pp_complex[i][0], 1, ae_v_len(0,n-1), v); - } - } - trfac_cmatrixplurec(a, 0, m, n, pivots, &tmp, _state); - if( ae_fp_neq(mx,0) ) - { - v = ae_complex_from_d(mx); - for(i=0; i<=ae_minint(m, n, _state)-1; i++) - { - ae_v_cmulc(&a->ptr.pp_complex[i][i], 1, ae_v_len(i,n-1), v); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Recursive computational subroutine for SPDMatrixCholesky. - -INPUT PARAMETERS: - A - matrix given by upper or lower triangle - Offs - offset of diagonal block to decompose - N - diagonal block size - IsUpper - what half is given - Tmp - temporary array; allocated by function, if its size is too - small; can be reused on subsequent calls. - -OUTPUT PARAMETERS: - A - upper (or lower) triangle contains Cholesky decomposition - -RESULT: - True, on success - False, on failure - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -ae_bool spdmatrixcholeskyrec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t n1; - ae_int_t n2; - ae_bool result; - - - - /* - * check N - */ - if( n<1 ) - { - result = ae_false; - return result; - } - - /* - * Prepare buffer - */ - if( tmp->cnt<2*n ) - { - ae_vector_set_length(tmp, 2*n, _state); - } - - /* - * special cases - */ - if( n==1 ) - { - if( ae_fp_greater(a->ptr.pp_double[offs][offs],0) ) - { - a->ptr.pp_double[offs][offs] = ae_sqrt(a->ptr.pp_double[offs][offs], _state); - result = ae_true; - } - else - { - result = ae_false; - } - return result; - } - if( n<=ablasblocksize(a, _state) ) - { - result = trfac_spdmatrixcholesky2(a, offs, n, isupper, tmp, _state); - return result; - } - - /* - * general case: split task in cache-oblivious manner - */ - result = ae_true; - ablassplitlength(a, n, &n1, &n2, _state); - result = spdmatrixcholeskyrec(a, offs, n1, isupper, tmp, _state); - if( !result ) - { - return result; - } - if( n2>0 ) - { - if( isupper ) - { - rmatrixlefttrsm(n1, n2, a, offs, offs, isupper, ae_false, 1, a, offs, offs+n1, _state); - rmatrixsyrk(n2, n1, -1.0, a, offs, offs+n1, 1, 1.0, a, offs+n1, offs+n1, isupper, _state); - } - else - { - rmatrixrighttrsm(n2, n1, a, offs, offs, isupper, ae_false, 1, a, offs+n1, offs, _state); - rmatrixsyrk(n2, n1, -1.0, a, offs+n1, offs, 0, 1.0, a, offs+n1, offs+n1, isupper, _state); - } - result = spdmatrixcholeskyrec(a, offs+n1, n2, isupper, tmp, _state); - if( !result ) - { - return result; - } - } - return result; -} - - -/************************************************************************* -Recurrent complex LU subroutine. -Never call it directly. - - -- ALGLIB routine -- - 04.01.2010 - Bochkanov Sergey -*************************************************************************/ -static void trfac_cmatrixluprec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t m1; - ae_int_t m2; - - - - /* - * Kernel case - */ - if( ae_minint(m, n, _state)<=ablascomplexblocksize(a, _state) ) - { - trfac_cmatrixlup2(a, offs, m, n, pivots, tmp, _state); - return; - } - - /* - * Preliminary step, make N>=M - * - * ( A1 ) - * A = ( ), where A1 is square - * ( A2 ) - * - * Factorize A1, update A2 - */ - if( m>n ) - { - trfac_cmatrixluprec(a, offs, n, n, pivots, tmp, _state); - for(i=0; i<=n-1; i++) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+n][offs+i], a->stride, "N", ae_v_len(0,m-n-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+n][offs+i], a->stride, &a->ptr.pp_complex[offs+n][pivots->ptr.p_int[offs+i]], a->stride, "N", ae_v_len(offs+n,offs+m-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+n][pivots->ptr.p_int[offs+i]], a->stride, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs+n,offs+m-1)); - } - cmatrixrighttrsm(m-n, n, a, offs, offs, ae_true, ae_true, 0, a, offs+n, offs, _state); - return; - } - - /* - * Non-kernel case - */ - ablascomplexsplitlength(a, m, &m1, &m2, _state); - trfac_cmatrixluprec(a, offs, m1, n, pivots, tmp, _state); - if( m2>0 ) - { - for(i=0; i<=m1-1; i++) - { - if( offs+i!=pivots->ptr.p_int[offs+i] ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+m1][offs+i], a->stride, "N", ae_v_len(0,m2-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+m1][offs+i], a->stride, &a->ptr.pp_complex[offs+m1][pivots->ptr.p_int[offs+i]], a->stride, "N", ae_v_len(offs+m1,offs+m-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+m1][pivots->ptr.p_int[offs+i]], a->stride, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs+m1,offs+m-1)); - } - } - cmatrixrighttrsm(m2, m1, a, offs, offs, ae_true, ae_true, 0, a, offs+m1, offs, _state); - cmatrixgemm(m-m1, n-m1, m1, ae_complex_from_d(-1.0), a, offs+m1, offs, 0, a, offs, offs+m1, 0, ae_complex_from_d(1.0), a, offs+m1, offs+m1, _state); - trfac_cmatrixluprec(a, offs+m1, m-m1, n-m1, pivots, tmp, _state); - for(i=0; i<=m2-1; i++) - { - if( offs+m1+i!=pivots->ptr.p_int[offs+m1+i] ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs][offs+m1+i], a->stride, "N", ae_v_len(0,m1-1)); - ae_v_cmove(&a->ptr.pp_complex[offs][offs+m1+i], a->stride, &a->ptr.pp_complex[offs][pivots->ptr.p_int[offs+m1+i]], a->stride, "N", ae_v_len(offs,offs+m1-1)); - ae_v_cmove(&a->ptr.pp_complex[offs][pivots->ptr.p_int[offs+m1+i]], a->stride, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs,offs+m1-1)); - } - } - } -} - - -/************************************************************************* -Recurrent real LU subroutine. -Never call it directly. - - -- ALGLIB routine -- - 04.01.2010 - Bochkanov Sergey -*************************************************************************/ -static void trfac_rmatrixluprec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t m1; - ae_int_t m2; - - - - /* - * Kernel case - */ - if( ae_minint(m, n, _state)<=ablasblocksize(a, _state) ) - { - trfac_rmatrixlup2(a, offs, m, n, pivots, tmp, _state); - return; - } - - /* - * Preliminary step, make N>=M - * - * ( A1 ) - * A = ( ), where A1 is square - * ( A2 ) - * - * Factorize A1, update A2 - */ - if( m>n ) - { - trfac_rmatrixluprec(a, offs, n, n, pivots, tmp, _state); - for(i=0; i<=n-1; i++) - { - if( offs+i!=pivots->ptr.p_int[offs+i] ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+n][offs+i], a->stride, ae_v_len(0,m-n-1)); - ae_v_move(&a->ptr.pp_double[offs+n][offs+i], a->stride, &a->ptr.pp_double[offs+n][pivots->ptr.p_int[offs+i]], a->stride, ae_v_len(offs+n,offs+m-1)); - ae_v_move(&a->ptr.pp_double[offs+n][pivots->ptr.p_int[offs+i]], a->stride, &tmp->ptr.p_double[0], 1, ae_v_len(offs+n,offs+m-1)); - } - } - rmatrixrighttrsm(m-n, n, a, offs, offs, ae_true, ae_true, 0, a, offs+n, offs, _state); - return; - } - - /* - * Non-kernel case - */ - ablassplitlength(a, m, &m1, &m2, _state); - trfac_rmatrixluprec(a, offs, m1, n, pivots, tmp, _state); - if( m2>0 ) - { - for(i=0; i<=m1-1; i++) - { - if( offs+i!=pivots->ptr.p_int[offs+i] ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+m1][offs+i], a->stride, ae_v_len(0,m2-1)); - ae_v_move(&a->ptr.pp_double[offs+m1][offs+i], a->stride, &a->ptr.pp_double[offs+m1][pivots->ptr.p_int[offs+i]], a->stride, ae_v_len(offs+m1,offs+m-1)); - ae_v_move(&a->ptr.pp_double[offs+m1][pivots->ptr.p_int[offs+i]], a->stride, &tmp->ptr.p_double[0], 1, ae_v_len(offs+m1,offs+m-1)); - } - } - rmatrixrighttrsm(m2, m1, a, offs, offs, ae_true, ae_true, 0, a, offs+m1, offs, _state); - rmatrixgemm(m-m1, n-m1, m1, -1.0, a, offs+m1, offs, 0, a, offs, offs+m1, 0, 1.0, a, offs+m1, offs+m1, _state); - trfac_rmatrixluprec(a, offs+m1, m-m1, n-m1, pivots, tmp, _state); - for(i=0; i<=m2-1; i++) - { - if( offs+m1+i!=pivots->ptr.p_int[offs+m1+i] ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs][offs+m1+i], a->stride, ae_v_len(0,m1-1)); - ae_v_move(&a->ptr.pp_double[offs][offs+m1+i], a->stride, &a->ptr.pp_double[offs][pivots->ptr.p_int[offs+m1+i]], a->stride, ae_v_len(offs,offs+m1-1)); - ae_v_move(&a->ptr.pp_double[offs][pivots->ptr.p_int[offs+m1+i]], a->stride, &tmp->ptr.p_double[0], 1, ae_v_len(offs,offs+m1-1)); - } - } - } -} - - -/************************************************************************* -Recurrent complex LU subroutine. -Never call it directly. - - -- ALGLIB routine -- - 04.01.2010 - Bochkanov Sergey -*************************************************************************/ -static void trfac_cmatrixplurec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n1; - ae_int_t n2; - - - - /* - * Kernel case - */ - if( ae_minint(m, n, _state)<=ablascomplexblocksize(a, _state) ) - { - trfac_cmatrixplu2(a, offs, m, n, pivots, tmp, _state); - return; - } - - /* - * Preliminary step, make M>=N. - * - * A = (A1 A2), where A1 is square - * Factorize A1, update A2 - */ - if( n>m ) - { - trfac_cmatrixplurec(a, offs, m, m, pivots, tmp, _state); - for(i=0; i<=m-1; i++) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+i][offs+m], 1, "N", ae_v_len(0,n-m-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+i][offs+m], 1, &a->ptr.pp_complex[pivots->ptr.p_int[offs+i]][offs+m], 1, "N", ae_v_len(offs+m,offs+n-1)); - ae_v_cmove(&a->ptr.pp_complex[pivots->ptr.p_int[offs+i]][offs+m], 1, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs+m,offs+n-1)); - } - cmatrixlefttrsm(m, n-m, a, offs, offs, ae_false, ae_true, 0, a, offs, offs+m, _state); - return; - } - - /* - * Non-kernel case - */ - ablascomplexsplitlength(a, n, &n1, &n2, _state); - trfac_cmatrixplurec(a, offs, m, n1, pivots, tmp, _state); - if( n2>0 ) - { - for(i=0; i<=n1-1; i++) - { - if( offs+i!=pivots->ptr.p_int[offs+i] ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+i][offs+n1], 1, "N", ae_v_len(0,n2-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+i][offs+n1], 1, &a->ptr.pp_complex[pivots->ptr.p_int[offs+i]][offs+n1], 1, "N", ae_v_len(offs+n1,offs+n-1)); - ae_v_cmove(&a->ptr.pp_complex[pivots->ptr.p_int[offs+i]][offs+n1], 1, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs+n1,offs+n-1)); - } - } - cmatrixlefttrsm(n1, n2, a, offs, offs, ae_false, ae_true, 0, a, offs, offs+n1, _state); - cmatrixgemm(m-n1, n-n1, n1, ae_complex_from_d(-1.0), a, offs+n1, offs, 0, a, offs, offs+n1, 0, ae_complex_from_d(1.0), a, offs+n1, offs+n1, _state); - trfac_cmatrixplurec(a, offs+n1, m-n1, n-n1, pivots, tmp, _state); - for(i=0; i<=n2-1; i++) - { - if( offs+n1+i!=pivots->ptr.p_int[offs+n1+i] ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+n1+i][offs], 1, "N", ae_v_len(0,n1-1)); - ae_v_cmove(&a->ptr.pp_complex[offs+n1+i][offs], 1, &a->ptr.pp_complex[pivots->ptr.p_int[offs+n1+i]][offs], 1, "N", ae_v_len(offs,offs+n1-1)); - ae_v_cmove(&a->ptr.pp_complex[pivots->ptr.p_int[offs+n1+i]][offs], 1, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs,offs+n1-1)); - } - } - } -} - - -/************************************************************************* -Recurrent real LU subroutine. -Never call it directly. - - -- ALGLIB routine -- - 04.01.2010 - Bochkanov Sergey -*************************************************************************/ -static void trfac_rmatrixplurec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n1; - ae_int_t n2; - - - - /* - * Kernel case - */ - if( ae_minint(m, n, _state)<=ablasblocksize(a, _state) ) - { - trfac_rmatrixplu2(a, offs, m, n, pivots, tmp, _state); - return; - } - - /* - * Preliminary step, make M>=N. - * - * A = (A1 A2), where A1 is square - * Factorize A1, update A2 - */ - if( n>m ) - { - trfac_rmatrixplurec(a, offs, m, m, pivots, tmp, _state); - for(i=0; i<=m-1; i++) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+i][offs+m], 1, ae_v_len(0,n-m-1)); - ae_v_move(&a->ptr.pp_double[offs+i][offs+m], 1, &a->ptr.pp_double[pivots->ptr.p_int[offs+i]][offs+m], 1, ae_v_len(offs+m,offs+n-1)); - ae_v_move(&a->ptr.pp_double[pivots->ptr.p_int[offs+i]][offs+m], 1, &tmp->ptr.p_double[0], 1, ae_v_len(offs+m,offs+n-1)); - } - rmatrixlefttrsm(m, n-m, a, offs, offs, ae_false, ae_true, 0, a, offs, offs+m, _state); - return; - } - - /* - * Non-kernel case - */ - ablassplitlength(a, n, &n1, &n2, _state); - trfac_rmatrixplurec(a, offs, m, n1, pivots, tmp, _state); - if( n2>0 ) - { - for(i=0; i<=n1-1; i++) - { - if( offs+i!=pivots->ptr.p_int[offs+i] ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+i][offs+n1], 1, ae_v_len(0,n2-1)); - ae_v_move(&a->ptr.pp_double[offs+i][offs+n1], 1, &a->ptr.pp_double[pivots->ptr.p_int[offs+i]][offs+n1], 1, ae_v_len(offs+n1,offs+n-1)); - ae_v_move(&a->ptr.pp_double[pivots->ptr.p_int[offs+i]][offs+n1], 1, &tmp->ptr.p_double[0], 1, ae_v_len(offs+n1,offs+n-1)); - } - } - rmatrixlefttrsm(n1, n2, a, offs, offs, ae_false, ae_true, 0, a, offs, offs+n1, _state); - rmatrixgemm(m-n1, n-n1, n1, -1.0, a, offs+n1, offs, 0, a, offs, offs+n1, 0, 1.0, a, offs+n1, offs+n1, _state); - trfac_rmatrixplurec(a, offs+n1, m-n1, n-n1, pivots, tmp, _state); - for(i=0; i<=n2-1; i++) - { - if( offs+n1+i!=pivots->ptr.p_int[offs+n1+i] ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+n1+i][offs], 1, ae_v_len(0,n1-1)); - ae_v_move(&a->ptr.pp_double[offs+n1+i][offs], 1, &a->ptr.pp_double[pivots->ptr.p_int[offs+n1+i]][offs], 1, ae_v_len(offs,offs+n1-1)); - ae_v_move(&a->ptr.pp_double[pivots->ptr.p_int[offs+n1+i]][offs], 1, &tmp->ptr.p_double[0], 1, ae_v_len(offs,offs+n1-1)); - } - } - } -} - - -/************************************************************************* -Complex LUP kernel - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -static void trfac_cmatrixlup2(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t jp; - ae_complex s; - - - - /* - * Quick return if possible - */ - if( m==0||n==0 ) - { - return; - } - - /* - * main cycle - */ - for(j=0; j<=ae_minint(m-1, n-1, _state); j++) - { - - /* - * Find pivot, swap columns - */ - jp = j; - for(i=j+1; i<=n-1; i++) - { - if( ae_fp_greater(ae_c_abs(a->ptr.pp_complex[offs+j][offs+i], _state),ae_c_abs(a->ptr.pp_complex[offs+j][offs+jp], _state)) ) - { - jp = i; - } - } - pivots->ptr.p_int[offs+j] = offs+jp; - if( jp!=j ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs][offs+j], a->stride, "N", ae_v_len(0,m-1)); - ae_v_cmove(&a->ptr.pp_complex[offs][offs+j], a->stride, &a->ptr.pp_complex[offs][offs+jp], a->stride, "N", ae_v_len(offs,offs+m-1)); - ae_v_cmove(&a->ptr.pp_complex[offs][offs+jp], a->stride, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs,offs+m-1)); - } - - /* - * LU decomposition of 1x(N-J) matrix - */ - if( ae_c_neq_d(a->ptr.pp_complex[offs+j][offs+j],0)&&j+1<=n-1 ) - { - s = ae_c_d_div(1,a->ptr.pp_complex[offs+j][offs+j]); - ae_v_cmulc(&a->ptr.pp_complex[offs+j][offs+j+1], 1, ae_v_len(offs+j+1,offs+n-1), s); - } - - /* - * Update trailing (M-J-1)x(N-J-1) matrix - */ - if( jptr.p_complex[0], 1, &a->ptr.pp_complex[offs+j+1][offs+j], a->stride, "N", ae_v_len(0,m-j-2)); - ae_v_cmoveneg(&tmp->ptr.p_complex[m], 1, &a->ptr.pp_complex[offs+j][offs+j+1], 1, "N", ae_v_len(m,m+n-j-2)); - cmatrixrank1(m-j-1, n-j-1, a, offs+j+1, offs+j+1, tmp, 0, tmp, m, _state); - } - } -} - - -/************************************************************************* -Real LUP kernel - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -static void trfac_rmatrixlup2(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t jp; - double s; - - - - /* - * Quick return if possible - */ - if( m==0||n==0 ) - { - return; - } - - /* - * main cycle - */ - for(j=0; j<=ae_minint(m-1, n-1, _state); j++) - { - - /* - * Find pivot, swap columns - */ - jp = j; - for(i=j+1; i<=n-1; i++) - { - if( ae_fp_greater(ae_fabs(a->ptr.pp_double[offs+j][offs+i], _state),ae_fabs(a->ptr.pp_double[offs+j][offs+jp], _state)) ) - { - jp = i; - } - } - pivots->ptr.p_int[offs+j] = offs+jp; - if( jp!=j ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs][offs+j], a->stride, ae_v_len(0,m-1)); - ae_v_move(&a->ptr.pp_double[offs][offs+j], a->stride, &a->ptr.pp_double[offs][offs+jp], a->stride, ae_v_len(offs,offs+m-1)); - ae_v_move(&a->ptr.pp_double[offs][offs+jp], a->stride, &tmp->ptr.p_double[0], 1, ae_v_len(offs,offs+m-1)); - } - - /* - * LU decomposition of 1x(N-J) matrix - */ - if( ae_fp_neq(a->ptr.pp_double[offs+j][offs+j],0)&&j+1<=n-1 ) - { - s = 1/a->ptr.pp_double[offs+j][offs+j]; - ae_v_muld(&a->ptr.pp_double[offs+j][offs+j+1], 1, ae_v_len(offs+j+1,offs+n-1), s); - } - - /* - * Update trailing (M-J-1)x(N-J-1) matrix - */ - if( jptr.p_double[0], 1, &a->ptr.pp_double[offs+j+1][offs+j], a->stride, ae_v_len(0,m-j-2)); - ae_v_moveneg(&tmp->ptr.p_double[m], 1, &a->ptr.pp_double[offs+j][offs+j+1], 1, ae_v_len(m,m+n-j-2)); - rmatrixrank1(m-j-1, n-j-1, a, offs+j+1, offs+j+1, tmp, 0, tmp, m, _state); - } - } -} - - -/************************************************************************* -Complex PLU kernel - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - June 30, 1992 -*************************************************************************/ -static void trfac_cmatrixplu2(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t jp; - ae_complex s; - - - - /* - * Quick return if possible - */ - if( m==0||n==0 ) - { - return; - } - for(j=0; j<=ae_minint(m-1, n-1, _state); j++) - { - - /* - * Find pivot and test for singularity. - */ - jp = j; - for(i=j+1; i<=m-1; i++) - { - if( ae_fp_greater(ae_c_abs(a->ptr.pp_complex[offs+i][offs+j], _state),ae_c_abs(a->ptr.pp_complex[offs+jp][offs+j], _state)) ) - { - jp = i; - } - } - pivots->ptr.p_int[offs+j] = offs+jp; - if( ae_c_neq_d(a->ptr.pp_complex[offs+jp][offs+j],0) ) - { - - /* - *Apply the interchange to rows - */ - if( jp!=j ) - { - for(i=0; i<=n-1; i++) - { - s = a->ptr.pp_complex[offs+j][offs+i]; - a->ptr.pp_complex[offs+j][offs+i] = a->ptr.pp_complex[offs+jp][offs+i]; - a->ptr.pp_complex[offs+jp][offs+i] = s; - } - } - - /* - *Compute elements J+1:M of J-th column. - */ - if( j+1<=m-1 ) - { - s = ae_c_d_div(1,a->ptr.pp_complex[offs+j][offs+j]); - ae_v_cmulc(&a->ptr.pp_complex[offs+j+1][offs+j], a->stride, ae_v_len(offs+j+1,offs+m-1), s); - } - } - if( jptr.p_complex[0], 1, &a->ptr.pp_complex[offs+j+1][offs+j], a->stride, "N", ae_v_len(0,m-j-2)); - ae_v_cmoveneg(&tmp->ptr.p_complex[m], 1, &a->ptr.pp_complex[offs+j][offs+j+1], 1, "N", ae_v_len(m,m+n-j-2)); - cmatrixrank1(m-j-1, n-j-1, a, offs+j+1, offs+j+1, tmp, 0, tmp, m, _state); - } - } -} - - -/************************************************************************* -Real PLU kernel - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - June 30, 1992 -*************************************************************************/ -static void trfac_rmatrixplu2(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t jp; - double s; - - - - /* - * Quick return if possible - */ - if( m==0||n==0 ) - { - return; - } - for(j=0; j<=ae_minint(m-1, n-1, _state); j++) - { - - /* - * Find pivot and test for singularity. - */ - jp = j; - for(i=j+1; i<=m-1; i++) - { - if( ae_fp_greater(ae_fabs(a->ptr.pp_double[offs+i][offs+j], _state),ae_fabs(a->ptr.pp_double[offs+jp][offs+j], _state)) ) - { - jp = i; - } - } - pivots->ptr.p_int[offs+j] = offs+jp; - if( ae_fp_neq(a->ptr.pp_double[offs+jp][offs+j],0) ) - { - - /* - *Apply the interchange to rows - */ - if( jp!=j ) - { - for(i=0; i<=n-1; i++) - { - s = a->ptr.pp_double[offs+j][offs+i]; - a->ptr.pp_double[offs+j][offs+i] = a->ptr.pp_double[offs+jp][offs+i]; - a->ptr.pp_double[offs+jp][offs+i] = s; - } - } - - /* - *Compute elements J+1:M of J-th column. - */ - if( j+1<=m-1 ) - { - s = 1/a->ptr.pp_double[offs+j][offs+j]; - ae_v_muld(&a->ptr.pp_double[offs+j+1][offs+j], a->stride, ae_v_len(offs+j+1,offs+m-1), s); - } - } - if( jptr.p_double[0], 1, &a->ptr.pp_double[offs+j+1][offs+j], a->stride, ae_v_len(0,m-j-2)); - ae_v_moveneg(&tmp->ptr.p_double[m], 1, &a->ptr.pp_double[offs+j][offs+j+1], 1, ae_v_len(m,m+n-j-2)); - rmatrixrank1(m-j-1, n-j-1, a, offs+j+1, offs+j+1, tmp, 0, tmp, m, _state); - } - } -} - - -/************************************************************************* -Recursive computational subroutine for HPDMatrixCholesky - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -static ae_bool trfac_hpdmatrixcholeskyrec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t n1; - ae_int_t n2; - ae_bool result; - - - - /* - * check N - */ - if( n<1 ) - { - result = ae_false; - return result; - } - - /* - * Prepare buffer - */ - if( tmp->cnt<2*n ) - { - ae_vector_set_length(tmp, 2*n, _state); - } - - /* - * special cases - */ - if( n==1 ) - { - if( ae_fp_greater(a->ptr.pp_complex[offs][offs].x,0) ) - { - a->ptr.pp_complex[offs][offs] = ae_complex_from_d(ae_sqrt(a->ptr.pp_complex[offs][offs].x, _state)); - result = ae_true; - } - else - { - result = ae_false; - } - return result; - } - if( n<=ablascomplexblocksize(a, _state) ) - { - result = trfac_hpdmatrixcholesky2(a, offs, n, isupper, tmp, _state); - return result; - } - - /* - * general case: split task in cache-oblivious manner - */ - result = ae_true; - ablascomplexsplitlength(a, n, &n1, &n2, _state); - result = trfac_hpdmatrixcholeskyrec(a, offs, n1, isupper, tmp, _state); - if( !result ) - { - return result; - } - if( n2>0 ) - { - if( isupper ) - { - cmatrixlefttrsm(n1, n2, a, offs, offs, isupper, ae_false, 2, a, offs, offs+n1, _state); - cmatrixsyrk(n2, n1, -1.0, a, offs, offs+n1, 2, 1.0, a, offs+n1, offs+n1, isupper, _state); - } - else - { - cmatrixrighttrsm(n2, n1, a, offs, offs, isupper, ae_false, 2, a, offs+n1, offs, _state); - cmatrixsyrk(n2, n1, -1.0, a, offs+n1, offs, 0, 1.0, a, offs+n1, offs+n1, isupper, _state); - } - result = trfac_hpdmatrixcholeskyrec(a, offs+n1, n2, isupper, tmp, _state); - if( !result ) - { - return result; - } - } - return result; -} - - -/************************************************************************* -Level-2 Hermitian Cholesky subroutine. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static ae_bool trfac_hpdmatrixcholesky2(/* Complex */ ae_matrix* aaa, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double ajj; - ae_complex v; - double r; - ae_bool result; - - - result = ae_true; - if( n<0 ) - { - result = ae_false; - return result; - } - - /* - * Quick return if possible - */ - if( n==0 ) - { - return result; - } - if( isupper ) - { - - /* - * Compute the Cholesky factorization A = U'*U. - */ - for(j=0; j<=n-1; j++) - { - - /* - * Compute U(J,J) and test for non-positive-definiteness. - */ - v = ae_v_cdotproduct(&aaa->ptr.pp_complex[offs][offs+j], aaa->stride, "Conj", &aaa->ptr.pp_complex[offs][offs+j], aaa->stride, "N", ae_v_len(offs,offs+j-1)); - ajj = ae_c_sub(aaa->ptr.pp_complex[offs+j][offs+j],v).x; - if( ae_fp_less_eq(ajj,0) ) - { - aaa->ptr.pp_complex[offs+j][offs+j] = ae_complex_from_d(ajj); - result = ae_false; - return result; - } - ajj = ae_sqrt(ajj, _state); - aaa->ptr.pp_complex[offs+j][offs+j] = ae_complex_from_d(ajj); - - /* - * Compute elements J+1:N-1 of row J. - */ - if( j0 ) - { - ae_v_cmoveneg(&tmp->ptr.p_complex[0], 1, &aaa->ptr.pp_complex[offs][offs+j], aaa->stride, "Conj", ae_v_len(0,j-1)); - cmatrixmv(n-j-1, j, aaa, offs, offs+j+1, 1, tmp, 0, tmp, n, _state); - ae_v_cadd(&aaa->ptr.pp_complex[offs+j][offs+j+1], 1, &tmp->ptr.p_complex[n], 1, "N", ae_v_len(offs+j+1,offs+n-1)); - } - r = 1/ajj; - ae_v_cmuld(&aaa->ptr.pp_complex[offs+j][offs+j+1], 1, ae_v_len(offs+j+1,offs+n-1), r); - } - } - } - else - { - - /* - * Compute the Cholesky factorization A = L*L'. - */ - for(j=0; j<=n-1; j++) - { - - /* - * Compute L(J+1,J+1) and test for non-positive-definiteness. - */ - v = ae_v_cdotproduct(&aaa->ptr.pp_complex[offs+j][offs], 1, "Conj", &aaa->ptr.pp_complex[offs+j][offs], 1, "N", ae_v_len(offs,offs+j-1)); - ajj = ae_c_sub(aaa->ptr.pp_complex[offs+j][offs+j],v).x; - if( ae_fp_less_eq(ajj,0) ) - { - aaa->ptr.pp_complex[offs+j][offs+j] = ae_complex_from_d(ajj); - result = ae_false; - return result; - } - ajj = ae_sqrt(ajj, _state); - aaa->ptr.pp_complex[offs+j][offs+j] = ae_complex_from_d(ajj); - - /* - * Compute elements J+1:N of column J. - */ - if( j0 ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &aaa->ptr.pp_complex[offs+j][offs], 1, "Conj", ae_v_len(0,j-1)); - cmatrixmv(n-j-1, j, aaa, offs+j+1, offs, 0, tmp, 0, tmp, n, _state); - for(i=0; i<=n-j-2; i++) - { - aaa->ptr.pp_complex[offs+j+1+i][offs+j] = ae_c_div_d(ae_c_sub(aaa->ptr.pp_complex[offs+j+1+i][offs+j],tmp->ptr.p_complex[n+i]),ajj); - } - } - else - { - for(i=0; i<=n-j-2; i++) - { - aaa->ptr.pp_complex[offs+j+1+i][offs+j] = ae_c_div_d(aaa->ptr.pp_complex[offs+j+1+i][offs+j],ajj); - } - } - } - } - } - return result; -} - - -/************************************************************************* -Level-2 Cholesky subroutine - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static ae_bool trfac_spdmatrixcholesky2(/* Real */ ae_matrix* aaa, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double ajj; - double v; - double r; - ae_bool result; - - - result = ae_true; - if( n<0 ) - { - result = ae_false; - return result; - } - - /* - * Quick return if possible - */ - if( n==0 ) - { - return result; - } - if( isupper ) - { - - /* - * Compute the Cholesky factorization A = U'*U. - */ - for(j=0; j<=n-1; j++) - { - - /* - * Compute U(J,J) and test for non-positive-definiteness. - */ - v = ae_v_dotproduct(&aaa->ptr.pp_double[offs][offs+j], aaa->stride, &aaa->ptr.pp_double[offs][offs+j], aaa->stride, ae_v_len(offs,offs+j-1)); - ajj = aaa->ptr.pp_double[offs+j][offs+j]-v; - if( ae_fp_less_eq(ajj,0) ) - { - aaa->ptr.pp_double[offs+j][offs+j] = ajj; - result = ae_false; - return result; - } - ajj = ae_sqrt(ajj, _state); - aaa->ptr.pp_double[offs+j][offs+j] = ajj; - - /* - * Compute elements J+1:N-1 of row J. - */ - if( j0 ) - { - ae_v_moveneg(&tmp->ptr.p_double[0], 1, &aaa->ptr.pp_double[offs][offs+j], aaa->stride, ae_v_len(0,j-1)); - rmatrixmv(n-j-1, j, aaa, offs, offs+j+1, 1, tmp, 0, tmp, n, _state); - ae_v_add(&aaa->ptr.pp_double[offs+j][offs+j+1], 1, &tmp->ptr.p_double[n], 1, ae_v_len(offs+j+1,offs+n-1)); - } - r = 1/ajj; - ae_v_muld(&aaa->ptr.pp_double[offs+j][offs+j+1], 1, ae_v_len(offs+j+1,offs+n-1), r); - } - } - } - else - { - - /* - * Compute the Cholesky factorization A = L*L'. - */ - for(j=0; j<=n-1; j++) - { - - /* - * Compute L(J+1,J+1) and test for non-positive-definiteness. - */ - v = ae_v_dotproduct(&aaa->ptr.pp_double[offs+j][offs], 1, &aaa->ptr.pp_double[offs+j][offs], 1, ae_v_len(offs,offs+j-1)); - ajj = aaa->ptr.pp_double[offs+j][offs+j]-v; - if( ae_fp_less_eq(ajj,0) ) - { - aaa->ptr.pp_double[offs+j][offs+j] = ajj; - result = ae_false; - return result; - } - ajj = ae_sqrt(ajj, _state); - aaa->ptr.pp_double[offs+j][offs+j] = ajj; - - /* - * Compute elements J+1:N of column J. - */ - if( j0 ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &aaa->ptr.pp_double[offs+j][offs], 1, ae_v_len(0,j-1)); - rmatrixmv(n-j-1, j, aaa, offs+j+1, offs, 0, tmp, 0, tmp, n, _state); - for(i=0; i<=n-j-2; i++) - { - aaa->ptr.pp_double[offs+j+1+i][offs+j] = (aaa->ptr.pp_double[offs+j+1+i][offs+j]-tmp->ptr.p_double[n+i])/ajj; - } - } - else - { - for(i=0; i<=n-j-2; i++) - { - aaa->ptr.pp_double[offs+j+1+i][offs+j] = aaa->ptr.pp_double[offs+j+1+i][offs+j]/ajj; - } - } - } - } - } - return result; -} - - - - -/************************************************************************* -Estimate of a matrix condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixrcond1(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - ae_vector t; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "RMatrixRCond1: N<1!", _state); - ae_vector_set_length(&t, n, _state); - for(i=0; i<=n-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - t.ptr.p_double[j] = t.ptr.p_double[j]+ae_fabs(a->ptr.pp_double[i][j], _state); - } - } - nrm = 0; - for(i=0; i<=n-1; i++) - { - nrm = ae_maxreal(nrm, t.ptr.p_double[i], _state); - } - rmatrixlu(a, n, n, &pivots, _state); - rcond_rmatrixrcondluinternal(a, n, ae_true, ae_true, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixrcondinf(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "RMatrixRCondInf: N<1!", _state); - nrm = 0; - for(i=0; i<=n-1; i++) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+ae_fabs(a->ptr.pp_double[i][j], _state); - } - nrm = ae_maxreal(nrm, v, _state); - } - rmatrixlu(a, n, n, &pivots, _state); - rcond_rmatrixrcondluinternal(a, n, ae_false, ae_true, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Condition number estimate of a symmetric positive definite matrix. - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm of condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - A - symmetric positive definite matrix which is given by its - upper or lower triangle depending on the value of - IsUpper. Array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - -Result: - 1/LowerBound(cond(A)), if matrix A is positive definite, - -1, if matrix A is not positive definite, and its condition number - could not be found by this algorithm. - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double spdmatrixrcond(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - double v; - double nrm; - ae_vector t; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&t, n, _state); - for(i=0; i<=n-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - if( i==j ) - { - t.ptr.p_double[i] = t.ptr.p_double[i]+ae_fabs(a->ptr.pp_double[i][i], _state); - } - else - { - t.ptr.p_double[i] = t.ptr.p_double[i]+ae_fabs(a->ptr.pp_double[i][j], _state); - t.ptr.p_double[j] = t.ptr.p_double[j]+ae_fabs(a->ptr.pp_double[i][j], _state); - } - } - } - nrm = 0; - for(i=0; i<=n-1; i++) - { - nrm = ae_maxreal(nrm, t.ptr.p_double[i], _state); - } - if( spdmatrixcholesky(a, n, isupper, _state) ) - { - rcond_spdmatrixrcondcholeskyinternal(a, n, isupper, ae_true, nrm, &v, _state); - result = v; - } - else - { - result = -1; - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Triangular matrix: estimate of a condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array[0..N-1, 0..N-1]. - N - size of A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixtrrcond1(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - ae_vector t; - ae_int_t j1; - ae_int_t j2; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "RMatrixTRRCond1: N<1!", _state); - ae_vector_set_length(&t, n, _state); - for(i=0; i<=n-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i+1; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i-1; - } - for(j=j1; j<=j2; j++) - { - t.ptr.p_double[j] = t.ptr.p_double[j]+ae_fabs(a->ptr.pp_double[i][j], _state); - } - if( isunit ) - { - t.ptr.p_double[i] = t.ptr.p_double[i]+1; - } - else - { - t.ptr.p_double[i] = t.ptr.p_double[i]+ae_fabs(a->ptr.pp_double[i][i], _state); - } - } - nrm = 0; - for(i=0; i<=n-1; i++) - { - nrm = ae_maxreal(nrm, t.ptr.p_double[i], _state); - } - rcond_rmatrixrcondtrinternal(a, n, isupper, isunit, ae_true, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Triangular matrix: estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixtrrcondinf(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - ae_int_t j1; - ae_int_t j2; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "RMatrixTRRCondInf: N<1!", _state); - nrm = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i+1; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i-1; - } - v = 0; - for(j=j1; j<=j2; j++) - { - v = v+ae_fabs(a->ptr.pp_double[i][j], _state); - } - if( isunit ) - { - v = v+1; - } - else - { - v = v+ae_fabs(a->ptr.pp_double[i][i], _state); - } - nrm = ae_maxreal(nrm, v, _state); - } - rcond_rmatrixrcondtrinternal(a, n, isupper, isunit, ae_false, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Condition number estimate of a Hermitian positive definite matrix. - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm of condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - A - Hermitian positive definite matrix which is given by its - upper or lower triangle depending on the value of - IsUpper. Array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - -Result: - 1/LowerBound(cond(A)), if matrix A is positive definite, - -1, if matrix A is not positive definite, and its condition number - could not be found by this algorithm. - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double hpdmatrixrcond(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - double v; - double nrm; - ae_vector t; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&t, n, _state); - for(i=0; i<=n-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - if( i==j ) - { - t.ptr.p_double[i] = t.ptr.p_double[i]+ae_c_abs(a->ptr.pp_complex[i][i], _state); - } - else - { - t.ptr.p_double[i] = t.ptr.p_double[i]+ae_c_abs(a->ptr.pp_complex[i][j], _state); - t.ptr.p_double[j] = t.ptr.p_double[j]+ae_c_abs(a->ptr.pp_complex[i][j], _state); - } - } - } - nrm = 0; - for(i=0; i<=n-1; i++) - { - nrm = ae_maxreal(nrm, t.ptr.p_double[i], _state); - } - if( hpdmatrixcholesky(a, n, isupper, _state) ) - { - rcond_hpdmatrixrcondcholeskyinternal(a, n, isupper, ae_true, nrm, &v, _state); - result = v; - } - else - { - result = -1; - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Estimate of a matrix condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixrcond1(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - ae_vector t; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "CMatrixRCond1: N<1!", _state); - ae_vector_set_length(&t, n, _state); - for(i=0; i<=n-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - t.ptr.p_double[j] = t.ptr.p_double[j]+ae_c_abs(a->ptr.pp_complex[i][j], _state); - } - } - nrm = 0; - for(i=0; i<=n-1; i++) - { - nrm = ae_maxreal(nrm, t.ptr.p_double[i], _state); - } - cmatrixlu(a, n, n, &pivots, _state); - rcond_cmatrixrcondluinternal(a, n, ae_true, ae_true, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixrcondinf(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "CMatrixRCondInf: N<1!", _state); - nrm = 0; - for(i=0; i<=n-1; i++) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+ae_c_abs(a->ptr.pp_complex[i][j], _state); - } - nrm = ae_maxreal(nrm, v, _state); - } - cmatrixlu(a, n, n, &pivots, _state); - rcond_cmatrixrcondluinternal(a, n, ae_false, ae_true, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the RMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixlurcond1(/* Real */ ae_matrix* lua, - ae_int_t n, - ae_state *_state) -{ - double v; - double result; - - - rcond_rmatrixrcondluinternal(lua, n, ae_true, ae_false, 0, &v, _state); - result = v; - return result; -} - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition -(infinity norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the RMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixlurcondinf(/* Real */ ae_matrix* lua, - ae_int_t n, - ae_state *_state) -{ - double v; - double result; - - - rcond_rmatrixrcondluinternal(lua, n, ae_false, ae_false, 0, &v, _state); - result = v; - return result; -} - - -/************************************************************************* -Condition number estimate of a symmetric positive definite matrix given by -Cholesky decomposition. - -The algorithm calculates a lower bound of the condition number. In this -case, the algorithm does not return a lower bound of the condition number, -but an inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - CD - Cholesky decomposition of matrix A, - output of SMatrixCholesky subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double spdmatrixcholeskyrcond(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - double v; - double result; - - - rcond_spdmatrixrcondcholeskyinternal(a, n, isupper, ae_false, 0, &v, _state); - result = v; - return result; -} - - -/************************************************************************* -Condition number estimate of a Hermitian positive definite matrix given by -Cholesky decomposition. - -The algorithm calculates a lower bound of the condition number. In this -case, the algorithm does not return a lower bound of the condition number, -but an inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - CD - Cholesky decomposition of matrix A, - output of SMatrixCholesky subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double hpdmatrixcholeskyrcond(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - double v; - double result; - - - rcond_hpdmatrixrcondcholeskyinternal(a, n, isupper, ae_false, 0, &v, _state); - result = v; - return result; -} - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the CMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixlurcond1(/* Complex */ ae_matrix* lua, - ae_int_t n, - ae_state *_state) -{ - double v; - double result; - - - ae_assert(n>=1, "CMatrixLURCond1: N<1!", _state); - rcond_cmatrixrcondluinternal(lua, n, ae_true, ae_false, 0.0, &v, _state); - result = v; - return result; -} - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition -(infinity norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the CMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixlurcondinf(/* Complex */ ae_matrix* lua, - ae_int_t n, - ae_state *_state) -{ - double v; - double result; - - - ae_assert(n>=1, "CMatrixLURCondInf: N<1!", _state); - rcond_cmatrixrcondluinternal(lua, n, ae_false, ae_false, 0.0, &v, _state); - result = v; - return result; -} - - -/************************************************************************* -Triangular matrix: estimate of a condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array[0..N-1, 0..N-1]. - N - size of A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixtrrcond1(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - ae_vector t; - ae_int_t j1; - ae_int_t j2; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=1, "RMatrixTRRCond1: N<1!", _state); - ae_vector_set_length(&t, n, _state); - for(i=0; i<=n-1; i++) - { - t.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i+1; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i-1; - } - for(j=j1; j<=j2; j++) - { - t.ptr.p_double[j] = t.ptr.p_double[j]+ae_c_abs(a->ptr.pp_complex[i][j], _state); - } - if( isunit ) - { - t.ptr.p_double[i] = t.ptr.p_double[i]+1; - } - else - { - t.ptr.p_double[i] = t.ptr.p_double[i]+ae_c_abs(a->ptr.pp_complex[i][i], _state); - } - } - nrm = 0; - for(i=0; i<=n-1; i++) - { - nrm = ae_maxreal(nrm, t.ptr.p_double[i], _state); - } - rcond_cmatrixrcondtrinternal(a, n, isupper, isunit, ae_true, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Triangular matrix: estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixtrrcondinf(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double nrm; - ae_vector pivots; - ae_int_t j1; - ae_int_t j2; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "RMatrixTRRCondInf: N<1!", _state); - nrm = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i+1; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i-1; - } - v = 0; - for(j=j1; j<=j2; j++) - { - v = v+ae_c_abs(a->ptr.pp_complex[i][j], _state); - } - if( isunit ) - { - v = v+1; - } - else - { - v = v+ae_c_abs(a->ptr.pp_complex[i][i], _state); - } - nrm = ae_maxreal(nrm, v, _state); - } - rcond_cmatrixrcondtrinternal(a, n, isupper, isunit, ae_false, nrm, &v, _state); - result = v; - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Threshold for rcond: matrices with condition number beyond this threshold -are considered singular. - -Threshold must be far enough from underflow, at least Sqr(Threshold) must -be greater than underflow. -*************************************************************************/ -double rcondthreshold(ae_state *_state) -{ - double result; - - - result = ae_sqrt(ae_sqrt(ae_minrealnumber, _state), _state); - return result; -} - - -/************************************************************************* -Internal subroutine for condition number estimation - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static void rcond_rmatrixrcondtrinternal(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_bool onenorm, - double anorm, - double* rc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector ex; - ae_vector ev; - ae_vector iwork; - ae_vector tmp; - ae_int_t i; - ae_int_t j; - ae_int_t kase; - ae_int_t kase1; - ae_int_t j1; - ae_int_t j2; - double ainvnm; - double maxgrowth; - double s; - - ae_frame_make(_state, &_frame_block); - *rc = 0; - ae_vector_init(&ex, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ev, 0, DT_REAL, _state, ae_true); - ae_vector_init(&iwork, 0, DT_INT, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - - /* - * RC=0 if something happens - */ - *rc = 0; - - /* - * init - */ - if( onenorm ) - { - kase1 = 1; - } - else - { - kase1 = 2; - } - ae_vector_set_length(&iwork, n+1, _state); - ae_vector_set_length(&tmp, n, _state); - - /* - * prepare parameters for triangular solver - */ - maxgrowth = 1/rcondthreshold(_state); - s = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i+1; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i-1; - } - for(j=j1; j<=j2; j++) - { - s = ae_maxreal(s, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - if( isunit ) - { - s = ae_maxreal(s, 1, _state); - } - else - { - s = ae_maxreal(s, ae_fabs(a->ptr.pp_double[i][i], _state), _state); - } - } - if( ae_fp_eq(s,0) ) - { - s = 1; - } - s = 1/s; - - /* - * Scale according to S - */ - anorm = anorm*s; - - /* - * Quick return if possible - * We assume that ANORM<>0 after this block - */ - if( ae_fp_eq(anorm,0) ) - { - ae_frame_leave(_state); - return; - } - if( n==1 ) - { - *rc = 1; - ae_frame_leave(_state); - return; - } - - /* - * Estimate the norm of inv(A). - */ - ainvnm = 0; - kase = 0; - for(;;) - { - rcond_rmatrixestimatenorm(n, &ev, &ex, &iwork, &ainvnm, &kase, _state); - if( kase==0 ) - { - break; - } - - /* - * from 1-based array to 0-based - */ - for(i=0; i<=n-1; i++) - { - ex.ptr.p_double[i] = ex.ptr.p_double[i+1]; - } - - /* - * multiply by inv(A) or inv(A') - */ - if( kase==kase1 ) - { - - /* - * multiply by inv(A) - */ - if( !rmatrixscaledtrsafesolve(a, s, n, &ex, isupper, 0, isunit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - else - { - - /* - * multiply by inv(A') - */ - if( !rmatrixscaledtrsafesolve(a, s, n, &ex, isupper, 1, isunit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - - /* - * from 0-based array to 1-based - */ - for(i=n-1; i>=0; i--) - { - ex.ptr.p_double[i+1] = ex.ptr.p_double[i]; - } - } - - /* - * Compute the estimate of the reciprocal condition number. - */ - if( ae_fp_neq(ainvnm,0) ) - { - *rc = 1/ainvnm; - *rc = *rc/anorm; - if( ae_fp_less(*rc,rcondthreshold(_state)) ) - { - *rc = 0; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Condition number estimation - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - March 31, 1993 -*************************************************************************/ -static void rcond_cmatrixrcondtrinternal(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_bool onenorm, - double anorm, - double* rc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector ex; - ae_vector cwork2; - ae_vector cwork3; - ae_vector cwork4; - ae_vector isave; - ae_vector rsave; - ae_int_t kase; - ae_int_t kase1; - double ainvnm; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - double s; - double maxgrowth; - - ae_frame_make(_state, &_frame_block); - *rc = 0; - ae_vector_init(&ex, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cwork2, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cwork3, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cwork4, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&isave, 0, DT_INT, _state, ae_true); - ae_vector_init(&rsave, 0, DT_REAL, _state, ae_true); - - - /* - * RC=0 if something happens - */ - *rc = 0; - - /* - * init - */ - if( n<=0 ) - { - ae_frame_leave(_state); - return; - } - if( n==0 ) - { - *rc = 1; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&cwork2, n+1, _state); - - /* - * prepare parameters for triangular solver - */ - maxgrowth = 1/rcondthreshold(_state); - s = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i+1; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i-1; - } - for(j=j1; j<=j2; j++) - { - s = ae_maxreal(s, ae_c_abs(a->ptr.pp_complex[i][j], _state), _state); - } - if( isunit ) - { - s = ae_maxreal(s, 1, _state); - } - else - { - s = ae_maxreal(s, ae_c_abs(a->ptr.pp_complex[i][i], _state), _state); - } - } - if( ae_fp_eq(s,0) ) - { - s = 1; - } - s = 1/s; - - /* - * Scale according to S - */ - anorm = anorm*s; - - /* - * Quick return if possible - */ - if( ae_fp_eq(anorm,0) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Estimate the norm of inv(A). - */ - ainvnm = 0; - if( onenorm ) - { - kase1 = 1; - } - else - { - kase1 = 2; - } - kase = 0; - for(;;) - { - rcond_cmatrixestimatenorm(n, &cwork4, &ex, &ainvnm, &kase, &isave, &rsave, _state); - if( kase==0 ) - { - break; - } - - /* - * From 1-based to 0-based - */ - for(i=0; i<=n-1; i++) - { - ex.ptr.p_complex[i] = ex.ptr.p_complex[i+1]; - } - - /* - * multiply by inv(A) or inv(A') - */ - if( kase==kase1 ) - { - - /* - * multiply by inv(A) - */ - if( !cmatrixscaledtrsafesolve(a, s, n, &ex, isupper, 0, isunit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - else - { - - /* - * multiply by inv(A') - */ - if( !cmatrixscaledtrsafesolve(a, s, n, &ex, isupper, 2, isunit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - - /* - * from 0-based to 1-based - */ - for(i=n-1; i>=0; i--) - { - ex.ptr.p_complex[i+1] = ex.ptr.p_complex[i]; - } - } - - /* - * Compute the estimate of the reciprocal condition number. - */ - if( ae_fp_neq(ainvnm,0) ) - { - *rc = 1/ainvnm; - *rc = *rc/anorm; - if( ae_fp_less(*rc,rcondthreshold(_state)) ) - { - *rc = 0; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine for condition number estimation - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static void rcond_spdmatrixrcondcholeskyinternal(/* Real */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - ae_bool isnormprovided, - double anorm, - double* rc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t kase; - double ainvnm; - ae_vector ex; - ae_vector ev; - ae_vector tmp; - ae_vector iwork; - double sa; - double v; - double maxgrowth; - - ae_frame_make(_state, &_frame_block); - *rc = 0; - ae_vector_init(&ex, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ev, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&iwork, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "Assertion failed", _state); - ae_vector_set_length(&tmp, n, _state); - - /* - * RC=0 if something happens - */ - *rc = 0; - - /* - * prepare parameters for triangular solver - */ - maxgrowth = 1/rcondthreshold(_state); - sa = 0; - if( isupper ) - { - for(i=0; i<=n-1; i++) - { - for(j=i; j<=n-1; j++) - { - sa = ae_maxreal(sa, ae_c_abs(ae_complex_from_d(cha->ptr.pp_double[i][j]), _state), _state); - } - } - } - else - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i; j++) - { - sa = ae_maxreal(sa, ae_c_abs(ae_complex_from_d(cha->ptr.pp_double[i][j]), _state), _state); - } - } - } - if( ae_fp_eq(sa,0) ) - { - sa = 1; - } - sa = 1/sa; - - /* - * Estimate the norm of A. - */ - if( !isnormprovided ) - { - kase = 0; - anorm = 0; - for(;;) - { - rcond_rmatrixestimatenorm(n, &ev, &ex, &iwork, &anorm, &kase, _state); - if( kase==0 ) - { - break; - } - if( isupper ) - { - - /* - * Multiply by U - */ - for(i=1; i<=n; i++) - { - v = ae_v_dotproduct(&cha->ptr.pp_double[i-1][i-1], 1, &ex.ptr.p_double[i], 1, ae_v_len(i-1,n-1)); - ex.ptr.p_double[i] = v; - } - ae_v_muld(&ex.ptr.p_double[1], 1, ae_v_len(1,n), sa); - - /* - * Multiply by U' - */ - for(i=0; i<=n-1; i++) - { - tmp.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = ex.ptr.p_double[i+1]; - ae_v_addd(&tmp.ptr.p_double[i], 1, &cha->ptr.pp_double[i][i], 1, ae_v_len(i,n-1), v); - } - ae_v_move(&ex.ptr.p_double[1], 1, &tmp.ptr.p_double[0], 1, ae_v_len(1,n)); - ae_v_muld(&ex.ptr.p_double[1], 1, ae_v_len(1,n), sa); - } - else - { - - /* - * Multiply by L' - */ - for(i=0; i<=n-1; i++) - { - tmp.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = ex.ptr.p_double[i+1]; - ae_v_addd(&tmp.ptr.p_double[0], 1, &cha->ptr.pp_double[i][0], 1, ae_v_len(0,i), v); - } - ae_v_move(&ex.ptr.p_double[1], 1, &tmp.ptr.p_double[0], 1, ae_v_len(1,n)); - ae_v_muld(&ex.ptr.p_double[1], 1, ae_v_len(1,n), sa); - - /* - * Multiply by L - */ - for(i=n; i>=1; i--) - { - v = ae_v_dotproduct(&cha->ptr.pp_double[i-1][0], 1, &ex.ptr.p_double[1], 1, ae_v_len(0,i-1)); - ex.ptr.p_double[i] = v; - } - ae_v_muld(&ex.ptr.p_double[1], 1, ae_v_len(1,n), sa); - } - } - } - - /* - * Quick return if possible - */ - if( ae_fp_eq(anorm,0) ) - { - ae_frame_leave(_state); - return; - } - if( n==1 ) - { - *rc = 1; - ae_frame_leave(_state); - return; - } - - /* - * Estimate the 1-norm of inv(A). - */ - kase = 0; - for(;;) - { - rcond_rmatrixestimatenorm(n, &ev, &ex, &iwork, &ainvnm, &kase, _state); - if( kase==0 ) - { - break; - } - for(i=0; i<=n-1; i++) - { - ex.ptr.p_double[i] = ex.ptr.p_double[i+1]; - } - if( isupper ) - { - - /* - * Multiply by inv(U'). - */ - if( !rmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 1, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(U). - */ - if( !rmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 0, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - else - { - - /* - * Multiply by inv(L). - */ - if( !rmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 0, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(L'). - */ - if( !rmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 1, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - for(i=n-1; i>=0; i--) - { - ex.ptr.p_double[i+1] = ex.ptr.p_double[i]; - } - } - - /* - * Compute the estimate of the reciprocal condition number. - */ - if( ae_fp_neq(ainvnm,0) ) - { - v = 1/ainvnm; - *rc = v/anorm; - if( ae_fp_less(*rc,rcondthreshold(_state)) ) - { - *rc = 0; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine for condition number estimation - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static void rcond_hpdmatrixrcondcholeskyinternal(/* Complex */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - ae_bool isnormprovided, - double anorm, - double* rc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector isave; - ae_vector rsave; - ae_vector ex; - ae_vector ev; - ae_vector tmp; - ae_int_t kase; - double ainvnm; - ae_complex v; - ae_int_t i; - ae_int_t j; - double sa; - double maxgrowth; - - ae_frame_make(_state, &_frame_block); - *rc = 0; - ae_vector_init(&isave, 0, DT_INT, _state, ae_true); - ae_vector_init(&rsave, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ex, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&ev, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&tmp, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>=1, "Assertion failed", _state); - ae_vector_set_length(&tmp, n, _state); - - /* - * RC=0 if something happens - */ - *rc = 0; - - /* - * prepare parameters for triangular solver - */ - maxgrowth = 1/rcondthreshold(_state); - sa = 0; - if( isupper ) - { - for(i=0; i<=n-1; i++) - { - for(j=i; j<=n-1; j++) - { - sa = ae_maxreal(sa, ae_c_abs(cha->ptr.pp_complex[i][j], _state), _state); - } - } - } - else - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i; j++) - { - sa = ae_maxreal(sa, ae_c_abs(cha->ptr.pp_complex[i][j], _state), _state); - } - } - } - if( ae_fp_eq(sa,0) ) - { - sa = 1; - } - sa = 1/sa; - - /* - * Estimate the norm of A - */ - if( !isnormprovided ) - { - anorm = 0; - kase = 0; - for(;;) - { - rcond_cmatrixestimatenorm(n, &ev, &ex, &anorm, &kase, &isave, &rsave, _state); - if( kase==0 ) - { - break; - } - if( isupper ) - { - - /* - * Multiply by U - */ - for(i=1; i<=n; i++) - { - v = ae_v_cdotproduct(&cha->ptr.pp_complex[i-1][i-1], 1, "N", &ex.ptr.p_complex[i], 1, "N", ae_v_len(i-1,n-1)); - ex.ptr.p_complex[i] = v; - } - ae_v_cmuld(&ex.ptr.p_complex[1], 1, ae_v_len(1,n), sa); - - /* - * Multiply by U' - */ - for(i=0; i<=n-1; i++) - { - tmp.ptr.p_complex[i] = ae_complex_from_d(0); - } - for(i=0; i<=n-1; i++) - { - v = ex.ptr.p_complex[i+1]; - ae_v_caddc(&tmp.ptr.p_complex[i], 1, &cha->ptr.pp_complex[i][i], 1, "Conj", ae_v_len(i,n-1), v); - } - ae_v_cmove(&ex.ptr.p_complex[1], 1, &tmp.ptr.p_complex[0], 1, "N", ae_v_len(1,n)); - ae_v_cmuld(&ex.ptr.p_complex[1], 1, ae_v_len(1,n), sa); - } - else - { - - /* - * Multiply by L' - */ - for(i=0; i<=n-1; i++) - { - tmp.ptr.p_complex[i] = ae_complex_from_d(0); - } - for(i=0; i<=n-1; i++) - { - v = ex.ptr.p_complex[i+1]; - ae_v_caddc(&tmp.ptr.p_complex[0], 1, &cha->ptr.pp_complex[i][0], 1, "Conj", ae_v_len(0,i), v); - } - ae_v_cmove(&ex.ptr.p_complex[1], 1, &tmp.ptr.p_complex[0], 1, "N", ae_v_len(1,n)); - ae_v_cmuld(&ex.ptr.p_complex[1], 1, ae_v_len(1,n), sa); - - /* - * Multiply by L - */ - for(i=n; i>=1; i--) - { - v = ae_v_cdotproduct(&cha->ptr.pp_complex[i-1][0], 1, "N", &ex.ptr.p_complex[1], 1, "N", ae_v_len(0,i-1)); - ex.ptr.p_complex[i] = v; - } - ae_v_cmuld(&ex.ptr.p_complex[1], 1, ae_v_len(1,n), sa); - } - } - } - - /* - * Quick return if possible - * After this block we assume that ANORM<>0 - */ - if( ae_fp_eq(anorm,0) ) - { - ae_frame_leave(_state); - return; - } - if( n==1 ) - { - *rc = 1; - ae_frame_leave(_state); - return; - } - - /* - * Estimate the norm of inv(A). - */ - ainvnm = 0; - kase = 0; - for(;;) - { - rcond_cmatrixestimatenorm(n, &ev, &ex, &ainvnm, &kase, &isave, &rsave, _state); - if( kase==0 ) - { - break; - } - for(i=0; i<=n-1; i++) - { - ex.ptr.p_complex[i] = ex.ptr.p_complex[i+1]; - } - if( isupper ) - { - - /* - * Multiply by inv(U'). - */ - if( !cmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 2, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(U). - */ - if( !cmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 0, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - else - { - - /* - * Multiply by inv(L). - */ - if( !cmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 0, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(L'). - */ - if( !cmatrixscaledtrsafesolve(cha, sa, n, &ex, isupper, 2, ae_false, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - for(i=n-1; i>=0; i--) - { - ex.ptr.p_complex[i+1] = ex.ptr.p_complex[i]; - } - } - - /* - * Compute the estimate of the reciprocal condition number. - */ - if( ae_fp_neq(ainvnm,0) ) - { - *rc = 1/ainvnm; - *rc = *rc/anorm; - if( ae_fp_less(*rc,rcondthreshold(_state)) ) - { - *rc = 0; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine for condition number estimation - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static void rcond_rmatrixrcondluinternal(/* Real */ ae_matrix* lua, - ae_int_t n, - ae_bool onenorm, - ae_bool isanormprovided, - double anorm, - double* rc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector ex; - ae_vector ev; - ae_vector iwork; - ae_vector tmp; - double v; - ae_int_t i; - ae_int_t j; - ae_int_t kase; - ae_int_t kase1; - double ainvnm; - double maxgrowth; - double su; - double sl; - ae_bool mupper; - ae_bool munit; - - ae_frame_make(_state, &_frame_block); - *rc = 0; - ae_vector_init(&ex, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ev, 0, DT_REAL, _state, ae_true); - ae_vector_init(&iwork, 0, DT_INT, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - - - /* - * RC=0 if something happens - */ - *rc = 0; - - /* - * init - */ - if( onenorm ) - { - kase1 = 1; - } - else - { - kase1 = 2; - } - mupper = ae_true; - munit = ae_true; - ae_vector_set_length(&iwork, n+1, _state); - ae_vector_set_length(&tmp, n, _state); - - /* - * prepare parameters for triangular solver - */ - maxgrowth = 1/rcondthreshold(_state); - su = 0; - sl = 1; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-1; j++) - { - sl = ae_maxreal(sl, ae_fabs(lua->ptr.pp_double[i][j], _state), _state); - } - for(j=i; j<=n-1; j++) - { - su = ae_maxreal(su, ae_fabs(lua->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_eq(su,0) ) - { - su = 1; - } - su = 1/su; - sl = 1/sl; - - /* - * Estimate the norm of A. - */ - if( !isanormprovided ) - { - kase = 0; - anorm = 0; - for(;;) - { - rcond_rmatrixestimatenorm(n, &ev, &ex, &iwork, &anorm, &kase, _state); - if( kase==0 ) - { - break; - } - if( kase==kase1 ) - { - - /* - * Multiply by U - */ - for(i=1; i<=n; i++) - { - v = ae_v_dotproduct(&lua->ptr.pp_double[i-1][i-1], 1, &ex.ptr.p_double[i], 1, ae_v_len(i-1,n-1)); - ex.ptr.p_double[i] = v; - } - - /* - * Multiply by L - */ - for(i=n; i>=1; i--) - { - if( i>1 ) - { - v = ae_v_dotproduct(&lua->ptr.pp_double[i-1][0], 1, &ex.ptr.p_double[1], 1, ae_v_len(0,i-2)); - } - else - { - v = 0; - } - ex.ptr.p_double[i] = ex.ptr.p_double[i]+v; - } - } - else - { - - /* - * Multiply by L' - */ - for(i=0; i<=n-1; i++) - { - tmp.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = ex.ptr.p_double[i+1]; - if( i>=1 ) - { - ae_v_addd(&tmp.ptr.p_double[0], 1, &lua->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), v); - } - tmp.ptr.p_double[i] = tmp.ptr.p_double[i]+v; - } - ae_v_move(&ex.ptr.p_double[1], 1, &tmp.ptr.p_double[0], 1, ae_v_len(1,n)); - - /* - * Multiply by U' - */ - for(i=0; i<=n-1; i++) - { - tmp.ptr.p_double[i] = 0; - } - for(i=0; i<=n-1; i++) - { - v = ex.ptr.p_double[i+1]; - ae_v_addd(&tmp.ptr.p_double[i], 1, &lua->ptr.pp_double[i][i], 1, ae_v_len(i,n-1), v); - } - ae_v_move(&ex.ptr.p_double[1], 1, &tmp.ptr.p_double[0], 1, ae_v_len(1,n)); - } - } - } - - /* - * Scale according to SU/SL - */ - anorm = anorm*su*sl; - - /* - * Quick return if possible - * We assume that ANORM<>0 after this block - */ - if( ae_fp_eq(anorm,0) ) - { - ae_frame_leave(_state); - return; - } - if( n==1 ) - { - *rc = 1; - ae_frame_leave(_state); - return; - } - - /* - * Estimate the norm of inv(A). - */ - ainvnm = 0; - kase = 0; - for(;;) - { - rcond_rmatrixestimatenorm(n, &ev, &ex, &iwork, &ainvnm, &kase, _state); - if( kase==0 ) - { - break; - } - - /* - * from 1-based array to 0-based - */ - for(i=0; i<=n-1; i++) - { - ex.ptr.p_double[i] = ex.ptr.p_double[i+1]; - } - - /* - * multiply by inv(A) or inv(A') - */ - if( kase==kase1 ) - { - - /* - * Multiply by inv(L). - */ - if( !rmatrixscaledtrsafesolve(lua, sl, n, &ex, !mupper, 0, munit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(U). - */ - if( !rmatrixscaledtrsafesolve(lua, su, n, &ex, mupper, 0, !munit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - else - { - - /* - * Multiply by inv(U'). - */ - if( !rmatrixscaledtrsafesolve(lua, su, n, &ex, mupper, 1, !munit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(L'). - */ - if( !rmatrixscaledtrsafesolve(lua, sl, n, &ex, !mupper, 1, munit, maxgrowth, _state) ) - { - ae_frame_leave(_state); - return; - } - } - - /* - * from 0-based array to 1-based - */ - for(i=n-1; i>=0; i--) - { - ex.ptr.p_double[i+1] = ex.ptr.p_double[i]; - } - } - - /* - * Compute the estimate of the reciprocal condition number. - */ - if( ae_fp_neq(ainvnm,0) ) - { - *rc = 1/ainvnm; - *rc = *rc/anorm; - if( ae_fp_less(*rc,rcondthreshold(_state)) ) - { - *rc = 0; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Condition number estimation - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - March 31, 1993 -*************************************************************************/ -static void rcond_cmatrixrcondluinternal(/* Complex */ ae_matrix* lua, - ae_int_t n, - ae_bool onenorm, - ae_bool isanormprovided, - double anorm, - double* rc, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector ex; - ae_vector cwork2; - ae_vector cwork3; - ae_vector cwork4; - ae_vector isave; - ae_vector rsave; - ae_int_t kase; - ae_int_t kase1; - double ainvnm; - ae_complex v; - ae_int_t i; - ae_int_t j; - double su; - double sl; - double maxgrowth; - - ae_frame_make(_state, &_frame_block); - *rc = 0; - ae_vector_init(&ex, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cwork2, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cwork3, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&cwork4, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&isave, 0, DT_INT, _state, ae_true); - ae_vector_init(&rsave, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&cwork2, n+1, _state); - *rc = 0; - if( n==0 ) - { - *rc = 1; - ae_frame_leave(_state); - return; - } - - /* - * prepare parameters for triangular solver - */ - maxgrowth = 1/rcondthreshold(_state); - su = 0; - sl = 1; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-1; j++) - { - sl = ae_maxreal(sl, ae_c_abs(lua->ptr.pp_complex[i][j], _state), _state); - } - for(j=i; j<=n-1; j++) - { - su = ae_maxreal(su, ae_c_abs(lua->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_eq(su,0) ) - { - su = 1; - } - su = 1/su; - sl = 1/sl; - - /* - * Estimate the norm of SU*SL*A. - */ - if( !isanormprovided ) - { - anorm = 0; - if( onenorm ) - { - kase1 = 1; - } - else - { - kase1 = 2; - } - kase = 0; - do - { - rcond_cmatrixestimatenorm(n, &cwork4, &ex, &anorm, &kase, &isave, &rsave, _state); - if( kase!=0 ) - { - if( kase==kase1 ) - { - - /* - * Multiply by U - */ - for(i=1; i<=n; i++) - { - v = ae_v_cdotproduct(&lua->ptr.pp_complex[i-1][i-1], 1, "N", &ex.ptr.p_complex[i], 1, "N", ae_v_len(i-1,n-1)); - ex.ptr.p_complex[i] = v; - } - - /* - * Multiply by L - */ - for(i=n; i>=1; i--) - { - v = ae_complex_from_d(0); - if( i>1 ) - { - v = ae_v_cdotproduct(&lua->ptr.pp_complex[i-1][0], 1, "N", &ex.ptr.p_complex[1], 1, "N", ae_v_len(0,i-2)); - } - ex.ptr.p_complex[i] = ae_c_add(v,ex.ptr.p_complex[i]); - } - } - else - { - - /* - * Multiply by L' - */ - for(i=1; i<=n; i++) - { - cwork2.ptr.p_complex[i] = ae_complex_from_d(0); - } - for(i=1; i<=n; i++) - { - v = ex.ptr.p_complex[i]; - if( i>1 ) - { - ae_v_caddc(&cwork2.ptr.p_complex[1], 1, &lua->ptr.pp_complex[i-1][0], 1, "Conj", ae_v_len(1,i-1), v); - } - cwork2.ptr.p_complex[i] = ae_c_add(cwork2.ptr.p_complex[i],v); - } - - /* - * Multiply by U' - */ - for(i=1; i<=n; i++) - { - ex.ptr.p_complex[i] = ae_complex_from_d(0); - } - for(i=1; i<=n; i++) - { - v = cwork2.ptr.p_complex[i]; - ae_v_caddc(&ex.ptr.p_complex[i], 1, &lua->ptr.pp_complex[i-1][i-1], 1, "Conj", ae_v_len(i,n), v); - } - } - } - } - while(kase!=0); - } - - /* - * Scale according to SU/SL - */ - anorm = anorm*su*sl; - - /* - * Quick return if possible - */ - if( ae_fp_eq(anorm,0) ) - { - ae_frame_leave(_state); - return; - } - - /* - * Estimate the norm of inv(A). - */ - ainvnm = 0; - if( onenorm ) - { - kase1 = 1; - } - else - { - kase1 = 2; - } - kase = 0; - for(;;) - { - rcond_cmatrixestimatenorm(n, &cwork4, &ex, &ainvnm, &kase, &isave, &rsave, _state); - if( kase==0 ) - { - break; - } - - /* - * From 1-based to 0-based - */ - for(i=0; i<=n-1; i++) - { - ex.ptr.p_complex[i] = ex.ptr.p_complex[i+1]; - } - - /* - * multiply by inv(A) or inv(A') - */ - if( kase==kase1 ) - { - - /* - * Multiply by inv(L). - */ - if( !cmatrixscaledtrsafesolve(lua, sl, n, &ex, ae_false, 0, ae_true, maxgrowth, _state) ) - { - *rc = 0; - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(U). - */ - if( !cmatrixscaledtrsafesolve(lua, su, n, &ex, ae_true, 0, ae_false, maxgrowth, _state) ) - { - *rc = 0; - ae_frame_leave(_state); - return; - } - } - else - { - - /* - * Multiply by inv(U'). - */ - if( !cmatrixscaledtrsafesolve(lua, su, n, &ex, ae_true, 2, ae_false, maxgrowth, _state) ) - { - *rc = 0; - ae_frame_leave(_state); - return; - } - - /* - * Multiply by inv(L'). - */ - if( !cmatrixscaledtrsafesolve(lua, sl, n, &ex, ae_false, 2, ae_true, maxgrowth, _state) ) - { - *rc = 0; - ae_frame_leave(_state); - return; - } - } - - /* - * from 0-based to 1-based - */ - for(i=n-1; i>=0; i--) - { - ex.ptr.p_complex[i+1] = ex.ptr.p_complex[i]; - } - } - - /* - * Compute the estimate of the reciprocal condition number. - */ - if( ae_fp_neq(ainvnm,0) ) - { - *rc = 1/ainvnm; - *rc = *rc/anorm; - if( ae_fp_less(*rc,rcondthreshold(_state)) ) - { - *rc = 0; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine for matrix norm estimation - - -- LAPACK auxiliary routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992 -*************************************************************************/ -static void rcond_rmatrixestimatenorm(ae_int_t n, - /* Real */ ae_vector* v, - /* Real */ ae_vector* x, - /* Integer */ ae_vector* isgn, - double* est, - ae_int_t* kase, - ae_state *_state) -{ - ae_int_t itmax; - ae_int_t i; - double t; - ae_bool flg; - ae_int_t positer; - ae_int_t posj; - ae_int_t posjlast; - ae_int_t posjump; - ae_int_t posaltsgn; - ae_int_t posestold; - ae_int_t postemp; - - - itmax = 5; - posaltsgn = n+1; - posestold = n+2; - postemp = n+3; - positer = n+1; - posj = n+2; - posjlast = n+3; - posjump = n+4; - if( *kase==0 ) - { - ae_vector_set_length(v, n+4, _state); - ae_vector_set_length(x, n+1, _state); - ae_vector_set_length(isgn, n+5, _state); - t = (double)1/(double)n; - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = t; - } - *kase = 1; - isgn->ptr.p_int[posjump] = 1; - return; - } - - /* - * ................ ENTRY (JUMP = 1) - * FIRST ITERATION. X HAS BEEN OVERWRITTEN BY A*X. - */ - if( isgn->ptr.p_int[posjump]==1 ) - { - if( n==1 ) - { - v->ptr.p_double[1] = x->ptr.p_double[1]; - *est = ae_fabs(v->ptr.p_double[1], _state); - *kase = 0; - return; - } - *est = 0; - for(i=1; i<=n; i++) - { - *est = *est+ae_fabs(x->ptr.p_double[i], _state); - } - for(i=1; i<=n; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],0) ) - { - x->ptr.p_double[i] = 1; - } - else - { - x->ptr.p_double[i] = -1; - } - isgn->ptr.p_int[i] = ae_sign(x->ptr.p_double[i], _state); - } - *kase = 2; - isgn->ptr.p_int[posjump] = 2; - return; - } - - /* - * ................ ENTRY (JUMP = 2) - * FIRST ITERATION. X HAS BEEN OVERWRITTEN BY TRANDPOSE(A)*X. - */ - if( isgn->ptr.p_int[posjump]==2 ) - { - isgn->ptr.p_int[posj] = 1; - for(i=2; i<=n; i++) - { - if( ae_fp_greater(ae_fabs(x->ptr.p_double[i], _state),ae_fabs(x->ptr.p_double[isgn->ptr.p_int[posj]], _state)) ) - { - isgn->ptr.p_int[posj] = i; - } - } - isgn->ptr.p_int[positer] = 2; - - /* - * MAIN LOOP - ITERATIONS 2,3,...,ITMAX. - */ - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = 0; - } - x->ptr.p_double[isgn->ptr.p_int[posj]] = 1; - *kase = 1; - isgn->ptr.p_int[posjump] = 3; - return; - } - - /* - * ................ ENTRY (JUMP = 3) - * X HAS BEEN OVERWRITTEN BY A*X. - */ - if( isgn->ptr.p_int[posjump]==3 ) - { - ae_v_move(&v->ptr.p_double[1], 1, &x->ptr.p_double[1], 1, ae_v_len(1,n)); - v->ptr.p_double[posestold] = *est; - *est = 0; - for(i=1; i<=n; i++) - { - *est = *est+ae_fabs(v->ptr.p_double[i], _state); - } - flg = ae_false; - for(i=1; i<=n; i++) - { - if( (ae_fp_greater_eq(x->ptr.p_double[i],0)&&isgn->ptr.p_int[i]<0)||(ae_fp_less(x->ptr.p_double[i],0)&&isgn->ptr.p_int[i]>=0) ) - { - flg = ae_true; - } - } - - /* - * REPEATED SIGN VECTOR DETECTED, HENCE ALGORITHM HAS CONVERGED. - * OR MAY BE CYCLING. - */ - if( !flg||ae_fp_less_eq(*est,v->ptr.p_double[posestold]) ) - { - v->ptr.p_double[posaltsgn] = 1; - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = v->ptr.p_double[posaltsgn]*(1+(double)(i-1)/(double)(n-1)); - v->ptr.p_double[posaltsgn] = -v->ptr.p_double[posaltsgn]; - } - *kase = 1; - isgn->ptr.p_int[posjump] = 5; - return; - } - for(i=1; i<=n; i++) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],0) ) - { - x->ptr.p_double[i] = 1; - isgn->ptr.p_int[i] = 1; - } - else - { - x->ptr.p_double[i] = -1; - isgn->ptr.p_int[i] = -1; - } - } - *kase = 2; - isgn->ptr.p_int[posjump] = 4; - return; - } - - /* - * ................ ENTRY (JUMP = 4) - * X HAS BEEN OVERWRITTEN BY TRANDPOSE(A)*X. - */ - if( isgn->ptr.p_int[posjump]==4 ) - { - isgn->ptr.p_int[posjlast] = isgn->ptr.p_int[posj]; - isgn->ptr.p_int[posj] = 1; - for(i=2; i<=n; i++) - { - if( ae_fp_greater(ae_fabs(x->ptr.p_double[i], _state),ae_fabs(x->ptr.p_double[isgn->ptr.p_int[posj]], _state)) ) - { - isgn->ptr.p_int[posj] = i; - } - } - if( ae_fp_neq(x->ptr.p_double[isgn->ptr.p_int[posjlast]],ae_fabs(x->ptr.p_double[isgn->ptr.p_int[posj]], _state))&&isgn->ptr.p_int[positer]ptr.p_int[positer] = isgn->ptr.p_int[positer]+1; - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = 0; - } - x->ptr.p_double[isgn->ptr.p_int[posj]] = 1; - *kase = 1; - isgn->ptr.p_int[posjump] = 3; - return; - } - - /* - * ITERATION COMPLETE. FINAL STAGE. - */ - v->ptr.p_double[posaltsgn] = 1; - for(i=1; i<=n; i++) - { - x->ptr.p_double[i] = v->ptr.p_double[posaltsgn]*(1+(double)(i-1)/(double)(n-1)); - v->ptr.p_double[posaltsgn] = -v->ptr.p_double[posaltsgn]; - } - *kase = 1; - isgn->ptr.p_int[posjump] = 5; - return; - } - - /* - * ................ ENTRY (JUMP = 5) - * X HAS BEEN OVERWRITTEN BY A*X. - */ - if( isgn->ptr.p_int[posjump]==5 ) - { - v->ptr.p_double[postemp] = 0; - for(i=1; i<=n; i++) - { - v->ptr.p_double[postemp] = v->ptr.p_double[postemp]+ae_fabs(x->ptr.p_double[i], _state); - } - v->ptr.p_double[postemp] = 2*v->ptr.p_double[postemp]/(3*n); - if( ae_fp_greater(v->ptr.p_double[postemp],*est) ) - { - ae_v_move(&v->ptr.p_double[1], 1, &x->ptr.p_double[1], 1, ae_v_len(1,n)); - *est = v->ptr.p_double[postemp]; - } - *kase = 0; - return; - } -} - - -static void rcond_cmatrixestimatenorm(ae_int_t n, - /* Complex */ ae_vector* v, - /* Complex */ ae_vector* x, - double* est, - ae_int_t* kase, - /* Integer */ ae_vector* isave, - /* Real */ ae_vector* rsave, - ae_state *_state) -{ - ae_int_t itmax; - ae_int_t i; - ae_int_t iter; - ae_int_t j; - ae_int_t jlast; - ae_int_t jump; - double absxi; - double altsgn; - double estold; - double safmin; - double temp; - - - - /* - *Executable Statements .. - */ - itmax = 5; - safmin = ae_minrealnumber; - if( *kase==0 ) - { - ae_vector_set_length(v, n+1, _state); - ae_vector_set_length(x, n+1, _state); - ae_vector_set_length(isave, 5, _state); - ae_vector_set_length(rsave, 4, _state); - for(i=1; i<=n; i++) - { - x->ptr.p_complex[i] = ae_complex_from_d((double)1/(double)n); - } - *kase = 1; - jump = 1; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - rcond_internalcomplexrcondloadall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - - /* - * ENTRY (JUMP = 1) - * FIRST ITERATION. X HAS BEEN OVERWRITTEN BY A*X. - */ - if( jump==1 ) - { - if( n==1 ) - { - v->ptr.p_complex[1] = x->ptr.p_complex[1]; - *est = ae_c_abs(v->ptr.p_complex[1], _state); - *kase = 0; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - *est = rcond_internalcomplexrcondscsum1(x, n, _state); - for(i=1; i<=n; i++) - { - absxi = ae_c_abs(x->ptr.p_complex[i], _state); - if( ae_fp_greater(absxi,safmin) ) - { - x->ptr.p_complex[i] = ae_c_div_d(x->ptr.p_complex[i],absxi); - } - else - { - x->ptr.p_complex[i] = ae_complex_from_d(1); - } - } - *kase = 2; - jump = 2; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - - /* - * ENTRY (JUMP = 2) - * FIRST ITERATION. X HAS BEEN OVERWRITTEN BY CTRANS(A)*X. - */ - if( jump==2 ) - { - j = rcond_internalcomplexrcondicmax1(x, n, _state); - iter = 2; - - /* - * MAIN LOOP - ITERATIONS 2,3,...,ITMAX. - */ - for(i=1; i<=n; i++) - { - x->ptr.p_complex[i] = ae_complex_from_d(0); - } - x->ptr.p_complex[j] = ae_complex_from_d(1); - *kase = 1; - jump = 3; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - - /* - * ENTRY (JUMP = 3) - * X HAS BEEN OVERWRITTEN BY A*X. - */ - if( jump==3 ) - { - ae_v_cmove(&v->ptr.p_complex[1], 1, &x->ptr.p_complex[1], 1, "N", ae_v_len(1,n)); - estold = *est; - *est = rcond_internalcomplexrcondscsum1(v, n, _state); - - /* - * TEST FOR CYCLING. - */ - if( ae_fp_less_eq(*est,estold) ) - { - - /* - * ITERATION COMPLETE. FINAL STAGE. - */ - altsgn = 1; - for(i=1; i<=n; i++) - { - x->ptr.p_complex[i] = ae_complex_from_d(altsgn*(1+(double)(i-1)/(double)(n-1))); - altsgn = -altsgn; - } - *kase = 1; - jump = 5; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - for(i=1; i<=n; i++) - { - absxi = ae_c_abs(x->ptr.p_complex[i], _state); - if( ae_fp_greater(absxi,safmin) ) - { - x->ptr.p_complex[i] = ae_c_div_d(x->ptr.p_complex[i],absxi); - } - else - { - x->ptr.p_complex[i] = ae_complex_from_d(1); - } - } - *kase = 2; - jump = 4; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - - /* - * ENTRY (JUMP = 4) - * X HAS BEEN OVERWRITTEN BY CTRANS(A)*X. - */ - if( jump==4 ) - { - jlast = j; - j = rcond_internalcomplexrcondicmax1(x, n, _state); - if( ae_fp_neq(ae_c_abs(x->ptr.p_complex[jlast], _state),ae_c_abs(x->ptr.p_complex[j], _state))&&iterptr.p_complex[i] = ae_complex_from_d(0); - } - x->ptr.p_complex[j] = ae_complex_from_d(1); - *kase = 1; - jump = 3; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - - /* - * ITERATION COMPLETE. FINAL STAGE. - */ - altsgn = 1; - for(i=1; i<=n; i++) - { - x->ptr.p_complex[i] = ae_complex_from_d(altsgn*(1+(double)(i-1)/(double)(n-1))); - altsgn = -altsgn; - } - *kase = 1; - jump = 5; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } - - /* - * ENTRY (JUMP = 5) - * X HAS BEEN OVERWRITTEN BY A*X. - */ - if( jump==5 ) - { - temp = 2*(rcond_internalcomplexrcondscsum1(x, n, _state)/(3*n)); - if( ae_fp_greater(temp,*est) ) - { - ae_v_cmove(&v->ptr.p_complex[1], 1, &x->ptr.p_complex[1], 1, "N", ae_v_len(1,n)); - *est = temp; - } - *kase = 0; - rcond_internalcomplexrcondsaveall(isave, rsave, &i, &iter, &j, &jlast, &jump, &absxi, &altsgn, &estold, &temp, _state); - return; - } -} - - -static double rcond_internalcomplexrcondscsum1(/* Complex */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - double result; - - - result = 0; - for(i=1; i<=n; i++) - { - result = result+ae_c_abs(x->ptr.p_complex[i], _state); - } - return result; -} - - -static ae_int_t rcond_internalcomplexrcondicmax1(/* Complex */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - double m; - ae_int_t result; - - - result = 1; - m = ae_c_abs(x->ptr.p_complex[1], _state); - for(i=2; i<=n; i++) - { - if( ae_fp_greater(ae_c_abs(x->ptr.p_complex[i], _state),m) ) - { - result = i; - m = ae_c_abs(x->ptr.p_complex[i], _state); - } - } - return result; -} - - -static void rcond_internalcomplexrcondsaveall(/* Integer */ ae_vector* isave, - /* Real */ ae_vector* rsave, - ae_int_t* i, - ae_int_t* iter, - ae_int_t* j, - ae_int_t* jlast, - ae_int_t* jump, - double* absxi, - double* altsgn, - double* estold, - double* temp, - ae_state *_state) -{ - - - isave->ptr.p_int[0] = *i; - isave->ptr.p_int[1] = *iter; - isave->ptr.p_int[2] = *j; - isave->ptr.p_int[3] = *jlast; - isave->ptr.p_int[4] = *jump; - rsave->ptr.p_double[0] = *absxi; - rsave->ptr.p_double[1] = *altsgn; - rsave->ptr.p_double[2] = *estold; - rsave->ptr.p_double[3] = *temp; -} - - -static void rcond_internalcomplexrcondloadall(/* Integer */ ae_vector* isave, - /* Real */ ae_vector* rsave, - ae_int_t* i, - ae_int_t* iter, - ae_int_t* j, - ae_int_t* jlast, - ae_int_t* jump, - double* absxi, - double* altsgn, - double* estold, - double* temp, - ae_state *_state) -{ - - - *i = isave->ptr.p_int[0]; - *iter = isave->ptr.p_int[1]; - *j = isave->ptr.p_int[2]; - *jlast = isave->ptr.p_int[3]; - *jump = isave->ptr.p_int[4]; - *absxi = rsave->ptr.p_double[0]; - *altsgn = rsave->ptr.p_double[1]; - *estold = rsave->ptr.p_double[2]; - *temp = rsave->ptr.p_double[3]; -} - - - - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of RMatrixLU subroutine). - Pivots - table of permutations - (the output of RMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code: - * -3 A is singular, or VERY close to singular. - it is filled by zeros in such cases. - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - A - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixluinverse(/* Real */ ae_matrix* a, - /* Integer */ ae_vector* pivots, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector work; - ae_int_t i; - ae_int_t j; - ae_int_t k; - double v; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _matinvreport_clear(rep); - ae_vector_init(&work, 0, DT_REAL, _state, ae_true); - - ae_assert(n>0, "RMatrixLUInverse: N<=0!", _state); - ae_assert(a->cols>=n, "RMatrixLUInverse: cols(A)rows>=n, "RMatrixLUInverse: rows(A)cnt>=n, "RMatrixLUInverse: len(Pivots)ptr.p_int[i]>n-1||pivots->ptr.p_int[i]0, "RMatrixLUInverse: incorrect Pivots array!", _state); - - /* - * calculate condition numbers - */ - rep->r1 = rmatrixlurcond1(a, n, _state); - rep->rinf = rmatrixlurcondinf(a, n, _state); - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Call cache-oblivious code - */ - ae_vector_set_length(&work, n, _state); - matinv_rmatrixluinverserec(a, 0, n, &work, info, rep, _state); - - /* - * apply permutations - */ - for(i=0; i<=n-1; i++) - { - for(j=n-2; j>=0; j--) - { - k = pivots->ptr.p_int[j]; - v = a->ptr.pp_double[i][j]; - a->ptr.pp_double[i][j] = a->ptr.pp_double[i][k]; - a->ptr.pp_double[i][k] = v; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - -Result: - True, if the matrix is not singular. - False, if the matrix is singular. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinverse(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector pivots; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _matinvreport_clear(rep); - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>0, "RMatrixInverse: N<=0!", _state); - ae_assert(a->cols>=n, "RMatrixInverse: cols(A)rows>=n, "RMatrixInverse: rows(A)0, "CMatrixLUInverse: N<=0!", _state); - ae_assert(a->cols>=n, "CMatrixLUInverse: cols(A)rows>=n, "CMatrixLUInverse: rows(A)cnt>=n, "CMatrixLUInverse: len(Pivots)ptr.p_int[i]>n-1||pivots->ptr.p_int[i]0, "CMatrixLUInverse: incorrect Pivots array!", _state); - - /* - * calculate condition numbers - */ - rep->r1 = cmatrixlurcond1(a, n, _state); - rep->rinf = cmatrixlurcondinf(a, n, _state); - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Call cache-oblivious code - */ - ae_vector_set_length(&work, n, _state); - matinv_cmatrixluinverserec(a, 0, n, &work, info, rep, _state); - - /* - * apply permutations - */ - for(i=0; i<=n-1; i++) - { - for(j=n-2; j>=0; j--) - { - k = pivots->ptr.p_int[j]; - v = a->ptr.pp_complex[i][j]; - a->ptr.pp_complex[i][j] = a->ptr.pp_complex[i][k]; - a->ptr.pp_complex[i][k] = v; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void cmatrixinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector pivots; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _matinvreport_clear(rep); - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>0, "CRMatrixInverse: N<=0!", _state); - ae_assert(a->cols>=n, "CRMatrixInverse: cols(A)rows>=n, "CRMatrixInverse: rows(A)0, "SPDMatrixCholeskyInverse: N<=0!", _state); - ae_assert(a->cols>=n, "SPDMatrixCholeskyInverse: cols(A)rows>=n, "SPDMatrixCholeskyInverse: rows(A)ptr.pp_double[i][i], _state); - } - ae_assert(f, "SPDMatrixCholeskyInverse: A contains infinite or NaN values!", _state); - - /* - * calculate condition numbers - */ - rep->r1 = spdmatrixcholeskyrcond(a, n, isupper, _state); - rep->rinf = rep->r1; - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - if( isupper ) - { - for(i=0; i<=n-1; i++) - { - for(j=i; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - } - else - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Inverse - */ - ae_vector_set_length(&tmp, n, _state); - matinv_spdmatrixcholeskyinverserec(a, 0, n, isupper, &tmp, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Inversion of a symmetric positive definite matrix. - -Given an upper or lower triangle of a symmetric positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixinverse(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - - *info = 0; - _matinvreport_clear(rep); - - ae_assert(n>0, "SPDMatrixInverse: N<=0!", _state); - ae_assert(a->cols>=n, "SPDMatrixInverse: cols(A)rows>=n, "SPDMatrixInverse: rows(A)0, "HPDMatrixCholeskyInverse: N<=0!", _state); - ae_assert(a->cols>=n, "HPDMatrixCholeskyInverse: cols(A)rows>=n, "HPDMatrixCholeskyInverse: rows(A)ptr.pp_complex[i][i].x, _state))&&ae_isfinite(a->ptr.pp_complex[i][i].y, _state); - } - ae_assert(f, "HPDMatrixCholeskyInverse: A contains infinite or NaN values!", _state); - *info = 1; - - /* - * calculate condition numbers - */ - rep->r1 = hpdmatrixcholeskyrcond(a, n, isupper, _state); - rep->rinf = rep->r1; - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - if( isupper ) - { - for(i=0; i<=n-1; i++) - { - for(j=i; j<=n-1; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - } - else - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Inverse - */ - ae_vector_set_length(&tmp, n, _state); - matinv_hpdmatrixcholeskyinverserec(a, 0, n, isupper, &tmp, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Inversion of a Hermitian positive definite matrix. - -Given an upper or lower triangle of a Hermitian positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - - *info = 0; - _matinvreport_clear(rep); - - ae_assert(n>0, "HPDMatrixInverse: N<=0!", _state); - ae_assert(a->cols>=n, "HPDMatrixInverse: cols(A)rows>=n, "HPDMatrixInverse: rows(A)0, "RMatrixTRInverse: N<=0!", _state); - ae_assert(a->cols>=n, "RMatrixTRInverse: cols(A)rows>=n, "RMatrixTRInverse: rows(A)r1 = rmatrixtrrcond1(a, n, isupper, isunit, _state); - rep->rinf = rmatrixtrrcondinf(a, n, isupper, isunit, _state); - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_double[i][j] = 0; - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Invert - */ - ae_vector_set_length(&tmp, n, _state); - matinv_rmatrixtrinverserec(a, 0, n, isupper, isunit, &tmp, info, rep, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Triangular matrix inverse (complex) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixtrinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_vector tmp; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _matinvreport_clear(rep); - ae_vector_init(&tmp, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(n>0, "CMatrixTRInverse: N<=0!", _state); - ae_assert(a->cols>=n, "CMatrixTRInverse: cols(A)rows>=n, "CMatrixTRInverse: rows(A)r1 = cmatrixtrrcond1(a, n, isupper, isunit, _state); - rep->rinf = cmatrixtrrcondinf(a, n, isupper, isunit, _state); - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - a->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - - /* - * Invert - */ - ae_vector_set_length(&tmp, n, _state); - matinv_cmatrixtrinverserec(a, 0, n, isupper, isunit, &tmp, info, rep, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Triangular matrix inversion, recursive subroutine - - -- ALGLIB -- - 05.02.2010, Bochkanov Sergey. - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992. -*************************************************************************/ -static void matinv_rmatrixtrinverserec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - /* Real */ ae_vector* tmp, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_int_t n1; - ae_int_t n2; - ae_int_t i; - ae_int_t j; - double v; - double ajj; - - - if( n<1 ) - { - *info = -1; - return; - } - - /* - * Base case - */ - if( n<=ablasblocksize(a, _state) ) - { - if( isupper ) - { - - /* - * Compute inverse of upper triangular matrix. - */ - for(j=0; j<=n-1; j++) - { - if( !isunit ) - { - if( ae_fp_eq(a->ptr.pp_double[offs+j][offs+j],0) ) - { - *info = -3; - return; - } - a->ptr.pp_double[offs+j][offs+j] = 1/a->ptr.pp_double[offs+j][offs+j]; - ajj = -a->ptr.pp_double[offs+j][offs+j]; - } - else - { - ajj = -1; - } - - /* - * Compute elements 1:j-1 of j-th column. - */ - if( j>0 ) - { - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+0][offs+j], a->stride, ae_v_len(0,j-1)); - for(i=0; i<=j-1; i++) - { - if( iptr.pp_double[offs+i][offs+i+1], 1, &tmp->ptr.p_double[i+1], 1, ae_v_len(offs+i+1,offs+j-1)); - } - else - { - v = 0; - } - if( !isunit ) - { - a->ptr.pp_double[offs+i][offs+j] = v+a->ptr.pp_double[offs+i][offs+i]*tmp->ptr.p_double[i]; - } - else - { - a->ptr.pp_double[offs+i][offs+j] = v+tmp->ptr.p_double[i]; - } - } - ae_v_muld(&a->ptr.pp_double[offs+0][offs+j], a->stride, ae_v_len(offs+0,offs+j-1), ajj); - } - } - } - else - { - - /* - * Compute inverse of lower triangular matrix. - */ - for(j=n-1; j>=0; j--) - { - if( !isunit ) - { - if( ae_fp_eq(a->ptr.pp_double[offs+j][offs+j],0) ) - { - *info = -3; - return; - } - a->ptr.pp_double[offs+j][offs+j] = 1/a->ptr.pp_double[offs+j][offs+j]; - ajj = -a->ptr.pp_double[offs+j][offs+j]; - } - else - { - ajj = -1; - } - if( jptr.p_double[j+1], 1, &a->ptr.pp_double[offs+j+1][offs+j], a->stride, ae_v_len(j+1,n-1)); - for(i=j+1; i<=n-1; i++) - { - if( i>j+1 ) - { - v = ae_v_dotproduct(&a->ptr.pp_double[offs+i][offs+j+1], 1, &tmp->ptr.p_double[j+1], 1, ae_v_len(offs+j+1,offs+i-1)); - } - else - { - v = 0; - } - if( !isunit ) - { - a->ptr.pp_double[offs+i][offs+j] = v+a->ptr.pp_double[offs+i][offs+i]*tmp->ptr.p_double[i]; - } - else - { - a->ptr.pp_double[offs+i][offs+j] = v+tmp->ptr.p_double[i]; - } - } - ae_v_muld(&a->ptr.pp_double[offs+j+1][offs+j], a->stride, ae_v_len(offs+j+1,offs+n-1), ajj); - } - } - } - return; - } - - /* - * Recursive case - */ - ablassplitlength(a, n, &n1, &n2, _state); - if( n2>0 ) - { - if( isupper ) - { - for(i=0; i<=n1-1; i++) - { - ae_v_muld(&a->ptr.pp_double[offs+i][offs+n1], 1, ae_v_len(offs+n1,offs+n-1), -1); - } - rmatrixlefttrsm(n1, n2, a, offs, offs, isupper, isunit, 0, a, offs, offs+n1, _state); - rmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, isupper, isunit, 0, a, offs, offs+n1, _state); - } - else - { - for(i=0; i<=n2-1; i++) - { - ae_v_muld(&a->ptr.pp_double[offs+n1+i][offs], 1, ae_v_len(offs,offs+n1-1), -1); - } - rmatrixrighttrsm(n2, n1, a, offs, offs, isupper, isunit, 0, a, offs+n1, offs, _state); - rmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, isupper, isunit, 0, a, offs+n1, offs, _state); - } - matinv_rmatrixtrinverserec(a, offs+n1, n2, isupper, isunit, tmp, info, rep, _state); - } - matinv_rmatrixtrinverserec(a, offs, n1, isupper, isunit, tmp, info, rep, _state); -} - - -/************************************************************************* -Triangular matrix inversion, recursive subroutine - - -- ALGLIB -- - 05.02.2010, Bochkanov Sergey. - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - February 29, 1992. -*************************************************************************/ -static void matinv_cmatrixtrinverserec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - /* Complex */ ae_vector* tmp, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_int_t n1; - ae_int_t n2; - ae_int_t i; - ae_int_t j; - ae_complex v; - ae_complex ajj; - - - if( n<1 ) - { - *info = -1; - return; - } - - /* - * Base case - */ - if( n<=ablascomplexblocksize(a, _state) ) - { - if( isupper ) - { - - /* - * Compute inverse of upper triangular matrix. - */ - for(j=0; j<=n-1; j++) - { - if( !isunit ) - { - if( ae_c_eq_d(a->ptr.pp_complex[offs+j][offs+j],0) ) - { - *info = -3; - return; - } - a->ptr.pp_complex[offs+j][offs+j] = ae_c_d_div(1,a->ptr.pp_complex[offs+j][offs+j]); - ajj = ae_c_neg(a->ptr.pp_complex[offs+j][offs+j]); - } - else - { - ajj = ae_complex_from_d(-1); - } - - /* - * Compute elements 1:j-1 of j-th column. - */ - if( j>0 ) - { - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+0][offs+j], a->stride, "N", ae_v_len(0,j-1)); - for(i=0; i<=j-1; i++) - { - if( iptr.pp_complex[offs+i][offs+i+1], 1, "N", &tmp->ptr.p_complex[i+1], 1, "N", ae_v_len(offs+i+1,offs+j-1)); - } - else - { - v = ae_complex_from_d(0); - } - if( !isunit ) - { - a->ptr.pp_complex[offs+i][offs+j] = ae_c_add(v,ae_c_mul(a->ptr.pp_complex[offs+i][offs+i],tmp->ptr.p_complex[i])); - } - else - { - a->ptr.pp_complex[offs+i][offs+j] = ae_c_add(v,tmp->ptr.p_complex[i]); - } - } - ae_v_cmulc(&a->ptr.pp_complex[offs+0][offs+j], a->stride, ae_v_len(offs+0,offs+j-1), ajj); - } - } - } - else - { - - /* - * Compute inverse of lower triangular matrix. - */ - for(j=n-1; j>=0; j--) - { - if( !isunit ) - { - if( ae_c_eq_d(a->ptr.pp_complex[offs+j][offs+j],0) ) - { - *info = -3; - return; - } - a->ptr.pp_complex[offs+j][offs+j] = ae_c_d_div(1,a->ptr.pp_complex[offs+j][offs+j]); - ajj = ae_c_neg(a->ptr.pp_complex[offs+j][offs+j]); - } - else - { - ajj = ae_complex_from_d(-1); - } - if( jptr.p_complex[j+1], 1, &a->ptr.pp_complex[offs+j+1][offs+j], a->stride, "N", ae_v_len(j+1,n-1)); - for(i=j+1; i<=n-1; i++) - { - if( i>j+1 ) - { - v = ae_v_cdotproduct(&a->ptr.pp_complex[offs+i][offs+j+1], 1, "N", &tmp->ptr.p_complex[j+1], 1, "N", ae_v_len(offs+j+1,offs+i-1)); - } - else - { - v = ae_complex_from_d(0); - } - if( !isunit ) - { - a->ptr.pp_complex[offs+i][offs+j] = ae_c_add(v,ae_c_mul(a->ptr.pp_complex[offs+i][offs+i],tmp->ptr.p_complex[i])); - } - else - { - a->ptr.pp_complex[offs+i][offs+j] = ae_c_add(v,tmp->ptr.p_complex[i]); - } - } - ae_v_cmulc(&a->ptr.pp_complex[offs+j+1][offs+j], a->stride, ae_v_len(offs+j+1,offs+n-1), ajj); - } - } - } - return; - } - - /* - * Recursive case - */ - ablascomplexsplitlength(a, n, &n1, &n2, _state); - if( n2>0 ) - { - if( isupper ) - { - for(i=0; i<=n1-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[offs+i][offs+n1], 1, ae_v_len(offs+n1,offs+n-1), -1); - } - cmatrixlefttrsm(n1, n2, a, offs, offs, isupper, isunit, 0, a, offs, offs+n1, _state); - cmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, isupper, isunit, 0, a, offs, offs+n1, _state); - } - else - { - for(i=0; i<=n2-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[offs+n1+i][offs], 1, ae_v_len(offs,offs+n1-1), -1); - } - cmatrixrighttrsm(n2, n1, a, offs, offs, isupper, isunit, 0, a, offs+n1, offs, _state); - cmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, isupper, isunit, 0, a, offs+n1, offs, _state); - } - matinv_cmatrixtrinverserec(a, offs+n1, n2, isupper, isunit, tmp, info, rep, _state); - } - matinv_cmatrixtrinverserec(a, offs, n1, isupper, isunit, tmp, info, rep, _state); -} - - -static void matinv_rmatrixluinverserec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - /* Real */ ae_vector* work, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - ae_int_t n1; - ae_int_t n2; - - - if( n<1 ) - { - *info = -1; - return; - } - - /* - * Base case - */ - if( n<=ablasblocksize(a, _state) ) - { - - /* - * Form inv(U) - */ - matinv_rmatrixtrinverserec(a, offs, n, ae_true, ae_false, work, info, rep, _state); - if( *info<=0 ) - { - return; - } - - /* - * Solve the equation inv(A)*L = inv(U) for inv(A). - */ - for(j=n-1; j>=0; j--) - { - - /* - * Copy current column of L to WORK and replace with zeros. - */ - for(i=j+1; i<=n-1; i++) - { - work->ptr.p_double[i] = a->ptr.pp_double[offs+i][offs+j]; - a->ptr.pp_double[offs+i][offs+j] = 0; - } - - /* - * Compute current column of inv(A). - */ - if( jptr.pp_double[offs+i][offs+j+1], 1, &work->ptr.p_double[j+1], 1, ae_v_len(offs+j+1,offs+n-1)); - a->ptr.pp_double[offs+i][offs+j] = a->ptr.pp_double[offs+i][offs+j]-v; - } - } - } - return; - } - - /* - * Recursive code: - * - * ( L1 ) ( U1 U12 ) - * A = ( ) * ( ) - * ( L12 L2 ) ( U2 ) - * - * ( W X ) - * A^-1 = ( ) - * ( Y Z ) - */ - ablassplitlength(a, n, &n1, &n2, _state); - ae_assert(n2>0, "LUInverseRec: internal error!", _state); - - /* - * X := inv(U1)*U12*inv(U2) - */ - rmatrixlefttrsm(n1, n2, a, offs, offs, ae_true, ae_false, 0, a, offs, offs+n1, _state); - rmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, ae_true, ae_false, 0, a, offs, offs+n1, _state); - - /* - * Y := inv(L2)*L12*inv(L1) - */ - rmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, ae_false, ae_true, 0, a, offs+n1, offs, _state); - rmatrixrighttrsm(n2, n1, a, offs, offs, ae_false, ae_true, 0, a, offs+n1, offs, _state); - - /* - * W := inv(L1*U1)+X*Y - */ - matinv_rmatrixluinverserec(a, offs, n1, work, info, rep, _state); - if( *info<=0 ) - { - return; - } - rmatrixgemm(n1, n1, n2, 1.0, a, offs, offs+n1, 0, a, offs+n1, offs, 0, 1.0, a, offs, offs, _state); - - /* - * X := -X*inv(L2) - * Y := -inv(U2)*Y - */ - rmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, ae_false, ae_true, 0, a, offs, offs+n1, _state); - for(i=0; i<=n1-1; i++) - { - ae_v_muld(&a->ptr.pp_double[offs+i][offs+n1], 1, ae_v_len(offs+n1,offs+n-1), -1); - } - rmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, ae_true, ae_false, 0, a, offs+n1, offs, _state); - for(i=0; i<=n2-1; i++) - { - ae_v_muld(&a->ptr.pp_double[offs+n1+i][offs], 1, ae_v_len(offs,offs+n1-1), -1); - } - - /* - * Z := inv(L2*U2) - */ - matinv_rmatrixluinverserec(a, offs+n1, n2, work, info, rep, _state); -} - - -static void matinv_cmatrixluinverserec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - /* Complex */ ae_vector* work, - ae_int_t* info, - matinvreport* rep, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_complex v; - ae_int_t n1; - ae_int_t n2; - - - if( n<1 ) - { - *info = -1; - return; - } - - /* - * Base case - */ - if( n<=ablascomplexblocksize(a, _state) ) - { - - /* - * Form inv(U) - */ - matinv_cmatrixtrinverserec(a, offs, n, ae_true, ae_false, work, info, rep, _state); - if( *info<=0 ) - { - return; - } - - /* - * Solve the equation inv(A)*L = inv(U) for inv(A). - */ - for(j=n-1; j>=0; j--) - { - - /* - * Copy current column of L to WORK and replace with zeros. - */ - for(i=j+1; i<=n-1; i++) - { - work->ptr.p_complex[i] = a->ptr.pp_complex[offs+i][offs+j]; - a->ptr.pp_complex[offs+i][offs+j] = ae_complex_from_d(0); - } - - /* - * Compute current column of inv(A). - */ - if( jptr.pp_complex[offs+i][offs+j+1], 1, "N", &work->ptr.p_complex[j+1], 1, "N", ae_v_len(offs+j+1,offs+n-1)); - a->ptr.pp_complex[offs+i][offs+j] = ae_c_sub(a->ptr.pp_complex[offs+i][offs+j],v); - } - } - } - return; - } - - /* - * Recursive code: - * - * ( L1 ) ( U1 U12 ) - * A = ( ) * ( ) - * ( L12 L2 ) ( U2 ) - * - * ( W X ) - * A^-1 = ( ) - * ( Y Z ) - */ - ablascomplexsplitlength(a, n, &n1, &n2, _state); - ae_assert(n2>0, "LUInverseRec: internal error!", _state); - - /* - * X := inv(U1)*U12*inv(U2) - */ - cmatrixlefttrsm(n1, n2, a, offs, offs, ae_true, ae_false, 0, a, offs, offs+n1, _state); - cmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, ae_true, ae_false, 0, a, offs, offs+n1, _state); - - /* - * Y := inv(L2)*L12*inv(L1) - */ - cmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, ae_false, ae_true, 0, a, offs+n1, offs, _state); - cmatrixrighttrsm(n2, n1, a, offs, offs, ae_false, ae_true, 0, a, offs+n1, offs, _state); - - /* - * W := inv(L1*U1)+X*Y - */ - matinv_cmatrixluinverserec(a, offs, n1, work, info, rep, _state); - if( *info<=0 ) - { - return; - } - cmatrixgemm(n1, n1, n2, ae_complex_from_d(1.0), a, offs, offs+n1, 0, a, offs+n1, offs, 0, ae_complex_from_d(1.0), a, offs, offs, _state); - - /* - * X := -X*inv(L2) - * Y := -inv(U2)*Y - */ - cmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, ae_false, ae_true, 0, a, offs, offs+n1, _state); - for(i=0; i<=n1-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[offs+i][offs+n1], 1, ae_v_len(offs+n1,offs+n-1), -1); - } - cmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, ae_true, ae_false, 0, a, offs+n1, offs, _state); - for(i=0; i<=n2-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[offs+n1+i][offs], 1, ae_v_len(offs,offs+n1-1), -1); - } - - /* - * Z := inv(L2*U2) - */ - matinv_cmatrixluinverserec(a, offs+n1, n2, work, info, rep, _state); -} - - -/************************************************************************* -Recursive subroutine for SPD inversion. - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -static void matinv_spdmatrixcholeskyinverserec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - ae_int_t n1; - ae_int_t n2; - ae_int_t info2; - matinvreport rep2; - - ae_frame_make(_state, &_frame_block); - _matinvreport_init(&rep2, _state, ae_true); - - if( n<1 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Base case - */ - if( n<=ablasblocksize(a, _state) ) - { - matinv_rmatrixtrinverserec(a, offs, n, isupper, ae_false, tmp, &info2, &rep2, _state); - if( isupper ) - { - - /* - * Compute the product U * U'. - * NOTE: we never assume that diagonal of U is real - */ - for(i=0; i<=n-1; i++) - { - if( i==0 ) - { - - /* - * 1x1 matrix - */ - a->ptr.pp_double[offs+i][offs+i] = ae_sqr(a->ptr.pp_double[offs+i][offs+i], _state); - } - else - { - - /* - * (I+1)x(I+1) matrix, - * - * ( A11 A12 ) ( A11^H ) ( A11*A11^H+A12*A12^H A12*A22^H ) - * ( ) * ( ) = ( ) - * ( A22 ) ( A12^H A22^H ) ( A22*A12^H A22*A22^H ) - * - * A11 is IxI, A22 is 1x1. - */ - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs][offs+i], a->stride, ae_v_len(0,i-1)); - for(j=0; j<=i-1; j++) - { - v = a->ptr.pp_double[offs+j][offs+i]; - ae_v_addd(&a->ptr.pp_double[offs+j][offs+j], 1, &tmp->ptr.p_double[j], 1, ae_v_len(offs+j,offs+i-1), v); - } - v = a->ptr.pp_double[offs+i][offs+i]; - ae_v_muld(&a->ptr.pp_double[offs][offs+i], a->stride, ae_v_len(offs,offs+i-1), v); - a->ptr.pp_double[offs+i][offs+i] = ae_sqr(a->ptr.pp_double[offs+i][offs+i], _state); - } - } - } - else - { - - /* - * Compute the product L' * L - * NOTE: we never assume that diagonal of L is real - */ - for(i=0; i<=n-1; i++) - { - if( i==0 ) - { - - /* - * 1x1 matrix - */ - a->ptr.pp_double[offs+i][offs+i] = ae_sqr(a->ptr.pp_double[offs+i][offs+i], _state); - } - else - { - - /* - * (I+1)x(I+1) matrix, - * - * ( A11^H A21^H ) ( A11 ) ( A11^H*A11+A21^H*A21 A21^H*A22 ) - * ( ) * ( ) = ( ) - * ( A22^H ) ( A21 A22 ) ( A22^H*A21 A22^H*A22 ) - * - * A11 is IxI, A22 is 1x1. - */ - ae_v_move(&tmp->ptr.p_double[0], 1, &a->ptr.pp_double[offs+i][offs], 1, ae_v_len(0,i-1)); - for(j=0; j<=i-1; j++) - { - v = a->ptr.pp_double[offs+i][offs+j]; - ae_v_addd(&a->ptr.pp_double[offs+j][offs], 1, &tmp->ptr.p_double[0], 1, ae_v_len(offs,offs+j), v); - } - v = a->ptr.pp_double[offs+i][offs+i]; - ae_v_muld(&a->ptr.pp_double[offs+i][offs], 1, ae_v_len(offs,offs+i-1), v); - a->ptr.pp_double[offs+i][offs+i] = ae_sqr(a->ptr.pp_double[offs+i][offs+i], _state); - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * Recursive code: triangular factor inversion merged with - * UU' or L'L multiplication - */ - ablassplitlength(a, n, &n1, &n2, _state); - - /* - * form off-diagonal block of trangular inverse - */ - if( isupper ) - { - for(i=0; i<=n1-1; i++) - { - ae_v_muld(&a->ptr.pp_double[offs+i][offs+n1], 1, ae_v_len(offs+n1,offs+n-1), -1); - } - rmatrixlefttrsm(n1, n2, a, offs, offs, isupper, ae_false, 0, a, offs, offs+n1, _state); - rmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, isupper, ae_false, 0, a, offs, offs+n1, _state); - } - else - { - for(i=0; i<=n2-1; i++) - { - ae_v_muld(&a->ptr.pp_double[offs+n1+i][offs], 1, ae_v_len(offs,offs+n1-1), -1); - } - rmatrixrighttrsm(n2, n1, a, offs, offs, isupper, ae_false, 0, a, offs+n1, offs, _state); - rmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, isupper, ae_false, 0, a, offs+n1, offs, _state); - } - - /* - * invert first diagonal block - */ - matinv_spdmatrixcholeskyinverserec(a, offs, n1, isupper, tmp, _state); - - /* - * update first diagonal block with off-diagonal block, - * update off-diagonal block - */ - if( isupper ) - { - rmatrixsyrk(n1, n2, 1.0, a, offs, offs+n1, 0, 1.0, a, offs, offs, isupper, _state); - rmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, isupper, ae_false, 1, a, offs, offs+n1, _state); - } - else - { - rmatrixsyrk(n1, n2, 1.0, a, offs+n1, offs, 1, 1.0, a, offs, offs, isupper, _state); - rmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, isupper, ae_false, 1, a, offs+n1, offs, _state); - } - - /* - * invert second diagonal block - */ - matinv_spdmatrixcholeskyinverserec(a, offs+n1, n2, isupper, tmp, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Recursive subroutine for HPD inversion. - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -static void matinv_hpdmatrixcholeskyinverserec(/* Complex */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_complex v; - ae_int_t n1; - ae_int_t n2; - ae_int_t info2; - matinvreport rep2; - - ae_frame_make(_state, &_frame_block); - _matinvreport_init(&rep2, _state, ae_true); - - if( n<1 ) - { - ae_frame_leave(_state); - return; - } - - /* - * Base case - */ - if( n<=ablascomplexblocksize(a, _state) ) - { - matinv_cmatrixtrinverserec(a, offs, n, isupper, ae_false, tmp, &info2, &rep2, _state); - if( isupper ) - { - - /* - * Compute the product U * U'. - * NOTE: we never assume that diagonal of U is real - */ - for(i=0; i<=n-1; i++) - { - if( i==0 ) - { - - /* - * 1x1 matrix - */ - a->ptr.pp_complex[offs+i][offs+i] = ae_complex_from_d(ae_sqr(a->ptr.pp_complex[offs+i][offs+i].x, _state)+ae_sqr(a->ptr.pp_complex[offs+i][offs+i].y, _state)); - } - else - { - - /* - * (I+1)x(I+1) matrix, - * - * ( A11 A12 ) ( A11^H ) ( A11*A11^H+A12*A12^H A12*A22^H ) - * ( ) * ( ) = ( ) - * ( A22 ) ( A12^H A22^H ) ( A22*A12^H A22*A22^H ) - * - * A11 is IxI, A22 is 1x1. - */ - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs][offs+i], a->stride, "Conj", ae_v_len(0,i-1)); - for(j=0; j<=i-1; j++) - { - v = a->ptr.pp_complex[offs+j][offs+i]; - ae_v_caddc(&a->ptr.pp_complex[offs+j][offs+j], 1, &tmp->ptr.p_complex[j], 1, "N", ae_v_len(offs+j,offs+i-1), v); - } - v = ae_c_conj(a->ptr.pp_complex[offs+i][offs+i], _state); - ae_v_cmulc(&a->ptr.pp_complex[offs][offs+i], a->stride, ae_v_len(offs,offs+i-1), v); - a->ptr.pp_complex[offs+i][offs+i] = ae_complex_from_d(ae_sqr(a->ptr.pp_complex[offs+i][offs+i].x, _state)+ae_sqr(a->ptr.pp_complex[offs+i][offs+i].y, _state)); - } - } - } - else - { - - /* - * Compute the product L' * L - * NOTE: we never assume that diagonal of L is real - */ - for(i=0; i<=n-1; i++) - { - if( i==0 ) - { - - /* - * 1x1 matrix - */ - a->ptr.pp_complex[offs+i][offs+i] = ae_complex_from_d(ae_sqr(a->ptr.pp_complex[offs+i][offs+i].x, _state)+ae_sqr(a->ptr.pp_complex[offs+i][offs+i].y, _state)); - } - else - { - - /* - * (I+1)x(I+1) matrix, - * - * ( A11^H A21^H ) ( A11 ) ( A11^H*A11+A21^H*A21 A21^H*A22 ) - * ( ) * ( ) = ( ) - * ( A22^H ) ( A21 A22 ) ( A22^H*A21 A22^H*A22 ) - * - * A11 is IxI, A22 is 1x1. - */ - ae_v_cmove(&tmp->ptr.p_complex[0], 1, &a->ptr.pp_complex[offs+i][offs], 1, "N", ae_v_len(0,i-1)); - for(j=0; j<=i-1; j++) - { - v = ae_c_conj(a->ptr.pp_complex[offs+i][offs+j], _state); - ae_v_caddc(&a->ptr.pp_complex[offs+j][offs], 1, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(offs,offs+j), v); - } - v = ae_c_conj(a->ptr.pp_complex[offs+i][offs+i], _state); - ae_v_cmulc(&a->ptr.pp_complex[offs+i][offs], 1, ae_v_len(offs,offs+i-1), v); - a->ptr.pp_complex[offs+i][offs+i] = ae_complex_from_d(ae_sqr(a->ptr.pp_complex[offs+i][offs+i].x, _state)+ae_sqr(a->ptr.pp_complex[offs+i][offs+i].y, _state)); - } - } - } - ae_frame_leave(_state); - return; - } - - /* - * Recursive code: triangular factor inversion merged with - * UU' or L'L multiplication - */ - ablascomplexsplitlength(a, n, &n1, &n2, _state); - - /* - * form off-diagonal block of trangular inverse - */ - if( isupper ) - { - for(i=0; i<=n1-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[offs+i][offs+n1], 1, ae_v_len(offs+n1,offs+n-1), -1); - } - cmatrixlefttrsm(n1, n2, a, offs, offs, isupper, ae_false, 0, a, offs, offs+n1, _state); - cmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, isupper, ae_false, 0, a, offs, offs+n1, _state); - } - else - { - for(i=0; i<=n2-1; i++) - { - ae_v_cmuld(&a->ptr.pp_complex[offs+n1+i][offs], 1, ae_v_len(offs,offs+n1-1), -1); - } - cmatrixrighttrsm(n2, n1, a, offs, offs, isupper, ae_false, 0, a, offs+n1, offs, _state); - cmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, isupper, ae_false, 0, a, offs+n1, offs, _state); - } - - /* - * invert first diagonal block - */ - matinv_hpdmatrixcholeskyinverserec(a, offs, n1, isupper, tmp, _state); - - /* - * update first diagonal block with off-diagonal block, - * update off-diagonal block - */ - if( isupper ) - { - cmatrixsyrk(n1, n2, 1.0, a, offs, offs+n1, 0, 1.0, a, offs, offs, isupper, _state); - cmatrixrighttrsm(n1, n2, a, offs+n1, offs+n1, isupper, ae_false, 2, a, offs, offs+n1, _state); - } - else - { - cmatrixsyrk(n1, n2, 1.0, a, offs+n1, offs, 2, 1.0, a, offs, offs, isupper, _state); - cmatrixlefttrsm(n2, n1, a, offs+n1, offs+n1, isupper, ae_false, 2, a, offs+n1, offs, _state); - } - - /* - * invert second diagonal block - */ - matinv_hpdmatrixcholeskyinverserec(a, offs+n1, n2, isupper, tmp, _state); - ae_frame_leave(_state); -} - - -ae_bool _matinvreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - matinvreport *p = (matinvreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _matinvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - matinvreport *dst = (matinvreport*)_dst; - matinvreport *src = (matinvreport*)_src; - dst->r1 = src->r1; - dst->rinf = src->rinf; - return ae_true; -} - - -void _matinvreport_clear(void* _p) -{ - matinvreport *p = (matinvreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _matinvreport_destroy(void* _p) -{ - matinvreport *p = (matinvreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -This function creates sparse matrix in a Hash-Table format. - -This function creates Hast-Table matrix, which can be converted to CRS -format after its initialization is over. Typical usage scenario for a -sparse matrix is: -1. creation in a Hash-Table format -2. insertion of the matrix elements -3. conversion to the CRS representation -4. matrix is passed to some linear algebra algorithm - -Some information about different matrix formats can be found below, in -the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - K - K>=0, expected number of non-zero elements in a matrix. - K can be inexact approximation, can be less than actual - number of elements (table will grow when needed) or - even zero). - It is important to understand that although hash-table - may grow automatically, it is better to provide good - estimate of data size. - -OUTPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - All elements of the matrix are zero. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreate(ae_int_t m, - ae_int_t n, - ae_int_t k, - sparsematrix* s, - ae_state *_state) -{ - ae_int_t i; - ae_int_t sz; - - _sparsematrix_clear(s); - - ae_assert(m>0, "SparseCreate: M<=0", _state); - ae_assert(n>0, "SparseCreate: N<=0", _state); - ae_assert(k>=0, "SparseCreate: K<0", _state); - sz = ae_round(k/sparse_desiredloadfactor+sparse_additional, _state); - s->matrixtype = 0; - s->m = m; - s->n = n; - s->nfree = sz; - ae_vector_set_length(&s->vals, sz, _state); - ae_vector_set_length(&s->idx, 2*sz, _state); - for(i=0; i<=sz-1; i++) - { - s->idx.ptr.p_int[2*i] = -1; - } -} - - -/************************************************************************* -This function creates sparse matrix in a CRS format (expert function for -situations when you are running out of memory). - -This function creates CRS matrix. Typical usage scenario for a CRS matrix -is: -1. creation (you have to tell number of non-zero elements at each row at - this moment) -2. insertion of the matrix elements (row by row, from left to right) -3. matrix is passed to some linear algebra algorithm - -This function is a memory-efficient alternative to SparseCreate(), but it -is more complex because it requires you to know in advance how large your -matrix is. Some information about different matrix formats can be found -below, in the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - NER - number of elements at each row, array[M], NER[I]>=0 - -OUTPUT PARAMETERS - S - sparse M*N matrix in CRS representation. - You have to fill ALL non-zero elements by calling - SparseSet() BEFORE you try to use this matrix. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreatecrs(ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* ner, - sparsematrix* s, - ae_state *_state) -{ - ae_int_t i; - ae_int_t noe; - - _sparsematrix_clear(s); - - ae_assert(m>0, "SparseCreateCRS: M<=0", _state); - ae_assert(n>0, "SparseCreateCRS: N<=0", _state); - ae_assert(ner->cnt>=m, "SparseCreateCRS: Length(NER)matrixtype = 1; - s->ninitialized = 0; - s->m = m; - s->n = n; - ae_vector_set_length(&s->ridx, s->m+1, _state); - s->ridx.ptr.p_int[0] = 0; - for(i=0; i<=s->m-1; i++) - { - ae_assert(ner->ptr.p_int[i]>=0, "SparseCreateCRS: NER[] contains negative elements", _state); - noe = noe+ner->ptr.p_int[i]; - s->ridx.ptr.p_int[i+1] = s->ridx.ptr.p_int[i]+ner->ptr.p_int[i]; - } - ae_vector_set_length(&s->vals, noe, _state); - ae_vector_set_length(&s->idx, noe, _state); - if( noe==0 ) - { - sparse_sparseinitduidx(s, _state); - } -} - - -/************************************************************************* -This function copies S0 to S1. - -NOTE: this function does not verify its arguments, it just copies all -fields of the structure. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecopy(sparsematrix* s0, sparsematrix* s1, ae_state *_state) -{ - ae_int_t l; - ae_int_t i; - - _sparsematrix_clear(s1); - - s1->matrixtype = s0->matrixtype; - s1->m = s0->m; - s1->n = s0->n; - s1->nfree = s0->nfree; - s1->ninitialized = s0->ninitialized; - - /* - * Initialization for arrays - */ - l = s0->vals.cnt; - ae_vector_set_length(&s1->vals, l, _state); - for(i=0; i<=l-1; i++) - { - s1->vals.ptr.p_double[i] = s0->vals.ptr.p_double[i]; - } - l = s0->ridx.cnt; - ae_vector_set_length(&s1->ridx, l, _state); - for(i=0; i<=l-1; i++) - { - s1->ridx.ptr.p_int[i] = s0->ridx.ptr.p_int[i]; - } - l = s0->idx.cnt; - ae_vector_set_length(&s1->idx, l, _state); - for(i=0; i<=l-1; i++) - { - s1->idx.ptr.p_int[i] = s0->idx.ptr.p_int[i]; - } - - /* - * Initalization for CRS-parameters - */ - l = s0->uidx.cnt; - ae_vector_set_length(&s1->uidx, l, _state); - for(i=0; i<=l-1; i++) - { - s1->uidx.ptr.p_int[i] = s0->uidx.ptr.p_int[i]; - } - l = s0->didx.cnt; - ae_vector_set_length(&s1->didx, l, _state); - for(i=0; i<=l-1; i++) - { - s1->didx.ptr.p_int[i] = s0->didx.ptr.p_int[i]; - } -} - - -/************************************************************************* -This function adds value to S[i,j] - element of the sparse matrix. Matrix -must be in a Hash-Table mode. - -In case S[i,j] already exists in the table, V i added to its value. In -case S[i,j] is non-existent, it is inserted in the table. Table -automatically grows when necessary. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - Exception will be thrown for CRS matrix. - I - row index of the element to modify, 0<=Imatrixtype==0, "SparseAdd: matrix must be in the Hash-Table mode to do this operation", _state); - ae_assert(i>=0, "SparseAdd: I<0", _state); - ae_assert(im, "SparseAdd: I>=M", _state); - ae_assert(j>=0, "SparseAdd: J<0", _state); - ae_assert(jn, "SparseAdd: J>=N", _state); - ae_assert(ae_isfinite(v, _state), "SparseAdd: V is not finite number", _state); - if( ae_fp_eq(v,0) ) - { - return; - } - tcode = -1; - k = s->vals.cnt; - if( ae_fp_greater_eq((1-sparse_maxloadfactor)*k,s->nfree) ) - { - sparseresizematrix(s, _state); - k = s->vals.cnt; - } - hashcode = sparse_hash(i, j, k, _state); - for(;;) - { - if( s->idx.ptr.p_int[2*hashcode]==-1 ) - { - if( tcode!=-1 ) - { - hashcode = tcode; - } - s->vals.ptr.p_double[hashcode] = v; - s->idx.ptr.p_int[2*hashcode] = i; - s->idx.ptr.p_int[2*hashcode+1] = j; - if( tcode==-1 ) - { - s->nfree = s->nfree-1; - } - return; - } - else - { - if( s->idx.ptr.p_int[2*hashcode]==i&&s->idx.ptr.p_int[2*hashcode+1]==j ) - { - s->vals.ptr.p_double[hashcode] = s->vals.ptr.p_double[hashcode]+v; - if( ae_fp_eq(s->vals.ptr.p_double[hashcode],0) ) - { - s->idx.ptr.p_int[2*hashcode] = -2; - } - return; - } - - /* - * Is it deleted element? - */ - if( tcode==-1&&s->idx.ptr.p_int[2*hashcode]==-2 ) - { - tcode = hashcode; - } - - /* - * Next step - */ - hashcode = (hashcode+1)%k; - } - } -} - - -/************************************************************************* -This function modifies S[i,j] - element of the sparse matrix. - -For Hash-based storage format: -* new value can be zero or non-zero. In case new value of S[i,j] is zero, - this element is deleted from the table. -* this function has no effect when called with zero V for non-existent - element. - -For CRS-bases storage format: -* new value MUST be non-zero. Exception will be thrown for zero V. -* elements must be initialized in correct order - from top row to bottom, - within row - from left to right. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - I - row index of the element to modify, 0<=I=0, "SparseSet: I<0", _state); - ae_assert(im, "SparseSet: I>=M", _state); - ae_assert(j>=0, "SparseSet: J<0", _state); - ae_assert(jn, "SparseSet: J>=N", _state); - ae_assert(ae_isfinite(v, _state), "SparseSet: V is not finite number", _state); - - /* - * Hash-table matrix - */ - if( s->matrixtype==0 ) - { - tcode = -1; - k = s->vals.cnt; - if( ae_fp_greater_eq((1-sparse_maxloadfactor)*k,s->nfree) ) - { - sparseresizematrix(s, _state); - k = s->vals.cnt; - } - hashcode = sparse_hash(i, j, k, _state); - for(;;) - { - if( s->idx.ptr.p_int[2*hashcode]==-1 ) - { - if( ae_fp_neq(v,0) ) - { - if( tcode!=-1 ) - { - hashcode = tcode; - } - s->vals.ptr.p_double[hashcode] = v; - s->idx.ptr.p_int[2*hashcode] = i; - s->idx.ptr.p_int[2*hashcode+1] = j; - if( tcode==-1 ) - { - s->nfree = s->nfree-1; - } - } - return; - } - else - { - if( s->idx.ptr.p_int[2*hashcode]==i&&s->idx.ptr.p_int[2*hashcode+1]==j ) - { - if( ae_fp_eq(v,0) ) - { - s->idx.ptr.p_int[2*hashcode] = -2; - } - else - { - s->vals.ptr.p_double[hashcode] = v; - } - return; - } - if( tcode==-1&&s->idx.ptr.p_int[2*hashcode]==-2 ) - { - tcode = hashcode; - } - - /* - * Next step - */ - hashcode = (hashcode+1)%k; - } - } - } - - /* - * CRS matrix - */ - if( s->matrixtype==1 ) - { - ae_assert(ae_fp_neq(v,0), "SparseSet: CRS format does not allow you to write zero elements", _state); - ae_assert(s->ridx.ptr.p_int[i]<=s->ninitialized, "SparseSet: too few initialized elements at some row (you have promised more when called SparceCreateCRS)", _state); - ae_assert(s->ridx.ptr.p_int[i+1]>s->ninitialized, "SparseSet: too many initialized elements at some row (you have promised less when called SparceCreateCRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[i]||s->idx.ptr.p_int[s->ninitialized-1]vals.ptr.p_double[s->ninitialized] = v; - s->idx.ptr.p_int[s->ninitialized] = j; - s->ninitialized = s->ninitialized+1; - - /* - * If matrix has been created then - * initiale 'S.UIdx' and 'S.DIdx' - */ - if( s->ninitialized==s->ridx.ptr.p_int[s->m] ) - { - sparse_sparseinitduidx(s, _state); - } - } -} - - -/************************************************************************* -This function returns S[i,j] - element of the sparse matrix. Matrix can -be in any mode (Hash-Table or CRS), but this function is less efficient -for CRS matrices. Hash-Table matrices can find element in O(1) time, -while CRS matrices need O(log(RS)) time, where RS is an number of non- -zero elements in a row. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - Exception will be thrown for CRS matrix. - I - row index of the element to modify, 0<=I=0, "SparseGet: I<0", _state); - ae_assert(im, "SparseGet: I>=M", _state); - ae_assert(j>=0, "SparseGet: J<0", _state); - ae_assert(jn, "SparseGet: J>=N", _state); - k = s->vals.cnt; - result = 0; - if( s->matrixtype==0 ) - { - hashcode = sparse_hash(i, j, k, _state); - for(;;) - { - if( s->idx.ptr.p_int[2*hashcode]==-1 ) - { - return result; - } - if( s->idx.ptr.p_int[2*hashcode]==i&&s->idx.ptr.p_int[2*hashcode+1]==j ) - { - result = s->vals.ptr.p_double[hashcode]; - return result; - } - hashcode = (hashcode+1)%k; - } - } - if( s->matrixtype==1 ) - { - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseGet: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - k0 = s->ridx.ptr.p_int[i]; - k1 = s->ridx.ptr.p_int[i+1]-1; - while(k0<=k1) - { - k = (k0+k1)/2; - if( s->idx.ptr.p_int[k]==j ) - { - result = s->vals.ptr.p_double[k]; - return result; - } - if( s->idx.ptr.p_int[k]=0, "SparseGetDiagonal: I<0", _state); - ae_assert(im, "SparseGetDiagonal: I>=M", _state); - ae_assert(in, "SparseGetDiagonal: I>=N", _state); - result = 0; - if( s->matrixtype==0 ) - { - result = sparseget(s, i, i, _state); - return result; - } - if( s->matrixtype==1 ) - { - if( s->didx.ptr.p_int[i]!=s->uidx.ptr.p_int[i] ) - { - result = s->vals.ptr.p_double[s->didx.ptr.p_int[i]]; - } - return result; - } - return result; -} - - -/************************************************************************* -This function converts matrix to CRS format. - -Some algorithms (linear algebra ones, for example) require matrices in -CRS format. - -INPUT PARAMETERS - S - sparse M*N matrix in any format - -OUTPUT PARAMETERS - S - matrix in CRS format - -NOTE: this function has no effect when called with matrix which is -already in CRS mode. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparseconverttocrs(sparsematrix* s, ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_vector tvals; - ae_vector tidx; - ae_vector temp; - ae_int_t nonne; - ae_int_t k; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tvals, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tidx, 0, DT_INT, _state, ae_true); - ae_vector_init(&temp, 0, DT_INT, _state, ae_true); - - ae_assert(s->matrixtype==0||s->matrixtype==1, "SparseConvertToCRS: invalid matrix type", _state); - if( s->matrixtype==1 ) - { - ae_frame_leave(_state); - return; - } - s->matrixtype = 1; - nonne = 0; - k = s->vals.cnt; - ae_swap_vectors(&s->vals, &tvals); - ae_swap_vectors(&s->idx, &tidx); - ae_vector_set_length(&s->ridx, s->m+1, _state); - for(i=0; i<=s->m; i++) - { - s->ridx.ptr.p_int[i] = 0; - } - ae_vector_set_length(&temp, s->m, _state); - for(i=0; i<=s->m-1; i++) - { - temp.ptr.p_int[i] = 0; - } - - /* - * Number of elements per row - */ - for(i=0; i<=k-1; i++) - { - if( tidx.ptr.p_int[2*i]>=0 ) - { - s->ridx.ptr.p_int[tidx.ptr.p_int[2*i]+1] = s->ridx.ptr.p_int[tidx.ptr.p_int[2*i]+1]+1; - nonne = nonne+1; - } - } - - /* - * Fill RIdx (offsets of rows) - */ - for(i=0; i<=s->m-1; i++) - { - s->ridx.ptr.p_int[i+1] = s->ridx.ptr.p_int[i+1]+s->ridx.ptr.p_int[i]; - } - - /* - * Allocate memory - */ - ae_vector_set_length(&s->vals, nonne, _state); - ae_vector_set_length(&s->idx, nonne, _state); - for(i=0; i<=k-1; i++) - { - if( tidx.ptr.p_int[2*i]>=0 ) - { - s->vals.ptr.p_double[s->ridx.ptr.p_int[tidx.ptr.p_int[2*i]]+temp.ptr.p_int[tidx.ptr.p_int[2*i]]] = tvals.ptr.p_double[i]; - s->idx.ptr.p_int[s->ridx.ptr.p_int[tidx.ptr.p_int[2*i]]+temp.ptr.p_int[tidx.ptr.p_int[2*i]]] = tidx.ptr.p_int[2*i+1]; - temp.ptr.p_int[tidx.ptr.p_int[2*i]] = temp.ptr.p_int[tidx.ptr.p_int[2*i]]+1; - } - } - - /* - * Set NInitialized - */ - s->ninitialized = s->ridx.ptr.p_int[s->m]; - - /* - * Sorting of elements - */ - for(i=0; i<=s->m-1; i++) - { - tagsortmiddleir(&s->idx, &s->vals, s->ridx.ptr.p_int[i], s->ridx.ptr.p_int[i+1]-s->ridx.ptr.p_int[i], _state); - } - - /* - * Initialization 'S.UIdx' and 'S.DIdx' - */ - sparse_sparseinitduidx(s, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This function calculates matrix-vector product S*x. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[M], S*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemv(sparsematrix* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - double tval; - ae_int_t i; - ae_int_t j; - ae_int_t lt; - ae_int_t rt; - - - ae_assert(s->matrixtype==1, "SparseMV: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseMV: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(x->cnt>=s->n, "SparseMV: length(X)m, _state); - for(i=0; i<=s->m-1; i++) - { - tval = 0; - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(j=lt; j<=rt-1; j++) - { - tval = tval+x->ptr.p_double[s->idx.ptr.p_int[j]]*s->vals.ptr.p_double[j]; - } - y->ptr.p_double[i] = tval; - } -} - - -/************************************************************************* -This function calculates matrix-vector product S^T*x. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - X - array[M], input vector. For performance reasons we - make only quick checks - we check that array size is - at least M, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[N], S^T*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemtv(sparsematrix* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t lt; - ae_int_t rt; - ae_int_t ct; - double v; - - - ae_assert(s->matrixtype==1, "SparseMTV: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseMTV: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(x->cnt>=s->m, "SparseMTV: Length(X)n, _state); - for(i=0; i<=s->n-1; i++) - { - y->ptr.p_double[i] = 0; - } - for(i=0; i<=s->m-1; i++) - { - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - v = x->ptr.p_double[i]; - for(j=lt; j<=rt-1; j++) - { - ct = s->idx.ptr.p_int[j]; - y->ptr.p_double[ct] = y->ptr.p_double[ct]+v*s->vals.ptr.p_double[j]; - } - } -} - - -/************************************************************************* -This function simultaneously calculates two matrix-vector products: - S*x and S^T*x. -S must be square (non-rectangular) matrix stored in CRS format (exception -will be thrown otherwise). - -INPUT PARAMETERS - S - sparse N*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y0 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - Y1 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y0 - array[N], S*x - Y1 - array[N], S^T*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. It also throws exception when S is non-square. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemv2(sparsematrix* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y0, - /* Real */ ae_vector* y1, - ae_state *_state) -{ - ae_int_t l; - double tval; - ae_int_t i; - ae_int_t j; - double vx; - double vs; - ae_int_t vi; - ae_int_t j0; - ae_int_t j1; - - - ae_assert(s->matrixtype==1, "SparseMV2: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseMV: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(s->m==s->n, "SparseMV2: matrix is non-square", _state); - l = x->cnt; - ae_assert(l>=s->n, "SparseMV2: Length(X)n-1; i++) - { - y1->ptr.p_double[i] = 0; - } - for(i=0; i<=s->m-1; i++) - { - tval = 0; - vx = x->ptr.p_double[i]; - j0 = s->ridx.ptr.p_int[i]; - j1 = s->ridx.ptr.p_int[i+1]-1; - for(j=j0; j<=j1; j++) - { - vi = s->idx.ptr.p_int[j]; - vs = s->vals.ptr.p_double[j]; - tval = tval+x->ptr.p_double[vi]*vs; - y1->ptr.p_double[vi] = y1->ptr.p_double[vi]+vx*vs; - } - y0->ptr.p_double[i] = tval; - } -} - - -/************************************************************************* -This function calculates matrix-vector product S*x, when S is symmetric -matrix. Matrix S must be stored in CRS format (exception will be -thrown otherwise). - -INPUT PARAMETERS - S - sparse M*M matrix in CRS format (you MUST convert it - to CRS before calling this function). - IsUpper - whether upper or lower triangle of S is given: - * if upper triangle is given, only S[i,j] for j>=i - are used, and lower triangle is ignored (it can be - empty - these elements are not referenced at all). - * if lower triangle is given, only S[i,j] for j<=i - are used, and upper triangle is ignored. - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[M], S*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsesmv(sparsematrix* s, - ae_bool isupper, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t id; - ae_int_t lt; - ae_int_t rt; - double v; - double vy; - double vx; - - - ae_assert(s->matrixtype==1, "SparseSMV: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseSMV: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(x->cnt>=s->n, "SparseSMV: length(X)m==s->n, "SparseSMV: non-square matrix", _state); - rvectorsetlengthatleast(y, s->m, _state); - for(i=0; i<=s->m-1; i++) - { - y->ptr.p_double[i] = 0; - } - for(i=0; i<=s->m-1; i++) - { - if( s->didx.ptr.p_int[i]!=s->uidx.ptr.p_int[i] ) - { - y->ptr.p_double[i] = y->ptr.p_double[i]+s->vals.ptr.p_double[s->didx.ptr.p_int[i]]*x->ptr.p_double[s->idx.ptr.p_int[s->didx.ptr.p_int[i]]]; - } - if( isupper ) - { - lt = s->uidx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - vy = 0; - vx = x->ptr.p_double[i]; - for(j=lt; j<=rt-1; j++) - { - id = s->idx.ptr.p_int[j]; - v = s->vals.ptr.p_double[j]; - vy = vy+x->ptr.p_double[id]*v; - y->ptr.p_double[id] = y->ptr.p_double[id]+vx*v; - } - y->ptr.p_double[i] = y->ptr.p_double[i]+vy; - } - else - { - lt = s->ridx.ptr.p_int[i]; - rt = s->didx.ptr.p_int[i]; - vy = 0; - vx = x->ptr.p_double[i]; - for(j=lt; j<=rt-1; j++) - { - id = s->idx.ptr.p_int[j]; - v = s->vals.ptr.p_double[j]; - vy = vy+x->ptr.p_double[id]*v; - y->ptr.p_double[id] = y->ptr.p_double[id]+vx*v; - } - y->ptr.p_double[i] = y->ptr.p_double[i]+vy; - } - } -} - - -/************************************************************************* -This function calculates matrix-matrix product S*A. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size - is at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[M][K], S*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemm(sparsematrix* s, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b, - ae_state *_state) -{ - double tval; - double v; - ae_int_t id; - ae_int_t i; - ae_int_t j; - ae_int_t k0; - ae_int_t lt; - ae_int_t rt; - - - ae_assert(s->matrixtype==1, "SparseMV: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseMV: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(a->rows>=s->n, "SparseMV: Rows(A)0, "SparseMV: K<=0", _state); - rmatrixsetlengthatleast(b, s->m, k, _state); - if( km-1; i++) - { - for(j=0; j<=k-1; j++) - { - tval = 0; - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(k0=lt; k0<=rt-1; k0++) - { - tval = tval+s->vals.ptr.p_double[k0]*a->ptr.pp_double[s->idx.ptr.p_int[k0]][j]; - } - b->ptr.pp_double[i][j] = tval; - } - } - } - else - { - for(i=0; i<=s->m-1; i++) - { - for(j=0; j<=k-1; j++) - { - b->ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=s->m-1; i++) - { - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(j=lt; j<=rt-1; j++) - { - id = s->idx.ptr.p_int[j]; - v = s->vals.ptr.p_double[j]; - ae_v_addd(&b->ptr.pp_double[i][0], 1, &a->ptr.pp_double[id][0], 1, ae_v_len(0,k-1), v); - } - } - } -} - - -/************************************************************************* -This function calculates matrix-matrix product S^T*A. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[M][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least M, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[N][K], S^T*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemtm(sparsematrix* s, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k0; - ae_int_t lt; - ae_int_t rt; - ae_int_t ct; - double v; - - - ae_assert(s->matrixtype==1, "SparseMTM: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseMTM: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(a->rows>=s->m, "SparseMTM: Rows(A)0, "SparseMTM: K<=0", _state); - rmatrixsetlengthatleast(b, s->n, k, _state); - for(i=0; i<=s->n-1; i++) - { - for(j=0; j<=k-1; j++) - { - b->ptr.pp_double[i][j] = 0; - } - } - if( km-1; i++) - { - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(k0=lt; k0<=rt-1; k0++) - { - v = s->vals.ptr.p_double[k0]; - ct = s->idx.ptr.p_int[k0]; - for(j=0; j<=k-1; j++) - { - b->ptr.pp_double[ct][j] = b->ptr.pp_double[ct][j]+v*a->ptr.pp_double[i][j]; - } - } - } - } - else - { - for(i=0; i<=s->m-1; i++) - { - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(j=lt; j<=rt-1; j++) - { - v = s->vals.ptr.p_double[j]; - ct = s->idx.ptr.p_int[j]; - ae_v_addd(&b->ptr.pp_double[ct][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,k-1), v); - } - } - } -} - - -/************************************************************************* -This function simultaneously calculates two matrix-matrix products: - S*A and S^T*A. -S must be square (non-rectangular) matrix stored in CRS format (exception -will be thrown otherwise). - -INPUT PARAMETERS - S - sparse N*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B0 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - B1 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B0 - array[N][K], S*A - B1 - array[N][K], S^T*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. It also throws exception when S is non-square. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemm2(sparsematrix* s, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b0, - /* Real */ ae_matrix* b1, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k0; - ae_int_t lt; - ae_int_t rt; - ae_int_t ct; - double v; - double tval; - - - ae_assert(s->matrixtype==1, "SparseMM2: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseMM2: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(s->m==s->n, "SparseMM2: matrix is non-square", _state); - ae_assert(a->rows>=s->n, "SparseMM2: Rows(A)0, "SparseMM2: K<=0", _state); - rmatrixsetlengthatleast(b0, s->m, k, _state); - rmatrixsetlengthatleast(b1, s->n, k, _state); - for(i=0; i<=s->n-1; i++) - { - for(j=0; j<=k-1; j++) - { - b1->ptr.pp_double[i][j] = 0; - } - } - if( km-1; i++) - { - for(j=0; j<=k-1; j++) - { - tval = 0; - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - v = a->ptr.pp_double[i][j]; - for(k0=lt; k0<=rt-1; k0++) - { - ct = s->idx.ptr.p_int[k0]; - b1->ptr.pp_double[ct][j] = b1->ptr.pp_double[ct][j]+s->vals.ptr.p_double[k0]*v; - tval = tval+s->vals.ptr.p_double[k0]*a->ptr.pp_double[ct][j]; - } - b0->ptr.pp_double[i][j] = tval; - } - } - } - else - { - for(i=0; i<=s->m-1; i++) - { - for(j=0; j<=k-1; j++) - { - b0->ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=s->m-1; i++) - { - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(j=lt; j<=rt-1; j++) - { - v = s->vals.ptr.p_double[j]; - ct = s->idx.ptr.p_int[j]; - ae_v_addd(&b0->ptr.pp_double[i][0], 1, &a->ptr.pp_double[ct][0], 1, ae_v_len(0,k-1), v); - ae_v_addd(&b1->ptr.pp_double[ct][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,k-1), v); - } - } - } -} - - -/************************************************************************* -This function calculates matrix-matrix product S*A, when S is symmetric -matrix. Matrix S must be stored in CRS format (exception will be -thrown otherwise). - -INPUT PARAMETERS - S - sparse M*M matrix in CRS format (you MUST convert it - to CRS before calling this function). - IsUpper - whether upper or lower triangle of S is given: - * if upper triangle is given, only S[i,j] for j>=i - are used, and lower triangle is ignored (it can be - empty - these elements are not referenced at all). - * if lower triangle is given, only S[i,j] for j<=i - are used, and upper triangle is ignored. - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[M][K], S*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsesmm(sparsematrix* s, - ae_bool isupper, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t k0; - ae_int_t id; - ae_int_t lt; - ae_int_t rt; - double v; - double vb; - double va; - - - ae_assert(s->matrixtype==1, "SparseSMM: incorrect matrix type (convert your matrix to CRS)", _state); - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseSMM: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - ae_assert(a->rows>=s->n, "SparseSMM: Rows(X)m==s->n, "SparseSMM: matrix is non-square", _state); - rmatrixsetlengthatleast(b, s->m, k, _state); - for(i=0; i<=s->m-1; i++) - { - for(j=0; j<=k-1; j++) - { - b->ptr.pp_double[i][j] = 0; - } - } - if( k>sparse_linalgswitch ) - { - for(i=0; i<=s->m-1; i++) - { - for(j=0; j<=k-1; j++) - { - if( s->didx.ptr.p_int[i]!=s->uidx.ptr.p_int[i] ) - { - id = s->didx.ptr.p_int[i]; - b->ptr.pp_double[i][j] = b->ptr.pp_double[i][j]+s->vals.ptr.p_double[id]*a->ptr.pp_double[s->idx.ptr.p_int[id]][j]; - } - if( isupper ) - { - lt = s->uidx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - vb = 0; - va = a->ptr.pp_double[i][j]; - for(k0=lt; k0<=rt-1; k0++) - { - id = s->idx.ptr.p_int[k0]; - v = s->vals.ptr.p_double[k0]; - vb = vb+a->ptr.pp_double[id][j]*v; - b->ptr.pp_double[id][j] = b->ptr.pp_double[id][j]+va*v; - } - b->ptr.pp_double[i][j] = b->ptr.pp_double[i][j]+vb; - } - else - { - lt = s->ridx.ptr.p_int[i]; - rt = s->didx.ptr.p_int[i]; - vb = 0; - va = a->ptr.pp_double[i][j]; - for(k0=lt; k0<=rt-1; k0++) - { - id = s->idx.ptr.p_int[k0]; - v = s->vals.ptr.p_double[k0]; - vb = vb+a->ptr.pp_double[id][j]*v; - b->ptr.pp_double[id][j] = b->ptr.pp_double[id][j]+va*v; - } - b->ptr.pp_double[i][j] = b->ptr.pp_double[i][j]+vb; - } - } - } - } - else - { - for(i=0; i<=s->m-1; i++) - { - if( s->didx.ptr.p_int[i]!=s->uidx.ptr.p_int[i] ) - { - id = s->didx.ptr.p_int[i]; - v = s->vals.ptr.p_double[id]; - ae_v_addd(&b->ptr.pp_double[i][0], 1, &a->ptr.pp_double[s->idx.ptr.p_int[id]][0], 1, ae_v_len(0,k-1), v); - } - if( isupper ) - { - lt = s->uidx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(j=lt; j<=rt-1; j++) - { - id = s->idx.ptr.p_int[j]; - v = s->vals.ptr.p_double[j]; - ae_v_addd(&b->ptr.pp_double[i][0], 1, &a->ptr.pp_double[id][0], 1, ae_v_len(0,k-1), v); - ae_v_addd(&b->ptr.pp_double[id][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,k-1), v); - } - } - else - { - lt = s->ridx.ptr.p_int[i]; - rt = s->didx.ptr.p_int[i]; - for(j=lt; j<=rt-1; j++) - { - id = s->idx.ptr.p_int[j]; - v = s->vals.ptr.p_double[j]; - ae_v_addd(&b->ptr.pp_double[i][0], 1, &a->ptr.pp_double[id][0], 1, ae_v_len(0,k-1), v); - ae_v_addd(&b->ptr.pp_double[id][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,k-1), v); - } - } - } - } -} - - -/************************************************************************* -This procedure resizes Hash-Table matrix. It can be called when you have -deleted too many elements from the matrix, and you want to free unneeded -memory. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparseresizematrix(sparsematrix* s, ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t k; - ae_int_t k1; - ae_int_t i; - ae_vector tvals; - ae_vector tidx; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tvals, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tidx, 0, DT_INT, _state, ae_true); - - ae_assert(s->matrixtype==0, "SparseResizeMatrix: incorrect matrix type", _state); - - /* - * Initialization for length and number of non-null elementd - */ - k = s->vals.cnt; - k1 = 0; - - /* - * Calculating number of non-null elements - */ - for(i=0; i<=k-1; i++) - { - if( s->idx.ptr.p_int[2*i]>=0 ) - { - k1 = k1+1; - } - } - - /* - * Initialization value for free space - */ - s->nfree = ae_round(k1/sparse_desiredloadfactor*sparse_growfactor+sparse_additional, _state)-k1; - ae_vector_set_length(&tvals, s->nfree+k1, _state); - ae_vector_set_length(&tidx, 2*(s->nfree+k1), _state); - ae_swap_vectors(&s->vals, &tvals); - ae_swap_vectors(&s->idx, &tidx); - for(i=0; i<=s->nfree+k1-1; i++) - { - s->idx.ptr.p_int[2*i] = -1; - } - for(i=0; i<=k-1; i++) - { - if( tidx.ptr.p_int[2*i]>=0 ) - { - sparseset(s, tidx.ptr.p_int[2*i], tidx.ptr.p_int[2*i+1], tvals.ptr.p_double[i], _state); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function return average length of chain at hash-table. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -double sparsegetaveragelengthofchain(sparsematrix* s, ae_state *_state) -{ - ae_int_t nchains; - ae_int_t talc; - ae_int_t l; - ae_int_t i; - ae_int_t ind0; - ae_int_t ind1; - ae_int_t hashcode; - double result; - - - - /* - * If matrix represent in CRS then return zero and exit - */ - if( s->matrixtype==1 ) - { - result = 0; - return result; - } - nchains = 0; - talc = 0; - l = s->vals.cnt; - for(i=0; i<=l-1; i++) - { - ind0 = 2*i; - if( s->idx.ptr.p_int[ind0]!=-1 ) - { - nchains = nchains+1; - hashcode = sparse_hash(s->idx.ptr.p_int[ind0], s->idx.ptr.p_int[ind0+1], l, _state); - for(;;) - { - talc = talc+1; - ind1 = 2*hashcode; - if( s->idx.ptr.p_int[ind0]==s->idx.ptr.p_int[ind1]&&s->idx.ptr.p_int[ind0+1]==s->idx.ptr.p_int[ind1+1] ) - { - break; - } - hashcode = (hashcode+1)%l; - } - } - } - if( nchains==0 ) - { - result = 0; - } - else - { - result = (double)talc/(double)nchains; - } - return result; -} - - -/************************************************************************* -This function is used to enumerate all elements of the sparse matrix. -Before first call user initializes T0 and T1 counters by zero. These -counters are used to remember current position in a matrix; after each -call they are updated by the function. - -Subsequent calls to this function return non-zero elements of the sparse -matrix, one by one. If you enumerate CRS matrix, matrix is traversed from -left to right, from top to bottom. In case you enumerate matrix stored as -Hash table, elements are returned in random order. - -EXAMPLE - > T0=0 - > T1=0 - > while SparseEnumerate(S,T0,T1,I,J,V) do - > ....do something with I,J,V - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - T0 - internal counter - T1 - internal counter - -OUTPUT PARAMETERS - T0 - new value of the internal counter - T1 - new value of the internal counter - I - row index of non-zero element, 0<=Imatrixtype==1&&*t1<0) ) - { - result = ae_false; - return result; - } - - /* - * Hash-table matrix - */ - if( s->matrixtype==0 ) - { - sz = s->vals.cnt; - for(i0=*t0; i0<=sz-1; i0++) - { - if( s->idx.ptr.p_int[2*i0]==-1||s->idx.ptr.p_int[2*i0]==-2 ) - { - continue; - } - else - { - *i = s->idx.ptr.p_int[2*i0]; - *j = s->idx.ptr.p_int[2*i0+1]; - *v = s->vals.ptr.p_double[i0]; - *t0 = i0+1; - result = ae_true; - return result; - } - } - *t0 = 0; - result = ae_false; - return result; - } - - /* - * CRS matrix - */ - if( s->matrixtype==1&&*t0ninitialized ) - { - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseEnumerate: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - while(*t0>s->ridx.ptr.p_int[*t1+1]-1&&*t1m) - { - *t1 = *t1+1; - } - *i = *t1; - *j = s->idx.ptr.p_int[*t0]; - *v = s->vals.ptr.p_double[*t0]; - *t0 = *t0+1; - result = ae_true; - return result; - } - *t0 = 0; - *t1 = 0; - result = ae_false; - return result; -} - - -/************************************************************************* -This function rewrites existing (non-zero) element. It returns True if -element exists or False, when it is called for non-existing (zero) -element. - -The purpose of this function is to provide convenient thread-safe way to -modify sparse matrix. Such modification (already existing element is -rewritten) is guaranteed to be thread-safe without any synchronization, as -long as different threads modify different elements. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - I - row index of non-zero element to modify, 0<=Im, "SparseRewriteExisting: invalid argument I(either I<0 or I>=S.M)", _state); - ae_assert(0<=j&&jn, "SparseRewriteExisting: invalid argument J(either J<0 or J>=S.N)", _state); - ae_assert(ae_isfinite(v, _state), "SparseRewriteExisting: invalid argument V(either V is infinite or V is NaN)", _state); - result = ae_false; - - /* - * Hash-table matrix - */ - if( s->matrixtype==0 ) - { - k = s->vals.cnt; - hashcode = sparse_hash(i, j, k, _state); - for(;;) - { - if( s->idx.ptr.p_int[2*hashcode]==-1 ) - { - return result; - } - if( s->idx.ptr.p_int[2*hashcode]==i&&s->idx.ptr.p_int[2*hashcode+1]==j ) - { - s->vals.ptr.p_double[hashcode] = v; - result = ae_true; - return result; - } - hashcode = (hashcode+1)%k; - } - } - - /* - * CRS matrix - */ - if( s->matrixtype==1 ) - { - ae_assert(s->ninitialized==s->ridx.ptr.p_int[s->m], "SparseRewriteExisting: some rows/elements of the CRS matrix were not initialized (you must initialize everything you promised to SparseCreateCRS)", _state); - k0 = s->ridx.ptr.p_int[i]; - k1 = s->ridx.ptr.p_int[i+1]-1; - while(k0<=k1) - { - k = (k0+k1)/2; - if( s->idx.ptr.p_int[k]==j ) - { - s->vals.ptr.p_double[k] = v; - result = ae_true; - return result; - } - if( s->idx.ptr.p_int[k]matrixtype==1, "SparseGetRow: S must be CRS-based matrix", _state); - ae_assert(i>=0&&im, "SparseGetRow: I<0 or I>=M", _state); - rvectorsetlengthatleast(irow, s->n, _state); - for(i0=0; i0<=s->n-1; i0++) - { - irow->ptr.p_double[i0] = 0; - } - for(i0=s->ridx.ptr.p_int[i]; i0<=s->ridx.ptr.p_int[i+1]-1; i0++) - { - irow->ptr.p_double[s->idx.ptr.p_int[i0]] = s->vals.ptr.p_double[i0]; - } -} - - -/************************************************************************* -This function performs in-place conversion from CRS format to Hash table -storage. - -INPUT PARAMETERS - S - sparse matrix in CRS format. - -OUTPUT PARAMETERS - S - sparse matrix in Hash table format. - -NOTE: this function has no effect when called with matrix which is -already in Hash table mode. - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparseconverttohash(sparsematrix* s, ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tidx; - ae_vector tridx; - ae_vector tvals; - ae_int_t tn; - ae_int_t tm; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&tidx, 0, DT_INT, _state, ae_true); - ae_vector_init(&tridx, 0, DT_INT, _state, ae_true); - ae_vector_init(&tvals, 0, DT_REAL, _state, ae_true); - - ae_assert(s->matrixtype==0||s->matrixtype==1, "SparseConvertToHash: invalid matrix type", _state); - if( s->matrixtype==0 ) - { - ae_frame_leave(_state); - return; - } - s->matrixtype = 0; - tm = s->m; - tn = s->n; - ae_swap_vectors(&s->idx, &tidx); - ae_swap_vectors(&s->ridx, &tridx); - ae_swap_vectors(&s->vals, &tvals); - - /* - * Delete RIdx - */ - ae_vector_set_length(&s->ridx, 0, _state); - sparsecreate(tm, tn, tridx.ptr.p_int[tm], s, _state); - - /* - * Fill the matrix - */ - for(i=0; i<=tm-1; i++) - { - for(j=tridx.ptr.p_int[i]; j<=tridx.ptr.p_int[i+1]-1; j++) - { - sparseset(s, i, tidx.ptr.p_int[j], tvals.ptr.p_double[j], _state); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function performs out-of-place conversion to Hash table storage -format. S0 is copied to S1 and converted on-the-fly. - -INPUT PARAMETERS - S0 - sparse matrix in any format. - -OUTPUT PARAMETERS - S1 - sparse matrix in Hash table format. - -NOTE: if S0 is stored as Hash-table, it is just copied without conversion. - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparsecopytohash(sparsematrix* s0, - sparsematrix* s1, - ae_state *_state) -{ - double val; - ae_int_t t0; - ae_int_t t1; - ae_int_t i; - ae_int_t j; - - _sparsematrix_clear(s1); - - ae_assert(s0->matrixtype==0||s0->matrixtype==1, "SparseCopyToHash: invalid matrix type", _state); - if( s0->matrixtype==0 ) - { - sparsecopy(s0, s1, _state); - } - else - { - t0 = 0; - t1 = 0; - sparsecreate(s0->m, s0->n, s0->ridx.ptr.p_int[s0->m], s1, _state); - while(sparseenumerate(s0, &t0, &t1, &i, &j, &val, _state)) - { - sparseset(s1, i, j, val, _state); - } - } -} - - -/************************************************************************* -This function performs out-of-place conversion to CRS format. S0 is -copied to S1 and converted on-the-fly. - -INPUT PARAMETERS - S0 - sparse matrix in any format. - -OUTPUT PARAMETERS - S1 - sparse matrix in CRS format. - -NOTE: if S0 is stored as CRS, it is just copied without conversion. - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparsecopytocrs(sparsematrix* s0, sparsematrix* s1, ae_state *_state) -{ - ae_frame _frame_block; - ae_vector temp; - ae_int_t nonne; - ae_int_t i; - ae_int_t k; - - ae_frame_make(_state, &_frame_block); - _sparsematrix_clear(s1); - ae_vector_init(&temp, 0, DT_INT, _state, ae_true); - - ae_assert(s0->matrixtype==0||s0->matrixtype==1, "SparseCopyToCRS: invalid matrix type", _state); - if( s0->matrixtype==1 ) - { - sparsecopy(s0, s1, _state); - } - else - { - - /* - * Done like ConvertToCRS function - */ - s1->matrixtype = 1; - s1->m = s0->m; - s1->n = s0->n; - s1->nfree = s0->nfree; - nonne = 0; - k = s0->vals.cnt; - ae_vector_set_length(&s1->ridx, s1->m+1, _state); - for(i=0; i<=s1->m; i++) - { - s1->ridx.ptr.p_int[i] = 0; - } - ae_vector_set_length(&temp, s1->m, _state); - for(i=0; i<=s1->m-1; i++) - { - temp.ptr.p_int[i] = 0; - } - - /* - * Number of elements per row - */ - for(i=0; i<=k-1; i++) - { - if( s0->idx.ptr.p_int[2*i]>=0 ) - { - s1->ridx.ptr.p_int[s0->idx.ptr.p_int[2*i]+1] = s1->ridx.ptr.p_int[s0->idx.ptr.p_int[2*i]+1]+1; - nonne = nonne+1; - } - } - - /* - * Fill RIdx (offsets of rows) - */ - for(i=0; i<=s1->m-1; i++) - { - s1->ridx.ptr.p_int[i+1] = s1->ridx.ptr.p_int[i+1]+s1->ridx.ptr.p_int[i]; - } - - /* - * Allocate memory - */ - ae_vector_set_length(&s1->vals, nonne, _state); - ae_vector_set_length(&s1->idx, nonne, _state); - for(i=0; i<=k-1; i++) - { - if( s0->idx.ptr.p_int[2*i]>=0 ) - { - s1->vals.ptr.p_double[s1->ridx.ptr.p_int[s0->idx.ptr.p_int[2*i]]+temp.ptr.p_int[s0->idx.ptr.p_int[2*i]]] = s0->vals.ptr.p_double[i]; - s1->idx.ptr.p_int[s1->ridx.ptr.p_int[s0->idx.ptr.p_int[2*i]]+temp.ptr.p_int[s0->idx.ptr.p_int[2*i]]] = s0->idx.ptr.p_int[2*i+1]; - temp.ptr.p_int[s0->idx.ptr.p_int[2*i]] = temp.ptr.p_int[s0->idx.ptr.p_int[2*i]]+1; - } - } - - /* - * Set NInitialized - */ - s1->ninitialized = s1->ridx.ptr.p_int[s1->m]; - - /* - * Sorting of elements - */ - for(i=0; i<=s1->m-1; i++) - { - tagsortmiddleir(&s1->idx, &s1->vals, s1->ridx.ptr.p_int[i], s1->ridx.ptr.p_int[i+1]-s1->ridx.ptr.p_int[i], _state); - } - - /* - * Initialization 'S.UIdx' and 'S.DIdx' - */ - sparse_sparseinitduidx(s1, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function returns type of the matrix storage format. - -INPUT PARAMETERS: - S - sparse matrix. - -RESULT: - sparse storage format used by matrix: - 0 - Hash-table - 1 - CRS-format - -NOTE: future versions of ALGLIB may include additional sparse storage - formats. - - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sparsegetmatrixtype(sparsematrix* s, ae_state *_state) -{ - ae_int_t result; - - - ae_assert(s->matrixtype==0||s->matrixtype==1, "SparseGetMatrixType: invalid matrix type", _state); - result = s->matrixtype; - return result; -} - - -/************************************************************************* -This function checks matrix storage format and returns True when matrix is -stored using Hash table representation. - -INPUT PARAMETERS: - S - sparse matrix. - -RESULT: - True if matrix type is Hash table - False if matrix type is not Hash table - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool sparseishash(sparsematrix* s, ae_state *_state) -{ - ae_bool result; - - - ae_assert(s->matrixtype==0||s->matrixtype==1, "SparseIsHash: invalid matrix type", _state); - result = s->matrixtype==0; - return result; -} - - -/************************************************************************* -This function checks matrix storage format and returns True when matrix is -stored using CRS representation. - -INPUT PARAMETERS: - S - sparse matrix. - -RESULT: - True if matrix type is CRS - False if matrix type is not CRS - - -- ALGLIB PROJECT -- - Copyright 20.07.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool sparseiscrs(sparsematrix* s, ae_state *_state) -{ - ae_bool result; - - - ae_assert(s->matrixtype==0||s->matrixtype==1, "SparseIsCRS: invalid matrix type", _state); - result = s->matrixtype==1; - return result; -} - - -/************************************************************************* -The function frees all memory occupied by sparse matrix. Sparse matrix -structure becomes unusable after this call. - -OUTPUT PARAMETERS - S - sparse matrix to delete - - -- ALGLIB PROJECT -- - Copyright 24.07.2012 by Bochkanov Sergey -*************************************************************************/ -void sparsefree(sparsematrix* s, ae_state *_state) -{ - - _sparsematrix_clear(s); - - s->matrixtype = -1; - s->m = 0; - s->n = 0; - s->nfree = 0; - s->ninitialized = 0; -} - - -/************************************************************************* -The function returns number of rows of a sparse matrix. - -RESULT: number of rows of a sparse matrix. - - -- ALGLIB PROJECT -- - Copyright 23.08.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sparsegetnrows(sparsematrix* s, ae_state *_state) -{ - ae_int_t result; - - - result = s->m; - return result; -} - - -/************************************************************************* -The function returns number of columns of a sparse matrix. - -RESULT: number of columns of a sparse matrix. - - -- ALGLIB PROJECT -- - Copyright 23.08.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sparsegetncols(sparsematrix* s, ae_state *_state) -{ - ae_int_t result; - - - result = s->n; - return result; -} - - -/************************************************************************* -Procedure for initialization 'S.DIdx' and 'S.UIdx' - - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -static void sparse_sparseinitduidx(sparsematrix* s, ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t lt; - ae_int_t rt; - - - ae_vector_set_length(&s->didx, s->m, _state); - ae_vector_set_length(&s->uidx, s->m, _state); - for(i=0; i<=s->m-1; i++) - { - s->uidx.ptr.p_int[i] = -1; - s->didx.ptr.p_int[i] = -1; - lt = s->ridx.ptr.p_int[i]; - rt = s->ridx.ptr.p_int[i+1]; - for(j=lt; j<=rt-1; j++) - { - if( iidx.ptr.p_int[j]&&s->uidx.ptr.p_int[i]==-1 ) - { - s->uidx.ptr.p_int[i] = j; - break; - } - else - { - if( i==s->idx.ptr.p_int[j] ) - { - s->didx.ptr.p_int[i] = j; - } - } - } - if( s->uidx.ptr.p_int[i]==-1 ) - { - s->uidx.ptr.p_int[i] = s->ridx.ptr.p_int[i+1]; - } - if( s->didx.ptr.p_int[i]==-1 ) - { - s->didx.ptr.p_int[i] = s->uidx.ptr.p_int[i]; - } - } -} - - -/************************************************************************* -This is hash function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -static ae_int_t sparse_hash(ae_int_t i, - ae_int_t j, - ae_int_t tabsize, - ae_state *_state) -{ - ae_frame _frame_block; - hqrndstate r; - ae_int_t result; - - ae_frame_make(_state, &_frame_block); - _hqrndstate_init(&r, _state, ae_true); - - hqrndseed(i, j, &r, _state); - result = hqrnduniformi(&r, tabsize, _state); - ae_frame_leave(_state); - return result; -} - - -ae_bool _sparsematrix_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sparsematrix *p = (sparsematrix*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->vals, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->idx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ridx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->didx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->uidx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _sparsematrix_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sparsematrix *dst = (sparsematrix*)_dst; - sparsematrix *src = (sparsematrix*)_src; - if( !ae_vector_init_copy(&dst->vals, &src->vals, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->idx, &src->idx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ridx, &src->ridx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->didx, &src->didx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->uidx, &src->uidx, _state, make_automatic) ) - return ae_false; - dst->matrixtype = src->matrixtype; - dst->m = src->m; - dst->n = src->n; - dst->nfree = src->nfree; - dst->ninitialized = src->ninitialized; - return ae_true; -} - - -void _sparsematrix_clear(void* _p) -{ - sparsematrix *p = (sparsematrix*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->vals); - ae_vector_clear(&p->idx); - ae_vector_clear(&p->ridx); - ae_vector_clear(&p->didx); - ae_vector_clear(&p->uidx); -} - - -void _sparsematrix_destroy(void* _p) -{ - sparsematrix *p = (sparsematrix*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->vals); - ae_vector_destroy(&p->idx); - ae_vector_destroy(&p->ridx); - ae_vector_destroy(&p->didx); - ae_vector_destroy(&p->uidx); -} - - - - -/************************************************************************* -Basic Cholesky solver for ScaleA*Cholesky(A)'*x = y. - -This subroutine assumes that: -* A*ScaleA is well scaled -* A is well-conditioned, so no zero divisions or overflow may occur - -INPUT PARAMETERS: - CHA - Cholesky decomposition of A - SqrtScaleA- square root of scale factor ScaleA - N - matrix size, N>=0. - IsUpper - storage type - XB - right part - Tmp - buffer; function automatically allocates it, if it is too - small. It can be reused if function is called several - times. - -OUTPUT PARAMETERS: - XB - solution - -NOTE 1: no assertion or tests are done during algorithm operation -NOTE 2: N=0 will force algorithm to silently return - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void fblscholeskysolve(/* Real */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* xb, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - double v; - - - if( n==0 ) - { - return; - } - if( tmp->cntptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - if( iptr.p_double[i]; - ae_v_moved(&tmp->ptr.p_double[i+1], 1, &cha->ptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), sqrtscalea); - ae_v_subd(&xb->ptr.p_double[i+1], 1, &tmp->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1), v); - } - } - - /* - * Solve U*x=y then. - */ - for(i=n-1; i>=0; i--) - { - if( iptr.p_double[i+1], 1, &cha->ptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), sqrtscalea); - v = ae_v_dotproduct(&tmp->ptr.p_double[i+1], 1, &xb->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1)); - xb->ptr.p_double[i] = xb->ptr.p_double[i]-v; - } - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - } - } - else - { - - /* - * Solve L*y=b first - */ - for(i=0; i<=n-1; i++) - { - if( i>0 ) - { - ae_v_moved(&tmp->ptr.p_double[0], 1, &cha->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), sqrtscalea); - v = ae_v_dotproduct(&tmp->ptr.p_double[0], 1, &xb->ptr.p_double[0], 1, ae_v_len(0,i-1)); - xb->ptr.p_double[i] = xb->ptr.p_double[i]-v; - } - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - } - - /* - * Solve L'*x=y then. - */ - for(i=n-1; i>=0; i--) - { - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - if( i>0 ) - { - v = xb->ptr.p_double[i]; - ae_v_moved(&tmp->ptr.p_double[0], 1, &cha->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), sqrtscalea); - ae_v_subd(&xb->ptr.p_double[0], 1, &tmp->ptr.p_double[0], 1, ae_v_len(0,i-1), v); - } - } - } -} - - -/************************************************************************* -Fast basic linear solver: linear SPD CG - -Solves (A^T*A + alpha*I)*x = b where: -* A is MxN matrix -* alpha>0 is a scalar -* I is NxN identity matrix -* b is Nx1 vector -* X is Nx1 unknown vector. - -N iterations of linear conjugate gradient are used to solve problem. - -INPUT PARAMETERS: - A - array[M,N], matrix - M - number of rows - N - number of unknowns - B - array[N], right part - X - initial approxumation, array[N] - Buf - buffer; function automatically allocates it, if it is too - small. It can be reused if function is called several times - with same M and N. - -OUTPUT PARAMETERS: - X - improved solution - -NOTES: -* solver checks quality of improved solution. If (because of problem - condition number, numerical noise, etc.) new solution is WORSE than - original approximation, then original approximation is returned. -* solver assumes that both A, B, Alpha are well scaled (i.e. they are - less than sqrt(overflow) and greater than sqrt(underflow)). - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void fblssolvecgx(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - double alpha, - /* Real */ ae_vector* b, - /* Real */ ae_vector* x, - /* Real */ ae_vector* buf, - ae_state *_state) -{ - ae_int_t k; - ae_int_t offsrk; - ae_int_t offsrk1; - ae_int_t offsxk; - ae_int_t offsxk1; - ae_int_t offspk; - ae_int_t offspk1; - ae_int_t offstmp1; - ae_int_t offstmp2; - ae_int_t bs; - double e1; - double e2; - double rk2; - double rk12; - double pap; - double s; - double betak; - double v1; - double v2; - - - - /* - * Test for special case: B=0 - */ - v1 = ae_v_dotproduct(&b->ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_eq(v1,0) ) - { - for(k=0; k<=n-1; k++) - { - x->ptr.p_double[k] = 0; - } - return; - } - - /* - * Offsets inside Buf for: - * * R[K], R[K+1] - * * X[K], X[K+1] - * * P[K], P[K+1] - * * Tmp1 - array[M], Tmp2 - array[N] - */ - offsrk = 0; - offsrk1 = offsrk+n; - offsxk = offsrk1+n; - offsxk1 = offsxk+n; - offspk = offsxk1+n; - offspk1 = offspk+n; - offstmp1 = offspk1+n; - offstmp2 = offstmp1+m; - bs = offstmp2+n; - if( buf->cntptr.p_double[offsxk], 1, &x->ptr.p_double[0], 1, ae_v_len(offsxk,offsxk+n-1)); - - /* - * r(0) = b-A*x(0) - * RK2 = r(0)'*r(0) - */ - rmatrixmv(m, n, a, 0, 0, 0, buf, offsxk, buf, offstmp1, _state); - rmatrixmv(n, m, a, 0, 0, 1, buf, offstmp1, buf, offstmp2, _state); - ae_v_addd(&buf->ptr.p_double[offstmp2], 1, &buf->ptr.p_double[offsxk], 1, ae_v_len(offstmp2,offstmp2+n-1), alpha); - ae_v_move(&buf->ptr.p_double[offsrk], 1, &b->ptr.p_double[0], 1, ae_v_len(offsrk,offsrk+n-1)); - ae_v_sub(&buf->ptr.p_double[offsrk], 1, &buf->ptr.p_double[offstmp2], 1, ae_v_len(offsrk,offsrk+n-1)); - rk2 = ae_v_dotproduct(&buf->ptr.p_double[offsrk], 1, &buf->ptr.p_double[offsrk], 1, ae_v_len(offsrk,offsrk+n-1)); - ae_v_move(&buf->ptr.p_double[offspk], 1, &buf->ptr.p_double[offsrk], 1, ae_v_len(offspk,offspk+n-1)); - e1 = ae_sqrt(rk2, _state); - - /* - * Cycle - */ - for(k=0; k<=n-1; k++) - { - - /* - * Calculate A*p(k) - store in Buf[OffsTmp2:OffsTmp2+N-1] - * and p(k)'*A*p(k) - store in PAP - * - * If PAP=0, break (iteration is over) - */ - rmatrixmv(m, n, a, 0, 0, 0, buf, offspk, buf, offstmp1, _state); - v1 = ae_v_dotproduct(&buf->ptr.p_double[offstmp1], 1, &buf->ptr.p_double[offstmp1], 1, ae_v_len(offstmp1,offstmp1+m-1)); - v2 = ae_v_dotproduct(&buf->ptr.p_double[offspk], 1, &buf->ptr.p_double[offspk], 1, ae_v_len(offspk,offspk+n-1)); - pap = v1+alpha*v2; - rmatrixmv(n, m, a, 0, 0, 1, buf, offstmp1, buf, offstmp2, _state); - ae_v_addd(&buf->ptr.p_double[offstmp2], 1, &buf->ptr.p_double[offspk], 1, ae_v_len(offstmp2,offstmp2+n-1), alpha); - if( ae_fp_eq(pap,0) ) - { - break; - } - - /* - * S = (r(k)'*r(k))/(p(k)'*A*p(k)) - */ - s = rk2/pap; - - /* - * x(k+1) = x(k) + S*p(k) - */ - ae_v_move(&buf->ptr.p_double[offsxk1], 1, &buf->ptr.p_double[offsxk], 1, ae_v_len(offsxk1,offsxk1+n-1)); - ae_v_addd(&buf->ptr.p_double[offsxk1], 1, &buf->ptr.p_double[offspk], 1, ae_v_len(offsxk1,offsxk1+n-1), s); - - /* - * r(k+1) = r(k) - S*A*p(k) - * RK12 = r(k+1)'*r(k+1) - * - * Break if r(k+1) small enough (when compared to r(k)) - */ - ae_v_move(&buf->ptr.p_double[offsrk1], 1, &buf->ptr.p_double[offsrk], 1, ae_v_len(offsrk1,offsrk1+n-1)); - ae_v_subd(&buf->ptr.p_double[offsrk1], 1, &buf->ptr.p_double[offstmp2], 1, ae_v_len(offsrk1,offsrk1+n-1), s); - rk12 = ae_v_dotproduct(&buf->ptr.p_double[offsrk1], 1, &buf->ptr.p_double[offsrk1], 1, ae_v_len(offsrk1,offsrk1+n-1)); - if( ae_fp_less_eq(ae_sqrt(rk12, _state),100*ae_machineepsilon*ae_sqrt(rk2, _state)) ) - { - - /* - * X(k) = x(k+1) before exit - - * - because we expect to find solution at x(k) - */ - ae_v_move(&buf->ptr.p_double[offsxk], 1, &buf->ptr.p_double[offsxk1], 1, ae_v_len(offsxk,offsxk+n-1)); - break; - } - - /* - * BetaK = RK12/RK2 - * p(k+1) = r(k+1)+betak*p(k) - */ - betak = rk12/rk2; - ae_v_move(&buf->ptr.p_double[offspk1], 1, &buf->ptr.p_double[offsrk1], 1, ae_v_len(offspk1,offspk1+n-1)); - ae_v_addd(&buf->ptr.p_double[offspk1], 1, &buf->ptr.p_double[offspk], 1, ae_v_len(offspk1,offspk1+n-1), betak); - - /* - * r(k) := r(k+1) - * x(k) := x(k+1) - * p(k) := p(k+1) - */ - ae_v_move(&buf->ptr.p_double[offsrk], 1, &buf->ptr.p_double[offsrk1], 1, ae_v_len(offsrk,offsrk+n-1)); - ae_v_move(&buf->ptr.p_double[offsxk], 1, &buf->ptr.p_double[offsxk1], 1, ae_v_len(offsxk,offsxk+n-1)); - ae_v_move(&buf->ptr.p_double[offspk], 1, &buf->ptr.p_double[offspk1], 1, ae_v_len(offspk,offspk+n-1)); - rk2 = rk12; - } - - /* - * Calculate E2 - */ - rmatrixmv(m, n, a, 0, 0, 0, buf, offsxk, buf, offstmp1, _state); - rmatrixmv(n, m, a, 0, 0, 1, buf, offstmp1, buf, offstmp2, _state); - ae_v_addd(&buf->ptr.p_double[offstmp2], 1, &buf->ptr.p_double[offsxk], 1, ae_v_len(offstmp2,offstmp2+n-1), alpha); - ae_v_move(&buf->ptr.p_double[offsrk], 1, &b->ptr.p_double[0], 1, ae_v_len(offsrk,offsrk+n-1)); - ae_v_sub(&buf->ptr.p_double[offsrk], 1, &buf->ptr.p_double[offstmp2], 1, ae_v_len(offsrk,offsrk+n-1)); - v1 = ae_v_dotproduct(&buf->ptr.p_double[offsrk], 1, &buf->ptr.p_double[offsrk], 1, ae_v_len(offsrk,offsrk+n-1)); - e2 = ae_sqrt(v1, _state); - - /* - * Output result (if it was improved) - */ - if( ae_fp_less(e2,e1) ) - { - ae_v_move(&x->ptr.p_double[0], 1, &buf->ptr.p_double[offsxk], 1, ae_v_len(0,n-1)); - } -} - - -/************************************************************************* -Construction of linear conjugate gradient solver. - -State parameter passed using "var" semantics (i.e. previous state is NOT -erased). When it is already initialized, we can reause prevously allocated -memory. - -INPUT PARAMETERS: - X - initial solution - B - right part - N - system size - State - structure; may be preallocated, if we want to reuse memory - -OUTPUT PARAMETERS: - State - structure which is used by FBLSCGIteration() to store - algorithm state between subsequent calls. - -NOTE: no error checking is done; caller must check all parameters, prevent - overflows, and so on. - - -- ALGLIB -- - Copyright 22.10.2009 by Bochkanov Sergey -*************************************************************************/ -void fblscgcreate(/* Real */ ae_vector* x, - /* Real */ ae_vector* b, - ae_int_t n, - fblslincgstate* state, - ae_state *_state) -{ - - - if( state->b.cntb, n, _state); - } - if( state->rk.cntrk, n, _state); - } - if( state->rk1.cntrk1, n, _state); - } - if( state->xk.cntxk, n, _state); - } - if( state->xk1.cntxk1, n, _state); - } - if( state->pk.cntpk, n, _state); - } - if( state->pk1.cntpk1, n, _state); - } - if( state->tmp2.cnttmp2, n, _state); - } - if( state->x.cntx, n, _state); - } - if( state->ax.cntax, n, _state); - } - state->n = n; - ae_v_move(&state->xk.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->b.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_vector_set_length(&state->rstate.ia, 1+1, _state); - ae_vector_set_length(&state->rstate.ra, 6+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -Linear CG solver, function relying on reverse communication to calculate -matrix-vector products. - -See comments for FBLSLinCGState structure for more info. - - -- ALGLIB -- - Copyright 22.10.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool fblscgiteration(fblslincgstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t k; - double rk2; - double rk12; - double pap; - double s; - double betak; - double v1; - double v2; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - k = state->rstate.ia.ptr.p_int[1]; - rk2 = state->rstate.ra.ptr.p_double[0]; - rk12 = state->rstate.ra.ptr.p_double[1]; - pap = state->rstate.ra.ptr.p_double[2]; - s = state->rstate.ra.ptr.p_double[3]; - betak = state->rstate.ra.ptr.p_double[4]; - v1 = state->rstate.ra.ptr.p_double[5]; - v2 = state->rstate.ra.ptr.p_double[6]; - } - else - { - n = -983; - k = -989; - rk2 = -834; - rk12 = 900; - pap = -287; - s = 364; - betak = 214; - v1 = -338; - v2 = -686; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - - /* - * Routine body - */ - - /* - * prepare locals - */ - n = state->n; - - /* - * Test for special case: B=0 - */ - v1 = ae_v_dotproduct(&state->b.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_eq(v1,0) ) - { - for(k=0; k<=n-1; k++) - { - state->xk.ptr.p_double[k] = 0; - } - result = ae_false; - return result; - } - - /* - * r(0) = b-A*x(0) - * RK2 = r(0)'*r(0) - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - ae_v_move(&state->rk.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_sub(&state->rk.ptr.p_double[0], 1, &state->ax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - rk2 = ae_v_dotproduct(&state->rk.ptr.p_double[0], 1, &state->rk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->pk.ptr.p_double[0], 1, &state->rk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->e1 = ae_sqrt(rk2, _state); - - /* - * Cycle - */ - k = 0; -lbl_3: - if( k>n-1 ) - { - goto lbl_5; - } - - /* - * Calculate A*p(k) - store in State.Tmp2 - * and p(k)'*A*p(k) - store in PAP - * - * If PAP=0, break (iteration is over) - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->pk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - ae_v_move(&state->tmp2.ptr.p_double[0], 1, &state->ax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - pap = state->xax; - if( !ae_isfinite(pap, _state) ) - { - goto lbl_5; - } - if( ae_fp_less_eq(pap,0) ) - { - goto lbl_5; - } - - /* - * S = (r(k)'*r(k))/(p(k)'*A*p(k)) - */ - s = rk2/pap; - - /* - * x(k+1) = x(k) + S*p(k) - */ - ae_v_move(&state->xk1.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->xk1.ptr.p_double[0], 1, &state->pk.ptr.p_double[0], 1, ae_v_len(0,n-1), s); - - /* - * r(k+1) = r(k) - S*A*p(k) - * RK12 = r(k+1)'*r(k+1) - * - * Break if r(k+1) small enough (when compared to r(k)) - */ - ae_v_move(&state->rk1.ptr.p_double[0], 1, &state->rk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_subd(&state->rk1.ptr.p_double[0], 1, &state->tmp2.ptr.p_double[0], 1, ae_v_len(0,n-1), s); - rk12 = ae_v_dotproduct(&state->rk1.ptr.p_double[0], 1, &state->rk1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_less_eq(ae_sqrt(rk12, _state),100*ae_machineepsilon*state->e1) ) - { - - /* - * X(k) = x(k+1) before exit - - * - because we expect to find solution at x(k) - */ - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->xk1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - goto lbl_5; - } - - /* - * BetaK = RK12/RK2 - * p(k+1) = r(k+1)+betak*p(k) - * - * NOTE: we expect that BetaK won't overflow because of - * "Sqrt(RK12)<=100*MachineEpsilon*E1" test above. - */ - betak = rk12/rk2; - ae_v_move(&state->pk1.ptr.p_double[0], 1, &state->rk1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->pk1.ptr.p_double[0], 1, &state->pk.ptr.p_double[0], 1, ae_v_len(0,n-1), betak); - - /* - * r(k) := r(k+1) - * x(k) := x(k+1) - * p(k) := p(k+1) - */ - ae_v_move(&state->rk.ptr.p_double[0], 1, &state->rk1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->xk1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->pk.ptr.p_double[0], 1, &state->pk1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - rk2 = rk12; - k = k+1; - goto lbl_3; -lbl_5: - - /* - * Calculate E2 - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - ae_v_move(&state->rk.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_sub(&state->rk.ptr.p_double[0], 1, &state->ax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v1 = ae_v_dotproduct(&state->rk.ptr.p_double[0], 1, &state->rk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->e2 = ae_sqrt(v1, _state); - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = k; - state->rstate.ra.ptr.p_double[0] = rk2; - state->rstate.ra.ptr.p_double[1] = rk12; - state->rstate.ra.ptr.p_double[2] = pap; - state->rstate.ra.ptr.p_double[3] = s; - state->rstate.ra.ptr.p_double[4] = betak; - state->rstate.ra.ptr.p_double[5] = v1; - state->rstate.ra.ptr.p_double[6] = v2; - return result; -} - - -/************************************************************************* -Fast least squares solver, solves well conditioned system without -performing any checks for degeneracy, and using user-provided buffers -(which are automatically reallocated if too small). - -This function is intended for solution of moderately sized systems. It -uses factorization algorithms based on Level 2 BLAS operations, thus it -won't work efficiently on large scale systems. - -INPUT PARAMETERS: - A - array[M,N], system matrix. - Contents of A is destroyed during solution. - B - array[M], right part - M - number of equations - N - number of variables, N<=M - Tmp0, Tmp1, Tmp2- - buffers; function automatically allocates them, if they are - too small. They can be reused if function is called - several times. - -OUTPUT PARAMETERS: - B - solution (first N components, next M-N are zero) - - -- ALGLIB -- - Copyright 20.01.2012 by Bochkanov Sergey -*************************************************************************/ -void fblssolvels(/* Real */ ae_matrix* a, - /* Real */ ae_vector* b, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tmp0, - /* Real */ ae_vector* tmp1, - /* Real */ ae_vector* tmp2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - double v; - - - ae_assert(n>0, "FBLSSolveLS: N<=0", _state); - ae_assert(m>=n, "FBLSSolveLS: Mrows>=m, "FBLSSolveLS: Rows(A)cols>=n, "FBLSSolveLS: Cols(A)cnt>=m, "FBLSSolveLS: Length(B)ptr.p_double[i] = 0; - } - ae_v_move(&tmp0->ptr.p_double[k], 1, &a->ptr.pp_double[k][k], a->stride, ae_v_len(k,m-1)); - tmp0->ptr.p_double[k] = 1; - v = ae_v_dotproduct(&tmp0->ptr.p_double[k], 1, &b->ptr.p_double[k], 1, ae_v_len(k,m-1)); - v = v*tmp2->ptr.p_double[k]; - ae_v_subd(&b->ptr.p_double[k], 1, &tmp0->ptr.p_double[k], 1, ae_v_len(k,m-1), v); - } - - /* - * Solve triangular system - */ - b->ptr.p_double[n-1] = b->ptr.p_double[n-1]/a->ptr.pp_double[n-1][n-1]; - for(i=n-2; i>=0; i--) - { - v = ae_v_dotproduct(&a->ptr.pp_double[i][i+1], 1, &b->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1)); - b->ptr.p_double[i] = (b->ptr.p_double[i]-v)/a->ptr.pp_double[i][i]; - } - for(i=n; i<=m-1; i++) - { - b->ptr.p_double[i] = 0.0; - } -} - - -ae_bool _fblslincgstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - fblslincgstate *p = (fblslincgstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ax, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rk1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xk1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->pk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->pk1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->b, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp2, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _fblslincgstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - fblslincgstate *dst = (fblslincgstate*)_dst; - fblslincgstate *src = (fblslincgstate*)_src; - dst->e1 = src->e1; - dst->e2 = src->e2; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ax, &src->ax, _state, make_automatic) ) - return ae_false; - dst->xax = src->xax; - dst->n = src->n; - if( !ae_vector_init_copy(&dst->rk, &src->rk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rk1, &src->rk1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xk, &src->xk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xk1, &src->xk1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->pk, &src->pk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->pk1, &src->pk1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->b, &src->b, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp2, &src->tmp2, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _fblslincgstate_clear(void* _p) -{ - fblslincgstate *p = (fblslincgstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->ax); - ae_vector_clear(&p->rk); - ae_vector_clear(&p->rk1); - ae_vector_clear(&p->xk); - ae_vector_clear(&p->xk1); - ae_vector_clear(&p->pk); - ae_vector_clear(&p->pk1); - ae_vector_clear(&p->b); - _rcommstate_clear(&p->rstate); - ae_vector_clear(&p->tmp2); -} - - -void _fblslincgstate_destroy(void* _p) -{ - fblslincgstate *p = (fblslincgstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->ax); - ae_vector_destroy(&p->rk); - ae_vector_destroy(&p->rk1); - ae_vector_destroy(&p->xk); - ae_vector_destroy(&p->xk1); - ae_vector_destroy(&p->pk); - ae_vector_destroy(&p->pk1); - ae_vector_destroy(&p->b); - _rcommstate_destroy(&p->rstate); - ae_vector_destroy(&p->tmp2); -} - - - - -/************************************************************************* -This procedure initializes matrix norm estimator. - -USAGE: -1. User initializes algorithm state with NormEstimatorCreate() call -2. User calls NormEstimatorEstimateSparse() (or NormEstimatorIteration()) -3. User calls NormEstimatorResults() to get solution. - -INPUT PARAMETERS: - M - number of rows in the matrix being estimated, M>0 - N - number of columns in the matrix being estimated, N>0 - NStart - number of random starting vectors - recommended value - at least 5. - NIts - number of iterations to do with best starting vector - recommended value - at least 5. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTE: this algorithm is effectively deterministic, i.e. it always returns -same result when repeatedly called for the same matrix. In fact, algorithm -uses randomized starting vectors, but internal random numbers generator -always generates same sequence of the random values (it is a feature, not -bug). - -Algorithm can be made non-deterministic with NormEstimatorSetSeed(0) call. - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorcreate(ae_int_t m, - ae_int_t n, - ae_int_t nstart, - ae_int_t nits, - normestimatorstate* state, - ae_state *_state) -{ - - _normestimatorstate_clear(state); - - ae_assert(m>0, "NormEstimatorCreate: M<=0", _state); - ae_assert(n>0, "NormEstimatorCreate: N<=0", _state); - ae_assert(nstart>0, "NormEstimatorCreate: NStart<=0", _state); - ae_assert(nits>0, "NormEstimatorCreate: NIts<=0", _state); - state->m = m; - state->n = n; - state->nstart = nstart; - state->nits = nits; - state->seedval = 11; - hqrndrandomize(&state->r, _state); - ae_vector_set_length(&state->x0, state->n, _state); - ae_vector_set_length(&state->t, state->m, _state); - ae_vector_set_length(&state->x1, state->n, _state); - ae_vector_set_length(&state->xbest, state->n, _state); - ae_vector_set_length(&state->x, ae_maxint(state->n, state->m, _state), _state); - ae_vector_set_length(&state->mv, state->m, _state); - ae_vector_set_length(&state->mtv, state->n, _state); - ae_vector_set_length(&state->rstate.ia, 3+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -This function changes seed value used by algorithm. In some cases we need -deterministic processing, i.e. subsequent calls must return equal results, -in other cases we need non-deterministic algorithm which returns different -results for the same matrix on every pass. - -Setting zero seed will lead to non-deterministic algorithm, while non-zero -value will make our algorithm deterministic. - -INPUT PARAMETERS: - State - norm estimator state, must be initialized with a call - to NormEstimatorCreate() - SeedVal - seed value, >=0. Zero value = non-deterministic algo. - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorsetseed(normestimatorstate* state, - ae_int_t seedval, - ae_state *_state) -{ - - - ae_assert(seedval>=0, "NormEstimatorSetSeed: SeedVal<0", _state); - state->seedval = seedval; -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -ae_bool normestimatoriteration(normestimatorstate* state, - ae_state *_state) -{ - ae_int_t n; - ae_int_t m; - ae_int_t i; - ae_int_t itcnt; - double v; - double growth; - double bestgrowth; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - i = state->rstate.ia.ptr.p_int[2]; - itcnt = state->rstate.ia.ptr.p_int[3]; - v = state->rstate.ra.ptr.p_double[0]; - growth = state->rstate.ra.ptr.p_double[1]; - bestgrowth = state->rstate.ra.ptr.p_double[2]; - } - else - { - n = -983; - m = -989; - i = -834; - itcnt = 900; - v = -287; - growth = 364; - bestgrowth = 214; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - - /* - * Routine body - */ - n = state->n; - m = state->m; - if( state->seedval>0 ) - { - hqrndseed(state->seedval, state->seedval+2, &state->r, _state); - } - bestgrowth = 0; - state->xbest.ptr.p_double[0] = 1; - for(i=1; i<=n-1; i++) - { - state->xbest.ptr.p_double[i] = 0; - } - itcnt = 0; -lbl_4: - if( itcnt>state->nstart-1 ) - { - goto lbl_6; - } - do - { - v = 0; - for(i=0; i<=n-1; i++) - { - state->x0.ptr.p_double[i] = hqrndnormal(&state->r, _state); - v = v+ae_sqr(state->x0.ptr.p_double[i], _state); - } - } - while(ae_fp_eq(v,0)); - v = 1/ae_sqrt(v, _state); - ae_v_muld(&state->x0.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->x0.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->needmv = ae_true; - state->needmtv = ae_false; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - ae_v_move(&state->x.ptr.p_double[0], 1, &state->mv.ptr.p_double[0], 1, ae_v_len(0,m-1)); - state->needmv = ae_false; - state->needmtv = ae_true; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - ae_v_move(&state->x1.ptr.p_double[0], 1, &state->mtv.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->x1.ptr.p_double[i], _state); - } - growth = ae_sqrt(ae_sqrt(v, _state), _state); - if( ae_fp_greater(growth,bestgrowth) ) - { - v = 1/ae_sqrt(v, _state); - ae_v_moved(&state->xbest.ptr.p_double[0], 1, &state->x1.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - bestgrowth = growth; - } - itcnt = itcnt+1; - goto lbl_4; -lbl_6: - ae_v_move(&state->x0.ptr.p_double[0], 1, &state->xbest.ptr.p_double[0], 1, ae_v_len(0,n-1)); - itcnt = 0; -lbl_7: - if( itcnt>state->nits-1 ) - { - goto lbl_9; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->x0.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->needmv = ae_true; - state->needmtv = ae_false; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - ae_v_move(&state->x.ptr.p_double[0], 1, &state->mv.ptr.p_double[0], 1, ae_v_len(0,m-1)); - state->needmv = ae_false; - state->needmtv = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - ae_v_move(&state->x1.ptr.p_double[0], 1, &state->mtv.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->x1.ptr.p_double[i], _state); - } - state->repnorm = ae_sqrt(ae_sqrt(v, _state), _state); - if( ae_fp_neq(v,0) ) - { - v = 1/ae_sqrt(v, _state); - ae_v_moved(&state->x0.ptr.p_double[0], 1, &state->x1.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - } - itcnt = itcnt+1; - goto lbl_7; -lbl_9: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = i; - state->rstate.ia.ptr.p_int[3] = itcnt; - state->rstate.ra.ptr.p_double[0] = v; - state->rstate.ra.ptr.p_double[1] = growth; - state->rstate.ra.ptr.p_double[2] = bestgrowth; - return result; -} - - -/************************************************************************* -This function estimates norm of the sparse M*N matrix A. - -INPUT PARAMETERS: - State - norm estimator state, must be initialized with a call - to NormEstimatorCreate() - A - sparse M*N matrix, must be converted to CRS format - prior to calling this function. - -After this function is over you can call NormEstimatorResults() to get -estimate of the norm(A). - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorestimatesparse(normestimatorstate* state, - sparsematrix* a, - ae_state *_state) -{ - - - normestimatorrestart(state, _state); - while(normestimatoriteration(state, _state)) - { - if( state->needmv ) - { - sparsemv(a, &state->x, &state->mv, _state); - continue; - } - if( state->needmtv ) - { - sparsemtv(a, &state->x, &state->mtv, _state); - continue; - } - } -} - - -/************************************************************************* -Matrix norm estimation results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - Nrm - estimate of the matrix norm, Nrm>=0 - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorresults(normestimatorstate* state, - double* nrm, - ae_state *_state) -{ - - *nrm = 0; - - *nrm = state->repnorm; -} - - -/************************************************************************* -This function restarts estimator and prepares it for the next estimation -round. - -INPUT PARAMETERS: - State - algorithm state - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorrestart(normestimatorstate* state, ae_state *_state) -{ - - - ae_vector_set_length(&state->rstate.ia, 3+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; -} - - -ae_bool _normestimatorstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - normestimatorstate *p = (normestimatorstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->t, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xbest, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_hqrndstate_init(&p->r, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mv, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mtv, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _normestimatorstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - normestimatorstate *dst = (normestimatorstate*)_dst; - normestimatorstate *src = (normestimatorstate*)_src; - dst->n = src->n; - dst->m = src->m; - dst->nstart = src->nstart; - dst->nits = src->nits; - dst->seedval = src->seedval; - if( !ae_vector_init_copy(&dst->x0, &src->x0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x1, &src->x1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->t, &src->t, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xbest, &src->xbest, _state, make_automatic) ) - return ae_false; - if( !_hqrndstate_init_copy(&dst->r, &src->r, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mv, &src->mv, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mtv, &src->mtv, _state, make_automatic) ) - return ae_false; - dst->needmv = src->needmv; - dst->needmtv = src->needmtv; - dst->repnorm = src->repnorm; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _normestimatorstate_clear(void* _p) -{ - normestimatorstate *p = (normestimatorstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x0); - ae_vector_clear(&p->x1); - ae_vector_clear(&p->t); - ae_vector_clear(&p->xbest); - _hqrndstate_clear(&p->r); - ae_vector_clear(&p->x); - ae_vector_clear(&p->mv); - ae_vector_clear(&p->mtv); - _rcommstate_clear(&p->rstate); -} - - -void _normestimatorstate_destroy(void* _p) -{ - normestimatorstate *p = (normestimatorstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x0); - ae_vector_destroy(&p->x1); - ae_vector_destroy(&p->t); - ae_vector_destroy(&p->xbest); - _hqrndstate_destroy(&p->r); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->mv); - ae_vector_destroy(&p->mtv); - _rcommstate_destroy(&p->rstate); -} - - - - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixludet(/* Real */ ae_matrix* a, - /* Integer */ ae_vector* pivots, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t s; - double result; - - - ae_assert(n>=1, "RMatrixLUDet: N<1!", _state); - ae_assert(pivots->cnt>=n, "RMatrixLUDet: Pivots array is too short!", _state); - ae_assert(a->rows>=n, "RMatrixLUDet: rows(A)cols>=n, "RMatrixLUDet: cols(A)ptr.pp_double[i][i]; - if( pivots->ptr.p_int[i]!=i ) - { - s = -s; - } - } - result = result*s; - return result; -} - - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixdet(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector pivots; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "RMatrixDet: N<1!", _state); - ae_assert(a->rows>=n, "RMatrixDet: rows(A)cols>=n, "RMatrixDet: cols(A)=1, "CMatrixLUDet: N<1!", _state); - ae_assert(pivots->cnt>=n, "CMatrixLUDet: Pivots array is too short!", _state); - ae_assert(a->rows>=n, "CMatrixLUDet: rows(A)cols>=n, "CMatrixLUDet: cols(A)ptr.pp_complex[i][i]); - if( pivots->ptr.p_int[i]!=i ) - { - s = -s; - } - } - result = ae_c_mul_d(result,s); - return result; -} - - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -ae_complex cmatrixdet(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_vector pivots; - ae_complex result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - ae_vector_init(&pivots, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "CMatrixDet: N<1!", _state); - ae_assert(a->rows>=n, "CMatrixDet: rows(A)cols>=n, "CMatrixDet: cols(A)=1, "SPDMatrixCholeskyDet: N<1!", _state); - ae_assert(a->rows>=n, "SPDMatrixCholeskyDet: rows(A)cols>=n, "SPDMatrixCholeskyDet: cols(A)ptr.pp_double[i][i], _state); - } - ae_assert(f, "SPDMatrixCholeskyDet: A contains infinite or NaN values!", _state); - result = 1; - for(i=0; i<=n-1; i++) - { - result = result*ae_sqr(a->ptr.pp_double[i][i], _state); - } - return result; -} - - -/************************************************************************* -Determinant calculation of the symmetric positive definite matrix. - -Input parameters: - A - matrix. Array with elements [0..N-1, 0..N-1]. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Result: - determinant of matrix A. - If matrix A is not positive definite, exception is thrown. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixdet(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _a; - ae_bool b; - double result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_a, a, _state, ae_true); - a = &_a; - - ae_assert(n>=1, "SPDMatrixDet: N<1!", _state); - ae_assert(a->rows>=n, "SPDMatrixDet: rows(A)cols>=n, "SPDMatrixDet: cols(A)ptr.pp_double[0][j] = 0.0; - } - for(i=1; i<=n-1; i++) - { - ae_v_move(&z->ptr.pp_double[i][0], 1, &z->ptr.pp_double[0][0], 1, ae_v_len(0,n-1)); - } - - /* - * Setup R properties - */ - if( isupperr ) - { - j1 = 0; - j2 = n-1; - j1inc = 1; - j2inc = 0; - } - else - { - j1 = 0; - j2 = 0; - j1inc = 0; - j2inc = 1; - } - - /* - * Calculate R*Z - */ - for(i=0; i<=n-1; i++) - { - for(j=j1; j<=j2; j++) - { - v = r.ptr.pp_double[i][j]; - ae_v_addd(&z->ptr.pp_double[i][0], 1, &t.ptr.pp_double[j][0], 1, ae_v_len(0,n-1), v); - } - j1 = j1+j1inc; - j2 = j2+j2inc; - } - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* -Algorithm for reduction of the following generalized symmetric positive- -definite eigenvalue problem: - A*x = lambda*B*x (1) or - A*B*x = lambda*x (2) or - B*A*x = lambda*x (3) -to the symmetric eigenvalues problem C*y = lambda*y (eigenvalues of this and -the given problems are the same, and the eigenvectors of the given problem -could be obtained by multiplying the obtained eigenvectors by the -transformation matrix x = R*y). - -Here A is a symmetric matrix, B - symmetric positive-definite matrix. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrices A and B. - IsUpperA - storage format of matrix A. - B - symmetric positive-definite matrix which is given by - its upper or lower triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperB - storage format of matrix B. - ProblemType - if ProblemType is equal to: - * 1, the following problem is solved: A*x = lambda*B*x; - * 2, the following problem is solved: A*B*x = lambda*x; - * 3, the following problem is solved: B*A*x = lambda*x. - -Output parameters: - A - symmetric matrix which is given by its upper or lower - triangle depending on IsUpperA. Contains matrix C. - Array whose indexes range within [0..N-1, 0..N-1]. - R - upper triangular or low triangular transformation matrix - which is used to obtain the eigenvectors of a given problem - as the product of eigenvectors of C (from the right) and - matrix R (from the left). If the matrix is upper - triangular, the elements below the main diagonal - are equal to 0 (and vice versa). Thus, we can perform - the multiplication without taking into account the - internal structure (which is an easier though less - effective way). - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperR - type of matrix R (upper or lower triangular). - -Result: - True, if the problem was reduced successfully. - False, if the error occurred during the Cholesky decomposition of - matrix B (the matrix is not positive-definite). - - -- ALGLIB -- - Copyright 1.28.2006 by Bochkanov Sergey -*************************************************************************/ -ae_bool smatrixgevdreduce(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isuppera, - /* Real */ ae_matrix* b, - ae_bool isupperb, - ae_int_t problemtype, - /* Real */ ae_matrix* r, - ae_bool* isupperr, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix t; - ae_vector w1; - ae_vector w2; - ae_vector w3; - ae_int_t i; - ae_int_t j; - double v; - matinvreport rep; - ae_int_t info; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(r); - *isupperr = ae_false; - ae_matrix_init(&t, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w2, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w3, 0, DT_REAL, _state, ae_true); - _matinvreport_init(&rep, _state, ae_true); - - ae_assert(n>0, "SMatrixGEVDReduce: N<=0!", _state); - ae_assert((problemtype==1||problemtype==2)||problemtype==3, "SMatrixGEVDReduce: incorrect ProblemType!", _state); - result = ae_true; - - /* - * Problem 1: A*x = lambda*B*x - * - * Reducing to: - * C*y = lambda*y - * C = L^(-1) * A * L^(-T) - * x = L^(-T) * y - */ - if( problemtype==1 ) - { - - /* - * Factorize B in T: B = LL' - */ - ae_matrix_set_length(&t, n-1+1, n-1+1, _state); - if( isupperb ) - { - for(i=0; i<=n-1; i++) - { - ae_v_move(&t.ptr.pp_double[i][i], t.stride, &b->ptr.pp_double[i][i], 1, ae_v_len(i,n-1)); - } - } - else - { - for(i=0; i<=n-1; i++) - { - ae_v_move(&t.ptr.pp_double[i][0], 1, &b->ptr.pp_double[i][0], 1, ae_v_len(0,i)); - } - } - if( !spdmatrixcholesky(&t, n, ae_false, _state) ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Invert L in T - */ - rmatrixtrinverse(&t, n, ae_false, ae_false, &info, &rep, _state); - if( info<=0 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Build L^(-1) * A * L^(-T) in R - */ - ae_vector_set_length(&w1, n+1, _state); - ae_vector_set_length(&w2, n+1, _state); - ae_matrix_set_length(r, n-1+1, n-1+1, _state); - for(j=1; j<=n; j++) - { - - /* - * Form w2 = A * l'(j) (here l'(j) is j-th column of L^(-T)) - */ - ae_v_move(&w1.ptr.p_double[1], 1, &t.ptr.pp_double[j-1][0], 1, ae_v_len(1,j)); - symmetricmatrixvectormultiply(a, isuppera, 0, j-1, &w1, 1.0, &w2, _state); - if( isuppera ) - { - matrixvectormultiply(a, 0, j-1, j, n-1, ae_true, &w1, 1, j, 1.0, &w2, j+1, n, 0.0, _state); - } - else - { - matrixvectormultiply(a, j, n-1, 0, j-1, ae_false, &w1, 1, j, 1.0, &w2, j+1, n, 0.0, _state); - } - - /* - * Form l(i)*w2 (here l(i) is i-th row of L^(-1)) - */ - for(i=1; i<=n; i++) - { - v = ae_v_dotproduct(&t.ptr.pp_double[i-1][0], 1, &w2.ptr.p_double[1], 1, ae_v_len(0,i-1)); - r->ptr.pp_double[i-1][j-1] = v; - } - } - - /* - * Copy R to A - */ - for(i=0; i<=n-1; i++) - { - ae_v_move(&a->ptr.pp_double[i][0], 1, &r->ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - } - - /* - * Copy L^(-1) from T to R and transpose - */ - *isupperr = ae_true; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-1; j++) - { - r->ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=n-1; i++) - { - ae_v_move(&r->ptr.pp_double[i][i], 1, &t.ptr.pp_double[i][i], t.stride, ae_v_len(i,n-1)); - } - ae_frame_leave(_state); - return result; - } - - /* - * Problem 2: A*B*x = lambda*x - * or - * problem 3: B*A*x = lambda*x - * - * Reducing to: - * C*y = lambda*y - * C = U * A * U' - * B = U'* U - */ - if( problemtype==2||problemtype==3 ) - { - - /* - * Factorize B in T: B = U'*U - */ - ae_matrix_set_length(&t, n-1+1, n-1+1, _state); - if( isupperb ) - { - for(i=0; i<=n-1; i++) - { - ae_v_move(&t.ptr.pp_double[i][i], 1, &b->ptr.pp_double[i][i], 1, ae_v_len(i,n-1)); - } - } - else - { - for(i=0; i<=n-1; i++) - { - ae_v_move(&t.ptr.pp_double[i][i], 1, &b->ptr.pp_double[i][i], b->stride, ae_v_len(i,n-1)); - } - } - if( !spdmatrixcholesky(&t, n, ae_true, _state) ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Build U * A * U' in R - */ - ae_vector_set_length(&w1, n+1, _state); - ae_vector_set_length(&w2, n+1, _state); - ae_vector_set_length(&w3, n+1, _state); - ae_matrix_set_length(r, n-1+1, n-1+1, _state); - for(j=1; j<=n; j++) - { - - /* - * Form w2 = A * u'(j) (here u'(j) is j-th column of U') - */ - ae_v_move(&w1.ptr.p_double[1], 1, &t.ptr.pp_double[j-1][j-1], 1, ae_v_len(1,n-j+1)); - symmetricmatrixvectormultiply(a, isuppera, j-1, n-1, &w1, 1.0, &w3, _state); - ae_v_move(&w2.ptr.p_double[j], 1, &w3.ptr.p_double[1], 1, ae_v_len(j,n)); - ae_v_move(&w1.ptr.p_double[j], 1, &t.ptr.pp_double[j-1][j-1], 1, ae_v_len(j,n)); - if( isuppera ) - { - matrixvectormultiply(a, 0, j-2, j-1, n-1, ae_false, &w1, j, n, 1.0, &w2, 1, j-1, 0.0, _state); - } - else - { - matrixvectormultiply(a, j-1, n-1, 0, j-2, ae_true, &w1, j, n, 1.0, &w2, 1, j-1, 0.0, _state); - } - - /* - * Form u(i)*w2 (here u(i) is i-th row of U) - */ - for(i=1; i<=n; i++) - { - v = ae_v_dotproduct(&t.ptr.pp_double[i-1][i-1], 1, &w2.ptr.p_double[i], 1, ae_v_len(i-1,n-1)); - r->ptr.pp_double[i-1][j-1] = v; - } - } - - /* - * Copy R to A - */ - for(i=0; i<=n-1; i++) - { - ae_v_move(&a->ptr.pp_double[i][0], 1, &r->ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - } - if( problemtype==2 ) - { - - /* - * Invert U in T - */ - rmatrixtrinverse(&t, n, ae_true, ae_false, &info, &rep, _state); - if( info<=0 ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - - /* - * Copy U^-1 from T to R - */ - *isupperr = ae_true; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=i-1; j++) - { - r->ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=n-1; i++) - { - ae_v_move(&r->ptr.pp_double[i][i], 1, &t.ptr.pp_double[i][i], 1, ae_v_len(i,n-1)); - } - } - else - { - - /* - * Copy U from T to R and transpose - */ - *isupperr = ae_false; - for(i=0; i<=n-1; i++) - { - for(j=i+1; j<=n-1; j++) - { - r->ptr.pp_double[i][j] = 0; - } - } - for(i=0; i<=n-1; i++) - { - ae_v_move(&r->ptr.pp_double[i][i], r->stride, &t.ptr.pp_double[i][i], 1, ae_v_len(i,n-1)); - } - } - } - ae_frame_leave(_state); - return result; -} - - - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a number to an element -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdRow - row where the element to be updated is stored. - UpdColumn - column where the element to be updated is stored. - UpdVal - a number to be added to the element. - - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdatesimple(/* Real */ ae_matrix* inva, - ae_int_t n, - ae_int_t updrow, - ae_int_t updcolumn, - double updval, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector t1; - ae_vector t2; - ae_int_t i; - double lambdav; - double vt; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&t1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t2, 0, DT_REAL, _state, ae_true); - - ae_assert(updrow>=0&&updrow=0&&updcolumnptr.pp_double[0][updrow], inva->stride, ae_v_len(0,n-1)); - - /* - * T2 = v*InvA - */ - ae_v_move(&t2.ptr.p_double[0], 1, &inva->ptr.pp_double[updcolumn][0], 1, ae_v_len(0,n-1)); - - /* - * Lambda = v * InvA * U - */ - lambdav = updval*inva->ptr.pp_double[updcolumn][updrow]; - - /* - * InvA = InvA - correction - */ - for(i=0; i<=n-1; i++) - { - vt = updval*t1.ptr.p_double[i]; - vt = vt/(1+lambdav); - ae_v_subd(&inva->ptr.pp_double[i][0], 1, &t2.ptr.p_double[0], 1, ae_v_len(0,n-1), vt); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a vector to a row -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdRow - the row of A whose vector V was added. - 0 <= Row <= N-1 - V - the vector to be added to a row. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdaterow(/* Real */ ae_matrix* inva, - ae_int_t n, - ae_int_t updrow, - /* Real */ ae_vector* v, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector t1; - ae_vector t2; - ae_int_t i; - ae_int_t j; - double lambdav; - double vt; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&t1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t2, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&t1, n-1+1, _state); - ae_vector_set_length(&t2, n-1+1, _state); - - /* - * T1 = InvA * U - */ - ae_v_move(&t1.ptr.p_double[0], 1, &inva->ptr.pp_double[0][updrow], inva->stride, ae_v_len(0,n-1)); - - /* - * T2 = v*InvA - * Lambda = v * InvA * U - */ - for(j=0; j<=n-1; j++) - { - vt = ae_v_dotproduct(&v->ptr.p_double[0], 1, &inva->ptr.pp_double[0][j], inva->stride, ae_v_len(0,n-1)); - t2.ptr.p_double[j] = vt; - } - lambdav = t2.ptr.p_double[updrow]; - - /* - * InvA = InvA - correction - */ - for(i=0; i<=n-1; i++) - { - vt = t1.ptr.p_double[i]/(1+lambdav); - ae_v_subd(&inva->ptr.pp_double[i][0], 1, &t2.ptr.p_double[0], 1, ae_v_len(0,n-1), vt); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a vector to a column -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdColumn - the column of A whose vector U was added. - 0 <= UpdColumn <= N-1 - U - the vector to be added to a column. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdatecolumn(/* Real */ ae_matrix* inva, - ae_int_t n, - ae_int_t updcolumn, - /* Real */ ae_vector* u, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector t1; - ae_vector t2; - ae_int_t i; - double lambdav; - double vt; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&t1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t2, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&t1, n-1+1, _state); - ae_vector_set_length(&t2, n-1+1, _state); - - /* - * T1 = InvA * U - * Lambda = v * InvA * U - */ - for(i=0; i<=n-1; i++) - { - vt = ae_v_dotproduct(&inva->ptr.pp_double[i][0], 1, &u->ptr.p_double[0], 1, ae_v_len(0,n-1)); - t1.ptr.p_double[i] = vt; - } - lambdav = t1.ptr.p_double[updcolumn]; - - /* - * T2 = v*InvA - */ - ae_v_move(&t2.ptr.p_double[0], 1, &inva->ptr.pp_double[updcolumn][0], 1, ae_v_len(0,n-1)); - - /* - * InvA = InvA - correction - */ - for(i=0; i<=n-1; i++) - { - vt = t1.ptr.p_double[i]/(1+lambdav); - ae_v_subd(&inva->ptr.pp_double[i][0], 1, &t2.ptr.p_double[0], 1, ae_v_len(0,n-1), vt); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm computes the inverse of matrix A+u*v’ by using the given matrix -A^-1 and the vectors u and v. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - U - the vector modifying the matrix. - Array whose index ranges within [0..N-1]. - V - the vector modifying the matrix. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of matrix A + u*v'. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdateuv(/* Real */ ae_matrix* inva, - ae_int_t n, - /* Real */ ae_vector* u, - /* Real */ ae_vector* v, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector t1; - ae_vector t2; - ae_int_t i; - ae_int_t j; - double lambdav; - double vt; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&t1, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t2, 0, DT_REAL, _state, ae_true); - - ae_vector_set_length(&t1, n-1+1, _state); - ae_vector_set_length(&t2, n-1+1, _state); - - /* - * T1 = InvA * U - * Lambda = v * T1 - */ - for(i=0; i<=n-1; i++) - { - vt = ae_v_dotproduct(&inva->ptr.pp_double[i][0], 1, &u->ptr.p_double[0], 1, ae_v_len(0,n-1)); - t1.ptr.p_double[i] = vt; - } - lambdav = ae_v_dotproduct(&v->ptr.p_double[0], 1, &t1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * T2 = v*InvA - */ - for(j=0; j<=n-1; j++) - { - vt = ae_v_dotproduct(&v->ptr.p_double[0], 1, &inva->ptr.pp_double[0][j], inva->stride, ae_v_len(0,n-1)); - t2.ptr.p_double[j] = vt; - } - - /* - * InvA = InvA - correction - */ - for(i=0; i<=n-1; i++) - { - vt = t1.ptr.p_double[i]/(1+lambdav); - ae_v_subd(&inva->ptr.pp_double[i][0], 1, &t2.ptr.p_double[0], 1, ae_v_len(0,n-1), vt); - } - ae_frame_leave(_state); -} - - - - -/************************************************************************* -Subroutine performing the Schur decomposition of a general matrix by using -the QR algorithm with multiple shifts. - -The source matrix A is represented as S'*A*S = T, where S is an orthogonal -matrix (Schur vectors), T - upper quasi-triangular matrix (with blocks of -sizes 1x1 and 2x2 on the main diagonal). - -Input parameters: - A - matrix to be decomposed. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of A, N>=0. - - -Output parameters: - A - contains matrix T. - Array whose indexes range within [0..N-1, 0..N-1]. - S - contains Schur vectors. - Array whose indexes range within [0..N-1, 0..N-1]. - -Note 1: - The block structure of matrix T can be easily recognized: since all - the elements below the blocks are zeros, the elements a[i+1,i] which - are equal to 0 show the block border. - -Note 2: - The algorithm performance depends on the value of the internal parameter - NS of the InternalSchurDecomposition subroutine which defines the number - of shifts in the QR algorithm (similarly to the block width in block-matrix - algorithms in linear algebra). If you require maximum performance on - your machine, it is recommended to adjust this parameter manually. - -Result: - True, - if the algorithm has converged and parameters A and S contain the result. - False, - if the algorithm has not converged. - -Algorithm implemented on the basis of the DHSEQR subroutine (LAPACK 3.0 library). -*************************************************************************/ -ae_bool rmatrixschur(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_matrix* s, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector tau; - ae_vector wi; - ae_vector wr; - ae_matrix a1; - ae_matrix s1; - ae_int_t info; - ae_int_t i; - ae_int_t j; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(s); - ae_vector_init(&tau, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wi, 0, DT_REAL, _state, ae_true); - ae_vector_init(&wr, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a1, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&s1, 0, 0, DT_REAL, _state, ae_true); - - - /* - * Upper Hessenberg form of the 0-based matrix - */ - rmatrixhessenberg(a, n, &tau, _state); - rmatrixhessenbergunpackq(a, n, &tau, s, _state); - - /* - * Convert from 0-based arrays to 1-based, - * then call InternalSchurDecomposition - * Awkward, of course, but Schur decompisiton subroutine - * is too complex to fix it. - * - */ - ae_matrix_set_length(&a1, n+1, n+1, _state); - ae_matrix_set_length(&s1, n+1, n+1, _state); - for(i=1; i<=n; i++) - { - for(j=1; j<=n; j++) - { - a1.ptr.pp_double[i][j] = a->ptr.pp_double[i-1][j-1]; - s1.ptr.pp_double[i][j] = s->ptr.pp_double[i-1][j-1]; - } - } - internalschurdecomposition(&a1, n, 1, 1, &wr, &wi, &s1, &info, _state); - result = info==0; - - /* - * convert from 1-based arrays to -based - */ - for(i=1; i<=n; i++) - { - for(j=1; j<=n; j++) - { - a->ptr.pp_double[i-1][j-1] = a1.ptr.pp_double[i][j]; - s->ptr.pp_double[i-1][j-1] = s1.ptr.pp_double[i][j]; - } - } - ae_frame_leave(_state); - return result; -} - - - -} - diff --git a/src/inc/alglib/linalg.h b/src/inc/alglib/linalg.h deleted file mode 100644 index e6364c1..0000000 --- a/src/inc/alglib/linalg.h +++ /dev/null @@ -1,5187 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _linalg_pkg_h -#define _linalg_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "alglibmisc.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - double r1; - double rinf; -} matinvreport; -typedef struct -{ - ae_vector vals; - ae_vector idx; - ae_vector ridx; - ae_vector didx; - ae_vector uidx; - ae_int_t matrixtype; - ae_int_t m; - ae_int_t n; - ae_int_t nfree; - ae_int_t ninitialized; -} sparsematrix; -typedef struct -{ - double e1; - double e2; - ae_vector x; - ae_vector ax; - double xax; - ae_int_t n; - ae_vector rk; - ae_vector rk1; - ae_vector xk; - ae_vector xk1; - ae_vector pk; - ae_vector pk1; - ae_vector b; - rcommstate rstate; - ae_vector tmp2; -} fblslincgstate; -typedef struct -{ - ae_int_t n; - ae_int_t m; - ae_int_t nstart; - ae_int_t nits; - ae_int_t seedval; - ae_vector x0; - ae_vector x1; - ae_vector t; - ae_vector xbest; - hqrndstate r; - ae_vector x; - ae_vector mv; - ae_vector mtv; - ae_bool needmv; - ae_bool needmtv; - double repnorm; - rcommstate rstate; -} normestimatorstate; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - - - - - - - - - - - - - - - - -/************************************************************************* -Matrix inverse report: -* R1 reciprocal of condition number in 1-norm -* RInf reciprocal of condition number in inf-norm -*************************************************************************/ -class _matinvreport_owner -{ -public: - _matinvreport_owner(); - _matinvreport_owner(const _matinvreport_owner &rhs); - _matinvreport_owner& operator=(const _matinvreport_owner &rhs); - virtual ~_matinvreport_owner(); - alglib_impl::matinvreport* c_ptr(); - alglib_impl::matinvreport* c_ptr() const; -protected: - alglib_impl::matinvreport *p_struct; -}; -class matinvreport : public _matinvreport_owner -{ -public: - matinvreport(); - matinvreport(const matinvreport &rhs); - matinvreport& operator=(const matinvreport &rhs); - virtual ~matinvreport(); - double &r1; - double &rinf; - -}; - -/************************************************************************* -Sparse matrix - -You should use ALGLIB functions to work with sparse matrix. -Never try to access its fields directly! -*************************************************************************/ -class _sparsematrix_owner -{ -public: - _sparsematrix_owner(); - _sparsematrix_owner(const _sparsematrix_owner &rhs); - _sparsematrix_owner& operator=(const _sparsematrix_owner &rhs); - virtual ~_sparsematrix_owner(); - alglib_impl::sparsematrix* c_ptr(); - alglib_impl::sparsematrix* c_ptr() const; -protected: - alglib_impl::sparsematrix *p_struct; -}; -class sparsematrix : public _sparsematrix_owner -{ -public: - sparsematrix(); - sparsematrix(const sparsematrix &rhs); - sparsematrix& operator=(const sparsematrix &rhs); - virtual ~sparsematrix(); - -}; - - - -/************************************************************************* -This object stores state of the iterative norm estimation algorithm. - -You should use ALGLIB functions to work with this object. -*************************************************************************/ -class _normestimatorstate_owner -{ -public: - _normestimatorstate_owner(); - _normestimatorstate_owner(const _normestimatorstate_owner &rhs); - _normestimatorstate_owner& operator=(const _normestimatorstate_owner &rhs); - virtual ~_normestimatorstate_owner(); - alglib_impl::normestimatorstate* c_ptr(); - alglib_impl::normestimatorstate* c_ptr() const; -protected: - alglib_impl::normestimatorstate *p_struct; -}; -class normestimatorstate : public _normestimatorstate_owner -{ -public: - normestimatorstate(); - normestimatorstate(const normestimatorstate &rhs); - normestimatorstate& operator=(const normestimatorstate &rhs); - virtual ~normestimatorstate(); - -}; - -/************************************************************************* -Cache-oblivous complex "copy-and-transpose" - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void cmatrixtranspose(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, complex_2d_array &b, const ae_int_t ib, const ae_int_t jb); - - -/************************************************************************* -Cache-oblivous real "copy-and-transpose" - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void rmatrixtranspose(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, real_2d_array &b, const ae_int_t ib, const ae_int_t jb); - - -/************************************************************************* -This code enforces symmetricy of the matrix by copying Upper part to lower -one (or vice versa). - -INPUT PARAMETERS: - A - matrix - N - number of rows/columns - IsUpper - whether we want to copy upper triangle to lower one (True) - or vice versa (False). -*************************************************************************/ -void rmatrixenforcesymmetricity(const real_2d_array &a, const ae_int_t n, const bool isupper); - - -/************************************************************************* -Copy - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void cmatrixcopy(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, complex_2d_array &b, const ae_int_t ib, const ae_int_t jb); - - -/************************************************************************* -Copy - -Input parameters: - M - number of rows - N - number of columns - A - source matrix, MxN submatrix is copied and transposed - IA - submatrix offset (row index) - JA - submatrix offset (column index) - B - destination matrix, must be large enough to store result - IB - submatrix offset (row index) - JB - submatrix offset (column index) -*************************************************************************/ -void rmatrixcopy(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, real_2d_array &b, const ae_int_t ib, const ae_int_t jb); - - -/************************************************************************* -Rank-1 correction: A := A + u*v' - -INPUT PARAMETERS: - M - number of rows - N - number of columns - A - target matrix, MxN submatrix is updated - IA - submatrix offset (row index) - JA - submatrix offset (column index) - U - vector #1 - IU - subvector offset - V - vector #2 - IV - subvector offset -*************************************************************************/ -void cmatrixrank1(const ae_int_t m, const ae_int_t n, complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, complex_1d_array &u, const ae_int_t iu, complex_1d_array &v, const ae_int_t iv); - - -/************************************************************************* -Rank-1 correction: A := A + u*v' - -INPUT PARAMETERS: - M - number of rows - N - number of columns - A - target matrix, MxN submatrix is updated - IA - submatrix offset (row index) - JA - submatrix offset (column index) - U - vector #1 - IU - subvector offset - V - vector #2 - IV - subvector offset -*************************************************************************/ -void rmatrixrank1(const ae_int_t m, const ae_int_t n, real_2d_array &a, const ae_int_t ia, const ae_int_t ja, real_1d_array &u, const ae_int_t iu, real_1d_array &v, const ae_int_t iv); - - -/************************************************************************* -Matrix-vector product: y := op(A)*x - -INPUT PARAMETERS: - M - number of rows of op(A) - M>=0 - N - number of columns of op(A) - N>=0 - A - target matrix - IA - submatrix offset (row index) - JA - submatrix offset (column index) - OpA - operation type: - * OpA=0 => op(A) = A - * OpA=1 => op(A) = A^T - * OpA=2 => op(A) = A^H - X - input vector - IX - subvector offset - IY - subvector offset - Y - preallocated matrix, must be large enough to store result - -OUTPUT PARAMETERS: - Y - vector which stores result - -if M=0, then subroutine does nothing. -if N=0, Y is filled by zeros. - - - -- ALGLIB routine -- - - 28.01.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixmv(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t opa, const complex_1d_array &x, const ae_int_t ix, complex_1d_array &y, const ae_int_t iy); - - -/************************************************************************* -Matrix-vector product: y := op(A)*x - -INPUT PARAMETERS: - M - number of rows of op(A) - N - number of columns of op(A) - A - target matrix - IA - submatrix offset (row index) - JA - submatrix offset (column index) - OpA - operation type: - * OpA=0 => op(A) = A - * OpA=1 => op(A) = A^T - X - input vector - IX - subvector offset - IY - subvector offset - Y - preallocated matrix, must be large enough to store result - -OUTPUT PARAMETERS: - Y - vector which stores result - -if M=0, then subroutine does nothing. -if N=0, Y is filled by zeros. - - - -- ALGLIB routine -- - - 28.01.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixmv(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t opa, const real_1d_array &x, const ae_int_t ix, real_1d_array &y, const ae_int_t iy); - - -/************************************************************************* - -*************************************************************************/ -void cmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2); -void smp_cmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2); - - -/************************************************************************* - -*************************************************************************/ -void cmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2); -void smp_cmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const complex_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const complex_2d_array &x, const ae_int_t i2, const ae_int_t j2); - - -/************************************************************************* - -*************************************************************************/ -void rmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2); -void smp_rmatrixrighttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2); - - -/************************************************************************* - -*************************************************************************/ -void rmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2); -void smp_rmatrixlefttrsm(const ae_int_t m, const ae_int_t n, const real_2d_array &a, const ae_int_t i1, const ae_int_t j1, const bool isupper, const bool isunit, const ae_int_t optype, const real_2d_array &x, const ae_int_t i2, const ae_int_t j2); - - -/************************************************************************* - -*************************************************************************/ -void cmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper); -void smp_cmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper); - - -/************************************************************************* - -*************************************************************************/ -void rmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper); -void smp_rmatrixsyrk(const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc, const bool isupper); - - -/************************************************************************* - -*************************************************************************/ -void cmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const alglib::complex alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const complex_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const alglib::complex beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc); -void smp_cmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const alglib::complex alpha, const complex_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const complex_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const alglib::complex beta, const complex_2d_array &c, const ae_int_t ic, const ae_int_t jc); - - -/************************************************************************* - -*************************************************************************/ -void rmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const real_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc); -void smp_rmatrixgemm(const ae_int_t m, const ae_int_t n, const ae_int_t k, const double alpha, const real_2d_array &a, const ae_int_t ia, const ae_int_t ja, const ae_int_t optypea, const real_2d_array &b, const ae_int_t ib, const ae_int_t jb, const ae_int_t optypeb, const double beta, const real_2d_array &c, const ae_int_t ic, const ae_int_t jc); - -/************************************************************************* -QR decomposition of a rectangular matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and R in compact form (see below). - Tau - array of scalar factors which are used to form - matrix Q. Array whose index ranges within [0.. Min(M-1,N-1)]. - -Matrix A is represented as A = QR, where Q is an orthogonal matrix of size -MxM, R - upper triangular (or upper trapezoid) matrix of size M x N. - -The elements of matrix R are located on and above the main diagonal of -matrix A. The elements which are located in Tau array and below the main -diagonal of matrix A are used to form matrix Q as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(0)*H(2)*...*H(k-1), - -where k = min(m,n), and each H(i) is in the form - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - real vector, -so that v(0:i-1) = 0, v(i) = 1, v(i+1:m-1) stored in A(i+1:m-1,i). - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqr(real_2d_array &a, const ae_int_t m, const ae_int_t n, real_1d_array &tau); - - -/************************************************************************* -LQ decomposition of a rectangular matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices L and Q in compact form (see below) - Tau - array of scalar factors which are used to form - matrix Q. Array whose index ranges within [0..Min(M,N)-1]. - -Matrix A is represented as A = LQ, where Q is an orthogonal matrix of size -MxM, L - lower triangular (or lower trapezoid) matrix of size M x N. - -The elements of matrix L are located on and below the main diagonal of -matrix A. The elements which are located in Tau array and above the main -diagonal of matrix A are used to form matrix Q as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(k-1)*H(k-2)*...*H(1)*H(0), - -where k = min(m,n), and each H(i) is of the form - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - real vector, so that v(0:i-1)=0, -v(i) = 1, v(i+1:n-1) stored in A(i,i+1:n-1). - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlq(real_2d_array &a, const ae_int_t m, const ae_int_t n, real_1d_array &tau); - - -/************************************************************************* -QR decomposition of a rectangular complex matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and R in compact form - Tau - array of scalar factors which are used to form matrix Q. Array - whose indexes range within [0.. Min(M,N)-1] - -Matrix A is represented as A = QR, where Q is an orthogonal matrix of size -MxM, R - upper triangular (or upper trapezoid) matrix of size MxN. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void cmatrixqr(complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_1d_array &tau); - - -/************************************************************************* -LQ decomposition of a rectangular complex matrix of size MxN - -Input parameters: - A - matrix A whose indexes range within [0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q and L in compact form - Tau - array of scalar factors which are used to form matrix Q. Array - whose indexes range within [0.. Min(M,N)-1] - -Matrix A is represented as A = LQ, where Q is an orthogonal matrix of size -MxM, L - lower triangular (or lower trapezoid) matrix of size MxN. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -void cmatrixlq(complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_1d_array &tau); - - -/************************************************************************* -Partial unpacking of matrix Q from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of RMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of the RMatrixQR subroutine. - QColumns - required number of columns of matrix Q. M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array whose indexes range within [0..M-1, 0..QColumns-1]. - If QColumns=0, the array remains unchanged. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqrunpackq(const real_2d_array &a, const ae_int_t m, const ae_int_t n, const real_1d_array &tau, const ae_int_t qcolumns, real_2d_array &q); - - -/************************************************************************* -Unpacking of matrix R from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of RMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - R - matrix R, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixqrunpackr(const real_2d_array &a, const ae_int_t m, const ae_int_t n, real_2d_array &r); - - -/************************************************************************* -Partial unpacking of matrix Q from the LQ decomposition of a matrix A - -Input parameters: - A - matrices L and Q in compact form. - Output of RMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of the RMatrixLQ subroutine. - QRows - required number of rows in matrix Q. N>=QRows>=0. - -Output parameters: - Q - first QRows rows of matrix Q. Array whose indexes range - within [0..QRows-1, 0..N-1]. If QRows=0, the array remains - unchanged. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlqunpackq(const real_2d_array &a, const ae_int_t m, const ae_int_t n, const real_1d_array &tau, const ae_int_t qrows, real_2d_array &q); - - -/************************************************************************* -Unpacking of matrix L from the LQ decomposition of a matrix A - -Input parameters: - A - matrices Q and L in compact form. - Output of RMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - L - matrix L, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlqunpackl(const real_2d_array &a, const ae_int_t m, const ae_int_t n, real_2d_array &l); - - -/************************************************************************* -Partial unpacking of matrix Q from QR decomposition of a complex matrix A. - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixQR subroutine . - M - number of rows in matrix A. M>=0. - N - number of columns in matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of CMatrixQR subroutine . - QColumns - required number of columns in matrix Q. M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array whose index ranges within [0..M-1, 0..QColumns-1]. - If QColumns=0, array isn't changed. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixqrunpackq(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, const complex_1d_array &tau, const ae_int_t qcolumns, complex_2d_array &q); - - -/************************************************************************* -Unpacking of matrix R from the QR decomposition of a matrix A - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixQR subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - R - matrix R, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixqrunpackr(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_2d_array &r); - - -/************************************************************************* -Partial unpacking of matrix Q from LQ decomposition of a complex matrix A. - -Input parameters: - A - matrices Q and R in compact form. - Output of CMatrixLQ subroutine . - M - number of rows in matrix A. M>=0. - N - number of columns in matrix A. N>=0. - Tau - scalar factors which are used to form Q. - Output of CMatrixLQ subroutine . - QRows - required number of rows in matrix Q. N>=QColumns>=0. - -Output parameters: - Q - first QRows rows of matrix Q. - Array whose index ranges within [0..QRows-1, 0..N-1]. - If QRows=0, array isn't changed. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlqunpackq(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, const complex_1d_array &tau, const ae_int_t qrows, complex_2d_array &q); - - -/************************************************************************* -Unpacking of matrix L from the LQ decomposition of a matrix A - -Input parameters: - A - matrices Q and L in compact form. - Output of CMatrixLQ subroutine. - M - number of rows in given matrix A. M>=0. - N - number of columns in given matrix A. N>=0. - -Output parameters: - L - matrix L, array[0..M-1, 0..N-1]. - - -- ALGLIB routine -- - 17.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlqunpackl(const complex_2d_array &a, const ae_int_t m, const ae_int_t n, complex_2d_array &l); - - -/************************************************************************* -Reduction of a rectangular matrix to bidiagonal form - -The algorithm reduces the rectangular matrix A to bidiagonal form by -orthogonal transformations P and Q: A = Q*B*P. - -Input parameters: - A - source matrix. array[0..M-1, 0..N-1] - M - number of rows in matrix A. - N - number of columns in matrix A. - -Output parameters: - A - matrices Q, B, P in compact form (see below). - TauQ - scalar factors which are used to form matrix Q. - TauP - scalar factors which are used to form matrix P. - -The main diagonal and one of the secondary diagonals of matrix A are -replaced with bidiagonal matrix B. Other elements contain elementary -reflections which form MxM matrix Q and NxN matrix P, respectively. - -If M>=N, B is the upper bidiagonal MxN matrix and is stored in the -corresponding elements of matrix A. Matrix Q is represented as a -product of elementary reflections Q = H(0)*H(1)*...*H(n-1), where -H(i) = 1-tau*v*v'. Here tau is a scalar which is stored in TauQ[i], and -vector v has the following structure: v(0:i-1)=0, v(i)=1, v(i+1:m-1) is -stored in elements A(i+1:m-1,i). Matrix P is as follows: P = -G(0)*G(1)*...*G(n-2), where G(i) = 1 - tau*u*u'. Tau is stored in TauP[i], -u(0:i)=0, u(i+1)=1, u(i+2:n-1) is stored in elements A(i,i+2:n-1). - -If M n): m=5, n=6 (m < n): - -( d e u1 u1 u1 ) ( d u1 u1 u1 u1 u1 ) -( v1 d e u2 u2 ) ( e d u2 u2 u2 u2 ) -( v1 v2 d e u3 ) ( v1 e d u3 u3 u3 ) -( v1 v2 v3 d e ) ( v1 v2 e d u4 u4 ) -( v1 v2 v3 v4 d ) ( v1 v2 v3 e d u5 ) -( v1 v2 v3 v4 v5 ) - -Here vi and ui are vectors which form H(i) and G(i), and d and e - -are the diagonal and off-diagonal elements of matrix B. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994. - Sergey Bochkanov, ALGLIB project, translation from FORTRAN to - pseudocode, 2007-2010. -*************************************************************************/ -void rmatrixbd(real_2d_array &a, const ae_int_t m, const ae_int_t n, real_1d_array &tauq, real_1d_array &taup); - - -/************************************************************************* -Unpacking matrix Q which reduces a matrix to bidiagonal form. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUQ - scalar factors which are used to form Q. - Output of ToBidiagonal subroutine. - QColumns - required number of columns in matrix Q. - M>=QColumns>=0. - -Output parameters: - Q - first QColumns columns of matrix Q. - Array[0..M-1, 0..QColumns-1] - If QColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackq(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &tauq, const ae_int_t qcolumns, real_2d_array &q); - - -/************************************************************************* -Multiplication by matrix Q which reduces matrix A to bidiagonal form. - -The algorithm allows pre- or post-multiply by Q or Q'. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUQ - scalar factors which are used to form Q. - Output of ToBidiagonal subroutine. - Z - multiplied matrix. - array[0..ZRows-1,0..ZColumns-1] - ZRows - number of rows in matrix Z. If FromTheRight=False, - ZRows=M, otherwise ZRows can be arbitrary. - ZColumns - number of columns in matrix Z. If FromTheRight=True, - ZColumns=M, otherwise ZColumns can be arbitrary. - FromTheRight - pre- or post-multiply. - DoTranspose - multiply by Q or Q'. - -Output parameters: - Z - product of Z and Q. - Array[0..ZRows-1,0..ZColumns-1] - If ZRows=0 or ZColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdmultiplybyq(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &tauq, real_2d_array &z, const ae_int_t zrows, const ae_int_t zcolumns, const bool fromtheright, const bool dotranspose); - - -/************************************************************************* -Unpacking matrix P which reduces matrix A to bidiagonal form. -The subroutine returns transposed matrix P. - -Input parameters: - QP - matrices Q and P in compact form. - Output of ToBidiagonal subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUP - scalar factors which are used to form P. - Output of ToBidiagonal subroutine. - PTRows - required number of rows of matrix P^T. N >= PTRows >= 0. - -Output parameters: - PT - first PTRows columns of matrix P^T - Array[0..PTRows-1, 0..N-1] - If PTRows=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackpt(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &taup, const ae_int_t ptrows, real_2d_array &pt); - - -/************************************************************************* -Multiplication by matrix P which reduces matrix A to bidiagonal form. - -The algorithm allows pre- or post-multiply by P or P'. - -Input parameters: - QP - matrices Q and P in compact form. - Output of RMatrixBD subroutine. - M - number of rows in matrix A. - N - number of columns in matrix A. - TAUP - scalar factors which are used to form P. - Output of RMatrixBD subroutine. - Z - multiplied matrix. - Array whose indexes range within [0..ZRows-1,0..ZColumns-1]. - ZRows - number of rows in matrix Z. If FromTheRight=False, - ZRows=N, otherwise ZRows can be arbitrary. - ZColumns - number of columns in matrix Z. If FromTheRight=True, - ZColumns=N, otherwise ZColumns can be arbitrary. - FromTheRight - pre- or post-multiply. - DoTranspose - multiply by P or P'. - -Output parameters: - Z - product of Z and P. - Array whose indexes range within [0..ZRows-1,0..ZColumns-1]. - If ZRows=0 or ZColumns=0, the array is not modified. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdmultiplybyp(const real_2d_array &qp, const ae_int_t m, const ae_int_t n, const real_1d_array &taup, real_2d_array &z, const ae_int_t zrows, const ae_int_t zcolumns, const bool fromtheright, const bool dotranspose); - - -/************************************************************************* -Unpacking of the main and secondary diagonals of bidiagonal decomposition -of matrix A. - -Input parameters: - B - output of RMatrixBD subroutine. - M - number of rows in matrix B. - N - number of columns in matrix B. - -Output parameters: - IsUpper - True, if the matrix is upper bidiagonal. - otherwise IsUpper is False. - D - the main diagonal. - Array whose index ranges within [0..Min(M,N)-1]. - E - the secondary diagonal (upper or lower, depending on - the value of IsUpper). - Array index ranges within [0..Min(M,N)-1], the last - element is not used. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixbdunpackdiagonals(const real_2d_array &b, const ae_int_t m, const ae_int_t n, bool &isupper, real_1d_array &d, real_1d_array &e); - - -/************************************************************************* -Reduction of a square matrix to upper Hessenberg form: Q'*A*Q = H, -where Q is an orthogonal matrix, H - Hessenberg matrix. - -Input parameters: - A - matrix A with elements [0..N-1, 0..N-1] - N - size of matrix A. - -Output parameters: - A - matrices Q and P in compact form (see below). - Tau - array of scalar factors which are used to form matrix Q. - Array whose index ranges within [0..N-2] - -Matrix H is located on the main diagonal, on the lower secondary diagonal -and above the main diagonal of matrix A. The elements which are used to -form matrix Q are situated in array Tau and below the lower secondary -diagonal of matrix A as follows: - -Matrix Q is represented as a product of elementary reflections - -Q = H(0)*H(2)*...*H(n-2), - -where each H(i) is given by - -H(i) = 1 - tau * v * (v^T) - -where tau is a scalar stored in Tau[I]; v - is a real vector, -so that v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) stored in A(i+2:n-1,i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void rmatrixhessenberg(real_2d_array &a, const ae_int_t n, real_1d_array &tau); - - -/************************************************************************* -Unpacking matrix Q which reduces matrix A to upper Hessenberg form - -Input parameters: - A - output of RMatrixHessenberg subroutine. - N - size of matrix A. - Tau - scalar factors which are used to form Q. - Output of RMatrixHessenberg subroutine. - -Output parameters: - Q - matrix Q. - Array whose indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixhessenbergunpackq(const real_2d_array &a, const ae_int_t n, const real_1d_array &tau, real_2d_array &q); - - -/************************************************************************* -Unpacking matrix H (the result of matrix A reduction to upper Hessenberg form) - -Input parameters: - A - output of RMatrixHessenberg subroutine. - N - size of matrix A. - -Output parameters: - H - matrix H. Array whose indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - 2005-2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixhessenbergunpackh(const real_2d_array &a, const ae_int_t n, real_2d_array &h); - - -/************************************************************************* -Reduction of a symmetric matrix which is given by its higher or lower -triangular part to a tridiagonal matrix using orthogonal similarity -transformation: Q'*A*Q=T. - -Input parameters: - A - matrix to be transformed - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. If IsUpper = True, then matrix A is given - by its upper triangle, and the lower triangle is not used - and not modified by the algorithm, and vice versa - if IsUpper = False. - -Output parameters: - A - matrices T and Q in compact form (see lower) - Tau - array of factors which are forming matrices H(i) - array with elements [0..N-2]. - D - main diagonal of symmetric matrix T. - array with elements [0..N-1]. - E - secondary diagonal of symmetric matrix T. - array with elements [0..N-2]. - - - If IsUpper=True, the matrix Q is represented as a product of elementary - reflectors - - Q = H(n-2) . . . H(2) H(0). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a real scalar, and v is a real vector with - v(i+1:n-1) = 0, v(i) = 1, v(0:i-1) is stored on exit in - A(0:i-1,i+1), and tau in TAU(i). - - If IsUpper=False, the matrix Q is represented as a product of elementary - reflectors - - Q = H(0) H(2) . . . H(n-2). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a real scalar, and v is a real vector with - v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) is stored on exit in A(i+2:n-1,i), - and tau in TAU(i). - - The contents of A on exit are illustrated by the following examples - with n = 5: - - if UPLO = 'U': if UPLO = 'L': - - ( d e v1 v2 v3 ) ( d ) - ( d e v2 v3 ) ( e d ) - ( d e v3 ) ( v0 e d ) - ( d e ) ( v0 v1 e d ) - ( d ) ( v0 v1 v2 e d ) - - where d and e denote diagonal and off-diagonal elements of T, and vi - denotes an element of the vector defining H(i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void smatrixtd(real_2d_array &a, const ae_int_t n, const bool isupper, real_1d_array &tau, real_1d_array &d, real_1d_array &e); - - -/************************************************************************* -Unpacking matrix Q which reduces symmetric matrix to a tridiagonal -form. - -Input parameters: - A - the result of a SMatrixTD subroutine - N - size of matrix A. - IsUpper - storage format (a parameter of SMatrixTD subroutine) - Tau - the result of a SMatrixTD subroutine - -Output parameters: - Q - transformation matrix. - array with elements [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void smatrixtdunpackq(const real_2d_array &a, const ae_int_t n, const bool isupper, const real_1d_array &tau, real_2d_array &q); - - -/************************************************************************* -Reduction of a Hermitian matrix which is given by its higher or lower -triangular part to a real tridiagonal matrix using unitary similarity -transformation: Q'*A*Q = T. - -Input parameters: - A - matrix to be transformed - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. If IsUpper = True, then matrix A is given - by its upper triangle, and the lower triangle is not used - and not modified by the algorithm, and vice versa - if IsUpper = False. - -Output parameters: - A - matrices T and Q in compact form (see lower) - Tau - array of factors which are forming matrices H(i) - array with elements [0..N-2]. - D - main diagonal of real symmetric matrix T. - array with elements [0..N-1]. - E - secondary diagonal of real symmetric matrix T. - array with elements [0..N-2]. - - - If IsUpper=True, the matrix Q is represented as a product of elementary - reflectors - - Q = H(n-2) . . . H(2) H(0). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a complex scalar, and v is a complex vector with - v(i+1:n-1) = 0, v(i) = 1, v(0:i-1) is stored on exit in - A(0:i-1,i+1), and tau in TAU(i). - - If IsUpper=False, the matrix Q is represented as a product of elementary - reflectors - - Q = H(0) H(2) . . . H(n-2). - - Each H(i) has the form - - H(i) = I - tau * v * v' - - where tau is a complex scalar, and v is a complex vector with - v(0:i) = 0, v(i+1) = 1, v(i+2:n-1) is stored on exit in A(i+2:n-1,i), - and tau in TAU(i). - - The contents of A on exit are illustrated by the following examples - with n = 5: - - if UPLO = 'U': if UPLO = 'L': - - ( d e v1 v2 v3 ) ( d ) - ( d e v2 v3 ) ( e d ) - ( d e v3 ) ( v0 e d ) - ( d e ) ( v0 v1 e d ) - ( d ) ( v0 v1 v2 e d ) - -where d and e denote diagonal and off-diagonal elements of T, and vi -denotes an element of the vector defining H(i). - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1992 -*************************************************************************/ -void hmatrixtd(complex_2d_array &a, const ae_int_t n, const bool isupper, complex_1d_array &tau, real_1d_array &d, real_1d_array &e); - - -/************************************************************************* -Unpacking matrix Q which reduces a Hermitian matrix to a real tridiagonal -form. - -Input parameters: - A - the result of a HMatrixTD subroutine - N - size of matrix A. - IsUpper - storage format (a parameter of HMatrixTD subroutine) - Tau - the result of a HMatrixTD subroutine - -Output parameters: - Q - transformation matrix. - array with elements [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void hmatrixtdunpackq(const complex_2d_array &a, const ae_int_t n, const bool isupper, const complex_1d_array &tau, complex_2d_array &q); - -/************************************************************************* -Singular value decomposition of a bidiagonal matrix (extended algorithm) - -The algorithm performs the singular value decomposition of a bidiagonal -matrix B (upper or lower) representing it as B = Q*S*P^T, where Q and P - -orthogonal matrices, S - diagonal matrix with non-negative elements on the -main diagonal, in descending order. - -The algorithm finds singular values. In addition, the algorithm can -calculate matrices Q and P (more precisely, not the matrices, but their -product with given matrices U and VT - U*Q and (P^T)*VT)). Of course, -matrices U and VT can be of any type, including identity. Furthermore, the -algorithm can calculate Q'*C (this product is calculated more effectively -than U*Q, because this calculation operates with rows instead of matrix -columns). - -The feature of the algorithm is its ability to find all singular values -including those which are arbitrarily close to 0 with relative accuracy -close to machine precision. If the parameter IsFractionalAccuracyRequired -is set to True, all singular values will have high relative accuracy close -to machine precision. If the parameter is set to False, only the biggest -singular value will have relative accuracy close to machine precision. -The absolute error of other singular values is equal to the absolute error -of the biggest singular value. - -Input parameters: - D - main diagonal of matrix B. - Array whose index ranges within [0..N-1]. - E - superdiagonal (or subdiagonal) of matrix B. - Array whose index ranges within [0..N-2]. - N - size of matrix B. - IsUpper - True, if the matrix is upper bidiagonal. - IsFractionalAccuracyRequired - - THIS PARAMETER IS IGNORED SINCE ALGLIB 3.5.0 - SINGULAR VALUES ARE ALWAYS SEARCHED WITH HIGH ACCURACY. - U - matrix to be multiplied by Q. - Array whose indexes range within [0..NRU-1, 0..N-1]. - The matrix can be bigger, in that case only the submatrix - [0..NRU-1, 0..N-1] will be multiplied by Q. - NRU - number of rows in matrix U. - C - matrix to be multiplied by Q'. - Array whose indexes range within [0..N-1, 0..NCC-1]. - The matrix can be bigger, in that case only the submatrix - [0..N-1, 0..NCC-1] will be multiplied by Q'. - NCC - number of columns in matrix C. - VT - matrix to be multiplied by P^T. - Array whose indexes range within [0..N-1, 0..NCVT-1]. - The matrix can be bigger, in that case only the submatrix - [0..N-1, 0..NCVT-1] will be multiplied by P^T. - NCVT - number of columns in matrix VT. - -Output parameters: - D - singular values of matrix B in descending order. - U - if NRU>0, contains matrix U*Q. - VT - if NCVT>0, contains matrix (P^T)*VT. - C - if NCC>0, contains matrix Q'*C. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - -Additional information: - The type of convergence is controlled by the internal parameter TOL. - If the parameter is greater than 0, the singular values will have - relative accuracy TOL. If TOL<0, the singular values will have - absolute accuracy ABS(TOL)*norm(B). - By default, |TOL| falls within the range of 10*Epsilon and 100*Epsilon, - where Epsilon is the machine precision. It is not recommended to use - TOL less than 10*Epsilon since this will considerably slow down the - algorithm and may not lead to error decreasing. -History: - * 31 March, 2007. - changed MAXITR from 6 to 12. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - October 31, 1999. -*************************************************************************/ -bool rmatrixbdsvd(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const bool isupper, const bool isfractionalaccuracyrequired, real_2d_array &u, const ae_int_t nru, real_2d_array &c, const ae_int_t ncc, real_2d_array &vt, const ae_int_t ncvt); - -/************************************************************************* -Singular value decomposition of a rectangular matrix. - -The algorithm calculates the singular value decomposition of a matrix of -size MxN: A = U * S * V^T - -The algorithm finds the singular values and, optionally, matrices U and V^T. -The algorithm can find both first min(M,N) columns of matrix U and rows of -matrix V^T (singular vectors), and matrices U and V^T wholly (of sizes MxM -and NxN respectively). - -Take into account that the subroutine does not return matrix V but V^T. - -Input parameters: - A - matrix to be decomposed. - Array whose indexes range within [0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - UNeeded - 0, 1 or 2. See the description of the parameter U. - VTNeeded - 0, 1 or 2. See the description of the parameter VT. - AdditionalMemory - - If the parameter: - * equals 0, the algorithm doesn’t use additional - memory (lower requirements, lower performance). - * equals 1, the algorithm uses additional - memory of size min(M,N)*min(M,N) of real numbers. - It often speeds up the algorithm. - * equals 2, the algorithm uses additional - memory of size M*min(M,N) of real numbers. - It allows to get a maximum performance. - The recommended value of the parameter is 2. - -Output parameters: - W - contains singular values in descending order. - U - if UNeeded=0, U isn't changed, the left singular vectors - are not calculated. - if Uneeded=1, U contains left singular vectors (first - min(M,N) columns of matrix U). Array whose indexes range - within [0..M-1, 0..Min(M,N)-1]. - if UNeeded=2, U contains matrix U wholly. Array whose - indexes range within [0..M-1, 0..M-1]. - VT - if VTNeeded=0, VT isn’t changed, the right singular vectors - are not calculated. - if VTNeeded=1, VT contains right singular vectors (first - min(M,N) rows of matrix V^T). Array whose indexes range - within [0..min(M,N)-1, 0..N-1]. - if VTNeeded=2, VT contains matrix V^T wholly. Array whose - indexes range within [0..N-1, 0..N-1]. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -bool rmatrixsvd(const real_2d_array &a, const ae_int_t m, const ae_int_t n, const ae_int_t uneeded, const ae_int_t vtneeded, const ae_int_t additionalmemory, real_1d_array &w, real_2d_array &u, real_2d_array &vt); - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a symmetric matrix - -The algorithm finds eigen pairs of a symmetric matrix by reducing it to -tridiagonal form and using the QL/QR algorithm. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpper - storage format. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -bool smatrixevd(const real_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, real_1d_array &d, real_2d_array &z); - - -/************************************************************************* -Subroutine for finding the eigenvalues (and eigenvectors) of a symmetric -matrix in a given half open interval (A, B] by using a bisection and -inverse iteration - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. Array [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - B1, B2 - half open interval (B1, B2] to search eigenvalues in. - -Output parameters: - M - number of eigenvalues found in a given half-interval (M>=0). - W - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..M-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if successful. M contains the number of eigenvalues in the given - half-interval (could be equal to 0), W contains the eigenvalues, - Z contains the eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned, - M is equal to 0. - - -- ALGLIB -- - Copyright 07.01.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixevdr(const real_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const double b1, const double b2, ae_int_t &m, real_1d_array &w, real_2d_array &z); - - -/************************************************************************* -Subroutine for finding the eigenvalues and eigenvectors of a symmetric -matrix with given indexes by using bisection and inverse iteration methods. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - -Output parameters: - W - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..I2-I1]. - In that case, the eigenvectors are stored in the matrix columns. - -Result: - True, if successful. W contains the eigenvalues, Z contains the - eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned. - - -- ALGLIB -- - Copyright 07.01.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixevdi(const real_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const ae_int_t i1, const ae_int_t i2, real_1d_array &w, real_2d_array &z); - - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a Hermitian matrix - -The algorithm finds eigen pairs of a Hermitian matrix by reducing it to -real tridiagonal form and using the QL/QR algorithm. - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged (rare case). - -Note: - eigenvectors of Hermitian matrix are defined up to multiplication by - a complex number L, such that |L|=1. - - -- ALGLIB -- - Copyright 2005, 23 March 2007 by Bochkanov Sergey -*************************************************************************/ -bool hmatrixevd(const complex_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, real_1d_array &d, complex_2d_array &z); - - -/************************************************************************* -Subroutine for finding the eigenvalues (and eigenvectors) of a Hermitian -matrix in a given half-interval (A, B] by using a bisection and inverse -iteration - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. Array whose indexes range within - [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - B1, B2 - half-interval (B1, B2] to search eigenvalues in. - -Output parameters: - M - number of eigenvalues found in a given half-interval, M>=0 - W - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..M-1]. - The eigenvectors are stored in the matrix columns. - -Result: - True, if successful. M contains the number of eigenvalues in the given - half-interval (could be equal to 0), W contains the eigenvalues, - Z contains the eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration - subroutine wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned, M is - equal to 0. - -Note: - eigen vectors of Hermitian matrix are defined up to multiplication by - a complex number L, such as |L|=1. - - -- ALGLIB -- - Copyright 07.01.2006, 24.03.2007 by Bochkanov Sergey. -*************************************************************************/ -bool hmatrixevdr(const complex_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const double b1, const double b2, ae_int_t &m, real_1d_array &w, complex_2d_array &z); - - -/************************************************************************* -Subroutine for finding the eigenvalues and eigenvectors of a Hermitian -matrix with given indexes by using bisection and inverse iteration methods - -Input parameters: - A - Hermitian matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or - not. If ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - IsUpperA - storage format of matrix A. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - -Output parameters: - W - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..I2-I1]. - In that case, the eigenvectors are stored in the matrix - columns. - -Result: - True, if successful. W contains the eigenvalues, Z contains the - eigenvectors (if needed). - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration - subroutine wasn't able to find all the corresponding eigenvectors. - In that case, the eigenvalues and eigenvectors are not returned. - -Note: - eigen vectors of Hermitian matrix are defined up to multiplication by - a complex number L, such as |L|=1. - - -- ALGLIB -- - Copyright 07.01.2006, 24.03.2007 by Bochkanov Sergey. -*************************************************************************/ -bool hmatrixevdi(const complex_2d_array &a, const ae_int_t n, const ae_int_t zneeded, const bool isupper, const ae_int_t i1, const ae_int_t i2, real_1d_array &w, complex_2d_array &z); - - -/************************************************************************* -Finding the eigenvalues and eigenvectors of a tridiagonal symmetric matrix - -The algorithm finds the eigen pairs of a tridiagonal symmetric matrix by -using an QL/QR algorithm with implicit shifts. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix A. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix - are multiplied by the square matrix Z. It is used if the - tridiagonal matrix is obtained by the similarity - transformation of a symmetric matrix; - * 2, the eigenvectors of a tridiagonal matrix replace the - square matrix Z; - * 3, matrix Z contains the first row of the eigenvectors - matrix. - Z - if ZNeeded=1, Z contains the square matrix by which the - eigenvectors are multiplied. - Array whose indexes range within [0..N-1, 0..N-1]. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains the product of a given matrix (from the left) - and the eigenvectors matrix (from the right); - * 2, Z contains the eigenvectors. - * 3, Z contains the first row of the eigenvectors matrix. - If ZNeeded<3, Z is the array whose indexes range within [0..N-1, 0..N-1]. - In that case, the eigenvectors are stored in the matrix columns. - If ZNeeded=3, Z is the array whose indexes range within [0..0, 0..N-1]. - -Result: - True, if the algorithm has converged. - False, if the algorithm hasn't converged. - - -- LAPACK routine (version 3.0) -- - Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd., - Courant Institute, Argonne National Lab, and Rice University - September 30, 1994 -*************************************************************************/ -bool smatrixtdevd(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const ae_int_t zneeded, real_2d_array &z); - - -/************************************************************************* -Subroutine for finding the tridiagonal matrix eigenvalues/vectors in a -given half-interval (A, B] by using bisection and inverse iteration. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix, N>=0. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix are multiplied - by the square matrix Z. It is used if the tridiagonal - matrix is obtained by the similarity transformation - of a symmetric matrix. - * 2, the eigenvectors of a tridiagonal matrix replace matrix Z. - A, B - half-interval (A, B] to search eigenvalues in. - Z - if ZNeeded is equal to: - * 0, Z isn't used and remains unchanged; - * 1, Z contains the square matrix (array whose indexes range - within [0..N-1, 0..N-1]) which reduces the given symmetric - matrix to tridiagonal form; - * 2, Z isn't used (but changed on the exit). - -Output parameters: - D - array of the eigenvalues found. - Array whose index ranges within [0..M-1]. - M - number of eigenvalues found in the given half-interval (M>=0). - Z - if ZNeeded is equal to: - * 0, doesn't contain any information; - * 1, contains the product of a given NxN matrix Z (from the - left) and NxM matrix of the eigenvectors found (from the - right). Array whose indexes range within [0..N-1, 0..M-1]. - * 2, contains the matrix of the eigenvectors found. - Array whose indexes range within [0..N-1, 0..M-1]. - -Result: - - True, if successful. In that case, M contains the number of eigenvalues - in the given half-interval (could be equal to 0), D contains the eigenvalues, - Z contains the eigenvectors (if needed). - It should be noted that the subroutine changes the size of arrays D and Z. - - False, if the bisection method subroutine wasn't able to find the - eigenvalues in the given interval or if the inverse iteration subroutine - wasn't able to find all the corresponding eigenvectors. In that case, - the eigenvalues and eigenvectors are not returned, M is equal to 0. - - -- ALGLIB -- - Copyright 31.03.2008 by Bochkanov Sergey -*************************************************************************/ -bool smatrixtdevdr(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const ae_int_t zneeded, const double a, const double b, ae_int_t &m, real_2d_array &z); - - -/************************************************************************* -Subroutine for finding tridiagonal matrix eigenvalues/vectors with given -indexes (in ascending order) by using the bisection and inverse iteraion. - -Input parameters: - D - the main diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-1]. - E - the secondary diagonal of a tridiagonal matrix. - Array whose index ranges within [0..N-2]. - N - size of matrix. N>=0. - ZNeeded - flag controlling whether the eigenvectors are needed or not. - If ZNeeded is equal to: - * 0, the eigenvectors are not needed; - * 1, the eigenvectors of a tridiagonal matrix are multiplied - by the square matrix Z. It is used if the - tridiagonal matrix is obtained by the similarity transformation - of a symmetric matrix. - * 2, the eigenvectors of a tridiagonal matrix replace - matrix Z. - I1, I2 - index interval for searching (from I1 to I2). - 0 <= I1 <= I2 <= N-1. - Z - if ZNeeded is equal to: - * 0, Z isn't used and remains unchanged; - * 1, Z contains the square matrix (array whose indexes range within [0..N-1, 0..N-1]) - which reduces the given symmetric matrix to tridiagonal form; - * 2, Z isn't used (but changed on the exit). - -Output parameters: - D - array of the eigenvalues found. - Array whose index ranges within [0..I2-I1]. - Z - if ZNeeded is equal to: - * 0, doesn't contain any information; - * 1, contains the product of a given NxN matrix Z (from the left) and - Nx(I2-I1) matrix of the eigenvectors found (from the right). - Array whose indexes range within [0..N-1, 0..I2-I1]. - * 2, contains the matrix of the eigenvalues found. - Array whose indexes range within [0..N-1, 0..I2-I1]. - - -Result: - - True, if successful. In that case, D contains the eigenvalues, - Z contains the eigenvectors (if needed). - It should be noted that the subroutine changes the size of arrays D and Z. - - False, if the bisection method subroutine wasn't able to find the eigenvalues - in the given interval or if the inverse iteration subroutine wasn't able - to find all the corresponding eigenvectors. In that case, the eigenvalues - and eigenvectors are not returned. - - -- ALGLIB -- - Copyright 25.12.2005 by Bochkanov Sergey -*************************************************************************/ -bool smatrixtdevdi(real_1d_array &d, const real_1d_array &e, const ae_int_t n, const ae_int_t zneeded, const ae_int_t i1, const ae_int_t i2, real_2d_array &z); - - -/************************************************************************* -Finding eigenvalues and eigenvectors of a general matrix - -The algorithm finds eigenvalues and eigenvectors of a general matrix by -using the QR algorithm with multiple shifts. The algorithm can find -eigenvalues and both left and right eigenvectors. - -The right eigenvector is a vector x such that A*x = w*x, and the left -eigenvector is a vector y such that y'*A = w*y' (here y' implies a complex -conjugate transposition of vector y). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - VNeeded - flag controlling whether eigenvectors are needed or not. - If VNeeded is equal to: - * 0, eigenvectors are not returned; - * 1, right eigenvectors are returned; - * 2, left eigenvectors are returned; - * 3, both left and right eigenvectors are returned. - -Output parameters: - WR - real parts of eigenvalues. - Array whose index ranges within [0..N-1]. - WR - imaginary parts of eigenvalues. - Array whose index ranges within [0..N-1]. - VL, VR - arrays of left and right eigenvectors (if they are needed). - If WI[i]=0, the respective eigenvalue is a real number, - and it corresponds to the column number I of matrices VL/VR. - If WI[i]>0, we have a pair of complex conjugate numbers with - positive and negative imaginary parts: - the first eigenvalue WR[i] + sqrt(-1)*WI[i]; - the second eigenvalue WR[i+1] + sqrt(-1)*WI[i+1]; - WI[i]>0 - WI[i+1] = -WI[i] < 0 - In that case, the eigenvector corresponding to the first - eigenvalue is located in i and i+1 columns of matrices - VL/VR (the column number i contains the real part, and the - column number i+1 contains the imaginary part), and the vector - corresponding to the second eigenvalue is a complex conjugate to - the first vector. - Arrays whose indexes range within [0..N-1, 0..N-1]. - -Result: - True, if the algorithm has converged. - False, if the algorithm has not converged. - -Note 1: - Some users may ask the following question: what if WI[N-1]>0? - WI[N] must contain an eigenvalue which is complex conjugate to the - N-th eigenvalue, but the array has only size N? - The answer is as follows: such a situation cannot occur because the - algorithm finds a pairs of eigenvalues, therefore, if WI[i]>0, I is - strictly less than N-1. - -Note 2: - The algorithm performance depends on the value of the internal parameter - NS of the InternalSchurDecomposition subroutine which defines the number - of shifts in the QR algorithm (similarly to the block width in block-matrix - algorithms of linear algebra). If you require maximum performance - on your machine, it is recommended to adjust this parameter manually. - - -See also the InternalTREVC subroutine. - -The algorithm is based on the LAPACK 3.0 library. -*************************************************************************/ -bool rmatrixevd(const real_2d_array &a, const ae_int_t n, const ae_int_t vneeded, real_1d_array &wr, real_1d_array &wi, real_2d_array &vl, real_2d_array &vr); - -/************************************************************************* -Generation of a random uniformly distributed (Haar) orthogonal matrix - -INPUT PARAMETERS: - N - matrix size, N>=1 - -OUTPUT PARAMETERS: - A - orthogonal NxN matrix, array[0..N-1,0..N-1] - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonal(const ae_int_t n, real_2d_array &a); - - -/************************************************************************* -Generation of random NxN matrix with given condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndcond(const ae_int_t n, const double c, real_2d_array &a); - - -/************************************************************************* -Generation of a random Haar distributed orthogonal complex matrix - -INPUT PARAMETERS: - N - matrix size, N>=1 - -OUTPUT PARAMETERS: - A - orthogonal NxN matrix, array[0..N-1,0..N-1] - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonal(const ae_int_t n, complex_2d_array &a); - - -/************************************************************************* -Generation of random NxN complex matrix with given condition number C and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndcond(const ae_int_t n, const double c, complex_2d_array &a); - - -/************************************************************************* -Generation of random NxN symmetric matrix with given condition number and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void smatrixrndcond(const ae_int_t n, const double c, real_2d_array &a); - - -/************************************************************************* -Generation of random NxN symmetric positive definite matrix with given -condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random SPD matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixrndcond(const ae_int_t n, const double c, real_2d_array &a); - - -/************************************************************************* -Generation of random NxN Hermitian matrix with given condition number and -norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hmatrixrndcond(const ae_int_t n, const double c, complex_2d_array &a); - - -/************************************************************************* -Generation of random NxN Hermitian positive definite matrix with given -condition number and norm2(A)=1 - -INPUT PARAMETERS: - N - matrix size - C - condition number (in 2-norm) - -OUTPUT PARAMETERS: - A - random HPD matrix with norm2(A)=1 and cond(A)=C - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixrndcond(const ae_int_t n, const double c, complex_2d_array &a); - - -/************************************************************************* -Multiplication of MxN matrix by NxN random Haar distributed orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonalfromtheright(real_2d_array &a, const ae_int_t m, const ae_int_t n); - - -/************************************************************************* -Multiplication of MxN matrix by MxM random Haar distributed orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - Q*A, where Q is random MxM orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void rmatrixrndorthogonalfromtheleft(real_2d_array &a, const ae_int_t m, const ae_int_t n); - - -/************************************************************************* -Multiplication of MxN complex matrix by NxN random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonalfromtheright(complex_2d_array &a, const ae_int_t m, const ae_int_t n); - - -/************************************************************************* -Multiplication of MxN complex matrix by MxM random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..M-1, 0..N-1] - M, N- matrix size - -OUTPUT PARAMETERS: - A - Q*A, where Q is random MxM orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void cmatrixrndorthogonalfromtheleft(complex_2d_array &a, const ae_int_t m, const ae_int_t n); - - -/************************************************************************* -Symmetric multiplication of NxN matrix by random Haar distributed -orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..N-1, 0..N-1] - N - matrix size - -OUTPUT PARAMETERS: - A - Q'*A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void smatrixrndmultiply(real_2d_array &a, const ae_int_t n); - - -/************************************************************************* -Hermitian multiplication of NxN matrix by random Haar distributed -complex orthogonal matrix - -INPUT PARAMETERS: - A - matrix, array[0..N-1, 0..N-1] - N - matrix size - -OUTPUT PARAMETERS: - A - Q^H*A*Q, where Q is random NxN orthogonal matrix - - -- ALGLIB routine -- - 04.12.2009 - Bochkanov Sergey -*************************************************************************/ -void hmatrixrndmultiply(complex_2d_array &a, const ae_int_t n); - -/************************************************************************* -LU decomposition of a general real matrix with row pivoting - -A is represented as A = P*L*U, where: -* L is lower unitriangular matrix -* U is upper triangular matrix -* P = P0*P1*...*PK, K=min(M,N)-1, - Pi - permutation matrix for I and Pivots[I] - -This is cache-oblivous implementation of LU decomposition. -It is optimized for square matrices. As for rectangular matrices: -* best case - M>>N -* worst case - N>>M, small M, large N, matrix does not fit in CPU cache - -INPUT PARAMETERS: - A - array[0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - - -OUTPUT PARAMETERS: - A - matrices L and U in compact form: - * L is stored under main diagonal - * U is stored on and above main diagonal - Pivots - permutation matrix in compact form. - array[0..Min(M-1,N-1)]. - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixlu(real_2d_array &a, const ae_int_t m, const ae_int_t n, integer_1d_array &pivots); - - -/************************************************************************* -LU decomposition of a general complex matrix with row pivoting - -A is represented as A = P*L*U, where: -* L is lower unitriangular matrix -* U is upper triangular matrix -* P = P0*P1*...*PK, K=min(M,N)-1, - Pi - permutation matrix for I and Pivots[I] - -This is cache-oblivous implementation of LU decomposition. It is optimized -for square matrices. As for rectangular matrices: -* best case - M>>N -* worst case - N>>M, small M, large N, matrix does not fit in CPU cache - -INPUT PARAMETERS: - A - array[0..M-1, 0..N-1]. - M - number of rows in matrix A. - N - number of columns in matrix A. - - -OUTPUT PARAMETERS: - A - matrices L and U in compact form: - * L is stored under main diagonal - * U is stored on and above main diagonal - Pivots - permutation matrix in compact form. - array[0..Min(M-1,N-1)]. - - -- ALGLIB routine -- - 10.01.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixlu(complex_2d_array &a, const ae_int_t m, const ae_int_t n, integer_1d_array &pivots); - - -/************************************************************************* -Cache-oblivious Cholesky decomposition - -The algorithm computes Cholesky decomposition of a Hermitian positive- -definite matrix. The result of an algorithm is a representation of A as -A=U'*U or A=L*L' (here X' detones conj(X^T)). - -INPUT PARAMETERS: - A - upper or lower triangle of a factorized matrix. - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - if IsUpper=True, then A contains an upper triangle of - a symmetric matrix, otherwise A contains a lower one. - -OUTPUT PARAMETERS: - A - the result of factorization. If IsUpper=True, then - the upper triangle contains matrix U, so that A = U'*U, - and the elements below the main diagonal are not modified. - Similarly, if IsUpper = False. - -RESULT: - If the matrix is positive-definite, the function returns True. - Otherwise, the function returns False. Contents of A is not determined - in such case. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -bool hpdmatrixcholesky(complex_2d_array &a, const ae_int_t n, const bool isupper); - - -/************************************************************************* -Cache-oblivious Cholesky decomposition - -The algorithm computes Cholesky decomposition of a symmetric positive- -definite matrix. The result of an algorithm is a representation of A as -A=U^T*U or A=L*L^T - -INPUT PARAMETERS: - A - upper or lower triangle of a factorized matrix. - array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - if IsUpper=True, then A contains an upper triangle of - a symmetric matrix, otherwise A contains a lower one. - -OUTPUT PARAMETERS: - A - the result of factorization. If IsUpper=True, then - the upper triangle contains matrix U, so that A = U^T*U, - and the elements below the main diagonal are not modified. - Similarly, if IsUpper = False. - -RESULT: - If the matrix is positive-definite, the function returns True. - Otherwise, the function returns False. Contents of A is not determined - in such case. - - -- ALGLIB routine -- - 15.12.2009 - Bochkanov Sergey -*************************************************************************/ -bool spdmatrixcholesky(real_2d_array &a, const ae_int_t n, const bool isupper); - -/************************************************************************* -Estimate of a matrix condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixrcond1(const real_2d_array &a, const ae_int_t n); - - -/************************************************************************* -Estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixrcondinf(const real_2d_array &a, const ae_int_t n); - - -/************************************************************************* -Condition number estimate of a symmetric positive definite matrix. - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm of condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - A - symmetric positive definite matrix which is given by its - upper or lower triangle depending on the value of - IsUpper. Array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - -Result: - 1/LowerBound(cond(A)), if matrix A is positive definite, - -1, if matrix A is not positive definite, and its condition number - could not be found by this algorithm. - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double spdmatrixrcond(const real_2d_array &a, const ae_int_t n, const bool isupper); - - -/************************************************************************* -Triangular matrix: estimate of a condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array[0..N-1, 0..N-1]. - N - size of A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixtrrcond1(const real_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit); - - -/************************************************************************* -Triangular matrix: estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixtrrcondinf(const real_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit); - - -/************************************************************************* -Condition number estimate of a Hermitian positive definite matrix. - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm of condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - A - Hermitian positive definite matrix which is given by its - upper or lower triangle depending on the value of - IsUpper. Array with elements [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - storage format. - -Result: - 1/LowerBound(cond(A)), if matrix A is positive definite, - -1, if matrix A is not positive definite, and its condition number - could not be found by this algorithm. - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double hpdmatrixrcond(const complex_2d_array &a, const ae_int_t n, const bool isupper); - - -/************************************************************************* -Estimate of a matrix condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixrcond1(const complex_2d_array &a, const ae_int_t n); - - -/************************************************************************* -Estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixrcondinf(const complex_2d_array &a, const ae_int_t n); - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the RMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixlurcond1(const real_2d_array &lua, const ae_int_t n); - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition -(infinity norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the RMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double rmatrixlurcondinf(const real_2d_array &lua, const ae_int_t n); - - -/************************************************************************* -Condition number estimate of a symmetric positive definite matrix given by -Cholesky decomposition. - -The algorithm calculates a lower bound of the condition number. In this -case, the algorithm does not return a lower bound of the condition number, -but an inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - CD - Cholesky decomposition of matrix A, - output of SMatrixCholesky subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double spdmatrixcholeskyrcond(const real_2d_array &a, const ae_int_t n, const bool isupper); - - -/************************************************************************* -Condition number estimate of a Hermitian positive definite matrix given by -Cholesky decomposition. - -The algorithm calculates a lower bound of the condition number. In this -case, the algorithm does not return a lower bound of the condition number, -but an inverse number (to avoid an overflow in case of a singular matrix). - -It should be noted that 1-norm and inf-norm condition numbers of symmetric -matrices are equal, so the algorithm doesn't take into account the -differences between these types of norms. - -Input parameters: - CD - Cholesky decomposition of matrix A, - output of SMatrixCholesky subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double hpdmatrixcholeskyrcond(const complex_2d_array &a, const ae_int_t n, const bool isupper); - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the CMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixlurcond1(const complex_2d_array &lua, const ae_int_t n); - - -/************************************************************************* -Estimate of the condition number of a matrix given by its LU decomposition -(infinity norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - LUA - LU decomposition of a matrix in compact form. Output of - the CMatrixLU subroutine. - N - size of matrix A. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixlurcondinf(const complex_2d_array &lua, const ae_int_t n); - - -/************************************************************************* -Triangular matrix: estimate of a condition number (1-norm) - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array[0..N-1, 0..N-1]. - N - size of A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixtrrcond1(const complex_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit); - - -/************************************************************************* -Triangular matrix: estimate of a matrix condition number (infinity-norm). - -The algorithm calculates a lower bound of the condition number. In this case, -the algorithm does not return a lower bound of the condition number, but an -inverse number (to avoid an overflow in case of a singular matrix). - -Input parameters: - A - matrix. Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - IsUpper - True, if the matrix is upper triangular. - IsUnit - True, if the matrix has a unit diagonal. - -Result: 1/LowerBound(cond(A)) - -NOTE: - if k(A) is very large, then matrix is assumed degenerate, k(A)=INF, - 0.0 is returned in such cases. -*************************************************************************/ -double cmatrixtrrcondinf(const complex_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit); - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of RMatrixLU subroutine). - Pivots - table of permutations - (the output of RMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code: - * -3 A is singular, or VERY close to singular. - it is filled by zeros in such cases. - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - A - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void rmatrixluinverse(real_2d_array &a, const integer_1d_array &pivots, const ae_int_t n, ae_int_t &info, matinvreport &rep); -void rmatrixluinverse(real_2d_array &a, const integer_1d_array &pivots, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - -Result: - True, if the matrix is not singular. - False, if the matrix is singular. - - -- ALGLIB -- - Copyright 2005-2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinverse(real_2d_array &a, const ae_int_t n, ae_int_t &info, matinvreport &rep); -void rmatrixinverse(real_2d_array &a, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a matrix given by its LU decomposition. - -INPUT PARAMETERS: - A - LU decomposition of the matrix - (output of CMatrixLU subroutine). - Pivots - table of permutations - (the output of CMatrixLU subroutine). - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -OUTPUT PARAMETERS: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 05.02.2010 - Bochkanov Sergey -*************************************************************************/ -void cmatrixluinverse(complex_2d_array &a, const integer_1d_array &pivots, const ae_int_t n, ae_int_t &info, matinvreport &rep); -void cmatrixluinverse(complex_2d_array &a, const integer_1d_array &pivots, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a general matrix. - -Input parameters: - A - matrix - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void cmatrixinverse(complex_2d_array &a, const ae_int_t n, ae_int_t &info, matinvreport &rep); -void cmatrixinverse(complex_2d_array &a, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a symmetric positive definite matrix which is given -by Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition of the matrix to be inverted: - A=U’*U or A = L*L'. - Output of SPDMatrixCholesky subroutine. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, lower half is used. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskyinverse(real_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep); -void spdmatrixcholeskyinverse(real_2d_array &a, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a symmetric positive definite matrix. - -Given an upper or lower triangle of a symmetric positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void spdmatrixinverse(real_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep); -void spdmatrixinverse(real_2d_array &a, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a Hermitian positive definite matrix which is given -by Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition of the matrix to be inverted: - A=U’*U or A = L*L'. - Output of HPDMatrixCholesky subroutine. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, lower half is used. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskyinverse(complex_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep); -void hpdmatrixcholeskyinverse(complex_2d_array &a, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Inversion of a Hermitian positive definite matrix. - -Given an upper or lower triangle of a Hermitian positive definite matrix, -the algorithm generates matrix A^-1 and saves the upper or lower triangle -depending on the input. - -Input parameters: - A - matrix to be inverted (upper or lower triangle). - Array with elements [0..N-1,0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - storage type (optional): - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Output parameters: - Info - return code, same as in RMatrixLUInverse - Rep - solver report, same as in RMatrixLUInverse - A - inverse of matrix A, same as in RMatrixLUInverse - - -- ALGLIB routine -- - 10.02.2010 - Bochkanov Sergey -*************************************************************************/ -void hpdmatrixinverse(complex_2d_array &a, const ae_int_t n, const bool isupper, ae_int_t &info, matinvreport &rep); -void hpdmatrixinverse(complex_2d_array &a, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Triangular matrix inverse (real) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixtrinverse(real_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit, ae_int_t &info, matinvreport &rep); -void rmatrixtrinverse(real_2d_array &a, const bool isupper, ae_int_t &info, matinvreport &rep); - - -/************************************************************************* -Triangular matrix inverse (complex) - -The subroutine inverts the following types of matrices: - * upper triangular - * upper triangular with unit diagonal - * lower triangular - * lower triangular with unit diagonal - -In case of an upper (lower) triangular matrix, the inverse matrix will -also be upper (lower) triangular, and after the end of the algorithm, the -inverse matrix replaces the source matrix. The elements below (above) the -main diagonal are not changed by the algorithm. - -If the matrix has a unit diagonal, the inverse matrix also has a unit -diagonal, and the diagonal elements are not passed to the algorithm. - -Input parameters: - A - matrix, array[0..N-1, 0..N-1]. - N - size of matrix A (optional) : - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, size is automatically determined from - matrix size (A must be square matrix) - IsUpper - True, if the matrix is upper triangular. - IsUnit - diagonal type (optional): - * if True, matrix has unit diagonal (a[i,i] are NOT used) - * if False, matrix diagonal is arbitrary - * if not given, False is assumed - -Output parameters: - Info - same as for RMatrixLUInverse - Rep - same as for RMatrixLUInverse - A - same as for RMatrixLUInverse. - - -- ALGLIB -- - Copyright 05.02.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixtrinverse(complex_2d_array &a, const ae_int_t n, const bool isupper, const bool isunit, ae_int_t &info, matinvreport &rep); -void cmatrixtrinverse(complex_2d_array &a, const bool isupper, ae_int_t &info, matinvreport &rep); - -/************************************************************************* -This function creates sparse matrix in a Hash-Table format. - -This function creates Hast-Table matrix, which can be converted to CRS -format after its initialization is over. Typical usage scenario for a -sparse matrix is: -1. creation in a Hash-Table format -2. insertion of the matrix elements -3. conversion to the CRS representation -4. matrix is passed to some linear algebra algorithm - -Some information about different matrix formats can be found below, in -the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - K - K>=0, expected number of non-zero elements in a matrix. - K can be inexact approximation, can be less than actual - number of elements (table will grow when needed) or - even zero). - It is important to understand that although hash-table - may grow automatically, it is better to provide good - estimate of data size. - -OUTPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - All elements of the matrix are zero. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreate(const ae_int_t m, const ae_int_t n, const ae_int_t k, sparsematrix &s); -void sparsecreate(const ae_int_t m, const ae_int_t n, sparsematrix &s); - - -/************************************************************************* -This function creates sparse matrix in a CRS format (expert function for -situations when you are running out of memory). - -This function creates CRS matrix. Typical usage scenario for a CRS matrix -is: -1. creation (you have to tell number of non-zero elements at each row at - this moment) -2. insertion of the matrix elements (row by row, from left to right) -3. matrix is passed to some linear algebra algorithm - -This function is a memory-efficient alternative to SparseCreate(), but it -is more complex because it requires you to know in advance how large your -matrix is. Some information about different matrix formats can be found -below, in the "NOTES" section. - -INPUT PARAMETERS - M - number of rows in a matrix, M>=1 - N - number of columns in a matrix, N>=1 - NER - number of elements at each row, array[M], NER[I]>=0 - -OUTPUT PARAMETERS - S - sparse M*N matrix in CRS representation. - You have to fill ALL non-zero elements by calling - SparseSet() BEFORE you try to use this matrix. - -NOTE 1. - -Sparse matrices can be stored using either Hash-Table representation or -Compressed Row Storage representation. Hast-table is better suited for -querying and dynamic operations (thus, it is used for matrix -initialization), but it is inefficient when you want to make some linear -algebra operations. - -From the other side, CRS is better suited for linear algebra operations, -but initialization is less convenient - you have to tell row sizes at the -initialization, and you can fill matrix only row by row, from left to -right. CRS is also very inefficient when you want to find matrix element -by its index. - -Thus, Hash-Table representation does not support linear algebra -operations, while CRS format does not support modification of the table. -Tables below outline information about these two formats: - - OPERATIONS WITH MATRIX HASH CRS - create + + - read element + + - modify element + - add value to element + - A*x (dense vector) + - A'*x (dense vector) + - A*X (dense matrix) + - A'*X (dense matrix) + - -NOTE 2. - -Hash-tables use memory inefficiently, and they have to keep some amount -of the "spare memory" in order to have good performance. Hash table for -matrix with K non-zero elements will need C*K*(8+2*sizeof(int)) bytes, -where C is a small constant, about 1.5-2 in magnitude. - -CRS storage, from the other side, is more memory-efficient, and needs -just K*(8+sizeof(int))+M*sizeof(int) bytes, where M is a number of rows -in a matrix. - -When you convert from the Hash-Table to CRS representation, all unneeded -memory will be freed. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecreatecrs(const ae_int_t m, const ae_int_t n, const integer_1d_array &ner, sparsematrix &s); - - -/************************************************************************* -This function copies S0 to S1. - -NOTE: this function does not verify its arguments, it just copies all -fields of the structure. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsecopy(const sparsematrix &s0, sparsematrix &s1); - - -/************************************************************************* -This function adds value to S[i,j] - element of the sparse matrix. Matrix -must be in a Hash-Table mode. - -In case S[i,j] already exists in the table, V i added to its value. In -case S[i,j] is non-existent, it is inserted in the table. Table -automatically grows when necessary. - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table representation. - Exception will be thrown for CRS matrix. - I - row index of the element to modify, 0<=I=i - are used, and lower triangle is ignored (it can be - empty - these elements are not referenced at all). - * if lower triangle is given, only S[i,j] for j<=i - are used, and upper triangle is ignored. - X - array[N], input vector. For performance reasons we - make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - Y - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - Y - array[M], S*x - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsesmv(const sparsematrix &s, const bool isupper, const real_1d_array &x, real_1d_array &y); - - -/************************************************************************* -This function calculates matrix-matrix product S*A. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size - is at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[M][K], S*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemm(const sparsematrix &s, const real_2d_array &a, const ae_int_t k, real_2d_array &b); - - -/************************************************************************* -This function calculates matrix-matrix product S^T*A. Matrix S must be -stored in CRS format (exception will be thrown otherwise). - -INPUT PARAMETERS - S - sparse M*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[M][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least M, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[N][K], S^T*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemtm(const sparsematrix &s, const real_2d_array &a, const ae_int_t k, real_2d_array &b); - - -/************************************************************************* -This function simultaneously calculates two matrix-matrix products: - S*A and S^T*A. -S must be square (non-rectangular) matrix stored in CRS format (exception -will be thrown otherwise). - -INPUT PARAMETERS - S - sparse N*N matrix in CRS format (you MUST convert it - to CRS before calling this function). - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B0 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - B1 - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B0 - array[N][K], S*A - B1 - array[N][K], S^T*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. It also throws exception when S is non-square. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsemm2(const sparsematrix &s, const real_2d_array &a, const ae_int_t k, real_2d_array &b0, real_2d_array &b1); - - -/************************************************************************* -This function calculates matrix-matrix product S*A, when S is symmetric -matrix. Matrix S must be stored in CRS format (exception will be -thrown otherwise). - -INPUT PARAMETERS - S - sparse M*M matrix in CRS format (you MUST convert it - to CRS before calling this function). - IsUpper - whether upper or lower triangle of S is given: - * if upper triangle is given, only S[i,j] for j>=i - are used, and lower triangle is ignored (it can be - empty - these elements are not referenced at all). - * if lower triangle is given, only S[i,j] for j<=i - are used, and upper triangle is ignored. - A - array[N][K], input dense matrix. For performance reasons - we make only quick checks - we check that array size is - at least N, but we do not check for NAN's or INF's. - K - number of columns of matrix (A). - B - output buffer, possibly preallocated. In case buffer - size is too small to store result, this buffer is - automatically resized. - -OUTPUT PARAMETERS - B - array[M][K], S*A - -NOTE: this function throws exception when called for non-CRS matrix. You -must convert your matrix with SparseConvertToCRS() before using this -function. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparsesmm(const sparsematrix &s, const bool isupper, const real_2d_array &a, const ae_int_t k, real_2d_array &b); - - -/************************************************************************* -This procedure resizes Hash-Table matrix. It can be called when you have -deleted too many elements from the matrix, and you want to free unneeded -memory. - - -- ALGLIB PROJECT -- - Copyright 14.10.2011 by Bochkanov Sergey -*************************************************************************/ -void sparseresizematrix(const sparsematrix &s); - - -/************************************************************************* -This function is used to enumerate all elements of the sparse matrix. -Before first call user initializes T0 and T1 counters by zero. These -counters are used to remember current position in a matrix; after each -call they are updated by the function. - -Subsequent calls to this function return non-zero elements of the sparse -matrix, one by one. If you enumerate CRS matrix, matrix is traversed from -left to right, from top to bottom. In case you enumerate matrix stored as -Hash table, elements are returned in random order. - -EXAMPLE - > T0=0 - > T1=0 - > while SparseEnumerate(S,T0,T1,I,J,V) do - > ....do something with I,J,V - -INPUT PARAMETERS - S - sparse M*N matrix in Hash-Table or CRS representation. - T0 - internal counter - T1 - internal counter - -OUTPUT PARAMETERS - T0 - new value of the internal counter - T1 - new value of the internal counter - I - row index of non-zero element, 0<=I0 - N - number of columns in the matrix being estimated, N>0 - NStart - number of random starting vectors - recommended value - at least 5. - NIts - number of iterations to do with best starting vector - recommended value - at least 5. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTE: this algorithm is effectively deterministic, i.e. it always returns -same result when repeatedly called for the same matrix. In fact, algorithm -uses randomized starting vectors, but internal random numbers generator -always generates same sequence of the random values (it is a feature, not -bug). - -Algorithm can be made non-deterministic with NormEstimatorSetSeed(0) call. - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorcreate(const ae_int_t m, const ae_int_t n, const ae_int_t nstart, const ae_int_t nits, normestimatorstate &state); - - -/************************************************************************* -This function changes seed value used by algorithm. In some cases we need -deterministic processing, i.e. subsequent calls must return equal results, -in other cases we need non-deterministic algorithm which returns different -results for the same matrix on every pass. - -Setting zero seed will lead to non-deterministic algorithm, while non-zero -value will make our algorithm deterministic. - -INPUT PARAMETERS: - State - norm estimator state, must be initialized with a call - to NormEstimatorCreate() - SeedVal - seed value, >=0. Zero value = non-deterministic algo. - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorsetseed(const normestimatorstate &state, const ae_int_t seedval); - - -/************************************************************************* -This function estimates norm of the sparse M*N matrix A. - -INPUT PARAMETERS: - State - norm estimator state, must be initialized with a call - to NormEstimatorCreate() - A - sparse M*N matrix, must be converted to CRS format - prior to calling this function. - -After this function is over you can call NormEstimatorResults() to get -estimate of the norm(A). - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorestimatesparse(const normestimatorstate &state, const sparsematrix &a); - - -/************************************************************************* -Matrix norm estimation results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - Nrm - estimate of the matrix norm, Nrm>=0 - - -- ALGLIB -- - Copyright 06.12.2011 by Bochkanov Sergey -*************************************************************************/ -void normestimatorresults(const normestimatorstate &state, double &nrm); - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixludet(const real_2d_array &a, const integer_1d_array &pivots, const ae_int_t n); -double rmatrixludet(const real_2d_array &a, const integer_1d_array &pivots); - - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -double rmatrixdet(const real_2d_array &a, const ae_int_t n); -double rmatrixdet(const real_2d_array &a); - - -/************************************************************************* -Determinant calculation of the matrix given by its LU decomposition. - -Input parameters: - A - LU decomposition of the matrix (output of - RMatrixLU subroutine). - Pivots - table of permutations which were made during - the LU decomposition. - Output of RMatrixLU subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: matrix determinant. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -alglib::complex cmatrixludet(const complex_2d_array &a, const integer_1d_array &pivots, const ae_int_t n); -alglib::complex cmatrixludet(const complex_2d_array &a, const integer_1d_array &pivots); - - -/************************************************************************* -Calculation of the determinant of a general matrix - -Input parameters: - A - matrix, array[0..N-1, 0..N-1] - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -Result: determinant of matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -alglib::complex cmatrixdet(const complex_2d_array &a, const ae_int_t n); -alglib::complex cmatrixdet(const complex_2d_array &a); - - -/************************************************************************* -Determinant calculation of the matrix given by the Cholesky decomposition. - -Input parameters: - A - Cholesky decomposition, - output of SMatrixCholesky subroutine. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - -As the determinant is equal to the product of squares of diagonal elements, -it’s not necessary to specify which triangle - lower or upper - the matrix -is stored in. - -Result: - matrix determinant. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixcholeskydet(const real_2d_array &a, const ae_int_t n); -double spdmatrixcholeskydet(const real_2d_array &a); - - -/************************************************************************* -Determinant calculation of the symmetric positive definite matrix. - -Input parameters: - A - matrix. Array with elements [0..N-1, 0..N-1]. - N - (optional) size of matrix A: - * if given, only principal NxN submatrix is processed and - overwritten. other elements are unchanged. - * if not given, automatically determined from matrix size - (A must be square matrix) - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used/changed by - function - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used/changed by - function - * if not given, both lower and upper triangles must be - filled. - -Result: - determinant of matrix A. - If matrix A is not positive definite, exception is thrown. - - -- ALGLIB -- - Copyright 2005-2008 by Bochkanov Sergey -*************************************************************************/ -double spdmatrixdet(const real_2d_array &a, const ae_int_t n, const bool isupper); -double spdmatrixdet(const real_2d_array &a); - -/************************************************************************* -Algorithm for solving the following generalized symmetric positive-definite -eigenproblem: - A*x = lambda*B*x (1) or - A*B*x = lambda*x (2) or - B*A*x = lambda*x (3). -where A is a symmetric matrix, B - symmetric positive-definite matrix. -The problem is solved by reducing it to an ordinary symmetric eigenvalue -problem. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrices A and B. - IsUpperA - storage format of matrix A. - B - symmetric positive-definite matrix which is given by - its upper or lower triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperB - storage format of matrix B. - ZNeeded - if ZNeeded is equal to: - * 0, the eigenvectors are not returned; - * 1, the eigenvectors are returned. - ProblemType - if ProblemType is equal to: - * 1, the following problem is solved: A*x = lambda*B*x; - * 2, the following problem is solved: A*B*x = lambda*x; - * 3, the following problem is solved: B*A*x = lambda*x. - -Output parameters: - D - eigenvalues in ascending order. - Array whose index ranges within [0..N-1]. - Z - if ZNeeded is equal to: - * 0, Z hasn’t changed; - * 1, Z contains eigenvectors. - Array whose indexes range within [0..N-1, 0..N-1]. - The eigenvectors are stored in matrix columns. It should - be noted that the eigenvectors in such problems do not - form an orthogonal system. - -Result: - True, if the problem was solved successfully. - False, if the error occurred during the Cholesky decomposition of matrix - B (the matrix isn’t positive-definite) or during the work of the iterative - algorithm for solving the symmetric eigenproblem. - -See also the GeneralizedSymmetricDefiniteEVDReduce subroutine. - - -- ALGLIB -- - Copyright 1.28.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixgevd(const real_2d_array &a, const ae_int_t n, const bool isuppera, const real_2d_array &b, const bool isupperb, const ae_int_t zneeded, const ae_int_t problemtype, real_1d_array &d, real_2d_array &z); - - -/************************************************************************* -Algorithm for reduction of the following generalized symmetric positive- -definite eigenvalue problem: - A*x = lambda*B*x (1) or - A*B*x = lambda*x (2) or - B*A*x = lambda*x (3) -to the symmetric eigenvalues problem C*y = lambda*y (eigenvalues of this and -the given problems are the same, and the eigenvectors of the given problem -could be obtained by multiplying the obtained eigenvectors by the -transformation matrix x = R*y). - -Here A is a symmetric matrix, B - symmetric positive-definite matrix. - -Input parameters: - A - symmetric matrix which is given by its upper or lower - triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrices A and B. - IsUpperA - storage format of matrix A. - B - symmetric positive-definite matrix which is given by - its upper or lower triangular part. - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperB - storage format of matrix B. - ProblemType - if ProblemType is equal to: - * 1, the following problem is solved: A*x = lambda*B*x; - * 2, the following problem is solved: A*B*x = lambda*x; - * 3, the following problem is solved: B*A*x = lambda*x. - -Output parameters: - A - symmetric matrix which is given by its upper or lower - triangle depending on IsUpperA. Contains matrix C. - Array whose indexes range within [0..N-1, 0..N-1]. - R - upper triangular or low triangular transformation matrix - which is used to obtain the eigenvectors of a given problem - as the product of eigenvectors of C (from the right) and - matrix R (from the left). If the matrix is upper - triangular, the elements below the main diagonal - are equal to 0 (and vice versa). Thus, we can perform - the multiplication without taking into account the - internal structure (which is an easier though less - effective way). - Array whose indexes range within [0..N-1, 0..N-1]. - IsUpperR - type of matrix R (upper or lower triangular). - -Result: - True, if the problem was reduced successfully. - False, if the error occurred during the Cholesky decomposition of - matrix B (the matrix is not positive-definite). - - -- ALGLIB -- - Copyright 1.28.2006 by Bochkanov Sergey -*************************************************************************/ -bool smatrixgevdreduce(real_2d_array &a, const ae_int_t n, const bool isuppera, const real_2d_array &b, const bool isupperb, const ae_int_t problemtype, real_2d_array &r, bool &isupperr); - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a number to an element -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdRow - row where the element to be updated is stored. - UpdColumn - column where the element to be updated is stored. - UpdVal - a number to be added to the element. - - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdatesimple(real_2d_array &inva, const ae_int_t n, const ae_int_t updrow, const ae_int_t updcolumn, const double updval); - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a vector to a row -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdRow - the row of A whose vector V was added. - 0 <= Row <= N-1 - V - the vector to be added to a row. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdaterow(real_2d_array &inva, const ae_int_t n, const ae_int_t updrow, const real_1d_array &v); - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm updates matrix A^-1 when adding a vector to a column -of matrix A. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - UpdColumn - the column of A whose vector U was added. - 0 <= UpdColumn <= N-1 - U - the vector to be added to a column. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of modified matrix A. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdatecolumn(real_2d_array &inva, const ae_int_t n, const ae_int_t updcolumn, const real_1d_array &u); - - -/************************************************************************* -Inverse matrix update by the Sherman-Morrison formula - -The algorithm computes the inverse of matrix A+u*v’ by using the given matrix -A^-1 and the vectors u and v. - -Input parameters: - InvA - inverse of matrix A. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of matrix A. - U - the vector modifying the matrix. - Array whose index ranges within [0..N-1]. - V - the vector modifying the matrix. - Array whose index ranges within [0..N-1]. - -Output parameters: - InvA - inverse of matrix A + u*v'. - - -- ALGLIB -- - Copyright 2005 by Bochkanov Sergey -*************************************************************************/ -void rmatrixinvupdateuv(real_2d_array &inva, const ae_int_t n, const real_1d_array &u, const real_1d_array &v); - -/************************************************************************* -Subroutine performing the Schur decomposition of a general matrix by using -the QR algorithm with multiple shifts. - -The source matrix A is represented as S'*A*S = T, where S is an orthogonal -matrix (Schur vectors), T - upper quasi-triangular matrix (with blocks of -sizes 1x1 and 2x2 on the main diagonal). - -Input parameters: - A - matrix to be decomposed. - Array whose indexes range within [0..N-1, 0..N-1]. - N - size of A, N>=0. - - -Output parameters: - A - contains matrix T. - Array whose indexes range within [0..N-1, 0..N-1]. - S - contains Schur vectors. - Array whose indexes range within [0..N-1, 0..N-1]. - -Note 1: - The block structure of matrix T can be easily recognized: since all - the elements below the blocks are zeros, the elements a[i+1,i] which - are equal to 0 show the block border. - -Note 2: - The algorithm performance depends on the value of the internal parameter - NS of the InternalSchurDecomposition subroutine which defines the number - of shifts in the QR algorithm (similarly to the block width in block-matrix - algorithms in linear algebra). If you require maximum performance on - your machine, it is recommended to adjust this parameter manually. - -Result: - True, - if the algorithm has converged and parameters A and S contain the result. - False, - if the algorithm has not converged. - -Algorithm implemented on the basis of the DHSEQR subroutine (LAPACK 3.0 library). -*************************************************************************/ -bool rmatrixschur(real_2d_array &a, const ae_int_t n, real_2d_array &s); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void ablassplitlength(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state); -void ablascomplexsplitlength(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t* n1, - ae_int_t* n2, - ae_state *_state); -ae_int_t ablasblocksize(/* Real */ ae_matrix* a, ae_state *_state); -ae_int_t ablascomplexblocksize(/* Complex */ ae_matrix* a, - ae_state *_state); -ae_int_t ablasmicroblocksize(ae_state *_state); -void cmatrixtranspose(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state); -void rmatrixtranspose(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state); -void rmatrixenforcesymmetricity(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -void cmatrixcopy(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state); -void rmatrixcopy(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_state *_state); -void cmatrixrank1(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Complex */ ae_vector* u, - ae_int_t iu, - /* Complex */ ae_vector* v, - ae_int_t iv, - ae_state *_state); -void rmatrixrank1(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - /* Real */ ae_vector* u, - ae_int_t iu, - /* Real */ ae_vector* v, - ae_int_t iv, - ae_state *_state); -void cmatrixmv(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Complex */ ae_vector* x, - ae_int_t ix, - /* Complex */ ae_vector* y, - ae_int_t iy, - ae_state *_state); -void rmatrixmv(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t opa, - /* Real */ ae_vector* x, - ae_int_t ix, - /* Real */ ae_vector* y, - ae_int_t iy, - ae_state *_state); -void cmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -void _pexec_cmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state); -void cmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -void _pexec_cmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Complex */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state); -void rmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -void _pexec_rmatrixrighttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state); -void rmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, - ae_state *_state); -void _pexec_rmatrixlefttrsm(ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_int_t i1, - ae_int_t j1, - ae_bool isupper, - ae_bool isunit, - ae_int_t optype, - /* Real */ ae_matrix* x, - ae_int_t i2, - ae_int_t j2, ae_state *_state); -void cmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); -void _pexec_cmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, ae_state *_state); -void rmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, - ae_state *_state); -void _pexec_rmatrixsyrk(ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_bool isupper, ae_state *_state); -void cmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void _pexec_cmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - ae_complex alpha, - /* Complex */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Complex */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - ae_complex beta, - /* Complex */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, ae_state *_state); -void rmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, - ae_state *_state); -void _pexec_rmatrixgemm(ae_int_t m, - ae_int_t n, - ae_int_t k, - double alpha, - /* Real */ ae_matrix* a, - ae_int_t ia, - ae_int_t ja, - ae_int_t optypea, - /* Real */ ae_matrix* b, - ae_int_t ib, - ae_int_t jb, - ae_int_t optypeb, - double beta, - /* Real */ ae_matrix* c, - ae_int_t ic, - ae_int_t jc, ae_state *_state); -void rmatrixqr(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state); -void rmatrixlq(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state); -void cmatrixqr(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_state *_state); -void cmatrixlq(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_state *_state); -void rmatrixqrunpackq(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_int_t qcolumns, - /* Real */ ae_matrix* q, - ae_state *_state); -void rmatrixqrunpackr(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* r, - ae_state *_state); -void rmatrixlqunpackq(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_int_t qrows, - /* Real */ ae_matrix* q, - ae_state *_state); -void rmatrixlqunpackl(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_matrix* l, - ae_state *_state); -void cmatrixqrunpackq(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_int_t qcolumns, - /* Complex */ ae_matrix* q, - ae_state *_state); -void cmatrixqrunpackr(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* r, - ae_state *_state); -void cmatrixlqunpackq(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_vector* tau, - ae_int_t qrows, - /* Complex */ ae_matrix* q, - ae_state *_state); -void cmatrixlqunpackl(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Complex */ ae_matrix* l, - ae_state *_state); -void rmatrixqrbasecase(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* work, - /* Real */ ae_vector* t, - /* Real */ ae_vector* tau, - ae_state *_state); -void rmatrixlqbasecase(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* work, - /* Real */ ae_vector* t, - /* Real */ ae_vector* tau, - ae_state *_state); -void rmatrixbd(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tauq, - /* Real */ ae_vector* taup, - ae_state *_state); -void rmatrixbdunpackq(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tauq, - ae_int_t qcolumns, - /* Real */ ae_matrix* q, - ae_state *_state); -void rmatrixbdmultiplybyq(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tauq, - /* Real */ ae_matrix* z, - ae_int_t zrows, - ae_int_t zcolumns, - ae_bool fromtheright, - ae_bool dotranspose, - ae_state *_state); -void rmatrixbdunpackpt(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* taup, - ae_int_t ptrows, - /* Real */ ae_matrix* pt, - ae_state *_state); -void rmatrixbdmultiplybyp(/* Real */ ae_matrix* qp, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* taup, - /* Real */ ae_matrix* z, - ae_int_t zrows, - ae_int_t zcolumns, - ae_bool fromtheright, - ae_bool dotranspose, - ae_state *_state); -void rmatrixbdunpackdiagonals(/* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t n, - ae_bool* isupper, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_state *_state); -void rmatrixhessenberg(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - ae_state *_state); -void rmatrixhessenbergunpackq(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* tau, - /* Real */ ae_matrix* q, - ae_state *_state); -void rmatrixhessenbergunpackh(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_matrix* h, - ae_state *_state); -void smatrixtd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tau, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_state *_state); -void smatrixtdunpackq(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tau, - /* Real */ ae_matrix* q, - ae_state *_state); -void hmatrixtd(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tau, - /* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_state *_state); -void hmatrixtdunpackq(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* tau, - /* Complex */ ae_matrix* q, - ae_state *_state); -ae_bool rmatrixbdsvd(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_bool isupper, - ae_bool isfractionalaccuracyrequired, - /* Real */ ae_matrix* u, - ae_int_t nru, - /* Real */ ae_matrix* c, - ae_int_t ncc, - /* Real */ ae_matrix* vt, - ae_int_t ncvt, - ae_state *_state); -ae_bool bidiagonalsvddecomposition(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_bool isupper, - ae_bool isfractionalaccuracyrequired, - /* Real */ ae_matrix* u, - ae_int_t nru, - /* Real */ ae_matrix* c, - ae_int_t ncc, - /* Real */ ae_matrix* vt, - ae_int_t ncvt, - ae_state *_state); -ae_bool rmatrixsvd(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_int_t uneeded, - ae_int_t vtneeded, - ae_int_t additionalmemory, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* u, - /* Real */ ae_matrix* vt, - ae_state *_state); -ae_bool smatrixevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - /* Real */ ae_vector* d, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool smatrixevdr(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - double b1, - double b2, - ae_int_t* m, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool smatrixevdi(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* w, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool hmatrixevd(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - /* Real */ ae_vector* d, - /* Complex */ ae_matrix* z, - ae_state *_state); -ae_bool hmatrixevdr(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - double b1, - double b2, - ae_int_t* m, - /* Real */ ae_vector* w, - /* Complex */ ae_matrix* z, - ae_state *_state); -ae_bool hmatrixevdi(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t zneeded, - ae_bool isupper, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_vector* w, - /* Complex */ ae_matrix* z, - ae_state *_state); -ae_bool smatrixtdevd(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool smatrixtdevdr(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - double a, - double b, - ae_int_t* m, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool smatrixtdevdi(/* Real */ ae_vector* d, - /* Real */ ae_vector* e, - ae_int_t n, - ae_int_t zneeded, - ae_int_t i1, - ae_int_t i2, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool rmatrixevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t vneeded, - /* Real */ ae_vector* wr, - /* Real */ ae_vector* wi, - /* Real */ ae_matrix* vl, - /* Real */ ae_matrix* vr, - ae_state *_state); -void rmatrixrndorthogonal(ae_int_t n, - /* Real */ ae_matrix* a, - ae_state *_state); -void rmatrixrndcond(ae_int_t n, - double c, - /* Real */ ae_matrix* a, - ae_state *_state); -void cmatrixrndorthogonal(ae_int_t n, - /* Complex */ ae_matrix* a, - ae_state *_state); -void cmatrixrndcond(ae_int_t n, - double c, - /* Complex */ ae_matrix* a, - ae_state *_state); -void smatrixrndcond(ae_int_t n, - double c, - /* Real */ ae_matrix* a, - ae_state *_state); -void spdmatrixrndcond(ae_int_t n, - double c, - /* Real */ ae_matrix* a, - ae_state *_state); -void hmatrixrndcond(ae_int_t n, - double c, - /* Complex */ ae_matrix* a, - ae_state *_state); -void hpdmatrixrndcond(ae_int_t n, - double c, - /* Complex */ ae_matrix* a, - ae_state *_state); -void rmatrixrndorthogonalfromtheright(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state); -void rmatrixrndorthogonalfromtheleft(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state); -void cmatrixrndorthogonalfromtheright(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state); -void cmatrixrndorthogonalfromtheleft(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - ae_state *_state); -void smatrixrndmultiply(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -void hmatrixrndmultiply(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -void rmatrixlu(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state); -void cmatrixlu(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state); -ae_bool hpdmatrixcholesky(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -ae_bool spdmatrixcholesky(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -void rmatrixlup(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state); -void cmatrixlup(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state); -void rmatrixplu(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state); -void cmatrixplu(/* Complex */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* pivots, - ae_state *_state); -ae_bool spdmatrixcholeskyrec(/* Real */ ae_matrix* a, - ae_int_t offs, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* tmp, - ae_state *_state); -double rmatrixrcond1(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -double rmatrixrcondinf(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -double spdmatrixrcond(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -double rmatrixtrrcond1(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state); -double rmatrixtrrcondinf(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state); -double hpdmatrixrcond(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -double cmatrixrcond1(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -double cmatrixrcondinf(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -double rmatrixlurcond1(/* Real */ ae_matrix* lua, - ae_int_t n, - ae_state *_state); -double rmatrixlurcondinf(/* Real */ ae_matrix* lua, - ae_int_t n, - ae_state *_state); -double spdmatrixcholeskyrcond(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -double hpdmatrixcholeskyrcond(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -double cmatrixlurcond1(/* Complex */ ae_matrix* lua, - ae_int_t n, - ae_state *_state); -double cmatrixlurcondinf(/* Complex */ ae_matrix* lua, - ae_int_t n, - ae_state *_state); -double cmatrixtrrcond1(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state); -double cmatrixtrrcondinf(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_state *_state); -double rcondthreshold(ae_state *_state); -void rmatrixluinverse(/* Real */ ae_matrix* a, - /* Integer */ ae_vector* pivots, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void rmatrixinverse(/* Real */ ae_matrix* a, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void cmatrixluinverse(/* Complex */ ae_matrix* a, - /* Integer */ ae_vector* pivots, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void cmatrixinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void spdmatrixcholeskyinverse(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void spdmatrixinverse(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void hpdmatrixcholeskyinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void hpdmatrixinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void rmatrixtrinverse(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -void cmatrixtrinverse(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_bool isunit, - ae_int_t* info, - matinvreport* rep, - ae_state *_state); -ae_bool _matinvreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _matinvreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _matinvreport_clear(void* _p); -void _matinvreport_destroy(void* _p); -void sparsecreate(ae_int_t m, - ae_int_t n, - ae_int_t k, - sparsematrix* s, - ae_state *_state); -void sparsecreatecrs(ae_int_t m, - ae_int_t n, - /* Integer */ ae_vector* ner, - sparsematrix* s, - ae_state *_state); -void sparsecopy(sparsematrix* s0, sparsematrix* s1, ae_state *_state); -void sparseadd(sparsematrix* s, - ae_int_t i, - ae_int_t j, - double v, - ae_state *_state); -void sparseset(sparsematrix* s, - ae_int_t i, - ae_int_t j, - double v, - ae_state *_state); -double sparseget(sparsematrix* s, - ae_int_t i, - ae_int_t j, - ae_state *_state); -double sparsegetdiagonal(sparsematrix* s, ae_int_t i, ae_state *_state); -void sparseconverttocrs(sparsematrix* s, ae_state *_state); -void sparsemv(sparsematrix* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void sparsemtv(sparsematrix* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void sparsemv2(sparsematrix* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y0, - /* Real */ ae_vector* y1, - ae_state *_state); -void sparsesmv(sparsematrix* s, - ae_bool isupper, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -void sparsemm(sparsematrix* s, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b, - ae_state *_state); -void sparsemtm(sparsematrix* s, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b, - ae_state *_state); -void sparsemm2(sparsematrix* s, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b0, - /* Real */ ae_matrix* b1, - ae_state *_state); -void sparsesmm(sparsematrix* s, - ae_bool isupper, - /* Real */ ae_matrix* a, - ae_int_t k, - /* Real */ ae_matrix* b, - ae_state *_state); -void sparseresizematrix(sparsematrix* s, ae_state *_state); -double sparsegetaveragelengthofchain(sparsematrix* s, ae_state *_state); -ae_bool sparseenumerate(sparsematrix* s, - ae_int_t* t0, - ae_int_t* t1, - ae_int_t* i, - ae_int_t* j, - double* v, - ae_state *_state); -ae_bool sparserewriteexisting(sparsematrix* s, - ae_int_t i, - ae_int_t j, - double v, - ae_state *_state); -void sparsegetrow(sparsematrix* s, - ae_int_t i, - /* Real */ ae_vector* irow, - ae_state *_state); -void sparseconverttohash(sparsematrix* s, ae_state *_state); -void sparsecopytohash(sparsematrix* s0, - sparsematrix* s1, - ae_state *_state); -void sparsecopytocrs(sparsematrix* s0, sparsematrix* s1, ae_state *_state); -ae_int_t sparsegetmatrixtype(sparsematrix* s, ae_state *_state); -ae_bool sparseishash(sparsematrix* s, ae_state *_state); -ae_bool sparseiscrs(sparsematrix* s, ae_state *_state); -void sparsefree(sparsematrix* s, ae_state *_state); -ae_int_t sparsegetnrows(sparsematrix* s, ae_state *_state); -ae_int_t sparsegetncols(sparsematrix* s, ae_state *_state); -ae_bool _sparsematrix_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sparsematrix_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sparsematrix_clear(void* _p); -void _sparsematrix_destroy(void* _p); -void fblscholeskysolve(/* Real */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* xb, - /* Real */ ae_vector* tmp, - ae_state *_state); -void fblssolvecgx(/* Real */ ae_matrix* a, - ae_int_t m, - ae_int_t n, - double alpha, - /* Real */ ae_vector* b, - /* Real */ ae_vector* x, - /* Real */ ae_vector* buf, - ae_state *_state); -void fblscgcreate(/* Real */ ae_vector* x, - /* Real */ ae_vector* b, - ae_int_t n, - fblslincgstate* state, - ae_state *_state); -ae_bool fblscgiteration(fblslincgstate* state, ae_state *_state); -void fblssolvels(/* Real */ ae_matrix* a, - /* Real */ ae_vector* b, - ae_int_t m, - ae_int_t n, - /* Real */ ae_vector* tmp0, - /* Real */ ae_vector* tmp1, - /* Real */ ae_vector* tmp2, - ae_state *_state); -ae_bool _fblslincgstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _fblslincgstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _fblslincgstate_clear(void* _p); -void _fblslincgstate_destroy(void* _p); -void normestimatorcreate(ae_int_t m, - ae_int_t n, - ae_int_t nstart, - ae_int_t nits, - normestimatorstate* state, - ae_state *_state); -void normestimatorsetseed(normestimatorstate* state, - ae_int_t seedval, - ae_state *_state); -ae_bool normestimatoriteration(normestimatorstate* state, - ae_state *_state); -void normestimatorestimatesparse(normestimatorstate* state, - sparsematrix* a, - ae_state *_state); -void normestimatorresults(normestimatorstate* state, - double* nrm, - ae_state *_state); -void normestimatorrestart(normestimatorstate* state, ae_state *_state); -ae_bool _normestimatorstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _normestimatorstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _normestimatorstate_clear(void* _p); -void _normestimatorstate_destroy(void* _p); -double rmatrixludet(/* Real */ ae_matrix* a, - /* Integer */ ae_vector* pivots, - ae_int_t n, - ae_state *_state); -double rmatrixdet(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -ae_complex cmatrixludet(/* Complex */ ae_matrix* a, - /* Integer */ ae_vector* pivots, - ae_int_t n, - ae_state *_state); -ae_complex cmatrixdet(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -double spdmatrixcholeskydet(/* Real */ ae_matrix* a, - ae_int_t n, - ae_state *_state); -double spdmatrixdet(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - ae_state *_state); -ae_bool smatrixgevd(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isuppera, - /* Real */ ae_matrix* b, - ae_bool isupperb, - ae_int_t zneeded, - ae_int_t problemtype, - /* Real */ ae_vector* d, - /* Real */ ae_matrix* z, - ae_state *_state); -ae_bool smatrixgevdreduce(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isuppera, - /* Real */ ae_matrix* b, - ae_bool isupperb, - ae_int_t problemtype, - /* Real */ ae_matrix* r, - ae_bool* isupperr, - ae_state *_state); -void rmatrixinvupdatesimple(/* Real */ ae_matrix* inva, - ae_int_t n, - ae_int_t updrow, - ae_int_t updcolumn, - double updval, - ae_state *_state); -void rmatrixinvupdaterow(/* Real */ ae_matrix* inva, - ae_int_t n, - ae_int_t updrow, - /* Real */ ae_vector* v, - ae_state *_state); -void rmatrixinvupdatecolumn(/* Real */ ae_matrix* inva, - ae_int_t n, - ae_int_t updcolumn, - /* Real */ ae_vector* u, - ae_state *_state); -void rmatrixinvupdateuv(/* Real */ ae_matrix* inva, - ae_int_t n, - /* Real */ ae_vector* u, - /* Real */ ae_vector* v, - ae_state *_state); -ae_bool rmatrixschur(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_matrix* s, - ae_state *_state); - -} -#endif - diff --git a/src/inc/alglib/optimization.cpp b/src/inc/alglib/optimization.cpp deleted file mode 100644 index 0b43330..0000000 --- a/src/inc/alglib/optimization.cpp +++ /dev/null @@ -1,25034 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "optimization.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - - - - - - - - - -/************************************************************************* -This object stores state of the nonlinear CG optimizer. - -You should use ALGLIB functions to work with this object. -*************************************************************************/ -_mincgstate_owner::_mincgstate_owner() -{ - p_struct = (alglib_impl::mincgstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::mincgstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mincgstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mincgstate_owner::_mincgstate_owner(const _mincgstate_owner &rhs) -{ - p_struct = (alglib_impl::mincgstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::mincgstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mincgstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mincgstate_owner& _mincgstate_owner::operator=(const _mincgstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mincgstate_clear(p_struct); - if( !alglib_impl::_mincgstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mincgstate_owner::~_mincgstate_owner() -{ - alglib_impl::_mincgstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mincgstate* _mincgstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mincgstate* _mincgstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mincgstate::mincgstate() : _mincgstate_owner() ,needf(p_struct->needf),needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -mincgstate::mincgstate(const mincgstate &rhs):_mincgstate_owner(rhs) ,needf(p_struct->needf),needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -mincgstate& mincgstate::operator=(const mincgstate &rhs) -{ - if( this==&rhs ) - return *this; - _mincgstate_owner::operator=(rhs); - return *this; -} - -mincgstate::~mincgstate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_mincgreport_owner::_mincgreport_owner() -{ - p_struct = (alglib_impl::mincgreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mincgreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mincgreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mincgreport_owner::_mincgreport_owner(const _mincgreport_owner &rhs) -{ - p_struct = (alglib_impl::mincgreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::mincgreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_mincgreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_mincgreport_owner& _mincgreport_owner::operator=(const _mincgreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_mincgreport_clear(p_struct); - if( !alglib_impl::_mincgreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_mincgreport_owner::~_mincgreport_owner() -{ - alglib_impl::_mincgreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::mincgreport* _mincgreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::mincgreport* _mincgreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -mincgreport::mincgreport() : _mincgreport_owner() ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),varidx(p_struct->varidx),terminationtype(p_struct->terminationtype) -{ -} - -mincgreport::mincgreport(const mincgreport &rhs):_mincgreport_owner(rhs) ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),varidx(p_struct->varidx),terminationtype(p_struct->terminationtype) -{ -} - -mincgreport& mincgreport::operator=(const mincgreport &rhs) -{ - if( this==&rhs ) - return *this; - _mincgreport_owner::operator=(rhs); - return *this; -} - -mincgreport::~mincgreport() -{ -} - -/************************************************************************* - NONLINEAR CONJUGATE GRADIENT METHOD - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using one of the -nonlinear conjugate gradient methods. - -These CG methods are globally convergent (even on non-convex functions) as -long as grad(f) is Lipschitz continuous in a some neighborhood of the -L = { x : f(x)<=f(x0) }. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinCGCreate() call -2. User tunes solver parameters with MinCGSetCond(), MinCGSetStpMax() and - other functions -3. User calls MinCGOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinCGResults() to get solution -5. Optionally, user may call MinCGRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinCGRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgcreate(const ae_int_t n, const real_1d_array &x, mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgcreate(n, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - NONLINEAR CONJUGATE GRADIENT METHOD - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using one of the -nonlinear conjugate gradient methods. - -These CG methods are globally convergent (even on non-convex functions) as -long as grad(f) is Lipschitz continuous in a some neighborhood of the -L = { x : f(x)<=f(x0) }. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinCGCreate() call -2. User tunes solver parameters with MinCGSetCond(), MinCGSetStpMax() and - other functions -3. User calls MinCGOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinCGResults() to get solution -5. Optionally, user may call MinCGRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinCGRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgcreate(const real_1d_array &x, mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgcreate(n, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The subroutine is finite difference variant of MinCGCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinCGCreate() in order to get more -information about creation of CG optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinCGSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. L-BFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgcreatef(const ae_int_t n, const real_1d_array &x, const double diffstep, mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgcreatef(n, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The subroutine is finite difference variant of MinCGCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinCGCreate() in order to get more -information about creation of CG optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinCGSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. L-BFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgcreatef(const real_1d_array &x, const double diffstep, mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgcreatef(n, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping conditions for CG optimization algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinCGSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetcond(const mincgstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetcond(const_cast(state.c_ptr()), epsg, epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets scaling coefficients for CG optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of CG optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the CG too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinCGSetPrec...() functions. - -There is special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgsetscale(const mincgstate &state, const real_1d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetscale(const_cast(state.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetxrep(const mincgstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets CG algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - CGType - algorithm type: - * -1 automatic selection of the best algorithm - * 0 DY (Dai and Yuan) algorithm - * 1 Hybrid DY-HS algorithm - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetcgtype(const mincgstate &state, const ae_int_t cgtype) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetcgtype(const_cast(state.c_ptr()), cgtype, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetstpmax(const mincgstate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function allows to suggest initial step length to the CG algorithm. - -Suggested step length is used as starting point for the line search. It -can be useful when you have badly scaled problem, i.e. when ||grad|| -(which is used as initial estimate for the first step) is many orders of -magnitude different from the desired step. - -Line search may fail on such problems without good estimate of initial -step length. Imagine, for example, problem with ||grad||=10^50 and desired -step equal to 0.1 Line search function will use 10^50 as initial step, -then it will decrease step length by 2 (up to 20 attempts) and will get -10^44, which is still too large. - -This function allows us to tell than line search should be started from -some moderate step length, like 1.0, so algorithm will be able to detect -desired step length in a several searches. - -Default behavior (when no step is suggested) is to use preconditioner, if -it is available, to generate initial estimate of step length. - -This function influences only first iteration of algorithm. It should be -called between MinCGCreate/MinCGRestartFrom() call and MinCGOptimize call. -Suggested step is ignored if you have preconditioner. - -INPUT PARAMETERS: - State - structure used to store algorithm state. - Stp - initial estimate of the step length. - Can be zero (no estimate). - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsuggeststep(const mincgstate &state, const double stp) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsuggeststep(const_cast(state.c_ptr()), stp, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: preconditioning is turned off. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdefault(const mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetprecdefault(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 3: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdiag(const mincgstate &state, const real_1d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetprecdiag(const_cast(state.c_ptr()), const_cast(d.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinCGSetScale() call -(before or after MinCGSetPrecScale() call). Without knowledge of the scale -of your variables scale-based preconditioner will be just unit matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecscale(const mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetprecscale(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool mincgiteration(const mincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::mincgiteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void mincgoptimize(mincgstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'mincgoptimize()' (func is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::mincgiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'mincgoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void mincgoptimize(mincgstate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'mincgoptimize()' (grad is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::mincgiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needfg ) - { - grad(state.x, state.f, state.g, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'mincgoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -Conjugate gradient results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -7 gradient verification failed. - See MinCGSetGradientCheck() for more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible, - we return best X found so far - * 8 terminated by user - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -void mincgresults(const mincgstate &state, real_1d_array &x, mincgreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conjugate gradient results - -Buffered implementation of MinCGResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -void mincgresultsbuf(const mincgstate &state, real_1d_array &x, mincgreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine restarts CG algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used to store algorithm state. - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgrestartfrom(const mincgstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgrestartfrom(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinCGOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinCGSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 31.05.2012 by Bochkanov Sergey -*************************************************************************/ -void mincgsetgradientcheck(const mincgstate &state, const double teststep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mincgsetgradientcheck(const_cast(state.c_ptr()), teststep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This object stores nonlinear optimizer state. -You should use functions provided by MinBLEIC subpackage to work with this -object -*************************************************************************/ -_minbleicstate_owner::_minbleicstate_owner() -{ - p_struct = (alglib_impl::minbleicstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minbleicstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minbleicstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minbleicstate_owner::_minbleicstate_owner(const _minbleicstate_owner &rhs) -{ - p_struct = (alglib_impl::minbleicstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minbleicstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minbleicstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minbleicstate_owner& _minbleicstate_owner::operator=(const _minbleicstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minbleicstate_clear(p_struct); - if( !alglib_impl::_minbleicstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minbleicstate_owner::~_minbleicstate_owner() -{ - alglib_impl::_minbleicstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minbleicstate* _minbleicstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minbleicstate* _minbleicstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minbleicstate::minbleicstate() : _minbleicstate_owner() ,needf(p_struct->needf),needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -minbleicstate::minbleicstate(const minbleicstate &rhs):_minbleicstate_owner(rhs) ,needf(p_struct->needf),needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -minbleicstate& minbleicstate::operator=(const minbleicstate &rhs) -{ - if( this==&rhs ) - return *this; - _minbleicstate_owner::operator=(rhs); - return *this; -} - -minbleicstate::~minbleicstate() -{ -} - - -/************************************************************************* -This structure stores optimization report: -* IterationsCount number of iterations -* NFEV number of gradient evaluations -* TerminationType termination type (see below) - -TERMINATION CODES - -TerminationType field contains completion code, which can be: - -7 gradient verification failed. - See MinBLEICSetGradientCheck() for more information. - -3 inconsistent constraints. Feasible point is - either nonexistent or too hard to find. Try to - restart optimizer with better initial approximation - 1 relative function improvement is no more than EpsF. - 2 relative step is no more than EpsX. - 4 gradient norm is no more than EpsG - 5 MaxIts steps was taken - 7 stopping conditions are too stringent, - further improvement is impossible, - X contains best point found so far. - -ADDITIONAL FIELDS - -There are additional fields which can be used for debugging: -* DebugEqErr error in the equality constraints (2-norm) -* DebugFS f, calculated at projection of initial point - to the feasible set -* DebugFF f, calculated at the final point -* DebugDX |X_start-X_final| -*************************************************************************/ -_minbleicreport_owner::_minbleicreport_owner() -{ - p_struct = (alglib_impl::minbleicreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minbleicreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minbleicreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minbleicreport_owner::_minbleicreport_owner(const _minbleicreport_owner &rhs) -{ - p_struct = (alglib_impl::minbleicreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minbleicreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minbleicreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minbleicreport_owner& _minbleicreport_owner::operator=(const _minbleicreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minbleicreport_clear(p_struct); - if( !alglib_impl::_minbleicreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minbleicreport_owner::~_minbleicreport_owner() -{ - alglib_impl::_minbleicreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minbleicreport* _minbleicreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minbleicreport* _minbleicreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minbleicreport::minbleicreport() : _minbleicreport_owner() ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),varidx(p_struct->varidx),terminationtype(p_struct->terminationtype),debugeqerr(p_struct->debugeqerr),debugfs(p_struct->debugfs),debugff(p_struct->debugff),debugdx(p_struct->debugdx),debugfeasqpits(p_struct->debugfeasqpits),debugfeasgpaits(p_struct->debugfeasgpaits),inneriterationscount(p_struct->inneriterationscount),outeriterationscount(p_struct->outeriterationscount) -{ -} - -minbleicreport::minbleicreport(const minbleicreport &rhs):_minbleicreport_owner(rhs) ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),varidx(p_struct->varidx),terminationtype(p_struct->terminationtype),debugeqerr(p_struct->debugeqerr),debugfs(p_struct->debugfs),debugff(p_struct->debugff),debugdx(p_struct->debugdx),debugfeasqpits(p_struct->debugfeasqpits),debugfeasgpaits(p_struct->debugfeasgpaits),inneriterationscount(p_struct->inneriterationscount),outeriterationscount(p_struct->outeriterationscount) -{ -} - -minbleicreport& minbleicreport::operator=(const minbleicreport &rhs) -{ - if( this==&rhs ) - return *this; - _minbleicreport_owner::operator=(rhs); - return *this; -} - -minbleicreport::~minbleicreport() -{ -} - -/************************************************************************* - BOUND CONSTRAINED OPTIMIZATION - WITH ADDITIONAL LINEAR EQUALITY AND INEQUALITY CONSTRAINTS - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments subject to any -combination of: -* bound constraints -* linear inequality constraints -* linear equality constraints - -REQUIREMENTS: -* user must provide function value and gradient -* starting point X0 must be feasible or - not too far away from the feasible set -* grad(f) must be Lipschitz continuous on a level set: - L = { x : f(x)<=f(x0) } -* function must be defined everywhere on the feasible set F - -USAGE: - -Constrained optimization if far more complex than the unconstrained one. -Here we give very brief outline of the BLEIC optimizer. We strongly recommend -you to read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide -on optimization, which is available at http://www.alglib.net/optimization/ - -1. User initializes algorithm state with MinBLEICCreate() call - -2. USer adds boundary and/or linear constraints by calling - MinBLEICSetBC() and MinBLEICSetLC() functions. - -3. User sets stopping conditions with MinBLEICSetCond(). - -4. User calls MinBLEICOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. - -5. User calls MinBLEICResults() to get solution - -6. Optionally user may call MinBLEICRestartFrom() to solve another problem - with same N but another starting point. - MinBLEICRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size ofX - X - starting point, array[N]: - * it is better to set X to a feasible point - * but X can be infeasible, in which case algorithm will try - to find feasible point first, using X as initial - approximation. - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreate(const ae_int_t n, const real_1d_array &x, minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleiccreate(n, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - BOUND CONSTRAINED OPTIMIZATION - WITH ADDITIONAL LINEAR EQUALITY AND INEQUALITY CONSTRAINTS - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments subject to any -combination of: -* bound constraints -* linear inequality constraints -* linear equality constraints - -REQUIREMENTS: -* user must provide function value and gradient -* starting point X0 must be feasible or - not too far away from the feasible set -* grad(f) must be Lipschitz continuous on a level set: - L = { x : f(x)<=f(x0) } -* function must be defined everywhere on the feasible set F - -USAGE: - -Constrained optimization if far more complex than the unconstrained one. -Here we give very brief outline of the BLEIC optimizer. We strongly recommend -you to read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide -on optimization, which is available at http://www.alglib.net/optimization/ - -1. User initializes algorithm state with MinBLEICCreate() call - -2. USer adds boundary and/or linear constraints by calling - MinBLEICSetBC() and MinBLEICSetLC() functions. - -3. User sets stopping conditions with MinBLEICSetCond(). - -4. User calls MinBLEICOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. - -5. User calls MinBLEICResults() to get solution - -6. Optionally user may call MinBLEICRestartFrom() to solve another problem - with same N but another starting point. - MinBLEICRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size ofX - X - starting point, array[N]: - * it is better to set X to a feasible point - * but X can be infeasible, in which case algorithm will try - to find feasible point first, using X as initial - approximation. - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreate(const real_1d_array &x, minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleiccreate(n, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The subroutine is finite difference variant of MinBLEICCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinBLEICCreate() in order to get -more information about creation of BLEIC optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinBLEICSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. CG needs exact gradient values. Imprecise - gradient may slow down convergence, especially on highly nonlinear - problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreatef(const ae_int_t n, const real_1d_array &x, const double diffstep, minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleiccreatef(n, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The subroutine is finite difference variant of MinBLEICCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinBLEICCreate() in order to get -more information about creation of BLEIC optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinBLEICSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. CG needs exact gradient values. Imprecise - gradient may slow down convergence, especially on highly nonlinear - problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreatef(const real_1d_array &x, const double diffstep, minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleiccreatef(n, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets boundary constraints for BLEIC optimizer. - -Boundary constraints are inactive by default (after initial creation). -They are preserved after algorithm restart with MinBLEICRestartFrom(). - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF. - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF. - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: this solver has following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints, - even when numerical differentiation is used (algorithm adjusts nodes - according to boundary constraints) - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbc(const minbleicstate &state, const real_1d_array &bndl, const real_1d_array &bndu) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetbc(const_cast(state.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets linear constraints for BLEIC optimizer. - -Linear constraints are inactive by default (after initial creation). -They are preserved after algorithm restart with MinBLEICRestartFrom(). - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately: -* there always exists some minor violation (about Epsilon in magnitude) - due to rounding errors -* numerical differentiation, if used, may lead to function evaluations - outside of the feasible area, because algorithm does NOT change - numerical differentiation formula according to linear constraints. -If you want constraints to be satisfied exactly, try to reformulate your -problem in such manner that all constraints will become boundary ones -(this kind of constraints is always satisfied exactly, both in the final -solution and in all intermediate points). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetlc(const minbleicstate &state, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetlc(const_cast(state.c_ptr()), const_cast(c.c_ptr()), const_cast(ct.c_ptr()), k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets linear constraints for BLEIC optimizer. - -Linear constraints are inactive by default (after initial creation). -They are preserved after algorithm restart with MinBLEICRestartFrom(). - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately: -* there always exists some minor violation (about Epsilon in magnitude) - due to rounding errors -* numerical differentiation, if used, may lead to function evaluations - outside of the feasible area, because algorithm does NOT change - numerical differentiation formula according to linear constraints. -If you want constraints to be satisfied exactly, try to reformulate your -problem in such manner that all constraints will become boundary ones -(this kind of constraints is always satisfied exactly, both in the final -solution and in all intermediate points). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetlc(const minbleicstate &state, const real_2d_array &c, const integer_1d_array &ct) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t k; - if( (c.rows()!=ct.length())) - throw ap_error("Error while calling 'minbleicsetlc': looks like one of arguments has wrong size"); - k = c.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetlc(const_cast(state.c_ptr()), const_cast(c.c_ptr()), const_cast(ct.c_ptr()), k, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping conditions for the optimizer. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - step vector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinBLEICSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0 and EpsX=0 and MaxIts=0 (simultaneously) will lead -to automatic stopping criterion selection. - -NOTE: when SetCond() called with non-zero MaxIts, BLEIC solver may perform - slightly more than MaxIts iterations. I.e., MaxIts sets non-strict - limit on iterations count. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetcond(const minbleicstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetcond(const_cast(state.c_ptr()), epsg, epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets scaling coefficients for BLEIC optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of the optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the BLEIC too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinBLEICSetPrec...() -functions. - -There is a special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetscale(const minbleicstate &state, const real_1d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetscale(const_cast(state.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: preconditioning is turned off. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecdefault(const minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetprecdefault(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE 1: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 2: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecdiag(const minbleicstate &state, const real_1d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetprecdiag(const_cast(state.c_ptr()), const_cast(d.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinBLEICSetScale() -call (before or after MinBLEICSetPrecScale() call). Without knowledge of -the scale of your variables scale-based preconditioner will be just unit -matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecscale(const minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetprecscale(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinBLEICOptimize(). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetxrep(const minbleicstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets maximum step length - -IMPORTANT: this feature is hard to combine with preconditioning. You can't -set upper limit on step length, when you solve optimization problem with -linear (non-boundary) constraints AND preconditioner turned on. - -When non-boundary constraints are present, you have to either a) use -preconditioner, or b) use upper limit on step length. YOU CAN'T USE BOTH! -In this case algorithm will terminate with appropriate error code. - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which lead to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetstpmax(const minbleicstate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minbleiciteration(const minbleicstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::minbleiciteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minbleicoptimize(minbleicstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'minbleicoptimize()' (func is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minbleiciteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minbleicoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minbleicoptimize(minbleicstate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'minbleicoptimize()' (grad is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minbleiciteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needfg ) - { - grad(state.x, state.f, state.g, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minbleicoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -BLEIC results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report. You should check Rep.TerminationType - in order to distinguish successful termination from - unsuccessful one: - * -7 gradient verification failed. - See MinBLEICSetGradientCheck() for more information. - * -3 inconsistent constraints. Feasible point is - either nonexistent or too hard to find. Try to - restart optimizer with better initial approximation - * 1 relative function improvement is no more than EpsF. - * 2 scaled step is no more than EpsX. - * 4 scaled gradient norm is no more than EpsG. - * 5 MaxIts steps was taken - More information about fields of this structure can be - found in the comments on MinBLEICReport datatype. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicresults(const minbleicstate &state, real_1d_array &x, minbleicreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -BLEIC results - -Buffered implementation of MinBLEICResults() which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicresultsbuf(const minbleicstate &state, real_1d_array &x, minbleicreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine restarts algorithm from new point. -All optimization parameters (including constraints) are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - X - new starting point. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicrestartfrom(const minbleicstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicrestartfrom(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinBLEICOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinBLEICSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetgradientcheck(const minbleicstate &state, const double teststep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetgradientcheck(const_cast(state.c_ptr()), teststep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_minlbfgsstate_owner::_minlbfgsstate_owner() -{ - p_struct = (alglib_impl::minlbfgsstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlbfgsstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlbfgsstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlbfgsstate_owner::_minlbfgsstate_owner(const _minlbfgsstate_owner &rhs) -{ - p_struct = (alglib_impl::minlbfgsstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlbfgsstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlbfgsstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlbfgsstate_owner& _minlbfgsstate_owner::operator=(const _minlbfgsstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minlbfgsstate_clear(p_struct); - if( !alglib_impl::_minlbfgsstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minlbfgsstate_owner::~_minlbfgsstate_owner() -{ - alglib_impl::_minlbfgsstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minlbfgsstate* _minlbfgsstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minlbfgsstate* _minlbfgsstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minlbfgsstate::minlbfgsstate() : _minlbfgsstate_owner() ,needf(p_struct->needf),needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -minlbfgsstate::minlbfgsstate(const minlbfgsstate &rhs):_minlbfgsstate_owner(rhs) ,needf(p_struct->needf),needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -minlbfgsstate& minlbfgsstate::operator=(const minlbfgsstate &rhs) -{ - if( this==&rhs ) - return *this; - _minlbfgsstate_owner::operator=(rhs); - return *this; -} - -minlbfgsstate::~minlbfgsstate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_minlbfgsreport_owner::_minlbfgsreport_owner() -{ - p_struct = (alglib_impl::minlbfgsreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlbfgsreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlbfgsreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlbfgsreport_owner::_minlbfgsreport_owner(const _minlbfgsreport_owner &rhs) -{ - p_struct = (alglib_impl::minlbfgsreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlbfgsreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlbfgsreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlbfgsreport_owner& _minlbfgsreport_owner::operator=(const _minlbfgsreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minlbfgsreport_clear(p_struct); - if( !alglib_impl::_minlbfgsreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minlbfgsreport_owner::~_minlbfgsreport_owner() -{ - alglib_impl::_minlbfgsreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minlbfgsreport* _minlbfgsreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minlbfgsreport* _minlbfgsreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minlbfgsreport::minlbfgsreport() : _minlbfgsreport_owner() ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),varidx(p_struct->varidx),terminationtype(p_struct->terminationtype) -{ -} - -minlbfgsreport::minlbfgsreport(const minlbfgsreport &rhs):_minlbfgsreport_owner(rhs) ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),varidx(p_struct->varidx),terminationtype(p_struct->terminationtype) -{ -} - -minlbfgsreport& minlbfgsreport::operator=(const minlbfgsreport &rhs) -{ - if( this==&rhs ) - return *this; - _minlbfgsreport_owner::operator=(rhs); - return *this; -} - -minlbfgsreport::~minlbfgsreport() -{ -} - -/************************************************************************* - LIMITED MEMORY BFGS METHOD FOR LARGE SCALE OPTIMIZATION - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using a quasi- -Newton method (LBFGS scheme) which is optimized to use a minimum amount -of memory. -The subroutine generates the approximation of an inverse Hessian matrix by -using information about the last M steps of the algorithm (instead of N). -It lessens a required amount of memory from a value of order N^2 to a -value of order 2*N*M. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinLBFGSCreate() call -2. User tunes solver parameters with MinLBFGSSetCond() MinLBFGSSetStpMax() - and other functions -3. User calls MinLBFGSOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinLBFGSResults() to get solution -5. Optionally user may call MinLBFGSRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLBFGSRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension. N>0 - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - initial solution approximation, array[0..N-1]. - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with MinLBFGSSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLBFGSSetStpMax() function to bound algorithm's steps. However, - L-BFGS rarely needs such a tuning. - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreate(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgscreate(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - LIMITED MEMORY BFGS METHOD FOR LARGE SCALE OPTIMIZATION - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using a quasi- -Newton method (LBFGS scheme) which is optimized to use a minimum amount -of memory. -The subroutine generates the approximation of an inverse Hessian matrix by -using information about the last M steps of the algorithm (instead of N). -It lessens a required amount of memory from a value of order N^2 to a -value of order 2*N*M. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinLBFGSCreate() call -2. User tunes solver parameters with MinLBFGSSetCond() MinLBFGSSetStpMax() - and other functions -3. User calls MinLBFGSOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinLBFGSResults() to get solution -5. Optionally user may call MinLBFGSRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLBFGSRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension. N>0 - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - initial solution approximation, array[0..N-1]. - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with MinLBFGSSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLBFGSSetStpMax() function to bound algorithm's steps. However, - L-BFGS rarely needs such a tuning. - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreate(const ae_int_t m, const real_1d_array &x, minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgscreate(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The subroutine is finite difference variant of MinLBFGSCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinLBFGSCreate() in order to get -more information about creation of LBFGS optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinLBFGSSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. LBFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreatef(const ae_int_t n, const ae_int_t m, const real_1d_array &x, const double diffstep, minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgscreatef(n, m, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -The subroutine is finite difference variant of MinLBFGSCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinLBFGSCreate() in order to get -more information about creation of LBFGS optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinLBFGSSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. LBFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreatef(const ae_int_t m, const real_1d_array &x, const double diffstep, minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgscreatef(n, m, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping conditions for L-BFGS optimization algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinLBFGSSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetcond(const minlbfgsstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetcond(const_cast(state.c_ptr()), epsg, epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinLBFGSOptimize(). - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetxrep(const minlbfgsstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0 (default), if - you don't want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetstpmax(const minlbfgsstate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets scaling coefficients for LBFGS optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of the optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the LBFGS too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinLBFGSSetPrec...() -functions. - -There is special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetscale(const minlbfgsstate &state, const real_1d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetscale(const_cast(state.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: default preconditioner (simple -scaling, same for all elements of X) is used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecdefault(const minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetprecdefault(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: Cholesky factorization of approximate -Hessian is used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - P - triangular preconditioner, Cholesky factorization of - the approximate Hessian. array[0..N-1,0..N-1], - (if larger, only leading N elements are used). - IsUpper - whether upper or lower triangle of P is given - (other triangle is not referenced) - -After call to this function preconditioner is changed to P (P is copied -into the internal buffer). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: P should be nonsingular. Exception will be thrown otherwise. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetpreccholesky(const minlbfgsstate &state, const real_2d_array &p, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetpreccholesky(const_cast(state.c_ptr()), const_cast(p.c_ptr()), isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 3: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecdiag(const minlbfgsstate &state, const real_1d_array &d) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetprecdiag(const_cast(state.c_ptr()), const_cast(d.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinLBFGSSetScale() -call (before or after MinLBFGSSetPrecScale() call). Without knowledge of -the scale of your variables scale-based preconditioner will be just unit -matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecscale(const minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetprecscale(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minlbfgsiteration(const minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::minlbfgsiteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlbfgsoptimize(minlbfgsstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'minlbfgsoptimize()' (func is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlbfgsiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlbfgsoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlbfgsoptimize(minlbfgsstate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'minlbfgsoptimize()' (grad is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlbfgsiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needfg ) - { - grad(state.x, state.f, state.g, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlbfgsoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -L-BFGS algorithm results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -7 gradient verification failed. - See MinLBFGSSetGradientCheck() for more information. - * -2 rounding errors prevent further improvement. - X contains best point found. - * -1 incorrect parameters were specified - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsresults(const minlbfgsstate &state, real_1d_array &x, minlbfgsreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgsresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -L-BFGS algorithm results - -Buffered implementation of MinLBFGSResults which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsresultsbuf(const minlbfgsstate &state, real_1d_array &x, minlbfgsreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgsresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine restarts LBFGS algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used to store algorithm state - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsrestartfrom(const minlbfgsstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgsrestartfrom(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinLBFGSOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinLBFGSSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 24.05.2012 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetgradientcheck(const minlbfgsstate &state, const double teststep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetgradientcheck(const_cast(state.c_ptr()), teststep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This object stores nonlinear optimizer state. -You should use functions provided by MinQP subpackage to work with this -object -*************************************************************************/ -_minqpstate_owner::_minqpstate_owner() -{ - p_struct = (alglib_impl::minqpstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minqpstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minqpstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minqpstate_owner::_minqpstate_owner(const _minqpstate_owner &rhs) -{ - p_struct = (alglib_impl::minqpstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minqpstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minqpstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minqpstate_owner& _minqpstate_owner::operator=(const _minqpstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minqpstate_clear(p_struct); - if( !alglib_impl::_minqpstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minqpstate_owner::~_minqpstate_owner() -{ - alglib_impl::_minqpstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minqpstate* _minqpstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minqpstate* _minqpstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minqpstate::minqpstate() : _minqpstate_owner() -{ -} - -minqpstate::minqpstate(const minqpstate &rhs):_minqpstate_owner(rhs) -{ -} - -minqpstate& minqpstate::operator=(const minqpstate &rhs) -{ - if( this==&rhs ) - return *this; - _minqpstate_owner::operator=(rhs); - return *this; -} - -minqpstate::~minqpstate() -{ -} - - -/************************************************************************* -This structure stores optimization report: -* InnerIterationsCount number of inner iterations -* OuterIterationsCount number of outer iterations -* NCholesky number of Cholesky decomposition -* NMV number of matrix-vector products - (only products calculated as part of iterative - process are counted) -* TerminationType completion code (see below) - -Completion codes: -* -5 inappropriate solver was used: - * Cholesky solver for semidefinite or indefinite problems - * Cholesky solver for problems with non-boundary constraints -* -4 BLEIC-QP algorithm found unconstrained direction - of negative curvature (function is unbounded from - below even under constraints), no meaningful - minimum can be found. -* -3 inconsistent constraints (or, maybe, feasible point is - too hard to find). If you are sure that constraints are feasible, - try to restart optimizer with better initial approximation. -* -1 solver error -* 4 successful completion -* 5 MaxIts steps was taken -* 7 stopping conditions are too stringent, - further improvement is impossible, - X contains best point found so far. -*************************************************************************/ -_minqpreport_owner::_minqpreport_owner() -{ - p_struct = (alglib_impl::minqpreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minqpreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minqpreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minqpreport_owner::_minqpreport_owner(const _minqpreport_owner &rhs) -{ - p_struct = (alglib_impl::minqpreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minqpreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minqpreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minqpreport_owner& _minqpreport_owner::operator=(const _minqpreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minqpreport_clear(p_struct); - if( !alglib_impl::_minqpreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minqpreport_owner::~_minqpreport_owner() -{ - alglib_impl::_minqpreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minqpreport* _minqpreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minqpreport* _minqpreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minqpreport::minqpreport() : _minqpreport_owner() ,inneriterationscount(p_struct->inneriterationscount),outeriterationscount(p_struct->outeriterationscount),nmv(p_struct->nmv),ncholesky(p_struct->ncholesky),terminationtype(p_struct->terminationtype) -{ -} - -minqpreport::minqpreport(const minqpreport &rhs):_minqpreport_owner(rhs) ,inneriterationscount(p_struct->inneriterationscount),outeriterationscount(p_struct->outeriterationscount),nmv(p_struct->nmv),ncholesky(p_struct->ncholesky),terminationtype(p_struct->terminationtype) -{ -} - -minqpreport& minqpreport::operator=(const minqpreport &rhs) -{ - if( this==&rhs ) - return *this; - _minqpreport_owner::operator=(rhs); - return *this; -} - -minqpreport::~minqpreport() -{ -} - -/************************************************************************* - CONSTRAINED QUADRATIC PROGRAMMING - -The subroutine creates QP optimizer. After initial creation, it contains -default optimization problem with zero quadratic and linear terms and no -constraints. You should set quadratic/linear terms with calls to functions -provided by MinQP subpackage. - -INPUT PARAMETERS: - N - problem size - -OUTPUT PARAMETERS: - State - optimizer with zero quadratic/linear terms - and no constraints - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpcreate(const ae_int_t n, minqpstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpcreate(n, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets linear term for QP solver. - -By default, linear term is zero. - -INPUT PARAMETERS: - State - structure which stores algorithm state - B - linear term, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlinearterm(const minqpstate &state, const real_1d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetlinearterm(const_cast(state.c_ptr()), const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets dense quadratic term for QP solver. By default, -quadratic term is zero. - -SUPPORT BY ALGLIB QP ALGORITHMS: - -Dense quadratic term can be handled by any of the QP algorithms supported -by ALGLIB QP Solver. - -IMPORTANT: - -This solver minimizes following function: - f(x) = 0.5*x'*A*x + b'*x. -Note that quadratic term has 0.5 before it. So if you want to minimize - f(x) = x^2 + x -you should rewrite your problem as follows: - f(x) = 0.5*(2*x^2) + x -and your matrix A will be equal to [[2.0]], not to [[1.0]] - -INPUT PARAMETERS: - State - structure which stores algorithm state - A - matrix, array[N,N] - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used - * if not given, both lower and upper triangles must be - filled. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetquadraticterm(const minqpstate &state, const real_2d_array &a, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetquadraticterm(const_cast(state.c_ptr()), const_cast(a.c_ptr()), isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets dense quadratic term for QP solver. By default, -quadratic term is zero. - -SUPPORT BY ALGLIB QP ALGORITHMS: - -Dense quadratic term can be handled by any of the QP algorithms supported -by ALGLIB QP Solver. - -IMPORTANT: - -This solver minimizes following function: - f(x) = 0.5*x'*A*x + b'*x. -Note that quadratic term has 0.5 before it. So if you want to minimize - f(x) = x^2 + x -you should rewrite your problem as follows: - f(x) = 0.5*(2*x^2) + x -and your matrix A will be equal to [[2.0]], not to [[1.0]] - -INPUT PARAMETERS: - State - structure which stores algorithm state - A - matrix, array[N,N] - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used - * if not given, both lower and upper triangles must be - filled. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetquadraticterm(const minqpstate &state, const real_2d_array &a) -{ - alglib_impl::ae_state _alglib_env_state; - bool isupper; - if( !alglib_impl::ae_is_symmetric(const_cast(a.c_ptr())) ) - throw ap_error("'a' parameter is not symmetric matrix"); - isupper = false; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetquadraticterm(const_cast(state.c_ptr()), const_cast(a.c_ptr()), isupper, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets sparse quadratic term for QP solver. By default, -quadratic term is zero. - -SUPPORT BY ALGLIB QP ALGORITHMS: - -Sparse quadratic term is supported only by BLEIC-based QP algorithm (one -which is activated by MinQPSetAlgoBLEIC function). Cholesky-based QP algo -won't be able to deal with sparse quadratic term and will terminate -abnormally. - -IF YOU CALLED THIS FUNCTION, YOU MUST SWITCH TO BLEIC-BASED QP ALGORITHM -BEFORE CALLING MINQPOPTIMIZE() FUNCTION. - -IMPORTANT: - -This solver minimizes following function: - f(x) = 0.5*x'*A*x + b'*x. -Note that quadratic term has 0.5 before it. So if you want to minimize - f(x) = x^2 + x -you should rewrite your problem as follows: - f(x) = 0.5*(2*x^2) + x -and your matrix A will be equal to [[2.0]], not to [[1.0]] - -INPUT PARAMETERS: - State - structure which stores algorithm state - A - matrix, array[N,N] - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used - * if not given, both lower and upper triangles must be - filled. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetquadratictermsparse(const minqpstate &state, const sparsematrix &a, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetquadratictermsparse(const_cast(state.c_ptr()), const_cast(a.c_ptr()), isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets starting point for QP solver. It is useful to have -good initial approximation to the solution, because it will increase -speed of convergence and identification of active constraints. - -INPUT PARAMETERS: - State - structure which stores algorithm state - X - starting point, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetstartingpoint(const minqpstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetstartingpoint(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets origin for QP solver. By default, following QP program -is solved: - - min(0.5*x'*A*x+b'*x) - -This function allows to solve different problem: - - min(0.5*(x-x_origin)'*A*(x-x_origin)+b'*(x-x_origin)) - -INPUT PARAMETERS: - State - structure which stores algorithm state - XOrigin - origin, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetorigin(const minqpstate &state, const real_1d_array &xorigin) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetorigin(const_cast(state.c_ptr()), const_cast(xorigin.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets scaling coefficients. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -BLEIC-based QP solver uses scale for two purposes: -* to evaluate stopping conditions -* for preconditioning of the underlying BLEIC solver - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetscale(const minqpstate &state, const real_1d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetscale(const_cast(state.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function tells solver to use Cholesky-based algorithm. This algorithm -is active by default. - -DESCRIPTION: - -Cholesky-based algorithm can be used only for problems which: -* have dense quadratic term, set by MinQPSetQuadraticTerm(), sparse or - structured problems are not supported. -* are strictly convex, i.e. quadratic term is symmetric positive definite, - indefinite or semidefinite problems are not supported by this algorithm. - -If anything of what listed above is violated, you may use BLEIC-based QP -algorithm which can be activated by MinQPSetAlgoBLEIC(). - -BENEFITS AND DRAWBACKS: - -This algorithm gives best precision amongst all QP solvers provided by -ALGLIB (Newton iterations have much higher precision than any other -optimization algorithm). This solver also gracefully handles problems with -very large amount of constraints. - -Performance of the algorithm is good because internally it uses Level 3 -Dense BLAS for its performance-critical parts. - - -From the other side, algorithm has O(N^3) complexity for unconstrained -problems and up to orders of magnitude slower on constrained problems -(these additional iterations are needed to identify active constraints). -So, its running time depends on number of constraints active at solution. - -Furthermore, this algorithm can not solve problems with sparse matrices or -problems with semidefinite/indefinite matrices of any kind (dense/sparse). - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetalgocholesky(const minqpstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetalgocholesky(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function tells solver to use BLEIC-based algorithm and sets stopping -criteria for the algorithm. - -DESCRIPTION: - -BLEIC-based QP algorithm can be used for any kind of QP problems: -* problems with both dense and sparse quadratic terms -* problems with positive definite, semidefinite, indefinite terms - -BLEIC-based algorithm can solve even indefinite problems - as long as they -are bounded from below on the feasible set. Of course, global minimum is -found only for positive definite and semidefinite problems. As for -indefinite ones - only local minimum is found. - -BENEFITS AND DRAWBACKS: - -This algorithm can be used to solve both convex and indefinite QP problems -and it can utilize sparsity of the quadratic term (algorithm calculates -matrix-vector products, which can be performed efficiently in case of -sparse matrix). - -Algorithm has iteration cost, which (assuming fixed amount of non-boundary -linear constraints) linearly depends on problem size. Boundary constraints -does not significantly change iteration cost. - -Thus, it outperforms Cholesky-based QP algorithm (CQP) on high-dimensional -sparse problems with moderate amount of constraints. - - -From the other side, unlike CQP solver, this algorithm does NOT make use -of Level 3 Dense BLAS. Thus, its performance on dense problems is inferior -to that of CQP solver. - -Its precision is also inferior to that of CQP. CQP performs Newton steps -which are know to achieve very good precision. In many cases Newton step -leads us exactly to the solution. BLEIC-QP performs LBFGS steps, which are -good at detecting neighborhood of the solution, buy need many iterations -to find solution with 6 digits of precision. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if exploratory steepest - descent step on k+1-th iteration satisfies following - condition: |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - EpsX - >=0 - The subroutine finishes its work if exploratory steepest - descent step on k+1-th iteration satisfies following - condition: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - step vector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinQPSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0 and EpsX=0 and MaxIts=0 (simultaneously) will lead -to automatic stopping criterion selection (presently it is small step -length, but it may change in the future versions of ALGLIB). - -IT IS VERY IMPORTANT THAT YOU CALL MinQPSetScale() WHEN YOU USE THIS ALGO! - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetalgobleic(const minqpstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetalgobleic(const_cast(state.c_ptr()), epsg, epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets boundary constraints for QP solver - -Boundary constraints are inactive by default (after initial creation). -After being set, they are preserved until explicitly turned off with -another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetbc(const minqpstate &state, const real_1d_array &bndl, const real_1d_array &bndu) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetbc(const_cast(state.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets linear constraints for QP optimizer. - -Linear constraints are inactive by default (after initial creation). - -INPUT PARAMETERS: - State - structure previously allocated with MinQPCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately - - there always exists some minor violation (about 10^-10...10^-13) - due to numerical errors. - - -- ALGLIB -- - Copyright 19.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlc(const minqpstate &state, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetlc(const_cast(state.c_ptr()), const_cast(c.c_ptr()), const_cast(ct.c_ptr()), k, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets linear constraints for QP optimizer. - -Linear constraints are inactive by default (after initial creation). - -INPUT PARAMETERS: - State - structure previously allocated with MinQPCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately - - there always exists some minor violation (about 10^-10...10^-13) - due to numerical errors. - - -- ALGLIB -- - Copyright 19.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlc(const minqpstate &state, const real_2d_array &c, const integer_1d_array &ct) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t k; - if( (c.rows()!=ct.length())) - throw ap_error("Error while calling 'minqpsetlc': looks like one of arguments has wrong size"); - k = c.rows(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpsetlc(const_cast(state.c_ptr()), const_cast(c.c_ptr()), const_cast(ct.c_ptr()), k, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function solves quadratic programming problem. -You should call it after setting solver options with MinQPSet...() calls. - -INPUT PARAMETERS: - State - algorithm state - -You should use MinQPResults() function to access results after calls -to this function. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey. - Special thanks to Elvira Illarionova for important suggestions on - the linearly constrained QP algorithm. -*************************************************************************/ -void minqpoptimize(const minqpstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpoptimize(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -QP solver results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution. - This array is allocated and initialized only when - Rep.TerminationType parameter is positive (success). - Rep - optimization report. You should check Rep.TerminationType, - which contains completion code, and you may check another - fields which contain another information about algorithm - functioning. - - Failure codes returned by algorithm are: - * -5 inappropriate solver was used: - * Cholesky solver for (semi)indefinite problems - * Cholesky solver for problems with sparse matrix - * -4 BLEIC-QP algorithm found unconstrained direction - of negative curvature (function is unbounded from - below even under constraints), no meaningful - minimum can be found. - * -3 inconsistent constraints (or maybe feasible point - is too hard to find). If you are sure that - constraints are feasible, try to restart optimizer - with better initial approximation. - - Completion codes specific for Cholesky algorithm: - * 4 successful completion - - Completion codes specific for BLEIC-based algorithm: - * 1 relative function improvement is no more than EpsF. - * 2 scaled step is no more than EpsX. - * 4 scaled gradient norm is no more than EpsG. - * 5 MaxIts steps was taken - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpresults(const minqpstate &state, real_1d_array &x, minqpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -QP results - -Buffered implementation of MinQPResults() which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpresultsbuf(const minqpstate &state, real_1d_array &x, minqpreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minqpresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Levenberg-Marquardt optimizer. - -This structure should be created using one of the MinLMCreate???() -functions. You should not access its fields directly; use ALGLIB functions -to work with it. -*************************************************************************/ -_minlmstate_owner::_minlmstate_owner() -{ - p_struct = (alglib_impl::minlmstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlmstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlmstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlmstate_owner::_minlmstate_owner(const _minlmstate_owner &rhs) -{ - p_struct = (alglib_impl::minlmstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlmstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlmstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlmstate_owner& _minlmstate_owner::operator=(const _minlmstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minlmstate_clear(p_struct); - if( !alglib_impl::_minlmstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minlmstate_owner::~_minlmstate_owner() -{ - alglib_impl::_minlmstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minlmstate* _minlmstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minlmstate* _minlmstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minlmstate::minlmstate() : _minlmstate_owner() ,needf(p_struct->needf),needfg(p_struct->needfg),needfgh(p_struct->needfgh),needfi(p_struct->needfi),needfij(p_struct->needfij),xupdated(p_struct->xupdated),f(p_struct->f),fi(&p_struct->fi),g(&p_struct->g),h(&p_struct->h),j(&p_struct->j),x(&p_struct->x) -{ -} - -minlmstate::minlmstate(const minlmstate &rhs):_minlmstate_owner(rhs) ,needf(p_struct->needf),needfg(p_struct->needfg),needfgh(p_struct->needfgh),needfi(p_struct->needfi),needfij(p_struct->needfij),xupdated(p_struct->xupdated),f(p_struct->f),fi(&p_struct->fi),g(&p_struct->g),h(&p_struct->h),j(&p_struct->j),x(&p_struct->x) -{ -} - -minlmstate& minlmstate::operator=(const minlmstate &rhs) -{ - if( this==&rhs ) - return *this; - _minlmstate_owner::operator=(rhs); - return *this; -} - -minlmstate::~minlmstate() -{ -} - - -/************************************************************************* -Optimization report, filled by MinLMResults() function - -FIELDS: -* TerminationType, completetion code: - * -7 derivative correctness check failed; - see Rep.WrongNum, Rep.WrongI, Rep.WrongJ for - more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient is no more than EpsG. - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible -* IterationsCount, contains iterations count -* NFunc, number of function calculations -* NJac, number of Jacobi matrix calculations -* NGrad, number of gradient calculations -* NHess, number of Hessian calculations -* NCholesky, number of Cholesky decomposition calculations -*************************************************************************/ -_minlmreport_owner::_minlmreport_owner() -{ - p_struct = (alglib_impl::minlmreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlmreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlmreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlmreport_owner::_minlmreport_owner(const _minlmreport_owner &rhs) -{ - p_struct = (alglib_impl::minlmreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minlmreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minlmreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minlmreport_owner& _minlmreport_owner::operator=(const _minlmreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minlmreport_clear(p_struct); - if( !alglib_impl::_minlmreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minlmreport_owner::~_minlmreport_owner() -{ - alglib_impl::_minlmreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minlmreport* _minlmreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minlmreport* _minlmreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minlmreport::minlmreport() : _minlmreport_owner() ,iterationscount(p_struct->iterationscount),terminationtype(p_struct->terminationtype),funcidx(p_struct->funcidx),varidx(p_struct->varidx),nfunc(p_struct->nfunc),njac(p_struct->njac),ngrad(p_struct->ngrad),nhess(p_struct->nhess),ncholesky(p_struct->ncholesky) -{ -} - -minlmreport::minlmreport(const minlmreport &rhs):_minlmreport_owner(rhs) ,iterationscount(p_struct->iterationscount),terminationtype(p_struct->terminationtype),funcidx(p_struct->funcidx),varidx(p_struct->varidx),nfunc(p_struct->nfunc),njac(p_struct->njac),ngrad(p_struct->ngrad),nhess(p_struct->nhess),ncholesky(p_struct->ncholesky) -{ -} - -minlmreport& minlmreport::operator=(const minlmreport &rhs) -{ - if( this==&rhs ) - return *this; - _minlmreport_owner::operator=(rhs); - return *this; -} - -minlmreport::~minlmreport() -{ -} - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] and Jacobian of f[]. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function vector f[] at given point X -* function vector f[] and Jacobian of f[] (simultaneously) at given point - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() and jac() callbacks. -First one is used to calculate f[] at given point, second one calculates -f[] and Jacobian df[i]/dx[j]. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not provide Jacobian), but it -will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateVJ() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatevj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] and Jacobian of f[]. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function vector f[] at given point X -* function vector f[] and Jacobian of f[] (simultaneously) at given point - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() and jac() callbacks. -First one is used to calculate f[] at given point, second one calculates -f[] and Jacobian df[i]/dx[j]. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not provide Jacobian), but it -will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateVJ() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevj(const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatevj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] only. Finite differences are used to -calculate Jacobian. - - -REQUIREMENTS: -This algorithm will request following information during its operation: -* function vector f[] at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() callback. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not accept function vector), but -it will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateV() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also MinLMIteration, MinLMResults. - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatev(const ae_int_t n, const ae_int_t m, const real_1d_array &x, const double diffstep, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatev(n, m, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] only. Finite differences are used to -calculate Jacobian. - - -REQUIREMENTS: -This algorithm will request following information during its operation: -* function vector f[] at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() callback. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not accept function vector), but -it will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateV() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also MinLMIteration, MinLMResults. - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatev(const ae_int_t m, const real_1d_array &x, const double diffstep, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatev(n, m, const_cast(x.c_ptr()), diffstep, const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE METHOD FOR NON-LINEAR OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of general form (not "sum-of- --squares") function - F = F(x[0], ..., x[n-1]) -using its gradient and Hessian. Levenberg-Marquardt modification with -L-BFGS pre-optimization and internal pre-conditioned L-BFGS optimization -after each Levenberg-Marquardt step is used. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function value F at given point X -* F and gradient G (simultaneously) at given point X -* F, G and Hessian H (simultaneously) at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts func(), grad() and hess() -function pointers. First pointer is used to calculate F at given point, -second one calculates F(x) and grad F(x), third one calculates F(x), -grad F(x), hess F(x). - -You can try to initialize MinLMState structure with FGH-function and then -use incorrect version of MinLMOptimize() (for example, version which does -not provide Hessian matrix), but it will lead to exception being thrown -after first attempt to calculate Hessian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateFGH() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - pointers (delegates, etc.) to callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgh(const ae_int_t n, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatefgh(n, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE METHOD FOR NON-LINEAR OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of general form (not "sum-of- --squares") function - F = F(x[0], ..., x[n-1]) -using its gradient and Hessian. Levenberg-Marquardt modification with -L-BFGS pre-optimization and internal pre-conditioned L-BFGS optimization -after each Levenberg-Marquardt step is used. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function value F at given point X -* F and gradient G (simultaneously) at given point X -* F, G and Hessian H (simultaneously) at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts func(), grad() and hess() -function pointers. First pointer is used to calculate F at given point, -second one calculates F(x) and grad F(x), third one calculates F(x), -grad F(x), hess F(x). - -You can try to initialize MinLMState structure with FGH-function and then -use incorrect version of MinLMOptimize() (for example, version which does -not provide Hessian matrix), but it will lead to exception being thrown -after first attempt to calculate Hessian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateFGH() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - pointers (delegates, etc.) to callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgh(const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatefgh(n, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping conditions for Levenberg-Marquardt optimization -algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinLMSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. Only Levenberg-Marquardt - iterations are counted (L-BFGS/CG iterations are NOT - counted because their cost is very low compared to that of - LM). - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetcond(const minlmstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetcond(const_cast(state.c_ptr()), epsg, epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinLMOptimize(). Both Levenberg-Marquardt and internal L-BFGS -iterations are reported. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetxrep(const minlmstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - -NOTE: non-zero StpMax leads to moderate performance degradation because -intermediate step of preconditioned L-BFGS optimization is incompatible -with limits on step size. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetstpmax(const minlmstate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets scaling coefficients for LM optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Generally, scale is NOT considered to be a form of preconditioner. But LM -optimizer is unique in that it uses scaling matrix both in the stopping -condition tests and as Marquardt damping factor. - -Proper scaling is very important for the algorithm performance. It is less -important for the quality of results, but still has some influence (it is -easier to converge when variables are properly scaled, so premature -stopping is possible when very badly scalled variables are combined with -relaxed stopping conditions). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlmsetscale(const minlmstate &state, const real_1d_array &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetscale(const_cast(state.c_ptr()), const_cast(s.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets boundary constraints for LM optimizer - -Boundary constraints are inactive by default (after initial creation). -They are preserved until explicitly turned off with another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: this solver has following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints - or at its boundary - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlmsetbc(const minlmstate &state, const real_1d_array &bndl, const real_1d_array &bndu) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetbc(const_cast(state.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is used to change acceleration settings - -You can choose between three acceleration strategies: -* AccType=0, no acceleration. -* AccType=1, secant updates are used to update quadratic model after each - iteration. After fixed number of iterations (or after model breakdown) - we recalculate quadratic model using analytic Jacobian or finite - differences. Number of secant-based iterations depends on optimization - settings: about 3 iterations - when we have analytic Jacobian, up to 2*N - iterations - when we use finite differences to calculate Jacobian. - -AccType=1 is recommended when Jacobian calculation cost is prohibitive -high (several Mx1 function vector calculations followed by several NxN -Cholesky factorizations are faster than calculation of one M*N Jacobian). -It should also be used when we have no Jacobian, because finite difference -approximation takes too much time to compute. - -Table below list optimization protocols (XYZ protocol corresponds to -MinLMCreateXYZ) and acceleration types they support (and use by default). - -ACCELERATION TYPES SUPPORTED BY OPTIMIZATION PROTOCOLS: - -protocol 0 1 comment -V + + -VJ + + -FGH + - -DAFAULT VALUES: - -protocol 0 1 comment -V x without acceleration it is so slooooooooow -VJ x -FGH x - -NOTE: this function should be called before optimization. Attempt to call -it during algorithm iterations may result in unexpected behavior. - -NOTE: attempt to call this function with unsupported protocol/acceleration -combination will result in exception being thrown. - - -- ALGLIB -- - Copyright 14.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetacctype(const minlmstate &state, const ae_int_t acctype) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetacctype(const_cast(state.c_ptr()), acctype, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minlmiteration(const minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::minlmiteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlmoptimize(minlmstate &state, - void (*fvec)(const real_1d_array &x, real_1d_array &fi, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( fvec==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (fvec is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlmiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needfi ) - { - fvec(state.x, state.fi, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlmoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlmoptimize(minlmstate &state, - void (*fvec)(const real_1d_array &x, real_1d_array &fi, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( fvec==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (fvec is NULL)"); - if( jac==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (jac is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlmiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needfi ) - { - fvec(state.x, state.fi, ptr); - continue; - } - if( state.needfij ) - { - jac(state.x, state.fi, state.j, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlmoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlmoptimize(minlmstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*hess)(const real_1d_array &x, double &func, real_1d_array &grad, real_2d_array &hess, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (func is NULL)"); - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (grad is NULL)"); - if( hess==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (hess is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlmiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.needfg ) - { - grad(state.x, state.f, state.g, ptr); - continue; - } - if( state.needfgh ) - { - hess(state.x, state.f, state.g, state.h, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlmoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlmoptimize(minlmstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (func is NULL)"); - if( jac==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (jac is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlmiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.needfij ) - { - jac(state.x, state.fi, state.j, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlmoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minlmoptimize(minlmstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (func is NULL)"); - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (grad is NULL)"); - if( jac==NULL ) - throw ap_error("ALGLIB: error in 'minlmoptimize()' (jac is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minlmiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.needfg ) - { - grad(state.x, state.f, state.g, ptr); - continue; - } - if( state.needfij ) - { - jac(state.x, state.fi, state.j, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minlmoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -Levenberg-Marquardt algorithm results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report; - see comments for this structure for more info. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmresults(const minlmstate &state, real_1d_array &x, minlmreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Levenberg-Marquardt algorithm results - -Buffered implementation of MinLMResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmresultsbuf(const minlmstate &state, real_1d_array &x, minlmreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine restarts LM algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used for reverse communication previously - allocated with MinLMCreateXXX call. - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmrestartfrom(const minlmstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmrestartfrom(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateVJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevgj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatevgj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateVJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevgj(const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatevgj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateFJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatefgj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateFJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgj(const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatefgj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is considered obsolete since ALGLIB 3.1.0 and is present for -backward compatibility only. We recommend to use MinLMCreateVJ, which -provides similar, but more consistent and feature-rich interface. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatefj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function is considered obsolete since ALGLIB 3.1.0 and is present for -backward compatibility only. We recommend to use MinLMCreateVJ, which -provides similar, but more consistent and feature-rich interface. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefj(const ae_int_t m, const real_1d_array &x, minlmstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmcreatefj(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinLMOptimize() is called -* prior to actual optimization, for each function Fi and each component - of parameters being optimized X[j] algorithm performs following steps: - * two trial steps are made to X[j]-TestStep*S[j] and X[j]+TestStep*S[j], - where X[j] is j-th parameter and S[j] is a scale of j-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * Fi(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative, - Rep.FuncIdx is set to index of the function. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) Jacobian evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided - by some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinLMSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minlmsetgradientcheck(const minlmstate &state, const double teststep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlmsetgradientcheck(const_cast(state.c_ptr()), teststep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_minasastate_owner::_minasastate_owner() -{ - p_struct = (alglib_impl::minasastate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minasastate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minasastate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minasastate_owner::_minasastate_owner(const _minasastate_owner &rhs) -{ - p_struct = (alglib_impl::minasastate*)alglib_impl::ae_malloc(sizeof(alglib_impl::minasastate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minasastate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minasastate_owner& _minasastate_owner::operator=(const _minasastate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minasastate_clear(p_struct); - if( !alglib_impl::_minasastate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minasastate_owner::~_minasastate_owner() -{ - alglib_impl::_minasastate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minasastate* _minasastate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minasastate* _minasastate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minasastate::minasastate() : _minasastate_owner() ,needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -minasastate::minasastate(const minasastate &rhs):_minasastate_owner(rhs) ,needfg(p_struct->needfg),xupdated(p_struct->xupdated),f(p_struct->f),g(&p_struct->g),x(&p_struct->x) -{ -} - -minasastate& minasastate::operator=(const minasastate &rhs) -{ - if( this==&rhs ) - return *this; - _minasastate_owner::operator=(rhs); - return *this; -} - -minasastate::~minasastate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_minasareport_owner::_minasareport_owner() -{ - p_struct = (alglib_impl::minasareport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minasareport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minasareport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minasareport_owner::_minasareport_owner(const _minasareport_owner &rhs) -{ - p_struct = (alglib_impl::minasareport*)alglib_impl::ae_malloc(sizeof(alglib_impl::minasareport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_minasareport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_minasareport_owner& _minasareport_owner::operator=(const _minasareport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_minasareport_clear(p_struct); - if( !alglib_impl::_minasareport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_minasareport_owner::~_minasareport_owner() -{ - alglib_impl::_minasareport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::minasareport* _minasareport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::minasareport* _minasareport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -minasareport::minasareport() : _minasareport_owner() ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),terminationtype(p_struct->terminationtype),activeconstraints(p_struct->activeconstraints) -{ -} - -minasareport::minasareport(const minasareport &rhs):_minasareport_owner(rhs) ,iterationscount(p_struct->iterationscount),nfev(p_struct->nfev),terminationtype(p_struct->terminationtype),activeconstraints(p_struct->activeconstraints) -{ -} - -minasareport& minasareport::operator=(const minasareport &rhs) -{ - if( this==&rhs ) - return *this; - _minasareport_owner::operator=(rhs); - return *this; -} - -minasareport::~minasareport() -{ -} - -/************************************************************************* -Obsolete function, use MinLBFGSSetPrecDefault() instead. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetdefaultpreconditioner(const minlbfgsstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetdefaultpreconditioner(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete function, use MinLBFGSSetCholeskyPreconditioner() instead. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetcholeskypreconditioner(const minlbfgsstate &state, const real_2d_array &p, const bool isupper) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minlbfgssetcholeskypreconditioner(const_cast(state.c_ptr()), const_cast(p.c_ptr()), isupper, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This is obsolete function which was used by previous version of the BLEIC -optimizer. It does nothing in the current version of BLEIC. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbarrierwidth(const minbleicstate &state, const double mu) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetbarrierwidth(const_cast(state.c_ptr()), mu, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This is obsolete function which was used by previous version of the BLEIC -optimizer. It does nothing in the current version of BLEIC. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbarrierdecay(const minbleicstate &state, const double mudecay) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minbleicsetbarrierdecay(const_cast(state.c_ptr()), mudecay, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void minasacreate(const ae_int_t n, const real_1d_array &x, const real_1d_array &bndl, const real_1d_array &bndu, minasastate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasacreate(n, const_cast(x.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void minasacreate(const real_1d_array &x, const real_1d_array &bndl, const real_1d_array &bndu, minasastate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=bndl.length()) || (x.length()!=bndu.length())) - throw ap_error("Error while calling 'minasacreate': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasacreate(n, const_cast(x.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetcond(const minasastate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasasetcond(const_cast(state.c_ptr()), epsg, epsf, epsx, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetxrep(const minasastate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasasetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetalgorithm(const minasastate &state, const ae_int_t algotype) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasasetalgorithm(const_cast(state.c_ptr()), algotype, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetstpmax(const minasastate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasasetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minasaiteration(const minasastate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::minasaiteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void minasaoptimize(minasastate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( grad==NULL ) - throw ap_error("ALGLIB: error in 'minasaoptimize()' (grad is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::minasaiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needfg ) - { - grad(state.x, state.f, state.g, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'minasaoptimize' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minasaresults(const minasastate &state, real_1d_array &x, minasareport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasaresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minasaresultsbuf(const minasastate &state, real_1d_array &x, minasareport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasaresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minasarestartfrom(const minasastate &state, const real_1d_array &x, const real_1d_array &bndl, const real_1d_array &bndu) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::minasarestartfrom(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(bndl.c_ptr()), const_cast(bndu.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - - -static ae_int_t cqmodels_newtonrefinementits = 3; -static ae_bool cqmodels_cqmrebuild(convexquadraticmodel* s, - ae_state *_state); -static void cqmodels_cqmsolveea(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* tmp, - ae_state *_state); - - -static ae_int_t snnls_iterativerefinementits = 3; -static ae_bool snnls_boundedstepandactivation(/* Real */ ae_vector* xc, - /* Real */ ae_vector* xn, - /* Boolean */ ae_vector* nnc, - ae_int_t n, - ae_state *_state); - - -static void sactivesets_constraineddescent(sactiveset* state, - /* Real */ ae_vector* g, - /* Real */ ae_vector* h, - /* Real */ ae_matrix* ha, - ae_bool normalize, - /* Real */ ae_vector* d, - ae_state *_state); -static void sactivesets_reactivateconstraints(sactiveset* state, - /* Real */ ae_vector* gc, - /* Real */ ae_vector* h, - ae_state *_state); - - -static ae_int_t mincg_rscountdownlen = 10; -static double mincg_gtol = 0.3; -static void mincg_clearrequestfields(mincgstate* state, ae_state *_state); -static void mincg_preconditionedmultiply(mincgstate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* work0, - /* Real */ ae_vector* work1, - ae_state *_state); -static double mincg_preconditionedmultiply2(mincgstate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* work0, - /* Real */ ae_vector* work1, - ae_state *_state); -static void mincg_mincginitinternal(ae_int_t n, - double diffstep, - mincgstate* state, - ae_state *_state); - - -static double minbleic_gtol = 0.4; -static double minbleic_maxnonmonotoniclen = 1.0E-5; -static double minbleic_initialdecay = 0.5; -static double minbleic_mindecay = 0.1; -static double minbleic_decaycorrection = 0.8; -static double minbleic_penaltyfactor = 100; -static void minbleic_clearrequestfields(minbleicstate* state, - ae_state *_state); -static void minbleic_minbleicinitinternal(ae_int_t n, - /* Real */ ae_vector* x, - double diffstep, - minbleicstate* state, - ae_state *_state); -static void minbleic_updateestimateofgoodstep(double* estimate, - double newstep, - ae_state *_state); - - -static double minlbfgs_gtol = 0.4; -static void minlbfgs_clearrequestfields(minlbfgsstate* state, - ae_state *_state); - - -static ae_int_t minqp_maxlagrangeits = 10; -static ae_int_t minqp_maxbadnewtonits = 7; -static double minqp_penaltyfactor = 100.0; -static ae_int_t minqp_minqpboundedstepandactivation(minqpstate* state, - /* Real */ ae_vector* xn, - /* Real */ ae_vector* buf, - ae_state *_state); -static double minqp_minqpmodelvalue(convexquadraticmodel* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* xc, - ae_int_t n, - /* Real */ ae_vector* tmp, - ae_state *_state); -static ae_bool minqp_minqpconstrainedoptimum(minqpstate* state, - convexquadraticmodel* a, - double anorm, - /* Real */ ae_vector* b, - /* Real */ ae_vector* xn, - /* Real */ ae_vector* tmp, - /* Boolean */ ae_vector* tmpb, - /* Real */ ae_vector* lagrangec, - ae_state *_state); - - -static double minlm_lambdaup = 2.0; -static double minlm_lambdadown = 0.33; -static double minlm_suspiciousnu = 16; -static ae_int_t minlm_smallmodelage = 3; -static ae_int_t minlm_additers = 5; -static void minlm_lmprepare(ae_int_t n, - ae_int_t m, - ae_bool havegrad, - minlmstate* state, - ae_state *_state); -static void minlm_clearrequestfields(minlmstate* state, ae_state *_state); -static ae_bool minlm_increaselambda(double* lambdav, - double* nu, - ae_state *_state); -static void minlm_decreaselambda(double* lambdav, - double* nu, - ae_state *_state); -static double minlm_boundedscaledantigradnorm(minlmstate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* g, - ae_state *_state); - - -static ae_int_t mincomp_n1 = 2; -static ae_int_t mincomp_n2 = 2; -static double mincomp_stpmin = 1.0E-300; -static double mincomp_gtol = 0.3; -static double mincomp_gpaftol = 0.0001; -static double mincomp_gpadecay = 0.5; -static double mincomp_asarho = 0.5; -static double mincomp_asaboundedantigradnorm(minasastate* state, - ae_state *_state); -static double mincomp_asaginorm(minasastate* state, ae_state *_state); -static double mincomp_asad1norm(minasastate* state, ae_state *_state); -static ae_bool mincomp_asauisempty(minasastate* state, ae_state *_state); -static void mincomp_clearrequestfields(minasastate* state, - ae_state *_state); - - - - - -/************************************************************************* -This subroutine is used to prepare threshold value which will be used for -trimming of the target function (see comments on TrimFunction() for more -information). - -This function accepts only one parameter: function value at the starting -point. It returns threshold which will be used for trimming. - - -- ALGLIB -- - Copyright 10.05.2011 by Bochkanov Sergey -*************************************************************************/ -void trimprepare(double f, double* threshold, ae_state *_state) -{ - - *threshold = 0; - - *threshold = 10*(ae_fabs(f, _state)+1); -} - - -/************************************************************************* -This subroutine is used to "trim" target function, i.e. to do following -transformation: - - { {F,G} if F=Threshold - -Such transformation allows us to solve problems with singularities by -redefining function in such way that it becomes bounded from above. - - -- ALGLIB -- - Copyright 10.05.2011 by Bochkanov Sergey -*************************************************************************/ -void trimfunction(double* f, - /* Real */ ae_vector* g, - ae_int_t n, - double threshold, - ae_state *_state) -{ - ae_int_t i; - - - if( ae_fp_greater_eq(*f,threshold) ) - { - *f = threshold; - for(i=0; i<=n-1; i++) - { - g->ptr.p_double[i] = 0.0; - } - } -} - - -/************************************************************************* -This function enforces boundary constraints in the X. - -This function correctly (although a bit inefficient) handles BL[i] which -are -INF and BU[i] which are +INF. - -We have NMain+NSlack dimensional X, with first NMain components bounded -by BL/BU, and next NSlack ones bounded by non-negativity constraints. - -INPUT PARAMETERS - X - array[NMain+NSlack], point - BL - array[NMain], lower bounds - (may contain -INF, when bound is not present) - HaveBL - array[NMain], if HaveBL[i] is False, - then i-th bound is not present - BU - array[NMain], upper bounds - (may contain +INF, when bound is not present) - HaveBU - array[NMain], if HaveBU[i] is False, - then i-th bound is not present - -OUTPUT PARAMETERS - X - X with all constraints being enforced - -It returns True when constraints are consistent, -False - when constraints are inconsistent. - - -- ALGLIB -- - Copyright 10.01.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool enforceboundaryconstraints(/* Real */ ae_vector* x, - /* Real */ ae_vector* bl, - /* Boolean */ ae_vector* havebl, - /* Real */ ae_vector* bu, - /* Boolean */ ae_vector* havebu, - ae_int_t nmain, - ae_int_t nslack, - ae_state *_state) -{ - ae_int_t i; - ae_bool result; - - - result = ae_false; - for(i=0; i<=nmain-1; i++) - { - if( (havebl->ptr.p_bool[i]&&havebu->ptr.p_bool[i])&&ae_fp_greater(bl->ptr.p_double[i],bu->ptr.p_double[i]) ) - { - return result; - } - if( havebl->ptr.p_bool[i]&&ae_fp_less(x->ptr.p_double[i],bl->ptr.p_double[i]) ) - { - x->ptr.p_double[i] = bl->ptr.p_double[i]; - } - if( havebu->ptr.p_bool[i]&&ae_fp_greater(x->ptr.p_double[i],bu->ptr.p_double[i]) ) - { - x->ptr.p_double[i] = bu->ptr.p_double[i]; - } - } - for(i=0; i<=nslack-1; i++) - { - if( ae_fp_less(x->ptr.p_double[nmain+i],0) ) - { - x->ptr.p_double[nmain+i] = 0; - } - } - result = ae_true; - return result; -} - - -/************************************************************************* -This function projects gradient into feasible area of boundary constrained -optimization problem. X can be infeasible with respect to boundary -constraints. We have NMain+NSlack dimensional X, with first NMain -components bounded by BL/BU, and next NSlack ones bounded by non-negativity -constraints. - -INPUT PARAMETERS - X - array[NMain+NSlack], point - G - array[NMain+NSlack], gradient - BL - lower bounds (may contain -INF, when bound is not present) - HaveBL - if HaveBL[i] is False, then i-th bound is not present - BU - upper bounds (may contain +INF, when bound is not present) - HaveBU - if HaveBU[i] is False, then i-th bound is not present - -OUTPUT PARAMETERS - G - projection of G. Components of G which satisfy one of the - following - (1) (X[I]<=BndL[I]) and (G[I]>0), OR - (2) (X[I]>=BndU[I]) and (G[I]<0) - are replaced by zeros. - -NOTE 1: this function assumes that constraints are feasible. It throws -exception otherwise. - -NOTE 2: in fact, projection of ANTI-gradient is calculated, because this -function trims components of -G which points outside of the feasible area. -However, working with -G is considered confusing, because all optimization -source work with G. - - -- ALGLIB -- - Copyright 10.01.2012 by Bochkanov Sergey -*************************************************************************/ -void projectgradientintobc(/* Real */ ae_vector* x, - /* Real */ ae_vector* g, - /* Real */ ae_vector* bl, - /* Boolean */ ae_vector* havebl, - /* Real */ ae_vector* bu, - /* Boolean */ ae_vector* havebu, - ae_int_t nmain, - ae_int_t nslack, - ae_state *_state) -{ - ae_int_t i; - - - for(i=0; i<=nmain-1; i++) - { - ae_assert((!havebl->ptr.p_bool[i]||!havebu->ptr.p_bool[i])||ae_fp_less_eq(bl->ptr.p_double[i],bu->ptr.p_double[i]), "ProjectGradientIntoBC: internal error (infeasible constraints)", _state); - if( (havebl->ptr.p_bool[i]&&ae_fp_less_eq(x->ptr.p_double[i],bl->ptr.p_double[i]))&&ae_fp_greater(g->ptr.p_double[i],0) ) - { - g->ptr.p_double[i] = 0; - } - if( (havebu->ptr.p_bool[i]&&ae_fp_greater_eq(x->ptr.p_double[i],bu->ptr.p_double[i]))&&ae_fp_less(g->ptr.p_double[i],0) ) - { - g->ptr.p_double[i] = 0; - } - } - for(i=0; i<=nslack-1; i++) - { - if( ae_fp_less_eq(x->ptr.p_double[nmain+i],0)&&ae_fp_greater(g->ptr.p_double[nmain+i],0) ) - { - g->ptr.p_double[nmain+i] = 0; - } - } -} - - -/************************************************************************* -Given - a) initial point X0[NMain+NSlack] - (feasible with respect to bound constraints) - b) step vector alpha*D[NMain+NSlack] - c) boundary constraints BndL[NMain], BndU[NMain] - d) implicit non-negativity constraints for slack variables -this function calculates bound on the step length subject to boundary -constraints. - -It returns: - * MaxStepLen - such step length that X0+MaxStepLen*alpha*D is exactly - at the boundary given by constraints - * VariableToFreeze - index of the constraint to be activated, - 0 <= VariableToFreeze < NMain+NSlack - * ValueToFreeze - value of the corresponding constraint. - -Notes: - * it is possible that several constraints can be activated by the step - at once. In such cases only one constraint is returned. It is caller - responsibility to check other constraints. This function makes sure - that we activate at least one constraint, and everything else is the - responsibility of the caller. - * steps smaller than MaxStepLen still can activate constraints due to - numerical errors. Thus purpose of this function is not to guard - against accidental activation of the constraints - quite the reverse, - its purpose is to activate at least constraint upon performing step - which is too long. - * in case there is no constraints to activate, we return negative - VariableToFreeze and zero MaxStepLen and ValueToFreeze. - * this function assumes that constraints are consistent; it throws - exception otherwise. - -INPUT PARAMETERS - X - array[NMain+NSlack], point. Must be feasible with respect - to bound constraints (exception will be thrown otherwise) - D - array[NMain+NSlack], step direction - alpha - scalar multiplier before D, alpha<>0 - BndL - lower bounds, array[NMain] - (may contain -INF, when bound is not present) - HaveBndL - array[NMain], if HaveBndL[i] is False, - then i-th bound is not present - BndU - array[NMain], upper bounds - (may contain +INF, when bound is not present) - HaveBndU - array[NMain], if HaveBndU[i] is False, - then i-th bound is not present - NMain - number of main variables - NSlack - number of slack variables - -OUTPUT PARAMETERS - VariableToFreeze: - * negative value = step is unbounded, ValueToFreeze=0, - MaxStepLen=0. - * non-negative value = at least one constraint, given by - this parameter, will be activated - upon performing maximum step. - ValueToFreeze- value of the variable which will be constrained - MaxStepLen - maximum length of the step. Can be zero when step vector - looks outside of the feasible area. - - -- ALGLIB -- - Copyright 10.01.2012 by Bochkanov Sergey -*************************************************************************/ -void calculatestepbound(/* Real */ ae_vector* x, - /* Real */ ae_vector* d, - double alpha, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - ae_int_t* variabletofreeze, - double* valuetofreeze, - double* maxsteplen, - ae_state *_state) -{ - ae_int_t i; - double prevmax; - double initval; - - *variabletofreeze = 0; - *valuetofreeze = 0; - *maxsteplen = 0; - - ae_assert(ae_fp_neq(alpha,0), "CalculateStepBound: zero alpha", _state); - *variabletofreeze = -1; - initval = ae_maxrealnumber; - *maxsteplen = initval; - for(i=0; i<=nmain-1; i++) - { - if( havebndl->ptr.p_bool[i]&&ae_fp_less(alpha*d->ptr.p_double[i],0) ) - { - ae_assert(ae_fp_greater_eq(x->ptr.p_double[i],bndl->ptr.p_double[i]), "CalculateStepBound: infeasible X", _state); - prevmax = *maxsteplen; - *maxsteplen = safeminposrv(x->ptr.p_double[i]-bndl->ptr.p_double[i], -alpha*d->ptr.p_double[i], *maxsteplen, _state); - if( ae_fp_less(*maxsteplen,prevmax) ) - { - *variabletofreeze = i; - *valuetofreeze = bndl->ptr.p_double[i]; - } - } - if( havebndu->ptr.p_bool[i]&&ae_fp_greater(alpha*d->ptr.p_double[i],0) ) - { - ae_assert(ae_fp_less_eq(x->ptr.p_double[i],bndu->ptr.p_double[i]), "CalculateStepBound: infeasible X", _state); - prevmax = *maxsteplen; - *maxsteplen = safeminposrv(bndu->ptr.p_double[i]-x->ptr.p_double[i], alpha*d->ptr.p_double[i], *maxsteplen, _state); - if( ae_fp_less(*maxsteplen,prevmax) ) - { - *variabletofreeze = i; - *valuetofreeze = bndu->ptr.p_double[i]; - } - } - } - for(i=0; i<=nslack-1; i++) - { - if( ae_fp_less(alpha*d->ptr.p_double[nmain+i],0) ) - { - ae_assert(ae_fp_greater_eq(x->ptr.p_double[nmain+i],0), "CalculateStepBound: infeasible X", _state); - prevmax = *maxsteplen; - *maxsteplen = safeminposrv(x->ptr.p_double[nmain+i], -alpha*d->ptr.p_double[nmain+i], *maxsteplen, _state); - if( ae_fp_less(*maxsteplen,prevmax) ) - { - *variabletofreeze = nmain+i; - *valuetofreeze = 0; - } - } - } - if( ae_fp_eq(*maxsteplen,initval) ) - { - *valuetofreeze = 0; - *maxsteplen = 0; - } -} - - -/************************************************************************* -This function postprocesses bounded step by: -* analysing step length (whether it is equal to MaxStepLen) and activating - constraint given by VariableToFreeze if needed -* checking for additional bound constraints to activate - -This function uses final point of the step, quantities calculated by the -CalculateStepBound() function. As result, it returns point which is -exactly feasible with respect to boundary constraints. - -NOTE 1: this function does NOT handle and check linear equality constraints -NOTE 2: when StepTaken=MaxStepLen we always activate at least one constraint - -INPUT PARAMETERS - X - array[NMain+NSlack], final point to postprocess - XPrev - array[NMain+NSlack], initial point - BndL - lower bounds, array[NMain] - (may contain -INF, when bound is not present) - HaveBndL - array[NMain], if HaveBndL[i] is False, - then i-th bound is not present - BndU - array[NMain], upper bounds - (may contain +INF, when bound is not present) - HaveBndU - array[NMain], if HaveBndU[i] is False, - then i-th bound is not present - NMain - number of main variables - NSlack - number of slack variables - VariableToFreeze-result of CalculateStepBound() - ValueToFreeze- result of CalculateStepBound() - StepTaken - actual step length (actual step is equal to the possibly - non-unit step direction vector times this parameter). - StepTaken<=MaxStepLen. - MaxStepLen - result of CalculateStepBound() - -OUTPUT PARAMETERS - X - point bounded with respect to constraints. - components corresponding to active constraints are exactly - equal to the boundary values. - -RESULT: - number of constraints activated in addition to previously active ones. - Constraints which were DEACTIVATED are ignored (do not influence - function value). - - -- ALGLIB -- - Copyright 10.01.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t postprocessboundedstep(/* Real */ ae_vector* x, - /* Real */ ae_vector* xprev, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - ae_int_t variabletofreeze, - double valuetofreeze, - double steptaken, - double maxsteplen, - ae_state *_state) -{ - ae_int_t i; - ae_bool wasactivated; - ae_int_t result; - - - ae_assert(variabletofreeze<0||ae_fp_less_eq(steptaken,maxsteplen), "Assertion failed", _state); - - /* - * Activate constraints - */ - if( variabletofreeze>=0&&ae_fp_eq(steptaken,maxsteplen) ) - { - x->ptr.p_double[variabletofreeze] = valuetofreeze; - } - for(i=0; i<=nmain-1; i++) - { - if( havebndl->ptr.p_bool[i]&&ae_fp_less(x->ptr.p_double[i],bndl->ptr.p_double[i]) ) - { - x->ptr.p_double[i] = bndl->ptr.p_double[i]; - } - if( havebndu->ptr.p_bool[i]&&ae_fp_greater(x->ptr.p_double[i],bndu->ptr.p_double[i]) ) - { - x->ptr.p_double[i] = bndu->ptr.p_double[i]; - } - } - for(i=0; i<=nslack-1; i++) - { - if( ae_fp_less_eq(x->ptr.p_double[nmain+i],0) ) - { - x->ptr.p_double[nmain+i] = 0; - } - } - - /* - * Calculate number of constraints being activated - */ - result = 0; - for(i=0; i<=nmain-1; i++) - { - wasactivated = ae_fp_neq(x->ptr.p_double[i],xprev->ptr.p_double[i])&&((havebndl->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndl->ptr.p_double[i]))||(havebndu->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndu->ptr.p_double[i]))); - wasactivated = wasactivated||variabletofreeze==i; - if( wasactivated ) - { - result = result+1; - } - } - for(i=0; i<=nslack-1; i++) - { - wasactivated = ae_fp_neq(x->ptr.p_double[nmain+i],xprev->ptr.p_double[nmain+i])&&ae_fp_eq(x->ptr.p_double[nmain+i],0.0); - wasactivated = wasactivated||variabletofreeze==nmain+i; - if( wasactivated ) - { - result = result+1; - } - } - return result; -} - - -/************************************************************************* -The purpose of this function is to prevent algorithm from "unsticking" -from the active bound constraints because of numerical noise in the -gradient or Hessian. - -It is done by zeroing some components of the search direction D. D[i] is -zeroed when both (a) and (b) are true: -a) corresponding X[i] is exactly at the boundary -b) |D[i]*S[i]| <= DropTol*Sqrt(SUM(D[i]^2*S[I]^2)) - -D can be step direction , antigradient, gradient, or anything similar. -Sign of D does not matter, nor matters step length. - -NOTE 1: boundary constraints are expected to be consistent, as well as X - is expected to be feasible. Exception will be thrown otherwise. - -INPUT PARAMETERS - D - array[NMain+NSlack], direction - X - array[NMain+NSlack], current point - BndL - lower bounds, array[NMain] - (may contain -INF, when bound is not present) - HaveBndL - array[NMain], if HaveBndL[i] is False, - then i-th bound is not present - BndU - array[NMain], upper bounds - (may contain +INF, when bound is not present) - HaveBndU - array[NMain], if HaveBndU[i] is False, - then i-th bound is not present - S - array[NMain+NSlack], scaling of the variables - NMain - number of main variables - NSlack - number of slack variables - DropTol - drop tolerance, >=0 - -OUTPUT PARAMETERS - X - point bounded with respect to constraints. - components corresponding to active constraints are exactly - equal to the boundary values. - - -- ALGLIB -- - Copyright 10.01.2012 by Bochkanov Sergey -*************************************************************************/ -void filterdirection(/* Real */ ae_vector* d, - /* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - /* Real */ ae_vector* s, - ae_int_t nmain, - ae_int_t nslack, - double droptol, - ae_state *_state) -{ - ae_int_t i; - double scalednorm; - ae_bool isactive; - - - scalednorm = 0.0; - for(i=0; i<=nmain+nslack-1; i++) - { - scalednorm = scalednorm+ae_sqr(d->ptr.p_double[i]*s->ptr.p_double[i], _state); - } - scalednorm = ae_sqrt(scalednorm, _state); - for(i=0; i<=nmain-1; i++) - { - ae_assert(!havebndl->ptr.p_bool[i]||ae_fp_greater_eq(x->ptr.p_double[i],bndl->ptr.p_double[i]), "FilterDirection: infeasible point", _state); - ae_assert(!havebndu->ptr.p_bool[i]||ae_fp_less_eq(x->ptr.p_double[i],bndu->ptr.p_double[i]), "FilterDirection: infeasible point", _state); - isactive = (havebndl->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndl->ptr.p_double[i]))||(havebndu->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndu->ptr.p_double[i])); - if( isactive&&ae_fp_less_eq(ae_fabs(d->ptr.p_double[i]*s->ptr.p_double[i], _state),droptol*scalednorm) ) - { - d->ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=nslack-1; i++) - { - ae_assert(ae_fp_greater_eq(x->ptr.p_double[nmain+i],0), "FilterDirection: infeasible point", _state); - if( ae_fp_eq(x->ptr.p_double[nmain+i],0)&&ae_fp_less_eq(ae_fabs(d->ptr.p_double[nmain+i]*s->ptr.p_double[nmain+i], _state),droptol*scalednorm) ) - { - d->ptr.p_double[nmain+i] = 0.0; - } - } -} - - -/************************************************************************* -This function returns number of bound constraints whose state was changed -(either activated or deactivated) when making step from XPrev to X. - -Constraints are considered: -* active - when we are exactly at the boundary -* inactive - when we are not at the boundary - -You should note that antigradient direction is NOT taken into account when -we make decions on the constraint status. - -INPUT PARAMETERS - X - array[NMain+NSlack], final point. - Must be feasible with respect to bound constraints. - XPrev - array[NMain+NSlack], initial point. - Must be feasible with respect to bound constraints. - BndL - lower bounds, array[NMain] - (may contain -INF, when bound is not present) - HaveBndL - array[NMain], if HaveBndL[i] is False, - then i-th bound is not present - BndU - array[NMain], upper bounds - (may contain +INF, when bound is not present) - HaveBndU - array[NMain], if HaveBndU[i] is False, - then i-th bound is not present - NMain - number of main variables - NSlack - number of slack variables - -RESULT: - number of constraints whose state was changed. - - -- ALGLIB -- - Copyright 10.01.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t numberofchangedconstraints(/* Real */ ae_vector* x, - /* Real */ ae_vector* xprev, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - ae_state *_state) -{ - ae_int_t i; - ae_bool statuschanged; - ae_int_t result; - - - result = 0; - for(i=0; i<=nmain-1; i++) - { - if( ae_fp_neq(x->ptr.p_double[i],xprev->ptr.p_double[i]) ) - { - statuschanged = ae_false; - if( havebndl->ptr.p_bool[i]&&(ae_fp_eq(x->ptr.p_double[i],bndl->ptr.p_double[i])||ae_fp_eq(xprev->ptr.p_double[i],bndl->ptr.p_double[i])) ) - { - statuschanged = ae_true; - } - if( havebndu->ptr.p_bool[i]&&(ae_fp_eq(x->ptr.p_double[i],bndu->ptr.p_double[i])||ae_fp_eq(xprev->ptr.p_double[i],bndu->ptr.p_double[i])) ) - { - statuschanged = ae_true; - } - if( statuschanged ) - { - result = result+1; - } - } - } - for(i=0; i<=nslack-1; i++) - { - if( ae_fp_neq(x->ptr.p_double[nmain+i],xprev->ptr.p_double[nmain+i])&&(ae_fp_eq(x->ptr.p_double[nmain+i],0)||ae_fp_eq(xprev->ptr.p_double[nmain+i],0)) ) - { - result = result+1; - } - } - return result; -} - - -/************************************************************************* -This function finds feasible point of (NMain+NSlack)-dimensional problem -subject to NMain explicit boundary constraints (some constraints can be -omitted), NSlack implicit non-negativity constraints, K linear equality -constraints. - -INPUT PARAMETERS - X - array[NMain+NSlack], initial point. - BndL - lower bounds, array[NMain] - (may contain -INF, when bound is not present) - HaveBndL - array[NMain], if HaveBndL[i] is False, - then i-th bound is not present - BndU - array[NMain], upper bounds - (may contain +INF, when bound is not present) - HaveBndU - array[NMain], if HaveBndU[i] is False, - then i-th bound is not present - NMain - number of main variables - NSlack - number of slack variables - CE - array[K,NMain+NSlack+1], equality constraints CE*x=b. - Rows contain constraints, first NMain+NSlack columns - contain coefficients before X[], last column contain - right part. - K - number of linear constraints - EpsI - infeasibility (error in the right part) allowed in the - solution - -OUTPUT PARAMETERS: - X - feasible point or best infeasible point found before - algorithm termination - QPIts - number of QP iterations (for debug purposes) - GPAIts - number of GPA iterations (for debug purposes) - -RESULT: - True in case X is feasible, False - if it is infeasible. - - -- ALGLIB -- - Copyright 20.01.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool findfeasiblepoint(/* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - /* Real */ ae_matrix* ce, - ae_int_t k, - double epsi, - ae_int_t* qpits, - ae_int_t* gpaits, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _ce; - ae_int_t i; - ae_int_t j; - ae_int_t idx0; - ae_int_t idx1; - ae_vector permx; - ae_vector xn; - ae_vector xa; - ae_vector newtonstep; - ae_vector g; - ae_vector pg; - ae_matrix a; - double armijostep; - double armijobeststep; - double armijobestfeas; - double v; - double mx; - double feaserr; - double feasold; - double feasnew; - double pgnorm; - double vn; - double vd; - double stp; - ae_int_t vartofreeze; - double valtofreeze; - double maxsteplen; - ae_bool werechangesinconstraints; - ae_bool stage1isover; - ae_bool converged; - ae_vector activeconstraints; - ae_vector tmpk; - ae_vector colnorms; - ae_int_t nactive; - ae_int_t nfree; - ae_int_t nsvd; - ae_vector p1; - ae_vector p2; - apbuffers buf; - ae_vector w; - ae_vector s; - ae_matrix u; - ae_matrix vt; - ae_int_t itscount; - ae_int_t itswithintolerance; - ae_int_t maxitswithintolerance; - ae_int_t gparuns; - ae_int_t maxarmijoruns; - ae_bool result; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_ce, ce, _state, ae_true); - ce = &_ce; - *qpits = 0; - *gpaits = 0; - ae_vector_init(&permx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xn, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xa, 0, DT_REAL, _state, ae_true); - ae_vector_init(&newtonstep, 0, DT_REAL, _state, ae_true); - ae_vector_init(&g, 0, DT_REAL, _state, ae_true); - ae_vector_init(&pg, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&a, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&activeconstraints, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmpk, 0, DT_REAL, _state, ae_true); - ae_vector_init(&colnorms, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p1, 0, DT_INT, _state, ae_true); - ae_vector_init(&p2, 0, DT_INT, _state, ae_true); - _apbuffers_init(&buf, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - ae_vector_init(&s, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&u, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vt, 0, 0, DT_REAL, _state, ae_true); - - maxitswithintolerance = 3; - maxarmijoruns = 5; - *qpits = 0; - *gpaits = 0; - - /* - * Initial enforcement of the feasibility with respect to boundary constraints - * NOTE: after this block we assume that boundary constraints are consistent. - */ - if( !enforceboundaryconstraints(x, bndl, havebndl, bndu, havebndu, nmain, nslack, _state) ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - if( k==0 ) - { - - /* - * No linear constraints, we can exit right now - */ - result = ae_true; - ae_frame_leave(_state); - return result; - } - - /* - * Scale rows of CE in such way that max(CE[i,0..nmain+nslack-1])=1 for any i=0..k-1 - */ - for(i=0; i<=k-1; i++) - { - v = 0.0; - for(j=0; j<=nmain+nslack-1; j++) - { - v = ae_maxreal(v, ae_fabs(ce->ptr.pp_double[i][j], _state), _state); - } - if( ae_fp_neq(v,0) ) - { - v = 1/v; - ae_v_muld(&ce->ptr.pp_double[i][0], 1, ae_v_len(0,nmain+nslack), v); - } - } - - /* - * Allocate temporaries - */ - ae_vector_set_length(&xn, nmain+nslack, _state); - ae_vector_set_length(&xa, nmain+nslack, _state); - ae_vector_set_length(&permx, nmain+nslack, _state); - ae_vector_set_length(&g, nmain+nslack, _state); - ae_vector_set_length(&pg, nmain+nslack, _state); - ae_vector_set_length(&tmpk, k, _state); - ae_matrix_set_length(&a, k, nmain+nslack, _state); - ae_vector_set_length(&activeconstraints, nmain+nslack, _state); - ae_vector_set_length(&newtonstep, nmain+nslack, _state); - ae_vector_set_length(&s, nmain+nslack, _state); - ae_vector_set_length(&colnorms, nmain+nslack, _state); - for(i=0; i<=nmain+nslack-1; i++) - { - s.ptr.p_double[i] = 1.0; - colnorms.ptr.p_double[i] = 0.0; - for(j=0; j<=k-1; j++) - { - colnorms.ptr.p_double[i] = colnorms.ptr.p_double[i]+ae_sqr(ce->ptr.pp_double[j][i], _state); - } - } - - /* - * K>0, we have linear equality constraints combined with bound constraints. - * - * Try to find feasible point as minimizer of the quadratic function - * F(x) = 0.5*||CE*x-b||^2 = 0.5*x'*(CE'*CE)*x - (b'*CE)*x + 0.5*b'*b - * subject to boundary constraints given by BL, BU and non-negativity of - * the slack variables. BTW, we drop constant term because it does not - * actually influences on the solution. - * - * Below we will assume that K>0. - */ - itswithintolerance = 0; - itscount = 0; - for(;;) - { - - /* - * Stage 0: check for exact convergence - */ - converged = ae_true; - feaserr = 0; - for(i=0; i<=k-1; i++) - { - - /* - * Calculate: - * * V - error in the right part - * * MX - maximum term in the left part - * - * Terminate if error in the right part is not greater than 100*Eps*MX. - * - * IMPORTANT: we must perform check for non-strict inequality, i.e. to use <= instead of <. - * it will allow us to easily handle situations with zero rows of CE. - */ - mx = 0; - v = -ce->ptr.pp_double[i][nmain+nslack]; - for(j=0; j<=nmain+nslack-1; j++) - { - mx = ae_maxreal(mx, ae_fabs(ce->ptr.pp_double[i][j]*x->ptr.p_double[j], _state), _state); - v = v+ce->ptr.pp_double[i][j]*x->ptr.p_double[j]; - } - feaserr = feaserr+ae_sqr(v, _state); - converged = converged&&ae_fp_less_eq(ae_fabs(v, _state),100*ae_machineepsilon*mx); - } - feaserr = ae_sqrt(feaserr, _state); - if( converged ) - { - result = ae_fp_less_eq(feaserr,epsi); - ae_frame_leave(_state); - return result; - } - - /* - * Stage 1: equality constrained quadratic programming - * - * * treat active bound constraints as equality ones (constraint is considered - * active when we are at the boundary, independently of the antigradient direction) - * * calculate unrestricted Newton step to point XM (which may be infeasible) - * calculate MaxStepLen = largest step in direction of XM which retains feasibility. - * * perform bounded step from X to XN: - * a) XN=XM (if XM is feasible) - * b) XN=X-MaxStepLen*(XM-X) (otherwise) - * * X := XN - * * if XM (Newton step subject to currently active constraints) was feasible, goto Stage 2 - * * repeat Stage 1 - * - * NOTE 1: in order to solve constrained qudratic subproblem we will have to reorder - * variables in such way that ones corresponding to inactive constraints will - * be first, and active ones will be last in the list. CE and X are now - * [ xi ] - * separated into two parts: CE = [CEi CEa], x = [ ], where CEi/Xi correspond - * [ xa ] - * to INACTIVE constraints, and CEa/Xa correspond to the ACTIVE ones. - * - * Now, instead of F=0.5*x'*(CE'*CE)*x - (b'*CE)*x + 0.5*b'*b, we have - * F(xi) = 0.5*(CEi*xi,CEi*xi) + (CEa*xa-b,CEi*xi) + (0.5*CEa*xa-b,CEa*xa). - * Here xa is considered constant, i.e. we optimize with respect to xi, leaving xa fixed. - * - * We can solve it by performing SVD of CEi and calculating pseudoinverse of the - * Hessian matrix. Of course, we do NOT calculate pseudoinverse explicitly - we - * just use singular vectors to perform implicit multiplication by it. - * - */ - for(;;) - { - - /* - * Calculate G - gradient subject to equality constraints, - * multiply it by inverse of the Hessian diagonal to obtain initial - * step vector. - * - * Bound step subject to constraints which can be activated, - * run Armijo search with increasing step size. - * Search is terminated when feasibility error stops to decrease. - * - * NOTE: it is important to test for "stops to decrease" instead - * of "starts to increase" in order to correctly handle cases with - * zero CE. - */ - armijobeststep = 0.0; - armijobestfeas = 0.0; - for(i=0; i<=nmain+nslack-1; i++) - { - g.ptr.p_double[i] = 0; - } - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - v = v-ce->ptr.pp_double[i][nmain+nslack]; - armijobestfeas = armijobestfeas+ae_sqr(v, _state); - ae_v_addd(&g.ptr.p_double[0], 1, &ce->ptr.pp_double[i][0], 1, ae_v_len(0,nmain+nslack-1), v); - } - armijobestfeas = ae_sqrt(armijobestfeas, _state); - for(i=0; i<=nmain-1; i++) - { - if( havebndl->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndl->ptr.p_double[i]) ) - { - g.ptr.p_double[i] = 0.0; - } - if( havebndu->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndu->ptr.p_double[i]) ) - { - g.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=nslack-1; i++) - { - if( ae_fp_eq(x->ptr.p_double[nmain+i],0.0) ) - { - g.ptr.p_double[nmain+i] = 0.0; - } - } - v = 0.0; - for(i=0; i<=nmain+nslack-1; i++) - { - if( ae_fp_neq(ae_sqr(colnorms.ptr.p_double[i], _state),0) ) - { - newtonstep.ptr.p_double[i] = -g.ptr.p_double[i]/ae_sqr(colnorms.ptr.p_double[i], _state); - } - else - { - newtonstep.ptr.p_double[i] = 0.0; - } - v = v+ae_sqr(newtonstep.ptr.p_double[i], _state); - } - if( ae_fp_eq(v,0) ) - { - - /* - * Constrained gradient is zero, QP iterations are over - */ - break; - } - calculatestepbound(x, &newtonstep, 1.0, bndl, havebndl, bndu, havebndu, nmain, nslack, &vartofreeze, &valtofreeze, &maxsteplen, _state); - if( vartofreeze>=0&&ae_fp_eq(maxsteplen,0) ) - { - - /* - * Can not perform step, QP iterations are over - */ - break; - } - if( vartofreeze>=0 ) - { - armijostep = ae_minreal(1.0, maxsteplen, _state); - } - else - { - armijostep = 1; - } - for(;;) - { - ae_v_move(&xa.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - ae_v_addd(&xa.ptr.p_double[0], 1, &newtonstep.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1), armijostep); - enforceboundaryconstraints(&xa, bndl, havebndl, bndu, havebndu, nmain, nslack, _state); - feaserr = 0.0; - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &xa.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - v = v-ce->ptr.pp_double[i][nmain+nslack]; - feaserr = feaserr+ae_sqr(v, _state); - } - feaserr = ae_sqrt(feaserr, _state); - if( ae_fp_greater_eq(feaserr,armijobestfeas) ) - { - break; - } - armijobestfeas = feaserr; - armijobeststep = armijostep; - armijostep = 2.0*armijostep; - } - ae_v_addd(&x->ptr.p_double[0], 1, &newtonstep.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1), armijobeststep); - enforceboundaryconstraints(x, bndl, havebndl, bndu, havebndu, nmain, nslack, _state); - - /* - * Determine number of active and free constraints - */ - nactive = 0; - for(i=0; i<=nmain-1; i++) - { - activeconstraints.ptr.p_double[i] = 0; - if( havebndl->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndl->ptr.p_double[i]) ) - { - activeconstraints.ptr.p_double[i] = 1; - } - if( havebndu->ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],bndu->ptr.p_double[i]) ) - { - activeconstraints.ptr.p_double[i] = 1; - } - if( ae_fp_greater(activeconstraints.ptr.p_double[i],0) ) - { - nactive = nactive+1; - } - } - for(i=0; i<=nslack-1; i++) - { - activeconstraints.ptr.p_double[nmain+i] = 0; - if( ae_fp_eq(x->ptr.p_double[nmain+i],0.0) ) - { - activeconstraints.ptr.p_double[nmain+i] = 1; - } - if( ae_fp_greater(activeconstraints.ptr.p_double[nmain+i],0) ) - { - nactive = nactive+1; - } - } - nfree = nmain+nslack-nactive; - if( nfree==0 ) - { - break; - } - *qpits = *qpits+1; - - /* - * Reorder variables - */ - tagsortbuf(&activeconstraints, nmain+nslack, &p1, &p2, &buf, _state); - for(i=0; i<=k-1; i++) - { - for(j=0; j<=nmain+nslack-1; j++) - { - a.ptr.pp_double[i][j] = ce->ptr.pp_double[i][j]; - } - } - for(j=0; j<=nmain+nslack-1; j++) - { - permx.ptr.p_double[j] = x->ptr.p_double[j]; - } - for(j=0; j<=nmain+nslack-1; j++) - { - if( p2.ptr.p_int[j]!=j ) - { - idx0 = p2.ptr.p_int[j]; - idx1 = j; - for(i=0; i<=k-1; i++) - { - v = a.ptr.pp_double[i][idx0]; - a.ptr.pp_double[i][idx0] = a.ptr.pp_double[i][idx1]; - a.ptr.pp_double[i][idx1] = v; - } - v = permx.ptr.p_double[idx0]; - permx.ptr.p_double[idx0] = permx.ptr.p_double[idx1]; - permx.ptr.p_double[idx1] = v; - } - } - - /* - * Calculate (unprojected) gradient: - * G(xi) = CEi'*(CEi*xi + CEa*xa - b) - */ - for(i=0; i<=nfree-1; i++) - { - g.ptr.p_double[i] = 0; - } - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&a.ptr.pp_double[i][0], 1, &permx.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - tmpk.ptr.p_double[i] = v-ce->ptr.pp_double[i][nmain+nslack]; - } - for(i=0; i<=k-1; i++) - { - v = tmpk.ptr.p_double[i]; - ae_v_addd(&g.ptr.p_double[0], 1, &a.ptr.pp_double[i][0], 1, ae_v_len(0,nfree-1), v); - } - - /* - * Calculate Newton step using SVD of CEi: - * F(xi) = 0.5*xi'*H*xi + g'*xi (Taylor decomposition) - * XN = -H^(-1)*g (new point, solution of the QP subproblem) - * H = CEi'*CEi - * CEi = U*W*V' (SVD of CEi) - * H = V*W^2*V' - * H^(-1) = V*W^(-2)*V' - * step = -V*W^(-2)*V'*g (it is better to perform multiplication from right to left) - * - * NOTE 1: we do NOT need left singular vectors to perform Newton step. - */ - nsvd = ae_minint(k, nfree, _state); - if( !rmatrixsvd(&a, k, nfree, 0, 1, 2, &w, &u, &vt, _state) ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - for(i=0; i<=nsvd-1; i++) - { - v = ae_v_dotproduct(&vt.ptr.pp_double[i][0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,nfree-1)); - tmpk.ptr.p_double[i] = v; - } - for(i=0; i<=nsvd-1; i++) - { - - /* - * It is important to have strict ">" in order to correctly - * handle zero singular values. - */ - if( ae_fp_greater(ae_sqr(w.ptr.p_double[i], _state),ae_sqr(w.ptr.p_double[0], _state)*(nmain+nslack)*ae_machineepsilon) ) - { - tmpk.ptr.p_double[i] = tmpk.ptr.p_double[i]/ae_sqr(w.ptr.p_double[i], _state); - } - else - { - tmpk.ptr.p_double[i] = 0; - } - } - for(i=0; i<=nmain+nslack-1; i++) - { - newtonstep.ptr.p_double[i] = 0; - } - for(i=0; i<=nsvd-1; i++) - { - v = tmpk.ptr.p_double[i]; - ae_v_subd(&newtonstep.ptr.p_double[0], 1, &vt.ptr.pp_double[i][0], 1, ae_v_len(0,nfree-1), v); - } - for(j=nmain+nslack-1; j>=0; j--) - { - if( p2.ptr.p_int[j]!=j ) - { - idx0 = p2.ptr.p_int[j]; - idx1 = j; - v = newtonstep.ptr.p_double[idx0]; - newtonstep.ptr.p_double[idx0] = newtonstep.ptr.p_double[idx1]; - newtonstep.ptr.p_double[idx1] = v; - } - } - - /* - * NewtonStep contains Newton step subject to active bound constraints. - * - * Such step leads us to the minimizer of the equality constrained F, - * but such minimizer may be infeasible because some constraints which - * are inactive at the initial point can be violated at the solution. - * - * Thus, we perform optimization in two stages: - * a) perform bounded Newton step, i.e. step in the Newton direction - * until activation of the first constraint - * b) in case (MaxStepLen>0)and(MaxStepLen<1), perform additional iteration - * of the Armijo line search in the rest of the Newton direction. - */ - calculatestepbound(x, &newtonstep, 1.0, bndl, havebndl, bndu, havebndu, nmain, nslack, &vartofreeze, &valtofreeze, &maxsteplen, _state); - if( vartofreeze>=0&&ae_fp_eq(maxsteplen,0) ) - { - - /* - * Activation of the constraints prevent us from performing step, - * QP iterations are over - */ - break; - } - if( vartofreeze>=0 ) - { - v = ae_minreal(1.0, maxsteplen, _state); - } - else - { - v = 1.0; - } - ae_v_moved(&xn.ptr.p_double[0], 1, &newtonstep.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1), v); - ae_v_add(&xn.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - postprocessboundedstep(&xn, x, bndl, havebndl, bndu, havebndu, nmain, nslack, vartofreeze, valtofreeze, v, maxsteplen, _state); - if( ae_fp_greater(maxsteplen,0)&&ae_fp_less(maxsteplen,1) ) - { - - /* - * Newton step was restricted by activation of the constraints, - * perform Armijo iteration. - * - * Initial estimate for best step is zero step. We try different - * step sizes, from the 1-MaxStepLen (residual of the full Newton - * step) to progressively smaller and smaller steps. - */ - armijobeststep = 0.0; - armijobestfeas = 0.0; - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &xn.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - v = v-ce->ptr.pp_double[i][nmain+nslack]; - armijobestfeas = armijobestfeas+ae_sqr(v, _state); - } - armijobestfeas = ae_sqrt(armijobestfeas, _state); - armijostep = 1-maxsteplen; - for(j=0; j<=maxarmijoruns-1; j++) - { - ae_v_move(&xa.ptr.p_double[0], 1, &xn.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - ae_v_addd(&xa.ptr.p_double[0], 1, &newtonstep.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1), armijostep); - enforceboundaryconstraints(&xa, bndl, havebndl, bndu, havebndu, nmain, nslack, _state); - feaserr = 0.0; - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &xa.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - v = v-ce->ptr.pp_double[i][nmain+nslack]; - feaserr = feaserr+ae_sqr(v, _state); - } - feaserr = ae_sqrt(feaserr, _state); - if( ae_fp_less(feaserr,armijobestfeas) ) - { - armijobestfeas = feaserr; - armijobeststep = armijostep; - } - armijostep = 0.5*armijostep; - } - ae_v_move(&xa.ptr.p_double[0], 1, &xn.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - ae_v_addd(&xa.ptr.p_double[0], 1, &newtonstep.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1), armijobeststep); - enforceboundaryconstraints(&xa, bndl, havebndl, bndu, havebndu, nmain, nslack, _state); - } - else - { - - /* - * Armijo iteration is not performed - */ - ae_v_move(&xa.ptr.p_double[0], 1, &xn.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - } - stage1isover = ae_fp_greater_eq(maxsteplen,1)||ae_fp_eq(maxsteplen,0); - - /* - * Calculate feasibility errors for old and new X. - * These quantinies are used for debugging purposes only. - * However, we can leave them in release code because performance impact is insignificant. - * - * Update X. Exit if needed. - */ - feasold = 0; - feasnew = 0; - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - feasold = feasold+ae_sqr(v-ce->ptr.pp_double[i][nmain+nslack], _state); - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &xa.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - feasnew = feasnew+ae_sqr(v-ce->ptr.pp_double[i][nmain+nslack], _state); - } - feasold = ae_sqrt(feasold, _state); - feasnew = ae_sqrt(feasnew, _state); - if( ae_fp_greater_eq(feasnew,feasold) ) - { - break; - } - ae_v_move(&x->ptr.p_double[0], 1, &xa.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - if( stage1isover ) - { - break; - } - } - - /* - * Stage 2: gradient projection algorithm (GPA) - * - * * calculate feasibility error (with respect to linear equality constraints) - * * calculate gradient G of F, project it into feasible area (G => PG) - * * exit if norm(PG) is exactly zero or feasibility error is smaller than EpsC - * * let XM be exact minimum of F along -PG (XM may be infeasible). - * calculate MaxStepLen = largest step in direction of -PG which retains feasibility. - * * perform bounded step from X to XN: - * a) XN=XM (if XM is feasible) - * b) XN=X-MaxStepLen*PG (otherwise) - * * X := XN - * * stop after specified number of iterations or when no new constraints was activated - * - * NOTES: - * * grad(F) = (CE'*CE)*x - (b'*CE)^T - * * CE[i] denotes I-th row of CE - * * XM = X+stp*(-PG) where stp=(grad(F(X)),PG)/(CE*PG,CE*PG). - * Here PG is a projected gradient, but in fact it can be arbitrary non-zero - * direction vector - formula for minimum of F along PG still will be correct. - */ - werechangesinconstraints = ae_false; - for(gparuns=1; gparuns<=k; gparuns++) - { - - /* - * calculate feasibility error and G - */ - feaserr = 0; - for(i=0; i<=nmain+nslack-1; i++) - { - g.ptr.p_double[i] = 0; - } - for(i=0; i<=k-1; i++) - { - - /* - * G += CE[i]^T * (CE[i]*x-b[i]) - */ - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - v = v-ce->ptr.pp_double[i][nmain+nslack]; - feaserr = feaserr+ae_sqr(v, _state); - ae_v_addd(&g.ptr.p_double[0], 1, &ce->ptr.pp_double[i][0], 1, ae_v_len(0,nmain+nslack-1), v); - } - - /* - * project G, filter it (strip numerical noise) - */ - ae_v_move(&pg.ptr.p_double[0], 1, &g.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - projectgradientintobc(x, &pg, bndl, havebndl, bndu, havebndu, nmain, nslack, _state); - filterdirection(&pg, x, bndl, havebndl, bndu, havebndu, &s, nmain, nslack, 1.0E-9, _state); - for(i=0; i<=nmain+nslack-1; i++) - { - if( ae_fp_neq(ae_sqr(colnorms.ptr.p_double[i], _state),0) ) - { - pg.ptr.p_double[i] = pg.ptr.p_double[i]/ae_sqr(colnorms.ptr.p_double[i], _state); - } - else - { - pg.ptr.p_double[i] = 0.0; - } - } - - /* - * Check GNorm and feasibility. - * Exit when GNorm is exactly zero. - */ - pgnorm = ae_v_dotproduct(&pg.ptr.p_double[0], 1, &pg.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - feaserr = ae_sqrt(feaserr, _state); - pgnorm = ae_sqrt(pgnorm, _state); - if( ae_fp_eq(pgnorm,0) ) - { - result = ae_fp_less_eq(feaserr,epsi); - ae_frame_leave(_state); - return result; - } - - /* - * calculate planned step length - */ - vn = ae_v_dotproduct(&g.ptr.p_double[0], 1, &pg.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - vd = 0; - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &pg.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - vd = vd+ae_sqr(v, _state); - } - stp = vn/vd; - - /* - * Calculate step bound. - * Perform bounded step and post-process it - */ - calculatestepbound(x, &pg, -1.0, bndl, havebndl, bndu, havebndu, nmain, nslack, &vartofreeze, &valtofreeze, &maxsteplen, _state); - if( vartofreeze>=0&&ae_fp_eq(maxsteplen,0) ) - { - result = ae_false; - ae_frame_leave(_state); - return result; - } - if( vartofreeze>=0 ) - { - v = ae_minreal(stp, maxsteplen, _state); - } - else - { - v = stp; - } - ae_v_move(&xn.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - ae_v_subd(&xn.ptr.p_double[0], 1, &pg.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1), v); - postprocessboundedstep(&xn, x, bndl, havebndl, bndu, havebndu, nmain, nslack, vartofreeze, valtofreeze, v, maxsteplen, _state); - - /* - * update X - * check stopping criteria - */ - werechangesinconstraints = werechangesinconstraints||numberofchangedconstraints(&xn, x, bndl, havebndl, bndu, havebndu, nmain, nslack, _state)>0; - ae_v_move(&x->ptr.p_double[0], 1, &xn.ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - *gpaits = *gpaits+1; - if( !werechangesinconstraints ) - { - break; - } - } - - /* - * Stage 3: decide to stop algorithm or not to stop - * - * 1. we can stop when last GPA run did NOT changed constraints status. - * It means that we've found final set of the active constraints even - * before GPA made its run. And it means that Newton step moved us to - * the minimum subject to the present constraints. - * Depending on feasibility error, True or False is returned. - */ - feaserr = 0; - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&ce->ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,nmain+nslack-1)); - v = v-ce->ptr.pp_double[i][nmain+nslack]; - feaserr = feaserr+ae_sqr(v, _state); - } - feaserr = ae_sqrt(feaserr, _state); - if( ae_fp_less_eq(feaserr,epsi) ) - { - itswithintolerance = itswithintolerance+1; - } - else - { - itswithintolerance = 0; - } - if( !werechangesinconstraints||itswithintolerance>=maxitswithintolerance ) - { - result = ae_fp_less_eq(feaserr,epsi); - ae_frame_leave(_state); - return result; - } - itscount = itscount+1; - } - ae_frame_leave(_state); - return result; -} - - -/************************************************************************* - This function check, that input derivatives are right. First it scale -parameters DF0 and DF1 from segment [A;B] to [0;1]. Than it build Hermite -spline and derivative of it in 0,5. Search scale as Max(DF0,DF1, |F0-F1|). -Right derivative has to satisfy condition: - |H-F|/S<=0,01, |H'-F'|/S<=0,01. - -INPUT PARAMETERS: - F0 - function's value in X-TestStep point; - DF0 - derivative's value in X-TestStep point; - F1 - function's value in X+TestStep point; - DF1 - derivative's value in X+TestStep point; - F - testing function's value; - DF - testing derivative's value; - Width- width of verification segment. - -RESULT: - If input derivatives is right then function returns true, else - function returns false. - - -- ALGLIB -- - Copyright 29.05.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool derivativecheck(double f0, - double df0, - double f1, - double df1, - double f, - double df, - double width, - ae_state *_state) -{ - double s; - double h; - double dh; - ae_bool result; - - - df = width*df; - df0 = width*df0; - df1 = width*df1; - s = ae_maxreal(ae_maxreal(ae_fabs(df0, _state), ae_fabs(df1, _state), _state), ae_fabs(f1-f0, _state), _state); - h = 0.5*f0+0.125*df0+0.5*f1-0.125*df1; - dh = -1.5*f0-0.25*df0+1.5*f1-0.25*df1; - if( ae_fp_neq(s,0) ) - { - if( ae_fp_greater(ae_fabs(h-f, _state)/s,0.001)||ae_fp_greater(ae_fabs(dh-df, _state)/s,0.001) ) - { - result = ae_false; - return result; - } - } - else - { - if( ae_fp_neq(h-f,0.0)||ae_fp_neq(dh-df,0.0) ) - { - result = ae_false; - return result; - } - } - result = ae_true; - return result; -} - - - - -/************************************************************************* -This subroutine is used to initialize CQM. By default, empty NxN model is -generated, with Alpha=Lambda=Theta=0.0 and zero b. - -Previously allocated buffer variables are reused as much as possible. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqminit(ae_int_t n, convexquadraticmodel* s, ae_state *_state) -{ - ae_int_t i; - - - s->n = n; - s->k = 0; - s->nfree = n; - s->ecakind = -1; - s->alpha = 0.0; - s->tau = 0.0; - s->theta = 0.0; - s->ismaintermchanged = ae_true; - s->issecondarytermchanged = ae_true; - s->islineartermchanged = ae_true; - s->isactivesetchanged = ae_true; - bvectorsetlengthatleast(&s->activeset, n, _state); - rvectorsetlengthatleast(&s->xc, n, _state); - rvectorsetlengthatleast(&s->eb, n, _state); - rvectorsetlengthatleast(&s->tq1, n, _state); - rvectorsetlengthatleast(&s->txc, n, _state); - rvectorsetlengthatleast(&s->tb, n, _state); - rvectorsetlengthatleast(&s->b, s->n, _state); - rvectorsetlengthatleast(&s->tk1, s->n, _state); - for(i=0; i<=n-1; i++) - { - s->activeset.ptr.p_bool[i] = ae_false; - s->xc.ptr.p_double[i] = 0.0; - s->b.ptr.p_double[i] = 0.0; - } -} - - -/************************************************************************* -This subroutine changes main quadratic term of the model. - -INPUT PARAMETERS: - S - model - A - NxN matrix, only upper or lower triangle is referenced - IsUpper - True, when matrix is stored in upper triangle - Alpha - multiplier; when Alpha=0, A is not referenced at all - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmseta(convexquadraticmodel* s, - /* Real */ ae_matrix* a, - ae_bool isupper, - double alpha, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - double v; - - - ae_assert(ae_isfinite(alpha, _state)&&ae_fp_greater_eq(alpha,0), "CQMSetA: Alpha<0 or is not finite number", _state); - ae_assert(ae_fp_eq(alpha,0)||isfinitertrmatrix(a, s->n, isupper, _state), "CQMSetA: A is not finite NxN matrix", _state); - s->alpha = alpha; - if( ae_fp_greater(alpha,0) ) - { - rmatrixsetlengthatleast(&s->a, s->n, s->n, _state); - rmatrixsetlengthatleast(&s->ecadense, s->n, s->n, _state); - rmatrixsetlengthatleast(&s->tq2dense, s->n, s->n, _state); - for(i=0; i<=s->n-1; i++) - { - for(j=i; j<=s->n-1; j++) - { - if( isupper ) - { - v = a->ptr.pp_double[i][j]; - } - else - { - v = a->ptr.pp_double[j][i]; - } - s->a.ptr.pp_double[i][j] = v; - s->a.ptr.pp_double[j][i] = v; - } - } - } - s->ismaintermchanged = ae_true; -} - - -/************************************************************************* -This subroutine rewrites diagonal of the main quadratic term of the model -(dense A) by vector Z/Alpha (current value of the Alpha coefficient is -used). - -IMPORTANT: in case model has no dense quadratic term, this function - allocates N*N dense matrix of zeros, and fills its diagonal by - non-zero values. - -INPUT PARAMETERS: - S - model - Z - new diagonal, array[N] - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmrewritedensediagonal(convexquadraticmodel* s, - /* Real */ ae_vector* z, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - - - n = s->n; - if( ae_fp_eq(s->alpha,0) ) - { - rmatrixsetlengthatleast(&s->a, s->n, s->n, _state); - rmatrixsetlengthatleast(&s->ecadense, s->n, s->n, _state); - rmatrixsetlengthatleast(&s->tq2dense, s->n, s->n, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - s->a.ptr.pp_double[i][j] = 0.0; - } - } - s->alpha = 1.0; - } - for(i=0; i<=s->n-1; i++) - { - s->a.ptr.pp_double[i][i] = z->ptr.p_double[i]/s->alpha; - } - s->ismaintermchanged = ae_true; -} - - -/************************************************************************* -This subroutine changes diagonal quadratic term of the model. - -INPUT PARAMETERS: - S - model - D - array[N], semidefinite diagonal matrix - Tau - multiplier; when Tau=0, D is not referenced at all - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmsetd(convexquadraticmodel* s, - /* Real */ ae_vector* d, - double tau, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(ae_isfinite(tau, _state)&&ae_fp_greater_eq(tau,0), "CQMSetD: Tau<0 or is not finite number", _state); - ae_assert(ae_fp_eq(tau,0)||isfinitevector(d, s->n, _state), "CQMSetD: D is not finite Nx1 vector", _state); - s->tau = tau; - if( ae_fp_greater(tau,0) ) - { - rvectorsetlengthatleast(&s->d, s->n, _state); - rvectorsetlengthatleast(&s->ecadiag, s->n, _state); - rvectorsetlengthatleast(&s->tq2diag, s->n, _state); - for(i=0; i<=s->n-1; i++) - { - ae_assert(ae_fp_greater_eq(d->ptr.p_double[i],0), "CQMSetD: D[i]<0", _state); - s->d.ptr.p_double[i] = d->ptr.p_double[i]; - } - } - s->ismaintermchanged = ae_true; -} - - -/************************************************************************* -This subroutine drops main quadratic term A from the model. It is same as -call to CQMSetA() with zero A, but gives better performance because -algorithm knows that matrix is zero and can optimize subsequent -calculations. - -INPUT PARAMETERS: - S - model - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmdropa(convexquadraticmodel* s, ae_state *_state) -{ - - - s->alpha = 0.0; - s->ismaintermchanged = ae_true; -} - - -/************************************************************************* -This subroutine changes linear term of the model - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmsetb(convexquadraticmodel* s, - /* Real */ ae_vector* b, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(isfinitevector(b, s->n, _state), "CQMSetB: B is not finite vector", _state); - rvectorsetlengthatleast(&s->b, s->n, _state); - for(i=0; i<=s->n-1; i++) - { - s->b.ptr.p_double[i] = b->ptr.p_double[i]; - } - s->islineartermchanged = ae_true; -} - - -/************************************************************************* -This subroutine changes linear term of the model - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmsetq(convexquadraticmodel* s, - /* Real */ ae_matrix* q, - /* Real */ ae_vector* r, - ae_int_t k, - double theta, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - - - ae_assert(k>=0, "CQMSetQ: K<0", _state); - ae_assert((k==0||ae_fp_eq(theta,0))||apservisfinitematrix(q, k, s->n, _state), "CQMSetQ: Q is not finite matrix", _state); - ae_assert((k==0||ae_fp_eq(theta,0))||isfinitevector(r, k, _state), "CQMSetQ: R is not finite vector", _state); - ae_assert(ae_isfinite(theta, _state)&&ae_fp_greater_eq(theta,0), "CQMSetQ: Theta<0 or is not finite number", _state); - - /* - * degenerate case: K=0 or Theta=0 - */ - if( k==0||ae_fp_eq(theta,0) ) - { - s->k = 0; - s->theta = 0; - s->issecondarytermchanged = ae_true; - return; - } - - /* - * General case: both Theta>0 and K>0 - */ - s->k = k; - s->theta = theta; - rmatrixsetlengthatleast(&s->q, s->k, s->n, _state); - rvectorsetlengthatleast(&s->r, s->k, _state); - rmatrixsetlengthatleast(&s->eq, s->k, s->n, _state); - rmatrixsetlengthatleast(&s->eccm, s->k, s->k, _state); - rmatrixsetlengthatleast(&s->tk2, s->k, s->n, _state); - for(i=0; i<=s->k-1; i++) - { - for(j=0; j<=s->n-1; j++) - { - s->q.ptr.pp_double[i][j] = q->ptr.pp_double[i][j]; - } - s->r.ptr.p_double[i] = r->ptr.p_double[i]; - } - s->issecondarytermchanged = ae_true; -} - - -/************************************************************************* -This subroutine changes active set - -INPUT PARAMETERS - S - model - X - array[N], constraint values - ActiveSet- array[N], active set. If ActiveSet[I]=True, then I-th - variables is constrained to X[I]. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmsetactiveset(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Boolean */ ae_vector* activeset, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(x->cnt>=s->n, "CQMSetActiveSet: Length(X)cnt>=s->n, "CQMSetActiveSet: Length(ActiveSet)n-1; i++) - { - s->isactivesetchanged = s->isactivesetchanged||(s->activeset.ptr.p_bool[i]&&!activeset->ptr.p_bool[i]); - s->isactivesetchanged = s->isactivesetchanged||(activeset->ptr.p_bool[i]&&!s->activeset.ptr.p_bool[i]); - s->activeset.ptr.p_bool[i] = activeset->ptr.p_bool[i]; - if( activeset->ptr.p_bool[i] ) - { - ae_assert(ae_isfinite(x->ptr.p_double[i], _state), "CQMSetActiveSet: X[] contains infinite constraints", _state); - s->isactivesetchanged = s->isactivesetchanged||ae_fp_neq(s->xc.ptr.p_double[i],x->ptr.p_double[i]); - s->xc.ptr.p_double[i] = x->ptr.p_double[i]; - } - } -} - - -/************************************************************************* -This subroutine evaluates model at X. Active constraints are ignored. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -double cqmeval(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - double v; - double result; - - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMEval: X is not finite vector", _state); - result = 0.0; - - /* - * main quadratic term - */ - if( ae_fp_greater(s->alpha,0) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - result = result+s->alpha*0.5*x->ptr.p_double[i]*s->a.ptr.pp_double[i][j]*x->ptr.p_double[j]; - } - } - } - if( ae_fp_greater(s->tau,0) ) - { - for(i=0; i<=n-1; i++) - { - result = result+0.5*ae_sqr(x->ptr.p_double[i], _state)*s->tau*s->d.ptr.p_double[i]; - } - } - - /* - * secondary quadratic term - */ - if( ae_fp_greater(s->theta,0) ) - { - for(i=0; i<=s->k-1; i++) - { - v = ae_v_dotproduct(&s->q.ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - result = result+0.5*s->theta*ae_sqr(v-s->r.ptr.p_double[i], _state); - } - } - - /* - * linear term - */ - for(i=0; i<=s->n-1; i++) - { - result = result+x->ptr.p_double[i]*s->b.ptr.p_double[i]; - } - return result; -} - - -/************************************************************************* -This subroutine evaluates model at X. Active constraints are ignored. -It returns: - R - model value - Noise- estimate of the numerical noise in data - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmevalx(convexquadraticmodel* s, - /* Real */ ae_vector* x, - double* r, - double* noise, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - double v; - double v2; - double mxq; - double eps; - - *r = 0; - *noise = 0; - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMEval: X is not finite vector", _state); - *r = 0.0; - *noise = 0.0; - eps = 2*ae_machineepsilon; - mxq = 0.0; - - /* - * Main quadratic term. - * - * Noise from the main quadratic term is equal to the - * maximum summand in the term. - */ - if( ae_fp_greater(s->alpha,0) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - v = s->alpha*0.5*x->ptr.p_double[i]*s->a.ptr.pp_double[i][j]*x->ptr.p_double[j]; - *r = *r+v; - *noise = ae_maxreal(*noise, eps*ae_fabs(v, _state), _state); - } - } - } - if( ae_fp_greater(s->tau,0) ) - { - for(i=0; i<=n-1; i++) - { - v = 0.5*ae_sqr(x->ptr.p_double[i], _state)*s->tau*s->d.ptr.p_double[i]; - *r = *r+v; - *noise = ae_maxreal(*noise, eps*ae_fabs(v, _state), _state); - } - } - - /* - * secondary quadratic term - * - * Noise from the secondary quadratic term is estimated as follows: - * * noise in qi*x-r[i] is estimated as - * Eps*MXQ = Eps*max(|r[i]|, |q[i,j]*x[j]|) - * * noise in (qi*x-r[i])^2 is estimated as - * NOISE = (|qi*x-r[i]|+Eps*MXQ)^2-(|qi*x-r[i]|)^2 - * = Eps*MXQ*(2*|qi*x-r[i]|+Eps*MXQ) - */ - if( ae_fp_greater(s->theta,0) ) - { - for(i=0; i<=s->k-1; i++) - { - v = 0.0; - mxq = ae_fabs(s->r.ptr.p_double[i], _state); - for(j=0; j<=n-1; j++) - { - v2 = s->q.ptr.pp_double[i][j]*x->ptr.p_double[j]; - v = v+v2; - mxq = ae_maxreal(mxq, ae_fabs(v2, _state), _state); - } - *r = *r+0.5*s->theta*ae_sqr(v-s->r.ptr.p_double[i], _state); - *noise = ae_maxreal(*noise, eps*mxq*(2*ae_fabs(v-s->r.ptr.p_double[i], _state)+eps*mxq), _state); - } - } - - /* - * linear term - */ - for(i=0; i<=s->n-1; i++) - { - *r = *r+x->ptr.p_double[i]*s->b.ptr.p_double[i]; - *noise = ae_maxreal(*noise, eps*ae_fabs(x->ptr.p_double[i]*s->b.ptr.p_double[i], _state), _state); - } - - /* - * Final update of the noise - */ - *noise = n*(*noise); -} - - -/************************************************************************* -This subroutine evaluates gradient of the model; active constraints are -ignored. - -INPUT PARAMETERS: - S - convex model - X - point, array[N] - G - possibly preallocated buffer; resized, if too small - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmgradunconstrained(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* g, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - double v; - - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMEvalGradUnconstrained: X is not finite vector", _state); - rvectorsetlengthatleast(g, n, _state); - for(i=0; i<=n-1; i++) - { - g->ptr.p_double[i] = 0; - } - - /* - * main quadratic term - */ - if( ae_fp_greater(s->alpha,0) ) - { - for(i=0; i<=n-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+s->alpha*s->a.ptr.pp_double[i][j]*x->ptr.p_double[j]; - } - g->ptr.p_double[i] = g->ptr.p_double[i]+v; - } - } - if( ae_fp_greater(s->tau,0) ) - { - for(i=0; i<=n-1; i++) - { - g->ptr.p_double[i] = g->ptr.p_double[i]+x->ptr.p_double[i]*s->tau*s->d.ptr.p_double[i]; - } - } - - /* - * secondary quadratic term - */ - if( ae_fp_greater(s->theta,0) ) - { - for(i=0; i<=s->k-1; i++) - { - v = ae_v_dotproduct(&s->q.ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = s->theta*(v-s->r.ptr.p_double[i]); - ae_v_addd(&g->ptr.p_double[0], 1, &s->q.ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - } - } - - /* - * linear term - */ - for(i=0; i<=n-1; i++) - { - g->ptr.p_double[i] = g->ptr.p_double[i]+s->b.ptr.p_double[i]; - } -} - - -/************************************************************************* -This subroutine evaluates x'*(0.5*alpha*A+tau*D)*x - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -double cqmxtadx2(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - double result; - - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMEval: X is not finite vector", _state); - result = 0.0; - - /* - * main quadratic term - */ - if( ae_fp_greater(s->alpha,0) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - result = result+s->alpha*0.5*x->ptr.p_double[i]*s->a.ptr.pp_double[i][j]*x->ptr.p_double[j]; - } - } - } - if( ae_fp_greater(s->tau,0) ) - { - for(i=0; i<=n-1; i++) - { - result = result+0.5*ae_sqr(x->ptr.p_double[i], _state)*s->tau*s->d.ptr.p_double[i]; - } - } - return result; -} - - -/************************************************************************* -This subroutine evaluates (0.5*alpha*A+tau*D)*x - -Y is automatically resized if needed - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmadx(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double v; - - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMEval: X is not finite vector", _state); - rvectorsetlengthatleast(y, n, _state); - - /* - * main quadratic term - */ - for(i=0; i<=n-1; i++) - { - y->ptr.p_double[i] = 0; - } - if( ae_fp_greater(s->alpha,0) ) - { - for(i=0; i<=n-1; i++) - { - v = ae_v_dotproduct(&s->a.ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - y->ptr.p_double[i] = y->ptr.p_double[i]+s->alpha*v; - } - } - if( ae_fp_greater(s->tau,0) ) - { - for(i=0; i<=n-1; i++) - { - y->ptr.p_double[i] = y->ptr.p_double[i]+x->ptr.p_double[i]*s->tau*s->d.ptr.p_double[i]; - } - } -} - - -/************************************************************************* -This subroutine finds optimum of the model. It returns False on failure -(indefinite/semidefinite matrix). Optimum is found subject to active -constraints. - -INPUT PARAMETERS - S - model - X - possibly preallocated buffer; automatically resized, if - too small enough. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool cqmconstrainedoptimum(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nfree; - ae_int_t k; - ae_int_t i; - double v; - ae_int_t cidx0; - ae_int_t itidx; - ae_bool result; - - - - /* - * Rebuild internal structures - */ - if( !cqmodels_cqmrebuild(s, _state) ) - { - result = ae_false; - return result; - } - n = s->n; - k = s->k; - nfree = s->nfree; - result = ae_true; - - /* - * Calculate initial point for the iterative refinement: - * * free components are set to zero - * * constrained components are set to their constrained values - */ - rvectorsetlengthatleast(x, n, _state); - for(i=0; i<=n-1; i++) - { - if( s->activeset.ptr.p_bool[i] ) - { - x->ptr.p_double[i] = s->xc.ptr.p_double[i]; - } - else - { - x->ptr.p_double[i] = 0; - } - } - - /* - * Iterative refinement. - * - * In an ideal world without numerical errors it would be enough - * to make just one Newton step from initial point: - * x_new = -H^(-1)*grad(x=0) - * However, roundoff errors can significantly deteriorate quality - * of the solution. So we have to recalculate gradient and to - * perform Newton steps several times. - * - * Below we perform fixed number of Newton iterations. - */ - for(itidx=0; itidx<=cqmodels_newtonrefinementits-1; itidx++) - { - - /* - * Calculate gradient at the current point. - * Move free components of the gradient in the beginning. - */ - cqmgradunconstrained(s, x, &s->tmpg, _state); - cidx0 = 0; - for(i=0; i<=n-1; i++) - { - if( !s->activeset.ptr.p_bool[i] ) - { - s->tmpg.ptr.p_double[cidx0] = s->tmpg.ptr.p_double[i]; - cidx0 = cidx0+1; - } - } - - /* - * Free components of the extrema are calculated in the first NFree elements of TXC. - * - * First, we have to calculate original Newton step, without rank-K perturbations - */ - ae_v_moveneg(&s->txc.ptr.p_double[0], 1, &s->tmpg.ptr.p_double[0], 1, ae_v_len(0,nfree-1)); - cqmodels_cqmsolveea(s, &s->txc, &s->tmp0, _state); - - /* - * Then, we account for rank-K correction. - * Woodbury matrix identity is used. - */ - if( s->k>0&&ae_fp_greater(s->theta,0) ) - { - rvectorsetlengthatleast(&s->tmp0, ae_maxint(nfree, k, _state), _state); - rvectorsetlengthatleast(&s->tmp1, ae_maxint(nfree, k, _state), _state); - ae_v_moveneg(&s->tmp1.ptr.p_double[0], 1, &s->tmpg.ptr.p_double[0], 1, ae_v_len(0,nfree-1)); - cqmodels_cqmsolveea(s, &s->tmp1, &s->tmp0, _state); - for(i=0; i<=k-1; i++) - { - v = ae_v_dotproduct(&s->eq.ptr.pp_double[i][0], 1, &s->tmp1.ptr.p_double[0], 1, ae_v_len(0,nfree-1)); - s->tmp0.ptr.p_double[i] = v; - } - fblscholeskysolve(&s->eccm, 1.0, k, ae_true, &s->tmp0, &s->tmp1, _state); - for(i=0; i<=nfree-1; i++) - { - s->tmp1.ptr.p_double[i] = 0.0; - } - for(i=0; i<=k-1; i++) - { - v = s->tmp0.ptr.p_double[i]; - ae_v_addd(&s->tmp1.ptr.p_double[0], 1, &s->eq.ptr.pp_double[i][0], 1, ae_v_len(0,nfree-1), v); - } - cqmodels_cqmsolveea(s, &s->tmp1, &s->tmp0, _state); - ae_v_sub(&s->txc.ptr.p_double[0], 1, &s->tmp1.ptr.p_double[0], 1, ae_v_len(0,nfree-1)); - } - - /* - * Unpack components from TXC into X. We pass through all - * free components of X and add our step. - */ - cidx0 = 0; - for(i=0; i<=n-1; i++) - { - if( !s->activeset.ptr.p_bool[i] ) - { - x->ptr.p_double[i] = x->ptr.p_double[i]+s->txc.ptr.p_double[cidx0]; - cidx0 = cidx0+1; - } - } - } - return result; -} - - -/************************************************************************* -This function scales vector by multiplying it by inverse of the diagonal -of the Hessian matrix. It should be used to accelerate steepest descent -phase of the QP solver. - -Although it is called "scale-grad", it can be called for any vector, -whether it is gradient, anti-gradient, or just some vector. - -This function does NOT takes into account current set of constraints, it -just performs matrix-vector multiplication without taking into account -constraints. - -INPUT PARAMETERS: - S - model - X - vector to scale - -OUTPUT PARAMETERS: - X - scaled vector - -NOTE: - when called for non-SPD matrices, it silently skips components of X - which correspond to zero or negative diagonal elements. - -NOTE: - this function uses diagonals of A and D; it ignores Q - rank-K term of - the quadratic model. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -void cqmscalevector(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double v; - - - n = s->n; - for(i=0; i<=n-1; i++) - { - v = 0.0; - if( ae_fp_greater(s->alpha,0) ) - { - v = v+s->a.ptr.pp_double[i][i]; - } - if( ae_fp_greater(s->tau,0) ) - { - v = v+s->d.ptr.p_double[i]; - } - if( ae_fp_greater(v,0) ) - { - x->ptr.p_double[i] = x->ptr.p_double[i]/v; - } - } -} - - -/************************************************************************* -This subroutine calls CQMRebuild() and evaluates model at X subject to -active constraints. - -It is intended for debug purposes only, because it evaluates model by -means of temporaries, which were calculated by CQMRebuild(). The only -purpose of this function is to check correctness of CQMRebuild() by -comparing results of this function with ones obtained by CQMEval(), which -is used as reference point. The idea is that significant deviation in -results of these two functions is evidence of some error in the -CQMRebuild(). - -NOTE: suffix T denotes that temporaries marked by T-prefix are used. There - is one more variant of this function, which uses "effective" model - built by CQMRebuild(). - -NOTE2: in case CQMRebuild() fails (due to model non-convexity), this - function returns NAN. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -double cqmdebugconstrainedevalt(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nfree; - ae_int_t i; - ae_int_t j; - double v; - double result; - - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMDebugConstrainedEvalT: X is not finite vector", _state); - if( !cqmodels_cqmrebuild(s, _state) ) - { - result = _state->v_nan; - return result; - } - result = 0.0; - nfree = s->nfree; - - /* - * Reorder variables - */ - j = 0; - for(i=0; i<=n-1; i++) - { - if( !s->activeset.ptr.p_bool[i] ) - { - ae_assert(jtxc.ptr.p_double[j] = x->ptr.p_double[i]; - j = j+1; - } - } - - /* - * TQ2, TQ1, TQ0 - * - */ - if( ae_fp_greater(s->alpha,0) ) - { - - /* - * Dense TQ2 - */ - for(i=0; i<=nfree-1; i++) - { - for(j=0; j<=nfree-1; j++) - { - result = result+0.5*s->txc.ptr.p_double[i]*s->tq2dense.ptr.pp_double[i][j]*s->txc.ptr.p_double[j]; - } - } - } - else - { - - /* - * Diagonal TQ2 - */ - for(i=0; i<=nfree-1; i++) - { - result = result+0.5*s->tq2diag.ptr.p_double[i]*ae_sqr(s->txc.ptr.p_double[i], _state); - } - } - for(i=0; i<=nfree-1; i++) - { - result = result+s->tq1.ptr.p_double[i]*s->txc.ptr.p_double[i]; - } - result = result+s->tq0; - - /* - * TK2, TK1, TK0 - */ - if( s->k>0&&ae_fp_greater(s->theta,0) ) - { - for(i=0; i<=s->k-1; i++) - { - v = 0; - for(j=0; j<=nfree-1; j++) - { - v = v+s->tk2.ptr.pp_double[i][j]*s->txc.ptr.p_double[j]; - } - result = result+0.5*ae_sqr(v, _state); - } - for(i=0; i<=nfree-1; i++) - { - result = result+s->tk1.ptr.p_double[i]*s->txc.ptr.p_double[i]; - } - result = result+s->tk0; - } - - /* - * TB (Bf and Bc parts) - */ - for(i=0; i<=n-1; i++) - { - result = result+s->tb.ptr.p_double[i]*s->txc.ptr.p_double[i]; - } - return result; -} - - -/************************************************************************* -This subroutine calls CQMRebuild() and evaluates model at X subject to -active constraints. - -It is intended for debug purposes only, because it evaluates model by -means of "effective" matrices built by CQMRebuild(). The only purpose of -this function is to check correctness of CQMRebuild() by comparing results -of this function with ones obtained by CQMEval(), which is used as -reference point. The idea is that significant deviation in results of -these two functions is evidence of some error in the CQMRebuild(). - -NOTE: suffix E denotes that effective matrices. There is one more variant - of this function, which uses temporary matrices built by - CQMRebuild(). - -NOTE2: in case CQMRebuild() fails (due to model non-convexity), this - function returns NAN. - - -- ALGLIB -- - Copyright 12.06.2012 by Bochkanov Sergey -*************************************************************************/ -double cqmdebugconstrainedevale(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nfree; - ae_int_t i; - ae_int_t j; - double v; - double result; - - - n = s->n; - ae_assert(isfinitevector(x, n, _state), "CQMDebugConstrainedEvalE: X is not finite vector", _state); - if( !cqmodels_cqmrebuild(s, _state) ) - { - result = _state->v_nan; - return result; - } - result = 0.0; - nfree = s->nfree; - - /* - * Reorder variables - */ - j = 0; - for(i=0; i<=n-1; i++) - { - if( !s->activeset.ptr.p_bool[i] ) - { - ae_assert(jtxc.ptr.p_double[j] = x->ptr.p_double[i]; - j = j+1; - } - } - - /* - * ECA - */ - ae_assert((s->ecakind==0||s->ecakind==1)||(s->ecakind==-1&&nfree==0), "CQMDebugConstrainedEvalE: unexpected ECAKind", _state); - if( s->ecakind==0 ) - { - - /* - * Dense ECA - */ - for(i=0; i<=nfree-1; i++) - { - v = 0.0; - for(j=i; j<=nfree-1; j++) - { - v = v+s->ecadense.ptr.pp_double[i][j]*s->txc.ptr.p_double[j]; - } - result = result+0.5*ae_sqr(v, _state); - } - } - if( s->ecakind==1 ) - { - - /* - * Diagonal ECA - */ - for(i=0; i<=nfree-1; i++) - { - result = result+0.5*ae_sqr(s->ecadiag.ptr.p_double[i]*s->txc.ptr.p_double[i], _state); - } - } - - /* - * EQ - */ - for(i=0; i<=s->k-1; i++) - { - v = 0.0; - for(j=0; j<=nfree-1; j++) - { - v = v+s->eq.ptr.pp_double[i][j]*s->txc.ptr.p_double[j]; - } - result = result+0.5*ae_sqr(v, _state); - } - - /* - * EB - */ - for(i=0; i<=nfree-1; i++) - { - result = result+s->eb.ptr.p_double[i]*s->txc.ptr.p_double[i]; - } - - /* - * EC - */ - result = result+s->ec; - return result; -} - - -/************************************************************************* -Internal function, rebuilds "effective" model subject to constraints. -Returns False on failure (non-SPD main quadratic term) - - -- ALGLIB -- - Copyright 10.05.2011 by Bochkanov Sergey -*************************************************************************/ -static ae_bool cqmodels_cqmrebuild(convexquadraticmodel* s, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nfree; - ae_int_t k; - ae_int_t i; - ae_int_t j; - ae_int_t ridx0; - ae_int_t ridx1; - ae_int_t cidx0; - ae_int_t cidx1; - double v; - ae_bool result; - - - if( ae_fp_eq(s->alpha,0)&&ae_fp_eq(s->tau,0) ) - { - - /* - * Non-SPD model, quick exit - */ - result = ae_false; - return result; - } - result = ae_true; - n = s->n; - k = s->k; - - /* - * Determine number of free variables. - * Fill TXC - array whose last N-NFree elements store constraints. - */ - if( s->isactivesetchanged ) - { - s->nfree = 0; - for(i=0; i<=n-1; i++) - { - if( !s->activeset.ptr.p_bool[i] ) - { - s->nfree = s->nfree+1; - } - } - j = s->nfree; - for(i=0; i<=n-1; i++) - { - if( s->activeset.ptr.p_bool[i] ) - { - s->txc.ptr.p_double[j] = s->xc.ptr.p_double[i]; - j = j+1; - } - } - } - nfree = s->nfree; - - /* - * Re-evaluate TQ2/TQ1/TQ0, if needed - */ - if( s->isactivesetchanged||s->ismaintermchanged ) - { - - /* - * Handle cases Alpha>0 and Alpha=0 separately: - * * in the first case we have dense matrix - * * in the second one we have diagonal matrix, which can be - * handled more efficiently - */ - if( ae_fp_greater(s->alpha,0) ) - { - - /* - * Alpha>0, dense QP - * - * Split variables into two groups - free (F) and constrained (C). Reorder - * variables in such way that free vars come first, constrained are last: - * x = [xf, xc]. - * - * Main quadratic term x'*(alpha*A+tau*D)*x now splits into quadratic part, - * linear part and constant part: - * ( alpha*Aff+tau*Df alpha*Afc ) ( xf ) - * 0.5*( xf' xc' )*( )*( ) = - * ( alpha*Acf alpha*Acc+tau*Dc ) ( xc ) - * - * = 0.5*xf'*(alpha*Aff+tau*Df)*xf + (alpha*Afc*xc)'*xf + 0.5*xc'(alpha*Acc+tau*Dc)*xc - * - * We store these parts into temporary variables: - * * alpha*Aff+tau*Df, alpha*Afc, alpha*Acc+tau*Dc are stored into upper - * triangle of TQ2 - * * alpha*Afc*xc is stored into TQ1 - * * 0.5*xc'(alpha*Acc+tau*Dc)*xc is stored into TQ0 - * - * Below comes first part of the work - generation of TQ2: - * * we pass through rows of A and copy I-th row into upper block (Aff/Afc) or - * lower one (Acf/Acc) of TQ2, depending on presence of X[i] in the active set. - * RIdx0 variable contains current position for insertion into upper block, - * RIdx1 contains current position for insertion into lower one. - * * within each row, we copy J-th element into left half (Aff/Acf) or right - * one (Afc/Acc), depending on presence of X[j] in the active set. CIdx0 - * contains current position for insertion into left block, CIdx1 contains - * position for insertion into right one. - * * during copying, we multiply elements by alpha and add diagonal matrix D. - */ - ridx0 = 0; - ridx1 = s->nfree; - for(i=0; i<=n-1; i++) - { - cidx0 = 0; - cidx1 = s->nfree; - for(j=0; j<=n-1; j++) - { - if( !s->activeset.ptr.p_bool[i]&&!s->activeset.ptr.p_bool[j] ) - { - - /* - * Element belongs to Aff - */ - v = s->alpha*s->a.ptr.pp_double[i][j]; - if( i==j&&ae_fp_greater(s->tau,0) ) - { - v = v+s->tau*s->d.ptr.p_double[i]; - } - s->tq2dense.ptr.pp_double[ridx0][cidx0] = v; - } - if( !s->activeset.ptr.p_bool[i]&&s->activeset.ptr.p_bool[j] ) - { - - /* - * Element belongs to Afc - */ - s->tq2dense.ptr.pp_double[ridx0][cidx1] = s->alpha*s->a.ptr.pp_double[i][j]; - } - if( s->activeset.ptr.p_bool[i]&&!s->activeset.ptr.p_bool[j] ) - { - - /* - * Element belongs to Acf - */ - s->tq2dense.ptr.pp_double[ridx1][cidx0] = s->alpha*s->a.ptr.pp_double[i][j]; - } - if( s->activeset.ptr.p_bool[i]&&s->activeset.ptr.p_bool[j] ) - { - - /* - * Element belongs to Acc - */ - v = s->alpha*s->a.ptr.pp_double[i][j]; - if( i==j&&ae_fp_greater(s->tau,0) ) - { - v = v+s->tau*s->d.ptr.p_double[i]; - } - s->tq2dense.ptr.pp_double[ridx1][cidx1] = v; - } - if( s->activeset.ptr.p_bool[j] ) - { - cidx1 = cidx1+1; - } - else - { - cidx0 = cidx0+1; - } - } - if( s->activeset.ptr.p_bool[i] ) - { - ridx1 = ridx1+1; - } - else - { - ridx0 = ridx0+1; - } - } - - /* - * Now we have TQ2, and we can evaluate TQ1. - * In the special case when we have Alpha=0, NFree=0 or NFree=N, - * TQ1 is filled by zeros. - */ - for(i=0; i<=n-1; i++) - { - s->tq1.ptr.p_double[i] = 0.0; - } - if( s->nfree>0&&s->nfreenfree, n-s->nfree, &s->tq2dense, 0, s->nfree, 0, &s->txc, s->nfree, &s->tq1, 0, _state); - } - - /* - * And finally, we evaluate TQ0. - */ - v = 0.0; - for(i=s->nfree; i<=n-1; i++) - { - for(j=s->nfree; j<=n-1; j++) - { - v = v+0.5*s->txc.ptr.p_double[i]*s->tq2dense.ptr.pp_double[i][j]*s->txc.ptr.p_double[j]; - } - } - s->tq0 = v; - } - else - { - - /* - * Alpha=0, diagonal QP - * - * Split variables into two groups - free (F) and constrained (C). Reorder - * variables in such way that free vars come first, constrained are last: - * x = [xf, xc]. - * - * Main quadratic term x'*(tau*D)*x now splits into quadratic and constant - * parts: - * ( tau*Df ) ( xf ) - * 0.5*( xf' xc' )*( )*( ) = - * ( tau*Dc ) ( xc ) - * - * = 0.5*xf'*(tau*Df)*xf + 0.5*xc'(tau*Dc)*xc - * - * We store these parts into temporary variables: - * * tau*Df is stored in TQ2Diag - * * 0.5*xc'(tau*Dc)*xc is stored into TQ0 - */ - s->tq0 = 0.0; - ridx0 = 0; - for(i=0; i<=n-1; i++) - { - if( !s->activeset.ptr.p_bool[i] ) - { - s->tq2diag.ptr.p_double[ridx0] = s->tau*s->d.ptr.p_double[i]; - ridx0 = ridx0+1; - } - else - { - s->tq0 = s->tq0+0.5*s->tau*s->d.ptr.p_double[i]*ae_sqr(s->xc.ptr.p_double[i], _state); - } - } - for(i=0; i<=n-1; i++) - { - s->tq1.ptr.p_double[i] = 0.0; - } - } - } - - /* - * Re-evaluate TK2/TK1/TK0, if needed - */ - if( s->isactivesetchanged||s->issecondarytermchanged ) - { - - /* - * Split variables into two groups - free (F) and constrained (C). Reorder - * variables in such way that free vars come first, constrained are last: - * x = [xf, xc]. - * - * Secondary term theta*(Q*x-r)'*(Q*x-r) now splits into quadratic part, - * linear part and constant part: - * ( ( xf ) )' ( ( xf ) ) - * 0.5*theta*( (Qf Qc)'*( ) - r ) * ( (Qf Qc)'*( ) - r ) = - * ( ( xc ) ) ( ( xc ) ) - * - * = 0.5*theta*xf'*(Qf'*Qf)*xf + theta*((Qc*xc-r)'*Qf)*xf + - * + theta*(-r'*(Qc*xc-r)-0.5*r'*r+0.5*xc'*Qc'*Qc*xc) - * - * We store these parts into temporary variables: - * * sqrt(theta)*Qf is stored into TK2 - * * theta*((Qc*xc-r)'*Qf) is stored into TK1 - * * theta*(-r'*(Qc*xc-r)-0.5*r'*r+0.5*xc'*Qc'*Qc*xc) is stored into TK0 - * - * We use several other temporaries to store intermediate results: - * * Tmp0 - to store Qc*xc-r - * * Tmp1 - to store Qc*xc - * - * Generation of TK2/TK1/TK0 is performed as follows: - * * we fill TK2/TK1/TK0 (to handle K=0 or Theta=0) - * * other steps are performed only for K>0 and Theta>0 - * * we pass through columns of Q and copy I-th column into left block (Qf) or - * right one (Qc) of TK2, depending on presence of X[i] in the active set. - * CIdx0 variable contains current position for insertion into upper block, - * CIdx1 contains current position for insertion into lower one. - * * we calculate Qc*xc-r and store it into Tmp0 - * * we calculate TK0 and TK1 - * * we multiply leading part of TK2 which stores Qf by sqrt(theta) - * it is important to perform this step AFTER calculation of TK0 and TK1, - * because we need original (non-modified) Qf to calculate TK0 and TK1. - */ - for(j=0; j<=n-1; j++) - { - for(i=0; i<=k-1; i++) - { - s->tk2.ptr.pp_double[i][j] = 0.0; - } - s->tk1.ptr.p_double[j] = 0.0; - } - s->tk0 = 0.0; - if( s->k>0&&ae_fp_greater(s->theta,0) ) - { - - /* - * Split Q into Qf and Qc - * Calculate Qc*xc-r, store in Tmp0 - */ - rvectorsetlengthatleast(&s->tmp0, k, _state); - rvectorsetlengthatleast(&s->tmp1, k, _state); - cidx0 = 0; - cidx1 = nfree; - for(i=0; i<=k-1; i++) - { - s->tmp1.ptr.p_double[i] = 0.0; - } - for(j=0; j<=n-1; j++) - { - if( s->activeset.ptr.p_bool[j] ) - { - for(i=0; i<=k-1; i++) - { - s->tk2.ptr.pp_double[i][cidx1] = s->q.ptr.pp_double[i][j]; - s->tmp1.ptr.p_double[i] = s->tmp1.ptr.p_double[i]+s->q.ptr.pp_double[i][j]*s->txc.ptr.p_double[cidx1]; - } - cidx1 = cidx1+1; - } - else - { - for(i=0; i<=k-1; i++) - { - s->tk2.ptr.pp_double[i][cidx0] = s->q.ptr.pp_double[i][j]; - } - cidx0 = cidx0+1; - } - } - for(i=0; i<=k-1; i++) - { - s->tmp0.ptr.p_double[i] = s->tmp1.ptr.p_double[i]-s->r.ptr.p_double[i]; - } - - /* - * Calculate TK0 - */ - v = 0.0; - for(i=0; i<=k-1; i++) - { - v = v+s->theta*(0.5*ae_sqr(s->tmp1.ptr.p_double[i], _state)-s->r.ptr.p_double[i]*s->tmp0.ptr.p_double[i]-0.5*ae_sqr(s->r.ptr.p_double[i], _state)); - } - s->tk0 = v; - - /* - * Calculate TK1 - */ - if( nfree>0 ) - { - for(i=0; i<=k-1; i++) - { - v = s->theta*s->tmp0.ptr.p_double[i]; - ae_v_addd(&s->tk1.ptr.p_double[0], 1, &s->tk2.ptr.pp_double[i][0], 1, ae_v_len(0,nfree-1), v); - } - } - - /* - * Calculate TK2 - */ - if( nfree>0 ) - { - v = ae_sqrt(s->theta, _state); - for(i=0; i<=k-1; i++) - { - ae_v_muld(&s->tk2.ptr.pp_double[i][0], 1, ae_v_len(0,nfree-1), v); - } - } - } - } - - /* - * Re-evaluate TB - */ - if( s->isactivesetchanged||s->islineartermchanged ) - { - ridx0 = 0; - ridx1 = nfree; - for(i=0; i<=n-1; i++) - { - if( s->activeset.ptr.p_bool[i] ) - { - s->tb.ptr.p_double[ridx1] = s->b.ptr.p_double[i]; - ridx1 = ridx1+1; - } - else - { - s->tb.ptr.p_double[ridx0] = s->b.ptr.p_double[i]; - ridx0 = ridx0+1; - } - } - } - - /* - * Compose ECA: either dense ECA or diagonal ECA - */ - if( (s->isactivesetchanged||s->ismaintermchanged)&&nfree>0 ) - { - if( ae_fp_greater(s->alpha,0) ) - { - - /* - * Dense ECA - */ - s->ecakind = 0; - for(i=0; i<=nfree-1; i++) - { - for(j=i; j<=nfree-1; j++) - { - s->ecadense.ptr.pp_double[i][j] = s->tq2dense.ptr.pp_double[i][j]; - } - } - if( !spdmatrixcholeskyrec(&s->ecadense, 0, nfree, ae_true, &s->tmp0, _state) ) - { - result = ae_false; - return result; - } - } - else - { - - /* - * Diagonal ECA - */ - s->ecakind = 1; - for(i=0; i<=nfree-1; i++) - { - if( ae_fp_less(s->tq2diag.ptr.p_double[i],0) ) - { - result = ae_false; - return result; - } - s->ecadiag.ptr.p_double[i] = ae_sqrt(s->tq2diag.ptr.p_double[i], _state); - } - } - } - - /* - * Compose EQ - */ - if( s->isactivesetchanged||s->issecondarytermchanged ) - { - for(i=0; i<=k-1; i++) - { - for(j=0; j<=nfree-1; j++) - { - s->eq.ptr.pp_double[i][j] = s->tk2.ptr.pp_double[i][j]; - } - } - } - - /* - * Calculate ECCM - */ - if( ((((s->isactivesetchanged||s->ismaintermchanged)||s->issecondarytermchanged)&&s->k>0)&&ae_fp_greater(s->theta,0))&&nfree>0 ) - { - - /* - * Calculate ECCM - Cholesky factor of the "effective" capacitance - * matrix CM = I + EQ*inv(EffectiveA)*EQ'. - * - * We calculate CM as follows: - * CM = I + EQ*inv(EffectiveA)*EQ' - * = I + EQ*ECA^(-1)*ECA^(-T)*EQ' - * = I + (EQ*ECA^(-1))*(EQ*ECA^(-1))' - * - * Then we perform Cholesky decomposition of CM. - */ - rmatrixsetlengthatleast(&s->tmp2, k, n, _state); - rmatrixcopy(k, nfree, &s->eq, 0, 0, &s->tmp2, 0, 0, _state); - ae_assert(s->ecakind==0||s->ecakind==1, "CQMRebuild: unexpected ECAKind", _state); - if( s->ecakind==0 ) - { - rmatrixrighttrsm(k, nfree, &s->ecadense, 0, 0, ae_true, ae_false, 0, &s->tmp2, 0, 0, _state); - } - if( s->ecakind==1 ) - { - for(i=0; i<=k-1; i++) - { - for(j=0; j<=nfree-1; j++) - { - s->tmp2.ptr.pp_double[i][j] = s->tmp2.ptr.pp_double[i][j]/s->ecadiag.ptr.p_double[j]; - } - } - } - for(i=0; i<=k-1; i++) - { - for(j=0; j<=k-1; j++) - { - s->eccm.ptr.pp_double[i][j] = 0.0; - } - s->eccm.ptr.pp_double[i][i] = 1.0; - } - rmatrixsyrk(k, nfree, 1.0, &s->tmp2, 0, 0, 0, 1.0, &s->eccm, 0, 0, ae_true, _state); - if( !spdmatrixcholeskyrec(&s->eccm, 0, k, ae_true, &s->tmp0, _state) ) - { - result = ae_false; - return result; - } - } - - /* - * Compose EB and EC - * - * NOTE: because these quantities are cheap to compute, we do not - * use caching here. - */ - for(i=0; i<=nfree-1; i++) - { - s->eb.ptr.p_double[i] = s->tq1.ptr.p_double[i]+s->tk1.ptr.p_double[i]+s->tb.ptr.p_double[i]; - } - s->ec = s->tq0+s->tk0; - for(i=nfree; i<=n-1; i++) - { - s->ec = s->ec+s->tb.ptr.p_double[i]*s->txc.ptr.p_double[i]; - } - - /* - * Change cache status - everything is cached - */ - s->ismaintermchanged = ae_false; - s->issecondarytermchanged = ae_false; - s->islineartermchanged = ae_false; - s->isactivesetchanged = ae_false; - return result; -} - - -/************************************************************************* -Internal function, solves system Effective_A*x = b. -It should be called after successful completion of CQMRebuild(). - -INPUT PARAMETERS: - S - quadratic model, after call to CQMRebuild() - X - right part B, array[S.NFree] - Tmp - temporary array, automatically reallocated if needed - -OUTPUT PARAMETERS: - X - solution, array[S.NFree] - -NOTE: when called with zero S.NFree, returns silently -NOTE: this function assumes that EA is non-degenerate - - -- ALGLIB -- - Copyright 10.05.2011 by Bochkanov Sergey -*************************************************************************/ -static void cqmodels_cqmsolveea(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert((s->ecakind==0||s->ecakind==1)||(s->ecakind==-1&&s->nfree==0), "CQMSolveEA: unexpected ECAKind", _state); - if( s->ecakind==0 ) - { - - /* - * Dense ECA, use FBLSCholeskySolve() dense solver. - */ - fblscholeskysolve(&s->ecadense, 1.0, s->nfree, ae_true, x, tmp, _state); - } - if( s->ecakind==1 ) - { - - /* - * Diagonal ECA - */ - for(i=0; i<=s->nfree-1; i++) - { - x->ptr.p_double[i] = x->ptr.p_double[i]/ae_sqr(s->ecadiag.ptr.p_double[i], _state); - } - } -} - - -ae_bool _convexquadraticmodel_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - convexquadraticmodel *p = (convexquadraticmodel*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->a, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->q, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->b, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->r, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->activeset, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tq2dense, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tk2, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tq2diag, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tq1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tk1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->txc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tb, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->ecadense, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->eq, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->eccm, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ecadiag, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->eb, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmp2, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _convexquadraticmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - convexquadraticmodel *dst = (convexquadraticmodel*)_dst; - convexquadraticmodel *src = (convexquadraticmodel*)_src; - dst->n = src->n; - dst->k = src->k; - dst->alpha = src->alpha; - dst->tau = src->tau; - dst->theta = src->theta; - if( !ae_matrix_init_copy(&dst->a, &src->a, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->q, &src->q, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->b, &src->b, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->r, &src->r, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xc, &src->xc, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->activeset, &src->activeset, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tq2dense, &src->tq2dense, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tk2, &src->tk2, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tq2diag, &src->tq2diag, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tq1, &src->tq1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tk1, &src->tk1, _state, make_automatic) ) - return ae_false; - dst->tq0 = src->tq0; - dst->tk0 = src->tk0; - if( !ae_vector_init_copy(&dst->txc, &src->txc, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tb, &src->tb, _state, make_automatic) ) - return ae_false; - dst->nfree = src->nfree; - dst->ecakind = src->ecakind; - if( !ae_matrix_init_copy(&dst->ecadense, &src->ecadense, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->eq, &src->eq, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->eccm, &src->eccm, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->ecadiag, &src->ecadiag, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->eb, &src->eb, _state, make_automatic) ) - return ae_false; - dst->ec = src->ec; - if( !ae_vector_init_copy(&dst->tmp0, &src->tmp0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp1, &src->tmp1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpg, &src->tmpg, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmp2, &src->tmp2, _state, make_automatic) ) - return ae_false; - dst->ismaintermchanged = src->ismaintermchanged; - dst->issecondarytermchanged = src->issecondarytermchanged; - dst->islineartermchanged = src->islineartermchanged; - dst->isactivesetchanged = src->isactivesetchanged; - return ae_true; -} - - -void _convexquadraticmodel_clear(void* _p) -{ - convexquadraticmodel *p = (convexquadraticmodel*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->a); - ae_matrix_clear(&p->q); - ae_vector_clear(&p->b); - ae_vector_clear(&p->r); - ae_vector_clear(&p->xc); - ae_vector_clear(&p->d); - ae_vector_clear(&p->activeset); - ae_matrix_clear(&p->tq2dense); - ae_matrix_clear(&p->tk2); - ae_vector_clear(&p->tq2diag); - ae_vector_clear(&p->tq1); - ae_vector_clear(&p->tk1); - ae_vector_clear(&p->txc); - ae_vector_clear(&p->tb); - ae_matrix_clear(&p->ecadense); - ae_matrix_clear(&p->eq); - ae_matrix_clear(&p->eccm); - ae_vector_clear(&p->ecadiag); - ae_vector_clear(&p->eb); - ae_vector_clear(&p->tmp0); - ae_vector_clear(&p->tmp1); - ae_vector_clear(&p->tmpg); - ae_matrix_clear(&p->tmp2); -} - - -void _convexquadraticmodel_destroy(void* _p) -{ - convexquadraticmodel *p = (convexquadraticmodel*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->a); - ae_matrix_destroy(&p->q); - ae_vector_destroy(&p->b); - ae_vector_destroy(&p->r); - ae_vector_destroy(&p->xc); - ae_vector_destroy(&p->d); - ae_vector_destroy(&p->activeset); - ae_matrix_destroy(&p->tq2dense); - ae_matrix_destroy(&p->tk2); - ae_vector_destroy(&p->tq2diag); - ae_vector_destroy(&p->tq1); - ae_vector_destroy(&p->tk1); - ae_vector_destroy(&p->txc); - ae_vector_destroy(&p->tb); - ae_matrix_destroy(&p->ecadense); - ae_matrix_destroy(&p->eq); - ae_matrix_destroy(&p->eccm); - ae_vector_destroy(&p->ecadiag); - ae_vector_destroy(&p->eb); - ae_vector_destroy(&p->tmp0); - ae_vector_destroy(&p->tmp1); - ae_vector_destroy(&p->tmpg); - ae_matrix_destroy(&p->tmp2); -} - - - - -/************************************************************************* -This subroutine is used to initialize SNNLS solver. - -By default, empty NNLS problem is produced, but we allocated enough space -to store problems with NSMax+NDMax columns and NRMax rows. It is good -place to provide algorithm with initial estimate of the space requirements, -although you may underestimate problem size or even pass zero estimates - -in this case buffer variables will be resized automatically when you set -NNLS problem. - -Previously allocated buffer variables are reused as much as possible. This -function does not clear structure completely, it tries to preserve as much -dynamically allocated memory as possible. - - -- ALGLIB -- - Copyright 10.10.2012 by Bochkanov Sergey -*************************************************************************/ -void snnlsinit(ae_int_t nsmax, - ae_int_t ndmax, - ae_int_t nrmax, - snnlssolver* s, - ae_state *_state) -{ - - - s->ns = 0; - s->nd = 0; - s->nr = 0; - rmatrixsetlengthatleast(&s->densea, nrmax, ndmax, _state); - rmatrixsetlengthatleast(&s->tmpca, nrmax, ndmax, _state); - rmatrixsetlengthatleast(&s->tmpz, ndmax, ndmax, _state); - rvectorsetlengthatleast(&s->b, nrmax, _state); - bvectorsetlengthatleast(&s->nnc, nsmax+ndmax, _state); - s->debugflops = 0.0; - s->debugmaxnewton = 0; - s->refinementits = snnls_iterativerefinementits; -} - - -/************************************************************************* -This subroutine is used to set NNLS problem: - - ( [ 1 | ] [ ] [ ] )^2 - ( [ 1 | ] [ ] [ ] ) - min ( [ 1 | Ad ] * [ x ] - [ b ] ) s.t. x>=0 - ( [ | ] [ ] [ ] ) - ( [ | ] [ ] [ ] ) - -where: -* identity matrix has NS*NS size (NS<=NR, NS can be zero) -* dense matrix Ad has NR*ND size -* b is NR*1 vector -* x is (NS+ND)*1 vector -* all elements of x are non-negative (this constraint can be removed later - by calling SNNLSDropNNC() function) - -Previously allocated buffer variables are reused as much as possible. -After you set problem, you can solve it with SNNLSSolve(). - -INPUT PARAMETERS: - S - SNNLS solver, must be initialized with SNNLSInit() call - A - array[NR,ND], dense part of the system - B - array[NR], right part - NS - size of the sparse part of the system, 0<=NS<=NR - ND - size of the dense part of the system, ND>=0 - NR - rows count, NR>0 - -NOTE: - 1. You can have NS+ND=0, solver will correctly accept such combination - and return empty array as problem solution. - - -- ALGLIB -- - Copyright 10.10.2012 by Bochkanov Sergey -*************************************************************************/ -void snnlssetproblem(snnlssolver* s, - /* Real */ ae_matrix* a, - /* Real */ ae_vector* b, - ae_int_t ns, - ae_int_t nd, - ae_int_t nr, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(nd>=0, "SNNLSSetProblem: ND<0", _state); - ae_assert(ns>=0, "SNNLSSetProblem: NS<0", _state); - ae_assert(nr>0, "SNNLSSetProblem: NR<=0", _state); - ae_assert(ns<=nr, "SNNLSSetProblem: NS>NR", _state); - ae_assert(a->rows>=nr||nd==0, "SNNLSSetProblem: rows(A)cols>=nd, "SNNLSSetProblem: cols(A)cnt>=nr, "SNNLSSetProblem: length(B)ns = ns; - s->nd = nd; - s->nr = nr; - if( nd>0 ) - { - rmatrixsetlengthatleast(&s->densea, nr, nd, _state); - for(i=0; i<=nr-1; i++) - { - ae_v_move(&s->densea.ptr.pp_double[i][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,nd-1)); - } - } - rvectorsetlengthatleast(&s->b, nr, _state); - ae_v_move(&s->b.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,nr-1)); - bvectorsetlengthatleast(&s->nnc, ns+nd, _state); - for(i=0; i<=ns+nd-1; i++) - { - s->nnc.ptr.p_bool[i] = ae_true; - } -} - - -/************************************************************************* -This subroutine drops non-negativity constraint from the problem set by -SNNLSSetProblem() call. This function must be called AFTER problem is set, -because each SetProblem() call resets constraints to their default state -(all constraints are present). - -INPUT PARAMETERS: - S - SNNLS solver, must be initialized with SNNLSInit() call, - problem must be set with SNNLSSetProblem() call. - Idx - constraint index, 0<=IDX=0, "SNNLSDropNNC: Idx<0", _state); - ae_assert(idxns+s->nd, "SNNLSDropNNC: Idx>=NS+ND", _state); - s->nnc.ptr.p_bool[idx] = ae_false; -} - - -/************************************************************************* -This subroutine is used to solve NNLS problem. - -INPUT PARAMETERS: - S - SNNLS solver, must be initialized with SNNLSInit() call and - problem must be set up with SNNLSSetProblem() call. - X - possibly preallocated buffer, automatically resized if needed - -OUTPUT PARAMETERS: - X - array[NS+ND], solution - -NOTE: - 1. You can have NS+ND=0, solver will correctly accept such combination - and return empty array as problem solution. - - 2. Internal field S.DebugFLOPS contains rough estimate of FLOPs used - to solve problem. It can be used for debugging purposes. This field - is real-valued. - - -- ALGLIB -- - Copyright 10.10.2012 by Bochkanov Sergey -*************************************************************************/ -void snnlssolve(snnlssolver* s, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t ns; - ae_int_t nd; - ae_int_t nr; - ae_int_t nsc; - ae_int_t ndc; - ae_int_t newtoncnt; - ae_bool terminationneeded; - double eps; - double fcur; - double fprev; - double fcand; - double noiselevel; - double noisetolerance; - double stplen; - double d2; - double d1; - double d0; - ae_bool wasactivation; - ae_int_t rfsits; - double lambdav; - double v0; - double v1; - double v; - - - - /* - * Prepare - */ - ns = s->ns; - nd = s->nd; - nr = s->nr; - s->debugflops = 0.0; - - /* - * Handle special cases: - * * NS+ND=0 - * * ND=0 - */ - if( ns+nd==0 ) - { - return; - } - if( nd==0 ) - { - rvectorsetlengthatleast(x, ns, _state); - for(i=0; i<=ns-1; i++) - { - x->ptr.p_double[i] = s->b.ptr.p_double[i]; - if( s->nnc.ptr.p_bool[i] ) - { - x->ptr.p_double[i] = ae_maxreal(x->ptr.p_double[i], 0.0, _state); - } - } - return; - } - - /* - * Main cycle of BLEIC-SNNLS algorithm. - * Below we assume that ND>0. - */ - rvectorsetlengthatleast(x, ns+nd, _state); - rvectorsetlengthatleast(&s->xn, ns+nd, _state); - rvectorsetlengthatleast(&s->g, ns+nd, _state); - rvectorsetlengthatleast(&s->d, ns+nd, _state); - rvectorsetlengthatleast(&s->r, nr, _state); - rvectorsetlengthatleast(&s->diagaa, nd, _state); - rvectorsetlengthatleast(&s->dx, ns+nd, _state); - for(i=0; i<=ns+nd-1; i++) - { - x->ptr.p_double[i] = 0.0; - } - eps = 2*ae_machineepsilon; - noisetolerance = 10.0; - lambdav = 1.0E6*ae_machineepsilon; - newtoncnt = 0; - for(;;) - { - - /* - * Phase 1: perform steepest descent step. - * - * TerminationNeeded control variable is set on exit from this loop: - * * TerminationNeeded=False in case we have to proceed to Phase 2 (Newton step) - * * TerminationNeeded=True in case we found solution (step along projected gradient is small enough) - * - * Temporaries used: - * * R (I|A)*x-b - * - * NOTE 1. It is assumed that initial point X is feasible. This feasibility - * is retained during all iterations. - */ - terminationneeded = ae_false; - for(;;) - { - - /* - * Calculate gradient G and constrained descent direction D - */ - for(i=0; i<=nr-1; i++) - { - v = ae_v_dotproduct(&s->densea.ptr.pp_double[i][0], 1, &x->ptr.p_double[ns], 1, ae_v_len(0,nd-1)); - if( iptr.p_double[i]; - } - s->r.ptr.p_double[i] = v-s->b.ptr.p_double[i]; - } - for(i=0; i<=ns-1; i++) - { - s->g.ptr.p_double[i] = s->r.ptr.p_double[i]; - } - for(i=ns; i<=ns+nd-1; i++) - { - s->g.ptr.p_double[i] = 0.0; - } - for(i=0; i<=nr-1; i++) - { - v = s->r.ptr.p_double[i]; - ae_v_addd(&s->g.ptr.p_double[ns], 1, &s->densea.ptr.pp_double[i][0], 1, ae_v_len(ns,ns+nd-1), v); - } - for(i=0; i<=ns+nd-1; i++) - { - if( (s->nnc.ptr.p_bool[i]&&ae_fp_less_eq(x->ptr.p_double[i],0))&&ae_fp_greater(s->g.ptr.p_double[i],0) ) - { - s->d.ptr.p_double[i] = 0.0; - } - else - { - s->d.ptr.p_double[i] = -s->g.ptr.p_double[i]; - } - } - s->debugflops = s->debugflops+2*2*nr*nd; - - /* - * Build quadratic model of F along descent direction: - * F(x+alpha*d) = D2*alpha^2 + D1*alpha + D0 - * - * Estimate numerical noise in the X (noise level is used - * to classify step as singificant or insignificant). Noise - * comes from two sources: - * * noise when calculating rows of (I|A)*x - * * noise when calculating norm of residual - * - * In case function curvature is negative or product of descent - * direction and gradient is non-negative, iterations are terminated. - * - * NOTE: D0 is not actually used, but we prefer to maintain it. - */ - fprev = ae_v_dotproduct(&s->r.ptr.p_double[0], 1, &s->r.ptr.p_double[0], 1, ae_v_len(0,nr-1)); - fprev = fprev/2; - noiselevel = 0.0; - for(i=0; i<=nr-1; i++) - { - - /* - * Estimate noise introduced by I-th row of (I|A)*x - */ - v = 0.0; - if( iptr.p_double[i]; - } - for(j=0; j<=nd-1; j++) - { - v = ae_maxreal(v, eps*ae_fabs(s->densea.ptr.pp_double[i][j]*x->ptr.p_double[ns+j], _state), _state); - } - v = 2*ae_fabs(s->r.ptr.p_double[i]*v, _state)+v*v; - - /* - * Add to summary noise in the model - */ - noiselevel = noiselevel+v; - } - noiselevel = ae_maxreal(noiselevel, eps*fprev, _state); - d2 = 0.0; - for(i=0; i<=nr-1; i++) - { - v = ae_v_dotproduct(&s->densea.ptr.pp_double[i][0], 1, &s->d.ptr.p_double[ns], 1, ae_v_len(0,nd-1)); - if( id.ptr.p_double[i]; - } - d2 = d2+0.5*ae_sqr(v, _state); - } - v = ae_v_dotproduct(&s->d.ptr.p_double[0], 1, &s->g.ptr.p_double[0], 1, ae_v_len(0,ns+nd-1)); - d1 = v; - d0 = fprev; - if( ae_fp_less_eq(d2,0)||ae_fp_greater_eq(d1,0) ) - { - terminationneeded = ae_true; - break; - } - s->debugflops = s->debugflops+2*nr*nd; - touchreal(&d0, _state); - - /* - * Perform full (unconstrained) step with length StpLen in direction D. - * - * We can terminate iterations in case one of two criteria is met: - * 1. function change is dominated by noise (or function actually increased - * instead of decreasing) - * 2. relative change in X is small enough - * - * First condition is not enough to guarantee algorithm termination because - * sometimes our noise estimate is too optimistic (say, in situations when - * function value at solition is zero). - */ - stplen = -d1/(2*d2); - ae_v_move(&s->xn.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,ns+nd-1)); - ae_v_addd(&s->xn.ptr.p_double[0], 1, &s->d.ptr.p_double[0], 1, ae_v_len(0,ns+nd-1), stplen); - fcand = 0.0; - for(i=0; i<=nr-1; i++) - { - v = ae_v_dotproduct(&s->densea.ptr.pp_double[i][0], 1, &s->xn.ptr.p_double[ns], 1, ae_v_len(0,nd-1)); - if( ixn.ptr.p_double[i]; - } - fcand = fcand+0.5*ae_sqr(v-s->b.ptr.p_double[i], _state); - } - s->debugflops = s->debugflops+2*nr*nd; - if( ae_fp_greater_eq(fcand,fprev-noiselevel*noisetolerance) ) - { - terminationneeded = ae_true; - break; - } - v = 0; - for(i=0; i<=ns+nd-1; i++) - { - v0 = ae_fabs(x->ptr.p_double[i], _state); - v1 = ae_fabs(s->xn.ptr.p_double[i], _state); - if( ae_fp_neq(v0,0)||ae_fp_neq(v1,0) ) - { - v = ae_maxreal(v, ae_fabs(x->ptr.p_double[i]-s->xn.ptr.p_double[i], _state)/ae_maxreal(v0, v1, _state), _state); - } - } - if( ae_fp_less_eq(v,eps*noisetolerance) ) - { - terminationneeded = ae_true; - break; - } - - /* - * Perform step one more time, now with non-negativity constraints. - * - * NOTE: complicated code below which deals with VarIdx temporary makes - * sure that in case unconstrained step leads us outside of feasible - * area, we activate at least one constraint. - */ - wasactivation = snnls_boundedstepandactivation(x, &s->xn, &s->nnc, ns+nd, _state); - fcur = 0.0; - for(i=0; i<=nr-1; i++) - { - v = ae_v_dotproduct(&s->densea.ptr.pp_double[i][0], 1, &x->ptr.p_double[ns], 1, ae_v_len(0,nd-1)); - if( iptr.p_double[i]; - } - fcur = fcur+0.5*ae_sqr(v-s->b.ptr.p_double[i], _state); - } - s->debugflops = s->debugflops+2*nr*nd; - - /* - * Depending on results, decide what to do: - * 1. In case step was performed without activation of constraints, - * we proceed to Newton method - * 2. In case there was activated at least one constraint, we repeat - * steepest descent step. - */ - if( !wasactivation ) - { - - /* - * Step without activation, proceed to Newton - */ - break; - } - } - if( terminationneeded ) - { - break; - } - - /* - * Phase 2: Newton method. - */ - rvectorsetlengthatleast(&s->cx, ns+nd, _state); - ivectorsetlengthatleast(&s->columnmap, ns+nd, _state); - ivectorsetlengthatleast(&s->rowmap, nr, _state); - rmatrixsetlengthatleast(&s->tmpca, nr, nd, _state); - rmatrixsetlengthatleast(&s->tmpz, nd, nd, _state); - rvectorsetlengthatleast(&s->cborg, nr, _state); - rvectorsetlengthatleast(&s->cb, nr, _state); - terminationneeded = ae_false; - for(;;) - { - - /* - * Prepare equality constrained subproblem with NSC<=NS "sparse" - * variables and NDC<=ND "dense" variables. - * - * First, we reorder variables (columns) and move all unconstrained - * variables "to the left", ColumnMap stores this permutation. - * - * Then, we reorder first NS rows of A and first NS elements of B in - * such way that we still have identity matrix in first NSC columns - * of problem. This permutation is stored in RowMap. - */ - nsc = 0; - ndc = 0; - for(i=0; i<=ns-1; i++) - { - if( !(s->nnc.ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],0)) ) - { - s->columnmap.ptr.p_int[nsc] = i; - nsc = nsc+1; - } - } - for(i=ns; i<=ns+nd-1; i++) - { - if( !(s->nnc.ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],0)) ) - { - s->columnmap.ptr.p_int[nsc+ndc] = i; - ndc = ndc+1; - } - } - for(i=0; i<=nsc-1; i++) - { - s->rowmap.ptr.p_int[i] = s->columnmap.ptr.p_int[i]; - } - j = nsc; - for(i=0; i<=ns-1; i++) - { - if( s->nnc.ptr.p_bool[i]&&ae_fp_eq(x->ptr.p_double[i],0) ) - { - s->rowmap.ptr.p_int[j] = i; - j = j+1; - } - } - for(i=ns; i<=nr-1; i++) - { - s->rowmap.ptr.p_int[i] = i; - } - - /* - * Now, permutations are ready, and we can copy/reorder - * A, B and X to CA, CB and CX. - */ - for(i=0; i<=nsc+ndc-1; i++) - { - s->cx.ptr.p_double[i] = x->ptr.p_double[s->columnmap.ptr.p_int[i]]; - } - for(i=0; i<=nr-1; i++) - { - for(j=0; j<=ndc-1; j++) - { - s->tmpca.ptr.pp_double[i][j] = s->densea.ptr.pp_double[s->rowmap.ptr.p_int[i]][s->columnmap.ptr.p_int[nsc+j]-ns]; - } - s->cb.ptr.p_double[i] = s->b.ptr.p_double[s->rowmap.ptr.p_int[i]]; - } - - /* - * Solve equality constrained subproblem. - */ - if( ndc>0 ) - { - - /* - * NDC>0. - * - * Solve subproblem using Newton-type algorithm. We have a - * NR*(NSC+NDC) linear least squares subproblem - * - * | ( I AU ) ( XU ) ( BU ) |^2 - * min | ( ) * ( ) - ( ) | - * | ( 0 AL ) ( XL ) ( BL ) | - * - * where: - * * I is a NSC*NSC identity matrix - * * AU is NSC*NDC dense matrix (first NSC rows of CA) - * * AL is (NR-NSC)*NDC dense matrix (next NR-NSC rows of CA) - * * BU and BL are correspondingly sized parts of CB - * - * After conversion to normal equations and small regularization, - * we get: - * - * ( I AU ) ( XU ) ( BU ) - * ( )*( ) = ( ) - * ( AU' Y ) ( XL ) ( AU'*BU+AL'*BL ) - * - * where Y = AU'*AU + AL'*AL + lambda*diag(AU'*AU+AL'*AL). - * - * With Schur Complement Method this system can be solved in - * O(NR*NDC^2+NDC^3) operations. In order to solve it we multiply - * first row by AU' and subtract it from the second one. As result, - * we get system - * - * Z*XL = AL'*BL, where Z=AL'*AL+lambda*diag(AU'*AU+AL'*AL) - * - * We can easily solve it for XL, and we can get XU as XU = BU-AU*XL. - * - * We will start solution from calculating Cholesky decomposition of Z. - */ - for(i=0; i<=nr-1; i++) - { - s->cborg.ptr.p_double[i] = s->cb.ptr.p_double[i]; - } - for(i=0; i<=ndc-1; i++) - { - s->diagaa.ptr.p_double[i] = 0; - } - for(i=0; i<=nr-1; i++) - { - for(j=0; j<=ndc-1; j++) - { - s->diagaa.ptr.p_double[j] = s->diagaa.ptr.p_double[j]+ae_sqr(s->tmpca.ptr.pp_double[i][j], _state); - } - } - for(j=0; j<=ndc-1; j++) - { - if( ae_fp_eq(s->diagaa.ptr.p_double[j],0) ) - { - s->diagaa.ptr.p_double[j] = 1; - } - } - for(;;) - { - - /* - * NOTE: we try to factorize Z. In case of failure we increase - * regularization parameter and try again. - */ - s->debugflops = s->debugflops+2*(nr-nsc)*ae_sqr(ndc, _state)+ae_pow(ndc, 3, _state)/3; - for(i=0; i<=ndc-1; i++) - { - for(j=0; j<=ndc-1; j++) - { - s->tmpz.ptr.pp_double[i][j] = 0.0; - } - } - rmatrixsyrk(ndc, nr-nsc, 1.0, &s->tmpca, nsc, 0, 2, 0.0, &s->tmpz, 0, 0, ae_true, _state); - for(i=0; i<=ndc-1; i++) - { - s->tmpz.ptr.pp_double[i][i] = s->tmpz.ptr.pp_double[i][i]+lambdav*s->diagaa.ptr.p_double[i]; - } - if( spdmatrixcholeskyrec(&s->tmpz, 0, ndc, ae_true, &s->tmpcholesky, _state) ) - { - break; - } - lambdav = lambdav*10; - } - - /* - * We have Cholesky decomposition of Z, now we can solve system: - * * we start from initial point CX - * * we perform several iterations of refinement: - * * BU_new := BU_orig - XU_cur - AU*XL_cur - * * BL_new := BL_orig - AL*XL_cur - * * solve for BU_new/BL_new, obtain solution dx - * * XU_cur := XU_cur + dx_u - * * XL_cur := XL_cur + dx_l - * * BU_new/BL_new are stored in CB, original right part is - * stored in CBOrg, correction to X is stored in DX, current - * X is stored in CX - */ - for(rfsits=1; rfsits<=s->refinementits; rfsits++) - { - for(i=0; i<=nr-1; i++) - { - v = ae_v_dotproduct(&s->tmpca.ptr.pp_double[i][0], 1, &s->cx.ptr.p_double[nsc], 1, ae_v_len(0,ndc-1)); - s->cb.ptr.p_double[i] = s->cborg.ptr.p_double[i]-v; - if( icb.ptr.p_double[i] = s->cb.ptr.p_double[i]-s->cx.ptr.p_double[i]; - } - } - s->debugflops = s->debugflops+2*nr*ndc; - for(i=0; i<=ndc-1; i++) - { - s->dx.ptr.p_double[i] = 0.0; - } - for(i=nsc; i<=nr-1; i++) - { - v = s->cb.ptr.p_double[i]; - ae_v_addd(&s->dx.ptr.p_double[0], 1, &s->tmpca.ptr.pp_double[i][0], 1, ae_v_len(0,ndc-1), v); - } - fblscholeskysolve(&s->tmpz, 1.0, ndc, ae_true, &s->dx, &s->tmpcholesky, _state); - s->debugflops = s->debugflops+2*ndc*ndc; - ae_v_add(&s->cx.ptr.p_double[nsc], 1, &s->dx.ptr.p_double[0], 1, ae_v_len(nsc,nsc+ndc-1)); - for(i=0; i<=nsc-1; i++) - { - v = ae_v_dotproduct(&s->tmpca.ptr.pp_double[i][0], 1, &s->dx.ptr.p_double[0], 1, ae_v_len(0,ndc-1)); - s->cx.ptr.p_double[i] = s->cx.ptr.p_double[i]+s->cb.ptr.p_double[i]-v; - } - s->debugflops = s->debugflops+2*nsc*ndc; - } - } - else - { - - /* - * NDC=0. - * - * We have a NR*NSC linear least squares subproblem - * - * min |XU-BU|^2 - * - * solution is easy to find - it is XU=BU! - */ - for(i=0; i<=nsc-1; i++) - { - s->cx.ptr.p_double[i] = s->cb.ptr.p_double[i]; - } - } - for(i=0; i<=ns+nd-1; i++) - { - s->xn.ptr.p_double[i] = x->ptr.p_double[i]; - } - for(i=0; i<=nsc+ndc-1; i++) - { - s->xn.ptr.p_double[s->columnmap.ptr.p_int[i]] = s->cx.ptr.p_double[i]; - } - newtoncnt = newtoncnt+1; - - /* - * Step to candidate point. - * If no constraints was added, accept candidate point XN and move to next phase. - * Terminate, if number of Newton iterations exceeded DebugMaxNewton counter. - */ - terminationneeded = s->debugmaxnewton>0&&newtoncnt>=s->debugmaxnewton; - if( !snnls_boundedstepandactivation(x, &s->xn, &s->nnc, ns+nd, _state) ) - { - break; - } - if( terminationneeded ) - { - break; - } - } - if( terminationneeded ) - { - break; - } - } -} - - -/************************************************************************* -Having feasible current point XC and possibly infeasible candidate point -XN, this function performs longest step from XC to XN which retains -feasibility. In case XN is found to be infeasible, at least one constraint -is activated. - -For example, if we have: - XC=0.5 - XN=-1.2 - x>=0 -then this function will move us to X=0 and activate constraint "x>=0". - -INPUT PARAMETERS: - XC - current point, must be feasible with respect to - all constraints - XN - candidate point, can be infeasible with respect to some - constraints - NNC - NNC[i] is True when I-th variable is non-negatively - constrained - N - variable count - -OUTPUT PARAMETERS: - XC - new position - -RESULT: - True in case at least one constraint was activated by step - - -- ALGLIB -- - Copyright 19.10.2012 by Bochkanov Sergey -*************************************************************************/ -static ae_bool snnls_boundedstepandactivation(/* Real */ ae_vector* xc, - /* Real */ ae_vector* xn, - /* Boolean */ ae_vector* nnc, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - ae_int_t varidx; - double vmax; - double v; - double stplen; - ae_bool result; - - - - /* - * Check constraints. - * - * NOTE: it is important to test for XN[i]ptr.p_bool[i]&&ae_fp_less(xn->ptr.p_double[i],xc->ptr.p_double[i]))&&ae_fp_less_eq(xn->ptr.p_double[i],0.0) ) - { - v = vmax; - vmax = safeminposrv(xc->ptr.p_double[i], xc->ptr.p_double[i]-xn->ptr.p_double[i], vmax, _state); - if( ae_fp_less(vmax,v) ) - { - varidx = i; - } - } - } - stplen = ae_minreal(vmax, 1.0, _state); - - /* - * Perform step with activation. - * - * NOTE: it is important to use (1-StpLen)*XC + StpLen*XN because - * it allows us to step exactly to XN when StpLen=1, even in - * the presence of numerical errors. - */ - for(i=0; i<=n-1; i++) - { - xc->ptr.p_double[i] = (1-stplen)*xc->ptr.p_double[i]+stplen*xn->ptr.p_double[i]; - } - if( varidx>=0 ) - { - xc->ptr.p_double[varidx] = 0.0; - result = ae_true; - } - for(i=0; i<=n-1; i++) - { - if( nnc->ptr.p_bool[i]&&ae_fp_less(xc->ptr.p_double[i],0.0) ) - { - xc->ptr.p_double[i] = 0.0; - result = ae_true; - } - } - return result; -} - - -ae_bool _snnlssolver_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - snnlssolver *p = (snnlssolver*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->densea, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->b, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->nnc, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmpz, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmpca, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->diagaa, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cb, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cborg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->columnmap, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rowmap, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpcholesky, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->r, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _snnlssolver_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - snnlssolver *dst = (snnlssolver*)_dst; - snnlssolver *src = (snnlssolver*)_src; - dst->ns = src->ns; - dst->nd = src->nd; - dst->nr = src->nr; - if( !ae_matrix_init_copy(&dst->densea, &src->densea, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->b, &src->b, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->nnc, &src->nnc, _state, make_automatic) ) - return ae_false; - dst->refinementits = src->refinementits; - dst->debugflops = src->debugflops; - dst->debugmaxnewton = src->debugmaxnewton; - if( !ae_vector_init_copy(&dst->xn, &src->xn, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmpz, &src->tmpz, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmpca, &src->tmpca, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dx, &src->dx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->diagaa, &src->diagaa, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cb, &src->cb, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cx, &src->cx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cborg, &src->cborg, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->columnmap, &src->columnmap, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rowmap, &src->rowmap, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpcholesky, &src->tmpcholesky, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->r, &src->r, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _snnlssolver_clear(void* _p) -{ - snnlssolver *p = (snnlssolver*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->densea); - ae_vector_clear(&p->b); - ae_vector_clear(&p->nnc); - ae_vector_clear(&p->xn); - ae_matrix_clear(&p->tmpz); - ae_matrix_clear(&p->tmpca); - ae_vector_clear(&p->g); - ae_vector_clear(&p->d); - ae_vector_clear(&p->dx); - ae_vector_clear(&p->diagaa); - ae_vector_clear(&p->cb); - ae_vector_clear(&p->cx); - ae_vector_clear(&p->cborg); - ae_vector_clear(&p->columnmap); - ae_vector_clear(&p->rowmap); - ae_vector_clear(&p->tmpcholesky); - ae_vector_clear(&p->r); -} - - -void _snnlssolver_destroy(void* _p) -{ - snnlssolver *p = (snnlssolver*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->densea); - ae_vector_destroy(&p->b); - ae_vector_destroy(&p->nnc); - ae_vector_destroy(&p->xn); - ae_matrix_destroy(&p->tmpz); - ae_matrix_destroy(&p->tmpca); - ae_vector_destroy(&p->g); - ae_vector_destroy(&p->d); - ae_vector_destroy(&p->dx); - ae_vector_destroy(&p->diagaa); - ae_vector_destroy(&p->cb); - ae_vector_destroy(&p->cx); - ae_vector_destroy(&p->cborg); - ae_vector_destroy(&p->columnmap); - ae_vector_destroy(&p->rowmap); - ae_vector_destroy(&p->tmpcholesky); - ae_vector_destroy(&p->r); -} - - - - -/************************************************************************* -This subroutine is used to initialize active set. By default, empty -N-variable model with no constraints is generated. Previously allocated -buffer variables are reused as much as possible. - -Two use cases for this object are described below. - -CASE 1 - STEEPEST DESCENT: - - SASInit() - repeat: - SASReactivateConstraints() - SASDescentDirection() - SASExploreDirection() - SASMoveTo() - until convergence - -CASE 1 - PRECONDITIONED STEEPEST DESCENT: - - SASInit() - repeat: - SASReactivateConstraintsPrec() - SASDescentDirectionPrec() - SASExploreDirection() - SASMoveTo() - until convergence - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasinit(ae_int_t n, sactiveset* s, ae_state *_state) -{ - ae_int_t i; - - - s->n = n; - s->algostate = 0; - - /* - * Constraints - */ - s->constraintschanged = ae_true; - s->nec = 0; - s->nic = 0; - rvectorsetlengthatleast(&s->bndl, n, _state); - bvectorsetlengthatleast(&s->hasbndl, n, _state); - rvectorsetlengthatleast(&s->bndu, n, _state); - bvectorsetlengthatleast(&s->hasbndu, n, _state); - for(i=0; i<=n-1; i++) - { - s->bndl.ptr.p_double[i] = _state->v_neginf; - s->bndu.ptr.p_double[i] = _state->v_posinf; - s->hasbndl.ptr.p_bool[i] = ae_false; - s->hasbndu.ptr.p_bool[i] = ae_false; - } - - /* - * current point, scale - */ - s->hasxc = ae_false; - rvectorsetlengthatleast(&s->xc, n, _state); - rvectorsetlengthatleast(&s->s, n, _state); - rvectorsetlengthatleast(&s->h, n, _state); - for(i=0; i<=n-1; i++) - { - s->xc.ptr.p_double[i] = 0.0; - s->s.ptr.p_double[i] = 1.0; - s->h.ptr.p_double[i] = 1.0; - } - - /* - * Other - */ - rvectorsetlengthatleast(&s->unitdiagonal, n, _state); - for(i=0; i<=n-1; i++) - { - s->unitdiagonal.ptr.p_double[i] = 1.0; - } -} - - -/************************************************************************* -This function sets scaling coefficients for SAS object. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -During orthogonalization phase, scale is used to calculate drop tolerances -(whether vector is significantly non-zero or not). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sassetscale(sactiveset* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(state->algostate==0, "SASSetScale: you may change scale only in modification mode", _state); - ae_assert(s->cnt>=state->n, "SASSetScale: Length(S)n-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "SASSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "SASSetScale: S contains zero elements", _state); - } - for(i=0; i<=state->n-1; i++) - { - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE 1: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 2: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sassetprecdiag(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(state->algostate==0, "SASSetPrecDiag: you may change preconditioner only in modification mode", _state); - ae_assert(d->cnt>=state->n, "SASSetPrecDiag: D is too short", _state); - for(i=0; i<=state->n-1; i++) - { - ae_assert(ae_isfinite(d->ptr.p_double[i], _state), "SASSetPrecDiag: D contains infinite or NAN elements", _state); - ae_assert(ae_fp_greater(d->ptr.p_double[i],0), "SASSetPrecDiag: D contains non-positive elements", _state); - } - for(i=0; i<=state->n-1; i++) - { - state->h.ptr.p_double[i] = d->ptr.p_double[i]; - } -} - - -/************************************************************************* -This function sets/changes boundary constraints. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF. - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF. - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sassetbc(sactiveset* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - - - ae_assert(state->algostate==0, "SASSetBC: you may change constraints only in modification mode", _state); - n = state->n; - ae_assert(bndl->cnt>=n, "SASSetBC: Length(BndL)cnt>=n, "SASSetBC: Length(BndU)ptr.p_double[i], _state)||ae_isneginf(bndl->ptr.p_double[i], _state), "SASSetBC: BndL contains NAN or +INF", _state); - ae_assert(ae_isfinite(bndu->ptr.p_double[i], _state)||ae_isposinf(bndu->ptr.p_double[i], _state), "SASSetBC: BndL contains NAN or -INF", _state); - state->bndl.ptr.p_double[i] = bndl->ptr.p_double[i]; - state->hasbndl.ptr.p_bool[i] = ae_isfinite(bndl->ptr.p_double[i], _state); - state->bndu.ptr.p_double[i] = bndu->ptr.p_double[i]; - state->hasbndu.ptr.p_bool[i] = ae_isfinite(bndu->ptr.p_double[i], _state); - } - state->constraintschanged = ae_true; -} - - -/************************************************************************* -This function sets linear constraints for SAS object. - -Linear constraints are inactive by default (after initial creation). - -INPUT PARAMETERS: - State - SAS structure - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0 - -NOTE 1: linear (non-bound) constraints are satisfied only approximately: -* there always exists some minor violation (about Epsilon in magnitude) - due to rounding errors -* numerical differentiation, if used, may lead to function evaluations - outside of the feasible area, because algorithm does NOT change - numerical differentiation formula according to linear constraints. -If you want constraints to be satisfied exactly, try to reformulate your -problem in such manner that all constraints will become boundary ones -(this kind of constraints is always satisfied exactly, both in the final -solution and in all intermediate points). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void sassetlc(sactiveset* state, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - - - ae_assert(state->algostate==0, "SASSetLC: you may change constraints only in modification mode", _state); - n = state->n; - - /* - * First, check for errors in the inputs - */ - ae_assert(k>=0, "SASSetLC: K<0", _state); - ae_assert(c->cols>=n+1||k==0, "SASSetLC: Cols(C)rows>=k, "SASSetLC: Rows(C)cnt>=k, "SASSetLC: Length(CT)nec = 0; - state->nic = 0; - state->constraintschanged = ae_true; - return; - } - - /* - * Equality constraints are stored first, in the upper - * NEC rows of State.CLEIC matrix. Inequality constraints - * are stored in the next NIC rows. - * - * NOTE: we convert inequality constraints to the form - * A*x<=b before copying them. - */ - rmatrixsetlengthatleast(&state->cleic, k, n+1, _state); - state->nec = 0; - state->nic = 0; - for(i=0; i<=k-1; i++) - { - if( ct->ptr.p_int[i]==0 ) - { - ae_v_move(&state->cleic.ptr.pp_double[state->nec][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - state->nec = state->nec+1; - } - } - for(i=0; i<=k-1; i++) - { - if( ct->ptr.p_int[i]!=0 ) - { - if( ct->ptr.p_int[i]>0 ) - { - ae_v_moveneg(&state->cleic.ptr.pp_double[state->nec+state->nic][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - } - else - { - ae_v_move(&state->cleic.ptr.pp_double[state->nec+state->nic][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - } - state->nic = state->nic+1; - } - } - - /* - * Mark state as changed - */ - state->constraintschanged = ae_true; -} - - -/************************************************************************* -Another variation of SASSetLC(), which accepts linear constraints using -another representation. - -Linear constraints are inactive by default (after initial creation). - -INPUT PARAMETERS: - State - SAS structure - CLEIC - linear constraints, array[NEC+NIC,N+1]. - Each row of C represents one constraint: - * first N elements correspond to coefficients, - * last element corresponds to the right part. - First NEC rows store equality constraints, next NIC - are - inequality ones. - All elements of C (including right part) must be finite. - NEC - number of equality constraints, NEC>=0 - NIC - number of inequality constraints, NIC>=0 - -NOTE 1: linear (non-bound) constraints are satisfied only approximately: -* there always exists some minor violation (about Epsilon in magnitude) - due to rounding errors -* numerical differentiation, if used, may lead to function evaluations - outside of the feasible area, because algorithm does NOT change - numerical differentiation formula according to linear constraints. -If you want constraints to be satisfied exactly, try to reformulate your -problem in such manner that all constraints will become boundary ones -(this kind of constraints is always satisfied exactly, both in the final -solution and in all intermediate points). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void sassetlcx(sactiveset* state, - /* Real */ ae_matrix* cleic, - ae_int_t nec, - ae_int_t nic, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - - - ae_assert(state->algostate==0, "SASSetLCX: you may change constraints only in modification mode", _state); - n = state->n; - - /* - * First, check for errors in the inputs - */ - ae_assert(nec>=0, "SASSetLCX: NEC<0", _state); - ae_assert(nic>=0, "SASSetLCX: NIC<0", _state); - ae_assert(cleic->cols>=n+1||nec+nic==0, "SASSetLCX: Cols(CLEIC)rows>=nec+nic, "SASSetLCX: Rows(CLEIC)cleic, nec+nic, n+1, _state); - state->nec = nec; - state->nic = nic; - for(i=0; i<=nec+nic-1; i++) - { - for(j=0; j<=n; j++) - { - state->cleic.ptr.pp_double[i][j] = cleic->ptr.pp_double[i][j]; - } - } - - /* - * Mark state as changed - */ - state->constraintschanged = ae_true; -} - - -/************************************************************************* -This subroutine turns on optimization mode: -1. feasibility in X is enforced (in case X=S.XC and constraints have not - changed, algorithm just uses X without any modifications at all) -2. constraints are marked as "candidate" or "inactive" - -INPUT PARAMETERS: - S - active set object - X - initial point (candidate), array[N]. It is expected that X - contains only finite values (we do not check it). - -OUTPUT PARAMETERS: - S - state is changed - X - initial point can be changed to enforce feasibility - -RESULT: - True in case feasible point was found (mode was changed to "optimization") - False in case no feasible point was found (mode was not changed) - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -ae_bool sasstartoptimization(sactiveset* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nec; - ae_int_t nic; - ae_int_t i; - ae_int_t j; - double v; - ae_bool result; - - - ae_assert(state->algostate==0, "SASStartOptimization: already in optimization mode", _state); - result = ae_false; - n = state->n; - nec = state->nec; - nic = state->nic; - - /* - * Enforce feasibility and calculate set of "candidate"/"active" constraints. - * Always active equality constraints are marked as "active", all other constraints - * are marked as "candidate". - */ - ivectorsetlengthatleast(&state->activeset, n+nec+nic, _state); - for(i=0; i<=n-1; i++) - { - if( state->hasbndl.ptr.p_bool[i]&&state->hasbndu.ptr.p_bool[i] ) - { - if( ae_fp_greater(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - return result; - } - } - } - ae_v_move(&state->xc.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( state->nec+state->nic>0 ) - { - - /* - * General linear constraints are present; general code is used. - */ - rvectorsetlengthatleast(&state->tmp0, n, _state); - rvectorsetlengthatleast(&state->tmpfeas, n+state->nic, _state); - rmatrixsetlengthatleast(&state->tmpm0, state->nec+state->nic, n+state->nic+1, _state); - for(i=0; i<=state->nec+state->nic-1; i++) - { - ae_v_move(&state->tmpm0.ptr.pp_double[i][0], 1, &state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - for(j=n; j<=n+state->nic-1; j++) - { - state->tmpm0.ptr.pp_double[i][j] = 0; - } - if( i>=state->nec ) - { - state->tmpm0.ptr.pp_double[i][n+i-state->nec] = 1.0; - } - state->tmpm0.ptr.pp_double[i][n+state->nic] = state->cleic.ptr.pp_double[i][n]; - } - ae_v_move(&state->tmpfeas.ptr.p_double[0], 1, &state->xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=state->nic-1; i++) - { - v = ae_v_dotproduct(&state->cleic.ptr.pp_double[i+state->nec][0], 1, &state->xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->tmpfeas.ptr.p_double[i+n] = ae_maxreal(state->cleic.ptr.pp_double[i+state->nec][n]-v, 0.0, _state); - } - if( !findfeasiblepoint(&state->tmpfeas, &state->bndl, &state->hasbndl, &state->bndu, &state->hasbndu, n, state->nic, &state->tmpm0, state->nec+state->nic, 1.0E-6, &i, &j, _state) ) - { - return result; - } - ae_v_move(&state->xc.ptr.p_double[0], 1, &state->tmpfeas.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - if( (state->hasbndl.ptr.p_bool[i]&&state->hasbndu.ptr.p_bool[i])&&ae_fp_eq(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - if( (state->hasbndl.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]))||(state->hasbndu.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i])) ) - { - state->activeset.ptr.p_int[i] = 0; - continue; - } - state->activeset.ptr.p_int[i] = -1; - } - for(i=0; i<=state->nec-1; i++) - { - state->activeset.ptr.p_int[n+i] = 1; - } - for(i=0; i<=state->nic-1; i++) - { - if( ae_fp_eq(state->tmpfeas.ptr.p_double[n+i],0) ) - { - state->activeset.ptr.p_int[n+state->nec+i] = 0; - } - else - { - state->activeset.ptr.p_int[n+state->nec+i] = -1; - } - } - } - else - { - - /* - * Only bound constraints are present, quick code can be used - */ - for(i=0; i<=n-1; i++) - { - state->activeset.ptr.p_int[i] = -1; - if( (state->hasbndl.ptr.p_bool[i]&&state->hasbndu.ptr.p_bool[i])&&ae_fp_eq(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->activeset.ptr.p_int[i] = 1; - state->xc.ptr.p_double[i] = state->bndl.ptr.p_double[i]; - continue; - } - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less_eq(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]) ) - { - state->xc.ptr.p_double[i] = state->bndl.ptr.p_double[i]; - state->activeset.ptr.p_int[i] = 0; - continue; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater_eq(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->xc.ptr.p_double[i] = state->bndu.ptr.p_double[i]; - state->activeset.ptr.p_int[i] = 0; - continue; - } - } - } - - /* - * Change state, allocate temporaries - */ - result = ae_true; - state->algostate = 1; - state->basisisready = ae_false; - state->hasxc = ae_true; - rmatrixsetlengthatleast(&state->pbasis, ae_minint(nec+nic, n, _state), n+1, _state); - rmatrixsetlengthatleast(&state->ibasis, ae_minint(nec+nic, n, _state), n+1, _state); - rmatrixsetlengthatleast(&state->sbasis, ae_minint(nec+nic, n, _state), n+1, _state); - return result; -} - - -/************************************************************************* -This function explores search direction and calculates bound for step as -well as information for activation of constraints. - -INPUT PARAMETERS: - State - SAS structure which stores current point and all other - active set related information - D - descent direction to explore - -OUTPUT PARAMETERS: - StpMax - upper limit on step length imposed by yet inactive - constraints. Can be zero in case some constraints - can be activated by zero step. Equal to some large - value in case step is unlimited. - CIdx - -1 for unlimited step, in [0,N+NEC+NIC) in case of - limited step. - VVal - value which is assigned to X[CIdx] during activation. - For CIdx<0 or CIdx>=N some dummy value is assigned to - this parameter. -*************************************************************************/ -void sasexploredirection(sactiveset* state, - /* Real */ ae_vector* d, - double* stpmax, - ae_int_t* cidx, - double* vval, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nec; - ae_int_t nic; - ae_int_t i; - double prevmax; - double vc; - double vd; - - *stpmax = 0; - *cidx = 0; - *vval = 0; - - ae_assert(state->algostate==1, "SASExploreDirection: is not in optimization mode", _state); - n = state->n; - nec = state->nec; - nic = state->nic; - *cidx = -1; - *vval = 0; - *stpmax = 1.0E50; - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]<=0 ) - { - ae_assert(!state->hasbndl.ptr.p_bool[i]||ae_fp_greater_eq(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]), "SASExploreDirection: internal error - infeasible X", _state); - ae_assert(!state->hasbndu.ptr.p_bool[i]||ae_fp_less_eq(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i]), "SASExploreDirection: internal error - infeasible X", _state); - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(d->ptr.p_double[i],0) ) - { - prevmax = *stpmax; - *stpmax = safeminposrv(state->xc.ptr.p_double[i]-state->bndl.ptr.p_double[i], -d->ptr.p_double[i], *stpmax, _state); - if( ae_fp_less(*stpmax,prevmax) ) - { - *cidx = i; - *vval = state->bndl.ptr.p_double[i]; - } - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(d->ptr.p_double[i],0) ) - { - prevmax = *stpmax; - *stpmax = safeminposrv(state->bndu.ptr.p_double[i]-state->xc.ptr.p_double[i], d->ptr.p_double[i], *stpmax, _state); - if( ae_fp_less(*stpmax,prevmax) ) - { - *cidx = i; - *vval = state->bndu.ptr.p_double[i]; - } - } - } - } - for(i=nec; i<=nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[n+i]<=0 ) - { - vc = ae_v_dotproduct(&state->cleic.ptr.pp_double[i][0], 1, &state->xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - vc = vc-state->cleic.ptr.pp_double[i][n]; - vd = ae_v_dotproduct(&state->cleic.ptr.pp_double[i][0], 1, &d->ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_less_eq(vd,0) ) - { - continue; - } - if( ae_fp_less(vc,0) ) - { - - /* - * XC is strictly feasible with respect to I-th constraint, - * we can perform non-zero step because there is non-zero distance - * between XC and bound. - */ - prevmax = *stpmax; - *stpmax = safeminposrv(-vc, vd, *stpmax, _state); - if( ae_fp_less(*stpmax,prevmax) ) - { - *cidx = n+i; - } - } - else - { - - /* - * XC is at the boundary (or slightly beyond it), and step vector - * points beyond the boundary. - * - * The only thing we can do is to perform zero step and activate - * I-th constraint. - */ - *stpmax = 0; - *cidx = n+i; - } - } - } -} - - -/************************************************************************* -This subroutine moves current point to XN, in the direction previously -explored with SASExploreDirection() function. - -Step may activate one constraint. It is assumed than XN is approximately -feasible (small error as large as several ulps is possible). Strict -feasibility with respect to bound constraints is enforced during -activation, feasibility with respect to general linear constraints is not -enforced. - -INPUT PARAMETERS: - S - active set object - XN - new point. - NeedAct - True in case one constraint needs activation - CIdx - index of constraint, in [0,N+NEC+NIC). - Ignored if NeedAct is false. - This value is calculated by SASExploreDirection(). - CVal - for CIdx in [0,N) this field stores value which is - assigned to XC[CIdx] during activation. CVal is ignored in - other cases. - This value is calculated by SASExploreDirection(). - -OUTPUT PARAMETERS: - S - current point and list of active constraints are changed. - -RESULT: - >0, in case at least one inactive non-candidate constraint was activated - =0, in case only "candidate" constraints were activated - <0, in case no constraints were activated by the step - -NOTE: in general case State.XC<>XN because activation of constraints may - slightly change current point (to enforce feasibility). - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -ae_int_t sasmoveto(sactiveset* state, - /* Real */ ae_vector* xn, - ae_bool needact, - ae_int_t cidx, - double cval, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nec; - ae_int_t nic; - ae_int_t i; - ae_bool wasactivation; - ae_int_t result; - - - ae_assert(state->algostate==1, "SASMoveTo: is not in optimization mode", _state); - n = state->n; - nec = state->nec; - nic = state->nic; - - /* - * Save previous state, update current point - */ - rvectorsetlengthatleast(&state->mtx, n, _state); - ivectorsetlengthatleast(&state->mtas, n+nec+nic, _state); - for(i=0; i<=n-1; i++) - { - state->mtx.ptr.p_double[i] = state->xc.ptr.p_double[i]; - state->xc.ptr.p_double[i] = xn->ptr.p_double[i]; - } - for(i=0; i<=n+nec+nic-1; i++) - { - state->mtas.ptr.p_int[i] = state->activeset.ptr.p_int[i]; - } - - /* - * Activate constraints - */ - wasactivation = ae_false; - if( needact ) - { - - /* - * Activation - */ - ae_assert(cidx>=0&&cidxxc.ptr.p_double[cidx] = cval; - } - state->activeset.ptr.p_int[cidx] = 1; - wasactivation = ae_true; - } - for(i=0; i<=n-1; i++) - { - - /* - * Post-check (some constraints may be activated because of numerical errors) - */ - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]) ) - { - state->xc.ptr.p_double[i] = state->bndl.ptr.p_double[i]; - state->activeset.ptr.p_int[i] = 1; - wasactivation = ae_true; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->xc.ptr.p_double[i] = state->bndu.ptr.p_double[i]; - state->activeset.ptr.p_int[i] = 1; - wasactivation = ae_true; - } - } - - /* - * Determine return status: - * * -1 in case no constraints were activated - * * 0 in case only "candidate" constraints were activated - * * +1 in case at least one "non-candidate" constraint was activated - */ - if( wasactivation ) - { - - /* - * Step activated one/several constraints, but sometimes it is spurious - * activation - RecalculateConstraints() tells us that constraint is - * inactive (negative Largrange multiplier), but step activates it - * because of numerical noise. - * - * This block of code checks whether step activated truly new constraints - * (ones which were not in the active set at the solution): - * - * * for non-boundary constraint it is enough to check that previous value - * of ActiveSet[i] is negative (=far from boundary), and new one is - * positive (=we are at the boundary, constraint is activated). - * - * * for boundary constraints previous criterion won't work. Each variable - * has two constraints, and simply checking their status is not enough - - * we have to correctly identify cases when we leave one boundary - * (PrevActiveSet[i]=0) and move to another boundary (ActiveSet[i]>0). - * Such cases can be identified if we compare previous X with new X. - * - * In case only "candidate" constraints were activated, result variable - * is set to 0. In case at least one new constraint was activated, result - * is set to 1. - */ - result = 0; - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]>0&&ae_fp_neq(state->xc.ptr.p_double[i],state->mtx.ptr.p_double[i]) ) - { - result = 1; - } - } - for(i=n; i<=n+state->nec+state->nic-1; i++) - { - if( state->mtas.ptr.p_int[i]<0&&state->activeset.ptr.p_int[i]>0 ) - { - result = 1; - } - } - } - else - { - - /* - * No activation, return -1 - */ - result = -1; - } - - /* - * Invalidate basis - */ - state->basisisready = ae_false; - return result; -} - - -/************************************************************************* -This subroutine performs immediate activation of one constraint: -* "immediate" means that we do not have to move to activate it -* in case boundary constraint is activated, we enforce current point to be - exactly at the boundary - -INPUT PARAMETERS: - S - active set object - CIdx - index of constraint, in [0,N+NEC+NIC). - This value is calculated by SASExploreDirection(). - CVal - for CIdx in [0,N) this field stores value which is - assigned to XC[CIdx] during activation. CVal is ignored in - other cases. - This value is calculated by SASExploreDirection(). - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasimmediateactivation(sactiveset* state, - ae_int_t cidx, - double cval, - ae_state *_state) -{ - - - ae_assert(state->algostate==1, "SASMoveTo: is not in optimization mode", _state); - if( cidxn ) - { - state->xc.ptr.p_double[cidx] = cval; - } - state->activeset.ptr.p_int[cidx] = 1; - state->basisisready = ae_false; -} - - -/************************************************************************* -This subroutine calculates descent direction subject to current active set. - -INPUT PARAMETERS: - S - active set object - G - array[N], gradient - D - possibly prealocated buffer; - automatically resized if needed. - -OUTPUT PARAMETERS: - D - descent direction projected onto current active set. - Components of D which correspond to active boundary - constraints are forced to be exactly zero. - In case D is non-zero, it is normalized to have unit norm. - -NOTE: in case active set has N active constraints (or more), descent - direction is forced to be exactly zero. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasconstraineddescent(sactiveset* state, - /* Real */ ae_vector* g, - /* Real */ ae_vector* d, - ae_state *_state) -{ - - - ae_assert(state->algostate==1, "SASConstrainedDescent: is not in optimization mode", _state); - sasrebuildbasis(state, _state); - sactivesets_constraineddescent(state, g, &state->unitdiagonal, &state->ibasis, ae_true, d, _state); -} - - -/************************************************************************* -This subroutine calculates preconditioned descent direction subject to -current active set. - -INPUT PARAMETERS: - S - active set object - G - array[N], gradient - D - possibly prealocated buffer; - automatically resized if needed. - -OUTPUT PARAMETERS: - D - descent direction projected onto current active set. - Components of D which correspond to active boundary - constraints are forced to be exactly zero. - In case D is non-zero, it is normalized to have unit norm. - -NOTE: in case active set has N active constraints (or more), descent - direction is forced to be exactly zero. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasconstraineddescentprec(sactiveset* state, - /* Real */ ae_vector* g, - /* Real */ ae_vector* d, - ae_state *_state) -{ - - - ae_assert(state->algostate==1, "SASConstrainedDescentPrec: is not in optimization mode", _state); - sasrebuildbasis(state, _state); - sactivesets_constraineddescent(state, g, &state->h, &state->pbasis, ae_true, d, _state); -} - - -/************************************************************************* -This subroutine calculates product of direction vector and preconditioner -multiplied subject to current active set. - -INPUT PARAMETERS: - S - active set object - D - array[N], direction - -OUTPUT PARAMETERS: - D - preconditioned direction projected onto current active set. - Components of D which correspond to active boundary - constraints are forced to be exactly zero. - -NOTE: in case active set has N active constraints (or more), descent - direction is forced to be exactly zero. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasconstraineddirection(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(state->algostate==1, "SASConstrainedAntigradientPrec: is not in optimization mode", _state); - sasrebuildbasis(state, _state); - sactivesets_constraineddescent(state, d, &state->unitdiagonal, &state->ibasis, ae_false, &state->cdtmp, _state); - for(i=0; i<=state->n-1; i++) - { - d->ptr.p_double[i] = -state->cdtmp.ptr.p_double[i]; - } -} - - -/************************************************************************* -This subroutine calculates product of direction vector and preconditioner -multiplied subject to current active set. - -INPUT PARAMETERS: - S - active set object - D - array[N], direction - -OUTPUT PARAMETERS: - D - preconditioned direction projected onto current active set. - Components of D which correspond to active boundary - constraints are forced to be exactly zero. - -NOTE: in case active set has N active constraints (or more), descent - direction is forced to be exactly zero. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasconstraineddirectionprec(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(state->algostate==1, "SASConstrainedAntigradientPrec: is not in optimization mode", _state); - sasrebuildbasis(state, _state); - sactivesets_constraineddescent(state, d, &state->h, &state->pbasis, ae_false, &state->cdtmp, _state); - for(i=0; i<=state->n-1; i++) - { - d->ptr.p_double[i] = -state->cdtmp.ptr.p_double[i]; - } -} - - -/************************************************************************* -This subroutine performs correction of some (possibly infeasible) point -with respect to a) current active set, b) all boundary constraints, both -active and inactive: - -0) we calculate L1 penalty term for violation of active linear constraints - (one which is returned by SASActiveLCPenalty1() function). -1) first, it performs projection (orthogonal with respect to scale matrix - S) of X into current active set: X -> X1. -2) next, we perform projection with respect to ALL boundary constraints - which are violated at X1: X1 -> X2. -3) X is replaced by X2. - -The idea is that this function can preserve and enforce feasibility during -optimization, and additional penalty parameter can be used to prevent algo -from leaving feasible set because of rounding errors. - -INPUT PARAMETERS: - S - active set object - X - array[N], candidate point - -OUTPUT PARAMETERS: - X - "improved" candidate point: - a) feasible with respect to all boundary constraints - b) feasibility with respect to active set is retained at - good level. - Penalty - penalty term, which can be added to function value if user - wants to penalize violation of constraints (recommended). - -NOTE: this function is not intended to find exact projection (i.e. best - approximation) of X into feasible set. It just improves situation a - bit. - Regular use of this function will help you to retain feasibility - - if you already have something to start with and constrain your - steps is such way that the only source of infeasibility are roundoff - errors. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sascorrection(sactiveset* state, - /* Real */ ae_vector* x, - double* penalty, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - double v; - - *penalty = 0; - - ae_assert(state->algostate==1, "SASCorrection: is not in optimization mode", _state); - sasrebuildbasis(state, _state); - n = state->n; - rvectorsetlengthatleast(&state->corrtmp, n, _state); - - /* - * Calculate penalty term. - */ - *penalty = sasactivelcpenalty1(state, x, _state); - - /* - * Perform projection 1. - * - * This projecton is given by: - * - * x_proj = x - S*S*As'*(As*x-b) - * - * where x is original x before projection, S is a scale matrix, - * As is a matrix of equality constraints (active set) which were - * orthogonalized with respect to inner product given by S (i.e. we - * have As*S*S'*As'=I), b is a right part of the orthogonalized - * constraints. - * - * NOTE: you can verify that x_proj is strictly feasible w.r.t. - * active set by multiplying it by As - you will get - * As*x_proj = As*x - As*x + b = b. - * - * This formula for projection can be obtained by solving - * following minimization problem. - * - * min ||inv(S)*(x_proj-x)||^2 s.t. As*x_proj=b - * - */ - ae_v_move(&state->corrtmp.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=state->basissize-1; i++) - { - v = -state->sbasis.ptr.pp_double[i][n]; - for(j=0; j<=n-1; j++) - { - v = v+state->sbasis.ptr.pp_double[i][j]*state->corrtmp.ptr.p_double[j]; - } - for(j=0; j<=n-1; j++) - { - state->corrtmp.ptr.p_double[j] = state->corrtmp.ptr.p_double[j]-v*state->sbasis.ptr.pp_double[i][j]*ae_sqr(state->s.ptr.p_double[j], _state); - } - } - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]>0 ) - { - state->corrtmp.ptr.p_double[i] = state->xc.ptr.p_double[i]; - } - } - - /* - * Perform projection 2 - */ - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = state->corrtmp.ptr.p_double[i]; - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(x->ptr.p_double[i],state->bndl.ptr.p_double[i]) ) - { - x->ptr.p_double[i] = state->bndl.ptr.p_double[i]; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(x->ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - x->ptr.p_double[i] = state->bndu.ptr.p_double[i]; - } - } -} - - -/************************************************************************* -This subroutine returns L1 penalty for violation of active general linear -constraints (violation of boundary or inactive linear constraints is not -added to penalty). - -Penalty term is equal to: - - Penalty = SUM( Abs((C_i*x-R_i)/Alpha_i) ) - -Here: -* summation is performed for I=0...NEC+NIC-1, ActiveSet[N+I]>0 - (only for rows of CLEIC which are in active set) -* C_i is I-th row of CLEIC -* R_i is corresponding right part -* S is a scale matrix -* Alpha_i = ||S*C_i|| - is a scaling coefficient which "normalizes" - I-th summation term according to its scale. - -INPUT PARAMETERS: - S - active set object - X - array[N], candidate point - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -double sasactivelcpenalty1(sactiveset* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - ae_int_t nec; - ae_int_t nic; - double v; - double alpha; - double p; - double result; - - - ae_assert(state->algostate==1, "SASActiveLCPenalty1: is not in optimization mode", _state); - sasrebuildbasis(state, _state); - n = state->n; - nec = state->nec; - nic = state->nic; - - /* - * Calculate penalty term. - */ - result = 0; - for(i=0; i<=nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[n+i]>0 ) - { - alpha = 0; - p = -state->cleic.ptr.pp_double[i][n]; - for(j=0; j<=n-1; j++) - { - v = state->cleic.ptr.pp_double[i][j]; - p = p+v*x->ptr.p_double[j]; - alpha = alpha+ae_sqr(v*state->s.ptr.p_double[j], _state); - } - alpha = ae_sqrt(alpha, _state); - if( ae_fp_neq(alpha,0) ) - { - result = result+ae_fabs(p/alpha, _state); - } - } - } - return result; -} - - -/************************************************************************* -This subroutine calculates scaled norm of vector after projection onto -subspace of active constraints. Most often this function is used to test -stopping conditions. - -INPUT PARAMETERS: - S - active set object - D - vector whose norm is calculated - -RESULT: - Vector norm (after projection and scaling) - -NOTE: projection is performed first, scaling is performed after projection - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -double sasscaledconstrainednorm(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - double v; - double result; - - - ae_assert(state->algostate==1, "SASMoveTo: is not in optimization mode", _state); - n = state->n; - rvectorsetlengthatleast(&state->scntmp, n, _state); - - /* - * Prepare basis (if needed) - */ - sasrebuildbasis(state, _state); - - /* - * Calculate descent direction - */ - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]>0 ) - { - state->scntmp.ptr.p_double[i] = 0; - } - else - { - state->scntmp.ptr.p_double[i] = d->ptr.p_double[i]; - } - } - for(i=0; i<=state->basissize-1; i++) - { - v = ae_v_dotproduct(&state->ibasis.ptr.pp_double[i][0], 1, &state->scntmp.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_subd(&state->scntmp.ptr.p_double[0], 1, &state->ibasis.ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - } - v = 0.0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->s.ptr.p_double[i]*state->scntmp.ptr.p_double[i], _state); - } - result = ae_sqrt(v, _state); - return result; -} - - -/************************************************************************* -This subroutine turns off optimization mode. - -INPUT PARAMETERS: - S - active set object - -OUTPUT PARAMETERS: - S - state is changed - -NOTE: this function can be called many times for optimizer which was - already stopped. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -void sasstopoptimization(sactiveset* state, ae_state *_state) -{ - - - state->algostate = 0; -} - - -/************************************************************************* -This function recalculates constraints - activates and deactivates them -according to gradient value at current point. Algorithm assumes that we -want to make steepest descent step from current point; constraints are -activated and deactivated in such way that we won't violate any constraint -by steepest descent step. - -After call to this function active set is ready to try steepest descent -step (SASDescentDirection-SASExploreDirection-SASMoveTo). - -Only already "active" and "candidate" elements of ActiveSet are examined; -constraints which are not active are not examined. - -INPUT PARAMETERS: - State - active set object - GC - array[N], gradient at XC - -OUTPUT PARAMETERS: - State - active set object, with new set of constraint - - -- ALGLIB -- - Copyright 26.09.2012 by Bochkanov Sergey -*************************************************************************/ -void sasreactivateconstraints(sactiveset* state, - /* Real */ ae_vector* gc, - ae_state *_state) -{ - - - ae_assert(state->algostate==1, "SASReactivateConstraints: must be in optimization mode", _state); - sactivesets_reactivateconstraints(state, gc, &state->unitdiagonal, _state); -} - - -/************************************************************************* -This function recalculates constraints - activates and deactivates them -according to gradient value at current point. - -Algorithm assumes that we want to make Quasi-Newton step from current -point with diagonal Quasi-Newton matrix H. Constraints are activated and -deactivated in such way that we won't violate any constraint by step. - -After call to this function active set is ready to try preconditioned -steepest descent step (SASDescentDirection-SASExploreDirection-SASMoveTo). - -Only already "active" and "candidate" elements of ActiveSet are examined; -constraints which are not active are not examined. - -INPUT PARAMETERS: - State - active set object - GC - array[N], gradient at XC - -OUTPUT PARAMETERS: - State - active set object, with new set of constraint - - -- ALGLIB -- - Copyright 26.09.2012 by Bochkanov Sergey -*************************************************************************/ -void sasreactivateconstraintsprec(sactiveset* state, - /* Real */ ae_vector* gc, - ae_state *_state) -{ - - - ae_assert(state->algostate==1, "SASReactivateConstraintsPrec: must be in optimization mode", _state); - sactivesets_reactivateconstraints(state, gc, &state->h, _state); -} - - -/************************************************************************* -This function builds three orthonormal basises for current active set: -* P-orthogonal one, which is orthogonalized with inner product - (x,y) = x'*P*y, where P=inv(H) is current preconditioner -* S-orthogonal one, which is orthogonalized with inner product - (x,y) = x'*S'*S*y, where S is diagonal scaling matrix -* I-orthogonal one, which is orthogonalized with standard dot product - -NOTE: all sets of orthogonal vectors are guaranteed to have same size. - P-orthogonal basis is built first, I/S-orthogonal basises are forced - to have same number of vectors as P-orthogonal one (padded by zero - vectors if needed). - -NOTE: this function tracks changes in active set; first call will result - in reorthogonalization - -INPUT PARAMETERS: - State - active set object - H - diagonal preconditioner, H[i]>0 - -OUTPUT PARAMETERS: - State - active set object with new basis - - -- ALGLIB -- - Copyright 20.06.2012 by Bochkanov Sergey -*************************************************************************/ -void sasrebuildbasis(sactiveset* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t nec; - ae_int_t nic; - ae_int_t i; - ae_int_t j; - ae_int_t t; - ae_int_t nactivelin; - ae_int_t nactivebnd; - double v; - double vmax; - ae_int_t kmax; - - - if( state->basisisready ) - { - return; - } - n = state->n; - nec = state->nec; - nic = state->nic; - rmatrixsetlengthatleast(&state->tmpbasis, nec+nic, n+1, _state); - state->basissize = 0; - state->basisisready = ae_true; - - /* - * Determine number of active boundary and non-boundary - * constraints, move them to TmpBasis. Quick exit if no - * non-boundary constraints were detected. - */ - nactivelin = 0; - nactivebnd = 0; - for(i=0; i<=nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[n+i]>0 ) - { - nactivelin = nactivelin+1; - } - } - for(j=0; j<=n-1; j++) - { - if( state->activeset.ptr.p_int[j]>0 ) - { - nactivebnd = nactivebnd+1; - } - } - if( nactivelin==0 ) - { - return; - } - - /* - * Orthogonalize linear constraints (inner product is given by preconditioner) - * with respect to each other and boundary ones: - * * normalize all constraints - * * orthogonalize with respect to boundary ones - * * repeat: - * * if basisSize+nactivebnd=n - TERMINATE - * * choose largest row from TmpBasis - * * if row norm is too small - TERMINATE - * * add row to basis, normalize - * * remove from TmpBasis, orthogonalize other constraints with respect to this one - */ - nactivelin = 0; - for(i=0; i<=nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[n+i]>0 ) - { - ae_v_move(&state->tmpbasis.ptr.pp_double[nactivelin][0], 1, &state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n)); - nactivelin = nactivelin+1; - } - } - for(i=0; i<=nactivelin-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->tmpbasis.ptr.pp_double[i][j], _state)/state->h.ptr.p_double[j]; - } - if( ae_fp_greater(v,0) ) - { - v = 1/ae_sqrt(v, _state); - for(j=0; j<=n; j++) - { - state->tmpbasis.ptr.pp_double[i][j] = state->tmpbasis.ptr.pp_double[i][j]*v; - } - } - } - for(j=0; j<=n-1; j++) - { - if( state->activeset.ptr.p_int[j]>0 ) - { - for(i=0; i<=nactivelin-1; i++) - { - state->tmpbasis.ptr.pp_double[i][n] = state->tmpbasis.ptr.pp_double[i][n]-state->tmpbasis.ptr.pp_double[i][j]*state->xc.ptr.p_double[j]; - state->tmpbasis.ptr.pp_double[i][j] = 0.0; - } - } - } - while(state->basissize+nactivebndtmpbasis.ptr.pp_double[i][j], _state)/state->h.ptr.p_double[j]; - } - v = ae_sqrt(v, _state); - if( ae_fp_greater(v,vmax) ) - { - vmax = v; - kmax = i; - } - } - if( ae_fp_less(vmax,1.0E4*ae_machineepsilon) ) - { - break; - } - v = 1/vmax; - ae_v_moved(&state->pbasis.ptr.pp_double[state->basissize][0], 1, &state->tmpbasis.ptr.pp_double[kmax][0], 1, ae_v_len(0,n), v); - state->basissize = state->basissize+1; - - /* - * Reorthogonalize other vectors with respect to chosen one. - * Remove it from the array. - */ - for(i=0; i<=nactivelin-1; i++) - { - if( i!=kmax ) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+state->pbasis.ptr.pp_double[state->basissize-1][j]*state->tmpbasis.ptr.pp_double[i][j]/state->h.ptr.p_double[j]; - } - ae_v_subd(&state->tmpbasis.ptr.pp_double[i][0], 1, &state->pbasis.ptr.pp_double[state->basissize-1][0], 1, ae_v_len(0,n), v); - } - } - for(j=0; j<=n; j++) - { - state->tmpbasis.ptr.pp_double[kmax][j] = 0; - } - } - - /* - * Orthogonalize linear constraints using traditional dot product - * with respect to each other and boundary ones. - * - * NOTE: we force basis size to be equal to one which was computed - * at the previous step, with preconditioner-based inner product. - */ - nactivelin = 0; - for(i=0; i<=nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[n+i]>0 ) - { - ae_v_move(&state->tmpbasis.ptr.pp_double[nactivelin][0], 1, &state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n)); - nactivelin = nactivelin+1; - } - } - for(i=0; i<=nactivelin-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->tmpbasis.ptr.pp_double[i][j], _state); - } - if( ae_fp_greater(v,0) ) - { - v = 1/ae_sqrt(v, _state); - for(j=0; j<=n; j++) - { - state->tmpbasis.ptr.pp_double[i][j] = state->tmpbasis.ptr.pp_double[i][j]*v; - } - } - } - for(j=0; j<=n-1; j++) - { - if( state->activeset.ptr.p_int[j]>0 ) - { - for(i=0; i<=nactivelin-1; i++) - { - state->tmpbasis.ptr.pp_double[i][n] = state->tmpbasis.ptr.pp_double[i][n]-state->tmpbasis.ptr.pp_double[i][j]*state->xc.ptr.p_double[j]; - state->tmpbasis.ptr.pp_double[i][j] = 0.0; - } - } - } - for(t=0; t<=state->basissize-1; t++) - { - - /* - * Find largest vector, add to basis. - */ - vmax = -1; - kmax = -1; - for(i=0; i<=nactivelin-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->tmpbasis.ptr.pp_double[i][j], _state); - } - v = ae_sqrt(v, _state); - if( ae_fp_greater(v,vmax) ) - { - vmax = v; - kmax = i; - } - } - if( ae_fp_eq(vmax,0) ) - { - for(j=0; j<=n; j++) - { - state->ibasis.ptr.pp_double[t][j] = 0.0; - } - continue; - } - v = 1/vmax; - ae_v_moved(&state->ibasis.ptr.pp_double[t][0], 1, &state->tmpbasis.ptr.pp_double[kmax][0], 1, ae_v_len(0,n), v); - - /* - * Reorthogonalize other vectors with respect to chosen one. - * Remove it from the array. - */ - for(i=0; i<=nactivelin-1; i++) - { - if( i!=kmax ) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+state->ibasis.ptr.pp_double[t][j]*state->tmpbasis.ptr.pp_double[i][j]; - } - ae_v_subd(&state->tmpbasis.ptr.pp_double[i][0], 1, &state->ibasis.ptr.pp_double[t][0], 1, ae_v_len(0,n), v); - } - } - for(j=0; j<=n; j++) - { - state->tmpbasis.ptr.pp_double[kmax][j] = 0; - } - } - - /* - * Orthogonalize linear constraints using inner product given by - * scale matrix. - * - * NOTE: we force basis size to be equal to one which was computed - * with preconditioner-based inner product. - */ - nactivelin = 0; - for(i=0; i<=nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[n+i]>0 ) - { - ae_v_move(&state->tmpbasis.ptr.pp_double[nactivelin][0], 1, &state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n)); - nactivelin = nactivelin+1; - } - } - for(i=0; i<=nactivelin-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->tmpbasis.ptr.pp_double[i][j]*state->s.ptr.p_double[j], _state); - } - if( ae_fp_greater(v,0) ) - { - v = 1/ae_sqrt(v, _state); - for(j=0; j<=n; j++) - { - state->tmpbasis.ptr.pp_double[i][j] = state->tmpbasis.ptr.pp_double[i][j]*v; - } - } - } - for(j=0; j<=n-1; j++) - { - if( state->activeset.ptr.p_int[j]>0 ) - { - for(i=0; i<=nactivelin-1; i++) - { - state->tmpbasis.ptr.pp_double[i][n] = state->tmpbasis.ptr.pp_double[i][n]-state->tmpbasis.ptr.pp_double[i][j]*state->xc.ptr.p_double[j]; - state->tmpbasis.ptr.pp_double[i][j] = 0.0; - } - } - } - for(t=0; t<=state->basissize-1; t++) - { - - /* - * Find largest vector, add to basis. - */ - vmax = -1; - kmax = -1; - for(i=0; i<=nactivelin-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->tmpbasis.ptr.pp_double[i][j]*state->s.ptr.p_double[j], _state); - } - v = ae_sqrt(v, _state); - if( ae_fp_greater(v,vmax) ) - { - vmax = v; - kmax = i; - } - } - if( ae_fp_eq(vmax,0) ) - { - for(j=0; j<=n; j++) - { - state->sbasis.ptr.pp_double[t][j] = 0.0; - } - continue; - } - v = 1/vmax; - ae_v_moved(&state->sbasis.ptr.pp_double[t][0], 1, &state->tmpbasis.ptr.pp_double[kmax][0], 1, ae_v_len(0,n), v); - - /* - * Reorthogonalize other vectors with respect to chosen one. - * Remove it from the array. - */ - for(i=0; i<=nactivelin-1; i++) - { - if( i!=kmax ) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+state->sbasis.ptr.pp_double[t][j]*state->tmpbasis.ptr.pp_double[i][j]*ae_sqr(state->s.ptr.p_double[j], _state); - } - ae_v_subd(&state->tmpbasis.ptr.pp_double[i][0], 1, &state->sbasis.ptr.pp_double[t][0], 1, ae_v_len(0,n), v); - } - } - for(j=0; j<=n; j++) - { - state->tmpbasis.ptr.pp_double[kmax][j] = 0; - } - } -} - - -/************************************************************************* -This subroutine calculates preconditioned descent direction subject to -current active set. - -INPUT PARAMETERS: - State - active set object - G - array[N], gradient - H - array[N], Hessian matrix - HA - active constraints orthogonalized in such way - that HA*inv(H)*HA'= I. - Normalize- whether we need normalized descent or not - D - possibly preallocated buffer; automatically resized. - -OUTPUT PARAMETERS: - D - descent direction projected onto current active set. - Components of D which correspond to active boundary - constraints are forced to be exactly zero. - In case D is non-zero and Normalize is True, it is - normalized to have unit norm. - - -- ALGLIB -- - Copyright 21.12.2012 by Bochkanov Sergey -*************************************************************************/ -static void sactivesets_constraineddescent(sactiveset* state, - /* Real */ ae_vector* g, - /* Real */ ae_vector* h, - /* Real */ ae_matrix* ha, - ae_bool normalize, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - double v; - ae_int_t nactive; - - - ae_assert(state->algostate==1, "SAS: internal error in ConstrainedDescent() - not in optimization mode", _state); - ae_assert(state->basisisready, "SAS: internal error in ConstrainedDescent() - no basis", _state); - n = state->n; - rvectorsetlengthatleast(d, n, _state); - - /* - * Calculate preconditioned constrained descent direction: - * - * d := -inv(H)*( g - HA'*(HA*inv(H)*g) ) - * - * Formula above always gives direction which is orthogonal to rows of HA. - * You can verify it by multiplication of both sides by HA[i] (I-th row), - * taking into account that HA*inv(H)*HA'= I (by definition of HA - it is - * orthogonal basis with inner product given by inv(H)). - */ - nactive = 0; - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]>0 ) - { - d->ptr.p_double[i] = 0; - nactive = nactive+1; - } - else - { - d->ptr.p_double[i] = g->ptr.p_double[i]; - } - } - for(i=0; i<=state->basissize-1; i++) - { - v = 0.0; - for(j=0; j<=n-1; j++) - { - v = v+ha->ptr.pp_double[i][j]*d->ptr.p_double[j]/h->ptr.p_double[j]; - } - ae_v_subd(&d->ptr.p_double[0], 1, &ha->ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - nactive = nactive+1; - } - v = 0.0; - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]>0 ) - { - d->ptr.p_double[i] = 0; - } - else - { - d->ptr.p_double[i] = -d->ptr.p_double[i]/h->ptr.p_double[i]; - v = v+ae_sqr(d->ptr.p_double[i], _state); - } - } - v = ae_sqrt(v, _state); - if( nactive>=n ) - { - v = 0; - for(i=0; i<=n-1; i++) - { - d->ptr.p_double[i] = 0; - } - } - if( normalize&&ae_fp_greater(v,0) ) - { - for(i=0; i<=n-1; i++) - { - d->ptr.p_double[i] = d->ptr.p_double[i]/v; - } - } -} - - -/************************************************************************* -This function recalculates constraints - activates and deactivates them -according to gradient value at current point. - -Algorithm assumes that we want to make Quasi-Newton step from current -point with diagonal Quasi-Newton matrix H. Constraints are activated and -deactivated in such way that we won't violate any constraint by step. - -Only already "active" and "candidate" elements of ActiveSet are examined; -constraints which are not active are not examined. - -INPUT PARAMETERS: - State - active set object - GC - array[N], gradient at XC - H - array[N], Hessian matrix - -OUTPUT PARAMETERS: - State - active set object, with new set of constraint - - -- ALGLIB -- - Copyright 26.09.2012 by Bochkanov Sergey -*************************************************************************/ -static void sactivesets_reactivateconstraints(sactiveset* state, - /* Real */ ae_vector* gc, - /* Real */ ae_vector* h, - ae_state *_state) -{ - ae_int_t n; - ae_int_t nec; - ae_int_t nic; - ae_int_t i; - ae_int_t j; - ae_int_t idx0; - ae_int_t idx1; - double v; - ae_int_t nactivebnd; - ae_int_t nactivelin; - ae_int_t nactiveconstraints; - double rowscale; - - - ae_assert(state->algostate==1, "SASReactivateConstraintsPrec: must be in optimization mode", _state); - - /* - * Prepare - */ - n = state->n; - nec = state->nec; - nic = state->nic; - state->basisisready = ae_false; - - /* - * Handle important special case - no linear constraints, - * only boundary constraints are present - */ - if( nec+nic==0 ) - { - for(i=0; i<=n-1; i++) - { - if( (state->hasbndl.ptr.p_bool[i]&&state->hasbndu.ptr.p_bool[i])&&ae_fp_eq(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - if( (state->hasbndl.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]))&&ae_fp_greater_eq(gc->ptr.p_double[i],0) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - if( (state->hasbndu.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i]))&&ae_fp_less_eq(gc->ptr.p_double[i],0) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - state->activeset.ptr.p_int[i] = -1; - } - return; - } - - /* - * General case. - * Allocate temporaries. - */ - rvectorsetlengthatleast(&state->rctmpg, n, _state); - rvectorsetlengthatleast(&state->rctmprightpart, n, _state); - rvectorsetlengthatleast(&state->rctmps, n, _state); - rmatrixsetlengthatleast(&state->rctmpdense0, n, nec+nic, _state); - rmatrixsetlengthatleast(&state->rctmpdense1, n, nec+nic, _state); - bvectorsetlengthatleast(&state->rctmpisequality, n+nec+nic, _state); - ivectorsetlengthatleast(&state->rctmpconstraintidx, n+nec+nic, _state); - - /* - * Calculate descent direction - */ - ae_v_moveneg(&state->rctmpg.ptr.p_double[0], 1, &gc->ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Determine candidates to the active set. - * - * After this block constraints become either "inactive" (ActiveSet[i]<0) - * or "candidates" (ActiveSet[i]=0). Previously active constraints always - * become "candidates". - */ - for(i=0; i<=n+nec+nic-1; i++) - { - if( state->activeset.ptr.p_int[i]>0 ) - { - state->activeset.ptr.p_int[i] = 0; - } - else - { - state->activeset.ptr.p_int[i] = -1; - } - } - nactiveconstraints = 0; - nactivebnd = 0; - nactivelin = 0; - for(i=0; i<=n-1; i++) - { - - /* - * Activate boundary constraints: - * * copy constraint index to RCTmpConstraintIdx - * * set corresponding element of ActiveSet[] to "candidate" - * * fill RCTmpS by either +1 (lower bound) or -1 (upper bound) - * * set RCTmpIsEquality to False (BndLhasbndl.ptr.p_bool[i]&&state->hasbndu.ptr.p_bool[i])&&ae_fp_eq(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - - /* - * Equality constraint is activated - */ - state->rctmpconstraintidx.ptr.p_int[nactiveconstraints] = i; - state->activeset.ptr.p_int[i] = 0; - state->rctmps.ptr.p_double[i] = 1.0; - state->rctmpisequality.ptr.p_bool[nactiveconstraints] = ae_true; - nactiveconstraints = nactiveconstraints+1; - nactivebnd = nactivebnd+1; - continue; - } - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]) ) - { - - /* - * Lower bound is activated - */ - state->rctmpconstraintidx.ptr.p_int[nactiveconstraints] = i; - state->activeset.ptr.p_int[i] = 0; - state->rctmps.ptr.p_double[i] = -1.0; - state->rctmpisequality.ptr.p_bool[nactiveconstraints] = ae_false; - nactiveconstraints = nactiveconstraints+1; - nactivebnd = nactivebnd+1; - continue; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - - /* - * Upper bound is activated - */ - state->rctmpconstraintidx.ptr.p_int[nactiveconstraints] = i; - state->activeset.ptr.p_int[i] = 0; - state->rctmps.ptr.p_double[i] = 1.0; - state->rctmpisequality.ptr.p_bool[nactiveconstraints] = ae_false; - nactiveconstraints = nactiveconstraints+1; - nactivebnd = nactivebnd+1; - continue; - } - } - for(i=0; i<=nec+nic-1; i++) - { - if( i>=nec ) - { - - /* - * Inequality constraints are skipped if we too far away from - * the boundary. - */ - rowscale = 0.0; - v = -state->cleic.ptr.pp_double[i][n]; - for(j=0; j<=n-1; j++) - { - v = v+state->cleic.ptr.pp_double[i][j]*state->xc.ptr.p_double[j]; - rowscale = ae_maxreal(rowscale, ae_fabs(state->cleic.ptr.pp_double[i][j]*state->s.ptr.p_double[j], _state), _state); - } - if( ae_fp_less_eq(v,-1.0E5*ae_machineepsilon*rowscale) ) - { - - /* - * NOTE: it is important to check for non-strict inequality - * because we have to correctly handle zero constraint - * 0*x<=0 - */ - continue; - } - } - ae_v_move(&state->rctmpdense0.ptr.pp_double[0][nactivelin], state->rctmpdense0.stride, &state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - state->rctmpconstraintidx.ptr.p_int[nactiveconstraints] = n+i; - state->activeset.ptr.p_int[n+i] = 0; - state->rctmpisequality.ptr.p_bool[nactiveconstraints] = ihasbndl.ptr.p_bool[i]&&state->hasbndu.ptr.p_bool[i])&&ae_fp_eq(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - if( (state->hasbndl.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndl.ptr.p_double[i]))&&ae_fp_greater_eq(gc->ptr.p_double[i],0) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - if( (state->hasbndu.ptr.p_bool[i]&&ae_fp_eq(state->xc.ptr.p_double[i],state->bndu.ptr.p_double[i]))&&ae_fp_less_eq(gc->ptr.p_double[i],0) ) - { - state->activeset.ptr.p_int[i] = 1; - continue; - } - } - return; - } - - /* - * General case. - * - * APPROACH TO CONSTRAINTS ACTIVATION/DEACTIVATION - * - * We have NActiveConstraints "candidates": NActiveBnd boundary candidates, - * NActiveLin linear candidates. Indexes of boundary constraints are stored - * in RCTmpConstraintIdx[0:NActiveBnd-1], indexes of linear ones are stored - * in RCTmpConstraintIdx[NActiveBnd:NActiveBnd+NActiveLin-1]. Some of the - * constraints are equality ones, some are inequality - as specified by - * RCTmpIsEquality[i]. - * - * Now we have to determine active subset of "candidates" set. In order to - * do so we solve following constrained minimization problem: - * ( )^2 - * min ( SUM(lambda[i]*A[i]) + G ) - * ( ) - * Here: - * * G is a gradient (column vector) - * * A[i] is a column vector, linear (left) part of I-th constraint. - * I=0..NActiveConstraints-1, first NActiveBnd elements of A are just - * subset of identity matrix (boundary constraints), next NActiveLin - * elements are subset of rows of the matrix of general linear constraints. - * * lambda[i] is a Lagrange multiplier corresponding to I-th constraint - * - * NOTE: for preconditioned setting A is replaced by A*H^(-0.5), G is - * replaced by G*H^(-0.5). We apply this scaling at the last stage, - * before passing data to NNLS solver. - * - * Minimization is performed subject to non-negativity constraints on - * lambda[i] corresponding to inequality constraints. Inequality constraints - * which correspond to non-zero lambda are activated, equality constraints - * are always considered active. - * - * Informally speaking, we "decompose" descent direction -G and represent - * it as sum of constraint vectors and "residual" part (which is equal to - * the actual descent direction subject to constraints). - * - * SOLUTION OF THE NNLS PROBLEM - * - * We solve this optimization problem with Non-Negative Least Squares solver, - * which can efficiently solve least squares problems of the form - * - * ( [ I | AU ] )^2 - * min ( [ | ]*x-b ) s.t. non-negativity constraints on some x[i] - * ( [ 0 | AL ] ) - * - * In order to use this solver we have to rearrange rows of A[] and G in - * such way that first NActiveBnd columns of A store identity matrix (before - * sorting non-zero elements are randomly distributed in the first NActiveBnd - * columns of A, during sorting we move them to first NActiveBnd rows). - * - * Then we create instance of NNLS solver (we reuse instance left from the - * previous run of the optimization problem) and solve NNLS problem. - */ - idx0 = 0; - idx1 = nactivebnd; - for(i=0; i<=n-1; i++) - { - if( state->activeset.ptr.p_int[i]>=0 ) - { - v = 1/ae_sqrt(h->ptr.p_double[i], _state); - for(j=0; j<=nactivelin-1; j++) - { - state->rctmpdense1.ptr.pp_double[idx0][j] = state->rctmpdense0.ptr.pp_double[i][j]/state->rctmps.ptr.p_double[i]*v; - } - state->rctmprightpart.ptr.p_double[idx0] = state->rctmpg.ptr.p_double[i]/state->rctmps.ptr.p_double[i]*v; - idx0 = idx0+1; - } - else - { - v = 1/ae_sqrt(h->ptr.p_double[i], _state); - for(j=0; j<=nactivelin-1; j++) - { - state->rctmpdense1.ptr.pp_double[idx1][j] = state->rctmpdense0.ptr.pp_double[i][j]*v; - } - state->rctmprightpart.ptr.p_double[idx1] = state->rctmpg.ptr.p_double[i]*v; - idx1 = idx1+1; - } - } - snnlsinit(n, nec+nic, n, &state->solver, _state); - snnlssetproblem(&state->solver, &state->rctmpdense1, &state->rctmprightpart, nactivebnd, nactiveconstraints-nactivebnd, n, _state); - for(i=0; i<=nactiveconstraints-1; i++) - { - if( state->rctmpisequality.ptr.p_bool[i] ) - { - snnlsdropnnc(&state->solver, i, _state); - } - } - snnlssolve(&state->solver, &state->rctmplambdas, _state); - - /* - * After solution of the problem we activate equality constraints (always active) - * and inequality constraints with non-zero Lagrange multipliers. Then we reorthogonalize - * active constraints. - */ - for(i=0; i<=nactiveconstraints-1; i++) - { - if( state->rctmpisequality.ptr.p_bool[i]||ae_fp_greater(state->rctmplambdas.ptr.p_double[i],0) ) - { - state->activeset.ptr.p_int[state->rctmpconstraintidx.ptr.p_int[i]] = 1; - } - else - { - state->activeset.ptr.p_int[state->rctmpconstraintidx.ptr.p_int[i]] = 0; - } - } - sasrebuildbasis(state, _state); -} - - -ae_bool _sactiveset_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - sactiveset *p = (sactiveset*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->xc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->h, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->activeset, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->sbasis, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->pbasis, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->ibasis, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hasbndl, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hasbndu, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->cleic, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mtx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mtas, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cdtmp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->corrtmp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->unitdiagonal, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_snnlssolver_init(&p->solver, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->scntmp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpfeas, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmpm0, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmps, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmpg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmprightpart, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->rctmpdense0, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->rctmpdense1, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmpisequality, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmpconstraintidx, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmplambdas, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->tmpbasis, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _sactiveset_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - sactiveset *dst = (sactiveset*)_dst; - sactiveset *src = (sactiveset*)_src; - dst->n = src->n; - dst->algostate = src->algostate; - if( !ae_vector_init_copy(&dst->xc, &src->xc, _state, make_automatic) ) - return ae_false; - dst->hasxc = src->hasxc; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->h, &src->h, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->activeset, &src->activeset, _state, make_automatic) ) - return ae_false; - dst->basisisready = src->basisisready; - if( !ae_matrix_init_copy(&dst->sbasis, &src->sbasis, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->pbasis, &src->pbasis, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->ibasis, &src->ibasis, _state, make_automatic) ) - return ae_false; - dst->basissize = src->basissize; - dst->constraintschanged = src->constraintschanged; - if( !ae_vector_init_copy(&dst->hasbndl, &src->hasbndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->hasbndu, &src->hasbndu, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->cleic, &src->cleic, _state, make_automatic) ) - return ae_false; - dst->nec = src->nec; - dst->nic = src->nic; - if( !ae_vector_init_copy(&dst->mtx, &src->mtx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mtas, &src->mtas, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cdtmp, &src->cdtmp, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->corrtmp, &src->corrtmp, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->unitdiagonal, &src->unitdiagonal, _state, make_automatic) ) - return ae_false; - if( !_snnlssolver_init_copy(&dst->solver, &src->solver, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->scntmp, &src->scntmp, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp0, &src->tmp0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpfeas, &src->tmpfeas, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmpm0, &src->tmpm0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmps, &src->rctmps, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmpg, &src->rctmpg, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmprightpart, &src->rctmprightpart, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->rctmpdense0, &src->rctmpdense0, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->rctmpdense1, &src->rctmpdense1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmpisequality, &src->rctmpisequality, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmpconstraintidx, &src->rctmpconstraintidx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmplambdas, &src->rctmplambdas, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->tmpbasis, &src->tmpbasis, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _sactiveset_clear(void* _p) -{ - sactiveset *p = (sactiveset*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->xc); - ae_vector_clear(&p->s); - ae_vector_clear(&p->h); - ae_vector_clear(&p->activeset); - ae_matrix_clear(&p->sbasis); - ae_matrix_clear(&p->pbasis); - ae_matrix_clear(&p->ibasis); - ae_vector_clear(&p->hasbndl); - ae_vector_clear(&p->hasbndu); - ae_vector_clear(&p->bndl); - ae_vector_clear(&p->bndu); - ae_matrix_clear(&p->cleic); - ae_vector_clear(&p->mtx); - ae_vector_clear(&p->mtas); - ae_vector_clear(&p->cdtmp); - ae_vector_clear(&p->corrtmp); - ae_vector_clear(&p->unitdiagonal); - _snnlssolver_clear(&p->solver); - ae_vector_clear(&p->scntmp); - ae_vector_clear(&p->tmp0); - ae_vector_clear(&p->tmpfeas); - ae_matrix_clear(&p->tmpm0); - ae_vector_clear(&p->rctmps); - ae_vector_clear(&p->rctmpg); - ae_vector_clear(&p->rctmprightpart); - ae_matrix_clear(&p->rctmpdense0); - ae_matrix_clear(&p->rctmpdense1); - ae_vector_clear(&p->rctmpisequality); - ae_vector_clear(&p->rctmpconstraintidx); - ae_vector_clear(&p->rctmplambdas); - ae_matrix_clear(&p->tmpbasis); -} - - -void _sactiveset_destroy(void* _p) -{ - sactiveset *p = (sactiveset*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->xc); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->h); - ae_vector_destroy(&p->activeset); - ae_matrix_destroy(&p->sbasis); - ae_matrix_destroy(&p->pbasis); - ae_matrix_destroy(&p->ibasis); - ae_vector_destroy(&p->hasbndl); - ae_vector_destroy(&p->hasbndu); - ae_vector_destroy(&p->bndl); - ae_vector_destroy(&p->bndu); - ae_matrix_destroy(&p->cleic); - ae_vector_destroy(&p->mtx); - ae_vector_destroy(&p->mtas); - ae_vector_destroy(&p->cdtmp); - ae_vector_destroy(&p->corrtmp); - ae_vector_destroy(&p->unitdiagonal); - _snnlssolver_destroy(&p->solver); - ae_vector_destroy(&p->scntmp); - ae_vector_destroy(&p->tmp0); - ae_vector_destroy(&p->tmpfeas); - ae_matrix_destroy(&p->tmpm0); - ae_vector_destroy(&p->rctmps); - ae_vector_destroy(&p->rctmpg); - ae_vector_destroy(&p->rctmprightpart); - ae_matrix_destroy(&p->rctmpdense0); - ae_matrix_destroy(&p->rctmpdense1); - ae_vector_destroy(&p->rctmpisequality); - ae_vector_destroy(&p->rctmpconstraintidx); - ae_vector_destroy(&p->rctmplambdas); - ae_matrix_destroy(&p->tmpbasis); -} - - - - -/************************************************************************* - NONLINEAR CONJUGATE GRADIENT METHOD - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using one of the -nonlinear conjugate gradient methods. - -These CG methods are globally convergent (even on non-convex functions) as -long as grad(f) is Lipschitz continuous in a some neighborhood of the -L = { x : f(x)<=f(x0) }. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinCGCreate() call -2. User tunes solver parameters with MinCGSetCond(), MinCGSetStpMax() and - other functions -3. User calls MinCGOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinCGResults() to get solution -5. Optionally, user may call MinCGRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinCGRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgcreate(ae_int_t n, - /* Real */ ae_vector* x, - mincgstate* state, - ae_state *_state) -{ - - _mincgstate_clear(state); - - ae_assert(n>=1, "MinCGCreate: N too small!", _state); - ae_assert(x->cnt>=n, "MinCGCreate: Length(X)0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinCGSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. L-BFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgcreatef(ae_int_t n, - /* Real */ ae_vector* x, - double diffstep, - mincgstate* state, - ae_state *_state) -{ - - _mincgstate_clear(state); - - ae_assert(n>=1, "MinCGCreateF: N too small!", _state); - ae_assert(x->cnt>=n, "MinCGCreateF: Length(X)=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinCGSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetcond(mincgstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsg, _state), "MinCGSetCond: EpsG is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsg,0), "MinCGSetCond: negative EpsG!", _state); - ae_assert(ae_isfinite(epsf, _state), "MinCGSetCond: EpsF is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "MinCGSetCond: negative EpsF!", _state); - ae_assert(ae_isfinite(epsx, _state), "MinCGSetCond: EpsX is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "MinCGSetCond: negative EpsX!", _state); - ae_assert(maxits>=0, "MinCGSetCond: negative MaxIts!", _state); - if( ((ae_fp_eq(epsg,0)&&ae_fp_eq(epsf,0))&&ae_fp_eq(epsx,0))&&maxits==0 ) - { - epsx = 1.0E-6; - } - state->epsg = epsg; - state->epsf = epsf; - state->epsx = epsx; - state->maxits = maxits; -} - - -/************************************************************************* -This function sets scaling coefficients for CG optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of CG optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the CG too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinCGSetPrec...() functions. - -There is special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgsetscale(mincgstate* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(s->cnt>=state->n, "MinCGSetScale: Length(S)n-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "MinCGSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "MinCGSetScale: S contains zero elements", _state); - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetxrep(mincgstate* state, ae_bool needxrep, ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function turns on/off line search reports. -These reports are described in more details in developer-only comments on -MinCGState object. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedDRep- whether line search reports are needed or not - -This function is intended for private use only. Turning it on artificially -may cause program failure. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetdrep(mincgstate* state, ae_bool needdrep, ae_state *_state) -{ - - - state->drep = needdrep; -} - - -/************************************************************************* -This function sets CG algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - CGType - algorithm type: - * -1 automatic selection of the best algorithm - * 0 DY (Dai and Yuan) algorithm - * 1 Hybrid DY-HS algorithm - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetcgtype(mincgstate* state, ae_int_t cgtype, ae_state *_state) -{ - - - ae_assert(cgtype>=-1&&cgtype<=1, "MinCGSetCGType: incorrect CGType!", _state); - if( cgtype==-1 ) - { - cgtype = 1; - } - state->cgtype = cgtype; -} - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetstpmax(mincgstate* state, double stpmax, ae_state *_state) -{ - - - ae_assert(ae_isfinite(stpmax, _state), "MinCGSetStpMax: StpMax is not finite!", _state); - ae_assert(ae_fp_greater_eq(stpmax,0), "MinCGSetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* -This function allows to suggest initial step length to the CG algorithm. - -Suggested step length is used as starting point for the line search. It -can be useful when you have badly scaled problem, i.e. when ||grad|| -(which is used as initial estimate for the first step) is many orders of -magnitude different from the desired step. - -Line search may fail on such problems without good estimate of initial -step length. Imagine, for example, problem with ||grad||=10^50 and desired -step equal to 0.1 Line search function will use 10^50 as initial step, -then it will decrease step length by 2 (up to 20 attempts) and will get -10^44, which is still too large. - -This function allows us to tell than line search should be started from -some moderate step length, like 1.0, so algorithm will be able to detect -desired step length in a several searches. - -Default behavior (when no step is suggested) is to use preconditioner, if -it is available, to generate initial estimate of step length. - -This function influences only first iteration of algorithm. It should be -called between MinCGCreate/MinCGRestartFrom() call and MinCGOptimize call. -Suggested step is ignored if you have preconditioner. - -INPUT PARAMETERS: - State - structure used to store algorithm state. - Stp - initial estimate of the step length. - Can be zero (no estimate). - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsuggeststep(mincgstate* state, double stp, ae_state *_state) -{ - - - ae_assert(ae_isfinite(stp, _state), "MinCGSuggestStep: Stp is infinite or NAN", _state); - ae_assert(ae_fp_greater_eq(stp,0), "MinCGSuggestStep: Stp<0", _state); - state->suggestedstep = stp; -} - - -/************************************************************************* -Modification of the preconditioner: preconditioning is turned off. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdefault(mincgstate* state, ae_state *_state) -{ - - - state->prectype = 0; - state->innerresetneeded = ae_true; -} - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 3: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdiag(mincgstate* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(d->cnt>=state->n, "MinCGSetPrecDiag: D is too short", _state); - for(i=0; i<=state->n-1; i++) - { - ae_assert(ae_isfinite(d->ptr.p_double[i], _state), "MinCGSetPrecDiag: D contains infinite or NAN elements", _state); - ae_assert(ae_fp_greater(d->ptr.p_double[i],0), "MinCGSetPrecDiag: D contains non-positive elements", _state); - } - mincgsetprecdiagfast(state, d, _state); -} - - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinCGSetScale() call -(before or after MinCGSetPrecScale() call). Without knowledge of the scale -of your variables scale-based preconditioner will be just unit matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecscale(mincgstate* state, ae_state *_state) -{ - - - state->prectype = 3; - state->innerresetneeded = ae_true; -} - - -/************************************************************************* -NOTES: - -1. This function has two different implementations: one which uses exact - (analytical) user-supplied gradient, and one which uses function value - only and numerically differentiates function in order to obtain - gradient. - - Depending on the specific function used to create optimizer object - (either MinCGCreate() for analytical gradient or MinCGCreateF() for - numerical differentiation) you should choose appropriate variant of - MinCGOptimize() - one which accepts function AND gradient or one which - accepts function ONLY. - - Be careful to choose variant of MinCGOptimize() which corresponds to - your optimization scheme! Table below lists different combinations of - callback (function/gradient) passed to MinCGOptimize() and specific - function used to create optimizer. - - - | USER PASSED TO MinCGOptimize() - CREATED WITH | function only | function and gradient - ------------------------------------------------------------ - MinCGCreateF() | work FAIL - MinCGCreate() | FAIL work - - Here "FAIL" denotes inappropriate combinations of optimizer creation - function and MinCGOptimize() version. Attemps to use such combination - (for example, to create optimizer with MinCGCreateF() and to pass - gradient information to MinCGOptimize()) will lead to exception being - thrown. Either you did not pass gradient when it WAS needed or you - passed gradient when it was NOT needed. - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool mincgiteration(mincgstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double betak; - double v; - double vv; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - i = state->rstate.ia.ptr.p_int[1]; - betak = state->rstate.ra.ptr.p_double[0]; - v = state->rstate.ra.ptr.p_double[1]; - vv = state->rstate.ra.ptr.p_double[2]; - } - else - { - n = -983; - i = -989; - betak = -834; - v = 900; - vv = -287; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - if( state->rstate.stage==8 ) - { - goto lbl_8; - } - if( state->rstate.stage==9 ) - { - goto lbl_9; - } - if( state->rstate.stage==10 ) - { - goto lbl_10; - } - if( state->rstate.stage==11 ) - { - goto lbl_11; - } - if( state->rstate.stage==12 ) - { - goto lbl_12; - } - if( state->rstate.stage==13 ) - { - goto lbl_13; - } - if( state->rstate.stage==14 ) - { - goto lbl_14; - } - if( state->rstate.stage==15 ) - { - goto lbl_15; - } - if( state->rstate.stage==16 ) - { - goto lbl_16; - } - if( state->rstate.stage==17 ) - { - goto lbl_17; - } - if( state->rstate.stage==18 ) - { - goto lbl_18; - } - if( state->rstate.stage==19 ) - { - goto lbl_19; - } - - /* - * Routine body - */ - - /* - * Prepare - */ - n = state->n; - state->repterminationtype = 0; - state->repiterationscount = 0; - state->repvaridx = -1; - state->repnfev = 0; - state->debugrestartscount = 0; - - /* - * Check, that transferred derivative value is right - */ - mincg_clearrequestfields(state, _state); - if( !(ae_fp_eq(state->diffstep,0)&&ae_fp_greater(state->teststep,0)) ) - { - goto lbl_20; - } - state->needfg = ae_true; - i = 0; -lbl_22: - if( i>n-1 ) - { - goto lbl_24; - } - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->teststep*state->s.ptr.p_double[i]; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->fm1 = state->f; - state->fp1 = state->g.ptr.p_double[i]; - state->x.ptr.p_double[i] = v+state->teststep*state->s.ptr.p_double[i]; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->fm2 = state->f; - state->fp2 = state->g.ptr.p_double[i]; - state->x.ptr.p_double[i] = v; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - - /* - * 2*State.TestStep - scale parameter - * width of segment [Xi-TestStep;Xi+TestStep] - */ - if( !derivativecheck(state->fm1, state->fp1, state->fm2, state->fp2, state->f, state->g.ptr.p_double[i], 2*state->teststep, _state) ) - { - state->repvaridx = i; - state->repterminationtype = -7; - result = ae_false; - return result; - } - i = i+1; - goto lbl_22; -lbl_24: - state->needfg = ae_false; -lbl_20: - - /* - * Preparations continue: - * * set XK - * * calculate F/G - * * set DK to -G - * * powerup algo (it may change preconditioner) - * * apply preconditioner to DK - * * report update of X - * * check stopping conditions for G - */ - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->terminationneeded = ae_false; - mincg_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_25; - } - state->needfg = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needfg = ae_false; - goto lbl_26; -lbl_25: - state->needf = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->fbase = state->f; - i = 0; -lbl_27: - if( i>n-1 ) - { - goto lbl_29; - } - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 8; - goto lbl_rcomm; -lbl_8: - state->fp2 = state->f; - state->x.ptr.p_double[i] = v; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - i = i+1; - goto lbl_27; -lbl_29: - state->f = state->fbase; - state->needf = ae_false; -lbl_26: - if( !state->drep ) - { - goto lbl_30; - } - - /* - * Report algorithm powerup (if needed) - */ - mincg_clearrequestfields(state, _state); - state->algpowerup = ae_true; - state->rstate.stage = 9; - goto lbl_rcomm; -lbl_9: - state->algpowerup = ae_false; -lbl_30: - trimprepare(state->f, &state->trimthreshold, _state); - ae_v_moveneg(&state->dk.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - mincg_preconditionedmultiply(state, &state->dk, &state->work0, &state->work1, _state); - if( !state->xrep ) - { - goto lbl_32; - } - mincg_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 10; - goto lbl_rcomm; -lbl_10: - state->xupdated = ae_false; -lbl_32: - if( state->terminationneeded ) - { - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->repterminationtype = 8; - result = ae_false; - return result; - } - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->g.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - if( ae_fp_less_eq(ae_sqrt(v, _state),state->epsg) ) - { - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->repterminationtype = 4; - result = ae_false; - return result; - } - state->repnfev = 1; - state->k = 0; - state->fold = state->f; - - /* - * Choose initial step. - * Apply preconditioner, if we have something other than default. - */ - if( state->prectype==2||state->prectype==3 ) - { - - /* - * because we use preconditioner, step length must be equal - * to the norm of DK - */ - v = ae_v_dotproduct(&state->dk.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->lastgoodstep = ae_sqrt(v, _state); - } - else - { - - /* - * No preconditioner is used, we try to use suggested step - */ - if( ae_fp_greater(state->suggestedstep,0) ) - { - state->lastgoodstep = state->suggestedstep; - } - else - { - state->lastgoodstep = 1.0; - } - } - - /* - * Main cycle - */ - state->rstimer = mincg_rscountdownlen; -lbl_34: - if( ae_false ) - { - goto lbl_35; - } - - /* - * * clear reset flag - * * clear termination flag - * * store G[k] for later calculation of Y[k] - * * prepare starting point and direction and step length for line search - */ - state->innerresetneeded = ae_false; - state->terminationneeded = ae_false; - ae_v_moveneg(&state->yk.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->d.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->mcstage = 0; - state->stp = 1.0; - linminnormalized(&state->d, &state->stp, n, _state); - if( ae_fp_neq(state->lastgoodstep,0) ) - { - state->stp = state->lastgoodstep; - } - state->curstpmax = state->stpmax; - - /* - * Report beginning of line search (if needed) - * Terminate algorithm, if user request was detected - */ - if( !state->drep ) - { - goto lbl_36; - } - mincg_clearrequestfields(state, _state); - state->lsstart = ae_true; - state->rstate.stage = 11; - goto lbl_rcomm; -lbl_11: - state->lsstart = ae_false; -lbl_36: - if( state->terminationneeded ) - { - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->repterminationtype = 8; - result = ae_false; - return result; - } - - /* - * Minimization along D - */ - mcsrch(n, &state->x, &state->f, &state->g, &state->d, &state->stp, state->curstpmax, mincg_gtol, &state->mcinfo, &state->nfev, &state->work0, &state->lstate, &state->mcstage, _state); -lbl_38: - if( state->mcstage==0 ) - { - goto lbl_39; - } - - /* - * Calculate function/gradient using either - * analytical gradient supplied by user - * or finite difference approximation. - * - * "Trim" function in order to handle near-singularity points. - */ - mincg_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_40; - } - state->needfg = ae_true; - state->rstate.stage = 12; - goto lbl_rcomm; -lbl_12: - state->needfg = ae_false; - goto lbl_41; -lbl_40: - state->needf = ae_true; - state->rstate.stage = 13; - goto lbl_rcomm; -lbl_13: - state->fbase = state->f; - i = 0; -lbl_42: - if( i>n-1 ) - { - goto lbl_44; - } - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 14; - goto lbl_rcomm; -lbl_14: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 15; - goto lbl_rcomm; -lbl_15: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 16; - goto lbl_rcomm; -lbl_16: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 17; - goto lbl_rcomm; -lbl_17: - state->fp2 = state->f; - state->x.ptr.p_double[i] = v; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - i = i+1; - goto lbl_42; -lbl_44: - state->f = state->fbase; - state->needf = ae_false; -lbl_41: - trimfunction(&state->f, &state->g, n, state->trimthreshold, _state); - - /* - * Call MCSRCH again - */ - mcsrch(n, &state->x, &state->f, &state->g, &state->d, &state->stp, state->curstpmax, mincg_gtol, &state->mcinfo, &state->nfev, &state->work0, &state->lstate, &state->mcstage, _state); - goto lbl_38; -lbl_39: - - /* - * * report end of line search - * * store current point to XN - * * report iteration - * * terminate algorithm if user request was detected - */ - if( !state->drep ) - { - goto lbl_45; - } - - /* - * Report end of line search (if needed) - */ - mincg_clearrequestfields(state, _state); - state->lsend = ae_true; - state->rstate.stage = 18; - goto lbl_rcomm; -lbl_18: - state->lsend = ae_false; -lbl_45: - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( !state->xrep ) - { - goto lbl_47; - } - mincg_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 19; - goto lbl_rcomm; -lbl_19: - state->xupdated = ae_false; -lbl_47: - if( state->terminationneeded ) - { - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->repterminationtype = 8; - result = ae_false; - return result; - } - - /* - * Line search is finished. - * * calculate BetaK - * * calculate DN - * * update timers - * * calculate step length: - * * LastScaledStep is ALWAYS calculated because it is used in the stopping criteria - * * LastGoodStep is updated only when MCINFO is equal to 1 (Wolfe conditions hold). - * See below for more explanation. - */ - if( state->mcinfo==1&&!state->innerresetneeded ) - { - - /* - * Standard Wolfe conditions hold - * Calculate Y[K] and D[K]'*Y[K] - */ - ae_v_add(&state->yk.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - vv = ae_v_dotproduct(&state->yk.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Calculate BetaK according to DY formula - */ - v = mincg_preconditionedmultiply2(state, &state->g, &state->g, &state->work0, &state->work1, _state); - state->betady = v/vv; - - /* - * Calculate BetaK according to HS formula - */ - v = mincg_preconditionedmultiply2(state, &state->g, &state->yk, &state->work0, &state->work1, _state); - state->betahs = v/vv; - - /* - * Choose BetaK - */ - if( state->cgtype==0 ) - { - betak = state->betady; - } - if( state->cgtype==1 ) - { - betak = ae_maxreal(0, ae_minreal(state->betady, state->betahs, _state), _state); - } - } - else - { - - /* - * Something is wrong (may be function is too wild or too flat) - * or we just have to restart algo. - * - * We'll set BetaK=0, which will restart CG algorithm. - * We can stop later (during normal checks) if stopping conditions are met. - */ - betak = 0; - state->debugrestartscount = state->debugrestartscount+1; - } - if( state->repiterationscount>0&&state->repiterationscount%(3+n)==0 ) - { - - /* - * clear Beta every N iterations - */ - betak = 0; - } - if( state->mcinfo==1||state->mcinfo==5 ) - { - state->rstimer = mincg_rscountdownlen; - } - else - { - state->rstimer = state->rstimer-1; - } - ae_v_moveneg(&state->dn.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - mincg_preconditionedmultiply(state, &state->dn, &state->work0, &state->work1, _state); - ae_v_addd(&state->dn.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1), betak); - state->lastscaledstep = 0.0; - for(i=0; i<=n-1; i++) - { - state->lastscaledstep = state->lastscaledstep+ae_sqr(state->d.ptr.p_double[i]/state->s.ptr.p_double[i], _state); - } - state->lastscaledstep = state->stp*ae_sqrt(state->lastscaledstep, _state); - if( state->mcinfo==1 ) - { - - /* - * Step is good (Wolfe conditions hold), update LastGoodStep. - * - * This check for MCINFO=1 is essential because sometimes in the - * constrained optimization setting we may take very short steps - * (like 1E-15) because we were very close to boundary of the - * feasible area. Such short step does not mean that we've converged - * to the solution - it was so short because we were close to the - * boundary and there was a limit on step length. - * - * So having such short step is quite normal situation. However, we - * should NOT start next iteration from step whose initial length is - * estimated as 1E-15 because it may lead to the failure of the - * linear minimizer (step is too short, function does not changes, - * line search stagnates). - */ - state->lastgoodstep = 0; - for(i=0; i<=n-1; i++) - { - state->lastgoodstep = state->lastgoodstep+ae_sqr(state->d.ptr.p_double[i], _state); - } - state->lastgoodstep = state->stp*ae_sqrt(state->lastgoodstep, _state); - } - - /* - * Update information. - * Check stopping conditions. - */ - state->repnfev = state->repnfev+state->nfev; - state->repiterationscount = state->repiterationscount+1; - if( state->repiterationscount>=state->maxits&&state->maxits>0 ) - { - - /* - * Too many iterations - */ - state->repterminationtype = 5; - result = ae_false; - return result; - } - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->g.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - if( ae_fp_less_eq(ae_sqrt(v, _state),state->epsg) ) - { - - /* - * Gradient is small enough - */ - state->repterminationtype = 4; - result = ae_false; - return result; - } - if( !state->innerresetneeded ) - { - - /* - * These conditions are checked only when no inner reset was requested by user - */ - if( ae_fp_less_eq(state->fold-state->f,state->epsf*ae_maxreal(ae_fabs(state->fold, _state), ae_maxreal(ae_fabs(state->f, _state), 1.0, _state), _state)) ) - { - - /* - * F(k+1)-F(k) is small enough - */ - state->repterminationtype = 1; - result = ae_false; - return result; - } - if( ae_fp_less_eq(state->lastscaledstep,state->epsx) ) - { - - /* - * X(k+1)-X(k) is small enough - */ - state->repterminationtype = 2; - result = ae_false; - return result; - } - } - if( state->rstimer<=0 ) - { - - /* - * Too many subsequent restarts - */ - state->repterminationtype = 7; - result = ae_false; - return result; - } - - /* - * Shift Xk/Dk, update other information - */ - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->xn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->dk.ptr.p_double[0], 1, &state->dn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fold = state->f; - state->k = state->k+1; - goto lbl_34; -lbl_35: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = i; - state->rstate.ra.ptr.p_double[0] = betak; - state->rstate.ra.ptr.p_double[1] = v; - state->rstate.ra.ptr.p_double[2] = vv; - return result; -} - - -/************************************************************************* -Conjugate gradient results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -7 gradient verification failed. - See MinCGSetGradientCheck() for more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible, - we return best X found so far - * 8 terminated by user - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -void mincgresults(mincgstate* state, - /* Real */ ae_vector* x, - mincgreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _mincgreport_clear(rep); - - mincgresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -Conjugate gradient results - -Buffered implementation of MinCGResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -void mincgresultsbuf(mincgstate* state, - /* Real */ ae_vector* x, - mincgreport* rep, - ae_state *_state) -{ - - - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->xn.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->nfev = state->repnfev; - rep->varidx = state->repvaridx; - rep->terminationtype = state->repterminationtype; -} - - -/************************************************************************* -This subroutine restarts CG algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used to store algorithm state. - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgrestartfrom(mincgstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - - - ae_assert(x->cnt>=state->n, "MinCGRestartFrom: Length(X)n, _state), "MinCGCreate: X contains infinite or NaN values!", _state); - ae_v_move(&state->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - mincgsuggeststep(state, 0.0, _state); - ae_vector_set_length(&state->rstate.ia, 1+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; - mincg_clearrequestfields(state, _state); -} - - -/************************************************************************* -Faster version of MinCGSetPrecDiag(), for time-critical parts of code, -without safety checks. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdiagfast(mincgstate* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - rvectorsetlengthatleast(&state->diagh, state->n, _state); - rvectorsetlengthatleast(&state->diaghl2, state->n, _state); - state->prectype = 2; - state->vcnt = 0; - state->innerresetneeded = ae_true; - for(i=0; i<=state->n-1; i++) - { - state->diagh.ptr.p_double[i] = d->ptr.p_double[i]; - state->diaghl2.ptr.p_double[i] = 0.0; - } -} - - -/************************************************************************* -This function sets low-rank preconditioner for Hessian matrix H=D+V'*C*V, -where: -* H is a Hessian matrix, which is approximated by D/V/C -* D=D1+D2 is a diagonal matrix, which includes two positive definite terms: - * constant term D1 (is not updated or infrequently updated) - * variable term D2 (can be cheaply updated from iteration to iteration) -* V is a low-rank correction -* C is a diagonal factor of low-rank correction - -Preconditioner P is calculated using approximate Woodburry formula: - P = D^(-1) - D^(-1)*V'*(C^(-1)+V*D1^(-1)*V')^(-1)*V*D^(-1) - = D^(-1) - D^(-1)*VC'*VC*D^(-1), -where - VC = sqrt(B)*V - B = (C^(-1)+V*D1^(-1)*V')^(-1) - -Note that B is calculated using constant term (D1) only, which allows us -to update D2 without recalculation of B or VC. Such preconditioner is -exact when D2 is zero. When D2 is non-zero, it is only approximation, but -very good and cheap one. - -This function accepts D1, V, C. -D2 is set to zero by default. - -Cost of this update is O(N*VCnt*VCnt), but D2 can be updated in just O(N) -by MinCGSetPrecVarPart. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetpreclowrankfast(mincgstate* state, - /* Real */ ae_vector* d1, - /* Real */ ae_vector* c, - /* Real */ ae_matrix* v, - ae_int_t vcnt, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t n; - double t; - ae_matrix b; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init(&b, 0, 0, DT_REAL, _state, ae_true); - - if( vcnt==0 ) - { - mincgsetprecdiagfast(state, d1, _state); - ae_frame_leave(_state); - return; - } - n = state->n; - ae_matrix_set_length(&b, vcnt, vcnt, _state); - rvectorsetlengthatleast(&state->diagh, n, _state); - rvectorsetlengthatleast(&state->diaghl2, n, _state); - rmatrixsetlengthatleast(&state->vcorr, vcnt, n, _state); - state->prectype = 2; - state->vcnt = vcnt; - state->innerresetneeded = ae_true; - for(i=0; i<=n-1; i++) - { - state->diagh.ptr.p_double[i] = d1->ptr.p_double[i]; - state->diaghl2.ptr.p_double[i] = 0.0; - } - for(i=0; i<=vcnt-1; i++) - { - for(j=i; j<=vcnt-1; j++) - { - t = 0; - for(k=0; k<=n-1; k++) - { - t = t+v->ptr.pp_double[i][k]*v->ptr.pp_double[j][k]/d1->ptr.p_double[k]; - } - b.ptr.pp_double[i][j] = t; - } - b.ptr.pp_double[i][i] = b.ptr.pp_double[i][i]+1.0/c->ptr.p_double[i]; - } - if( !spdmatrixcholeskyrec(&b, 0, vcnt, ae_true, &state->work0, _state) ) - { - state->vcnt = 0; - ae_frame_leave(_state); - return; - } - for(i=0; i<=vcnt-1; i++) - { - ae_v_move(&state->vcorr.ptr.pp_double[i][0], 1, &v->ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - for(j=0; j<=i-1; j++) - { - t = b.ptr.pp_double[j][i]; - ae_v_subd(&state->vcorr.ptr.pp_double[i][0], 1, &state->vcorr.ptr.pp_double[j][0], 1, ae_v_len(0,n-1), t); - } - t = 1/b.ptr.pp_double[i][i]; - ae_v_muld(&state->vcorr.ptr.pp_double[i][0], 1, ae_v_len(0,n-1), t); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -This function updates variable part (diagonal matrix D2) -of low-rank preconditioner. - -This update is very cheap and takes just O(N) time. - -It has no effect with default preconditioner. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecvarpart(mincgstate* state, - /* Real */ ae_vector* d2, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - - - n = state->n; - for(i=0; i<=n-1; i++) - { - state->diaghl2.ptr.p_double[i] = d2->ptr.p_double[i]; - } -} - - -/************************************************************************* - -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinCGOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinCGSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 31.05.2012 by Bochkanov Sergey -*************************************************************************/ -void mincgsetgradientcheck(mincgstate* state, - double teststep, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(teststep, _state), "MinCGSetGradientCheck: TestStep contains NaN or Infinite", _state); - ae_assert(ae_fp_greater_eq(teststep,0), "MinCGSetGradientCheck: invalid argument TestStep(TestStep<0)", _state); - state->teststep = teststep; -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void mincg_clearrequestfields(mincgstate* state, ae_state *_state) -{ - - - state->needf = ae_false; - state->needfg = ae_false; - state->xupdated = ae_false; - state->lsstart = ae_false; - state->lsend = ae_false; - state->algpowerup = ae_false; -} - - -/************************************************************************* -This function calculates preconditioned product H^(-1)*x and stores result -back into X. Work0[] and Work1[] are used as temporaries (size must be at -least N; this function doesn't allocate arrays). - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -static void mincg_preconditionedmultiply(mincgstate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* work0, - /* Real */ ae_vector* work1, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - ae_int_t vcnt; - double v; - - - n = state->n; - vcnt = state->vcnt; - if( state->prectype==0 ) - { - return; - } - if( state->prectype==3 ) - { - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = x->ptr.p_double[i]*state->s.ptr.p_double[i]*state->s.ptr.p_double[i]; - } - return; - } - ae_assert(state->prectype==2, "MinCG: internal error (unexpected PrecType)", _state); - - /* - * handle part common for VCnt=0 and VCnt<>0 - */ - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = x->ptr.p_double[i]/(state->diagh.ptr.p_double[i]+state->diaghl2.ptr.p_double[i]); - } - - /* - * if VCnt>0 - */ - if( vcnt>0 ) - { - for(i=0; i<=vcnt-1; i++) - { - v = ae_v_dotproduct(&state->vcorr.ptr.pp_double[i][0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - work0->ptr.p_double[i] = v; - } - for(i=0; i<=n-1; i++) - { - work1->ptr.p_double[i] = 0; - } - for(i=0; i<=vcnt-1; i++) - { - v = work0->ptr.p_double[i]; - ae_v_addd(&state->work1.ptr.p_double[0], 1, &state->vcorr.ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - } - for(i=0; i<=n-1; i++) - { - x->ptr.p_double[i] = x->ptr.p_double[i]-state->work1.ptr.p_double[i]/(state->diagh.ptr.p_double[i]+state->diaghl2.ptr.p_double[i]); - } - } -} - - -/************************************************************************* -This function calculates preconditioned product x'*H^(-1)*y. Work0[] and -Work1[] are used as temporaries (size must be at least N; this function -doesn't allocate arrays). - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -static double mincg_preconditionedmultiply2(mincgstate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - /* Real */ ae_vector* work0, - /* Real */ ae_vector* work1, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - ae_int_t vcnt; - double v0; - double v1; - double result; - - - n = state->n; - vcnt = state->vcnt; - - /* - * no preconditioning - */ - if( state->prectype==0 ) - { - v0 = ae_v_dotproduct(&x->ptr.p_double[0], 1, &y->ptr.p_double[0], 1, ae_v_len(0,n-1)); - result = v0; - return result; - } - if( state->prectype==3 ) - { - result = 0; - for(i=0; i<=n-1; i++) - { - result = result+x->ptr.p_double[i]*state->s.ptr.p_double[i]*state->s.ptr.p_double[i]*y->ptr.p_double[i]; - } - return result; - } - ae_assert(state->prectype==2, "MinCG: internal error (unexpected PrecType)", _state); - - /* - * low rank preconditioning - */ - result = 0.0; - for(i=0; i<=n-1; i++) - { - result = result+x->ptr.p_double[i]*y->ptr.p_double[i]/(state->diagh.ptr.p_double[i]+state->diaghl2.ptr.p_double[i]); - } - if( vcnt>0 ) - { - for(i=0; i<=n-1; i++) - { - work0->ptr.p_double[i] = x->ptr.p_double[i]/(state->diagh.ptr.p_double[i]+state->diaghl2.ptr.p_double[i]); - work1->ptr.p_double[i] = y->ptr.p_double[i]/(state->diagh.ptr.p_double[i]+state->diaghl2.ptr.p_double[i]); - } - for(i=0; i<=vcnt-1; i++) - { - v0 = ae_v_dotproduct(&work0->ptr.p_double[0], 1, &state->vcorr.ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - v1 = ae_v_dotproduct(&work1->ptr.p_double[0], 1, &state->vcorr.ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - result = result-v0*v1; - } - } - return result; -} - - -/************************************************************************* -Internal initialization subroutine - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -static void mincg_mincginitinternal(ae_int_t n, - double diffstep, - mincgstate* state, - ae_state *_state) -{ - ae_int_t i; - - - - /* - * Initialize - */ - state->teststep = 0; - state->n = n; - state->diffstep = diffstep; - mincgsetcond(state, 0, 0, 0, 0, _state); - mincgsetxrep(state, ae_false, _state); - mincgsetdrep(state, ae_false, _state); - mincgsetstpmax(state, 0, _state); - mincgsetcgtype(state, -1, _state); - mincgsetprecdefault(state, _state); - ae_vector_set_length(&state->xk, n, _state); - ae_vector_set_length(&state->dk, n, _state); - ae_vector_set_length(&state->xn, n, _state); - ae_vector_set_length(&state->dn, n, _state); - ae_vector_set_length(&state->x, n, _state); - ae_vector_set_length(&state->d, n, _state); - ae_vector_set_length(&state->g, n, _state); - ae_vector_set_length(&state->work0, n, _state); - ae_vector_set_length(&state->work1, n, _state); - ae_vector_set_length(&state->yk, n, _state); - ae_vector_set_length(&state->s, n, _state); - for(i=0; i<=n-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - } -} - - -ae_bool _mincgstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mincgstate *p = (mincgstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->diagh, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->diaghl2, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->vcorr, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->yk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !_linminstate_init(&p->lstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->work0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->work1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _mincgstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mincgstate *dst = (mincgstate*)_dst; - mincgstate *src = (mincgstate*)_src; - dst->n = src->n; - dst->epsg = src->epsg; - dst->epsf = src->epsf; - dst->epsx = src->epsx; - dst->maxits = src->maxits; - dst->stpmax = src->stpmax; - dst->suggestedstep = src->suggestedstep; - dst->xrep = src->xrep; - dst->drep = src->drep; - dst->cgtype = src->cgtype; - dst->prectype = src->prectype; - if( !ae_vector_init_copy(&dst->diagh, &src->diagh, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->diaghl2, &src->diaghl2, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->vcorr, &src->vcorr, _state, make_automatic) ) - return ae_false; - dst->vcnt = src->vcnt; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - dst->diffstep = src->diffstep; - dst->nfev = src->nfev; - dst->mcstage = src->mcstage; - dst->k = src->k; - if( !ae_vector_init_copy(&dst->xk, &src->xk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dk, &src->dk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xn, &src->xn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dn, &src->dn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - dst->fold = src->fold; - dst->stp = src->stp; - dst->curstpmax = src->curstpmax; - if( !ae_vector_init_copy(&dst->yk, &src->yk, _state, make_automatic) ) - return ae_false; - dst->lastgoodstep = src->lastgoodstep; - dst->lastscaledstep = src->lastscaledstep; - dst->mcinfo = src->mcinfo; - dst->innerresetneeded = src->innerresetneeded; - dst->terminationneeded = src->terminationneeded; - dst->trimthreshold = src->trimthreshold; - dst->rstimer = src->rstimer; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - dst->needf = src->needf; - dst->needfg = src->needfg; - dst->xupdated = src->xupdated; - dst->algpowerup = src->algpowerup; - dst->lsstart = src->lsstart; - dst->lsend = src->lsend; - dst->teststep = src->teststep; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - dst->repiterationscount = src->repiterationscount; - dst->repnfev = src->repnfev; - dst->repvaridx = src->repvaridx; - dst->repterminationtype = src->repterminationtype; - dst->debugrestartscount = src->debugrestartscount; - if( !_linminstate_init_copy(&dst->lstate, &src->lstate, _state, make_automatic) ) - return ae_false; - dst->fbase = src->fbase; - dst->fm2 = src->fm2; - dst->fm1 = src->fm1; - dst->fp1 = src->fp1; - dst->fp2 = src->fp2; - dst->betahs = src->betahs; - dst->betady = src->betady; - if( !ae_vector_init_copy(&dst->work0, &src->work0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->work1, &src->work1, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _mincgstate_clear(void* _p) -{ - mincgstate *p = (mincgstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->diagh); - ae_vector_clear(&p->diaghl2); - ae_matrix_clear(&p->vcorr); - ae_vector_clear(&p->s); - ae_vector_clear(&p->xk); - ae_vector_clear(&p->dk); - ae_vector_clear(&p->xn); - ae_vector_clear(&p->dn); - ae_vector_clear(&p->d); - ae_vector_clear(&p->yk); - ae_vector_clear(&p->x); - ae_vector_clear(&p->g); - _rcommstate_clear(&p->rstate); - _linminstate_clear(&p->lstate); - ae_vector_clear(&p->work0); - ae_vector_clear(&p->work1); -} - - -void _mincgstate_destroy(void* _p) -{ - mincgstate *p = (mincgstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->diagh); - ae_vector_destroy(&p->diaghl2); - ae_matrix_destroy(&p->vcorr); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->xk); - ae_vector_destroy(&p->dk); - ae_vector_destroy(&p->xn); - ae_vector_destroy(&p->dn); - ae_vector_destroy(&p->d); - ae_vector_destroy(&p->yk); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->g); - _rcommstate_destroy(&p->rstate); - _linminstate_destroy(&p->lstate); - ae_vector_destroy(&p->work0); - ae_vector_destroy(&p->work1); -} - - -ae_bool _mincgreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - mincgreport *p = (mincgreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _mincgreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - mincgreport *dst = (mincgreport*)_dst; - mincgreport *src = (mincgreport*)_src; - dst->iterationscount = src->iterationscount; - dst->nfev = src->nfev; - dst->varidx = src->varidx; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _mincgreport_clear(void* _p) -{ - mincgreport *p = (mincgreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _mincgreport_destroy(void* _p) -{ - mincgreport *p = (mincgreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* - BOUND CONSTRAINED OPTIMIZATION - WITH ADDITIONAL LINEAR EQUALITY AND INEQUALITY CONSTRAINTS - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments subject to any -combination of: -* bound constraints -* linear inequality constraints -* linear equality constraints - -REQUIREMENTS: -* user must provide function value and gradient -* starting point X0 must be feasible or - not too far away from the feasible set -* grad(f) must be Lipschitz continuous on a level set: - L = { x : f(x)<=f(x0) } -* function must be defined everywhere on the feasible set F - -USAGE: - -Constrained optimization if far more complex than the unconstrained one. -Here we give very brief outline of the BLEIC optimizer. We strongly recommend -you to read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide -on optimization, which is available at http://www.alglib.net/optimization/ - -1. User initializes algorithm state with MinBLEICCreate() call - -2. USer adds boundary and/or linear constraints by calling - MinBLEICSetBC() and MinBLEICSetLC() functions. - -3. User sets stopping conditions with MinBLEICSetCond(). - -4. User calls MinBLEICOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. - -5. User calls MinBLEICResults() to get solution - -6. Optionally user may call MinBLEICRestartFrom() to solve another problem - with same N but another starting point. - MinBLEICRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size ofX - X - starting point, array[N]: - * it is better to set X to a feasible point - * but X can be infeasible, in which case algorithm will try - to find feasible point first, using X as initial - approximation. - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreate(ae_int_t n, - /* Real */ ae_vector* x, - minbleicstate* state, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix c; - ae_vector ct; - - ae_frame_make(_state, &_frame_block); - _minbleicstate_clear(state); - ae_matrix_init(&c, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ct, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "MinBLEICCreate: N<1", _state); - ae_assert(x->cnt>=n, "MinBLEICCreate: Length(X)0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinBLEICSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. CG needs exact gradient values. Imprecise - gradient may slow down convergence, especially on highly nonlinear - problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreatef(ae_int_t n, - /* Real */ ae_vector* x, - double diffstep, - minbleicstate* state, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix c; - ae_vector ct; - - ae_frame_make(_state, &_frame_block); - _minbleicstate_clear(state); - ae_matrix_init(&c, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ct, 0, DT_INT, _state, ae_true); - - ae_assert(n>=1, "MinBLEICCreateF: N<1", _state); - ae_assert(x->cnt>=n, "MinBLEICCreateF: Length(X)nmain; - ae_assert(bndl->cnt>=n, "MinBLEICSetBC: Length(BndL)cnt>=n, "MinBLEICSetBC: Length(BndU)ptr.p_double[i], _state)||ae_isneginf(bndl->ptr.p_double[i], _state), "MinBLEICSetBC: BndL contains NAN or +INF", _state); - ae_assert(ae_isfinite(bndu->ptr.p_double[i], _state)||ae_isposinf(bndu->ptr.p_double[i], _state), "MinBLEICSetBC: BndL contains NAN or -INF", _state); - state->bndl.ptr.p_double[i] = bndl->ptr.p_double[i]; - state->hasbndl.ptr.p_bool[i] = ae_isfinite(bndl->ptr.p_double[i], _state); - state->bndu.ptr.p_double[i] = bndu->ptr.p_double[i]; - state->hasbndu.ptr.p_bool[i] = ae_isfinite(bndu->ptr.p_double[i], _state); - } - sassetbc(&state->sas, bndl, bndu, _state); -} - - -/************************************************************************* -This function sets linear constraints for BLEIC optimizer. - -Linear constraints are inactive by default (after initial creation). -They are preserved after algorithm restart with MinBLEICRestartFrom(). - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately: -* there always exists some minor violation (about Epsilon in magnitude) - due to rounding errors -* numerical differentiation, if used, may lead to function evaluations - outside of the feasible area, because algorithm does NOT change - numerical differentiation formula according to linear constraints. -If you want constraints to be satisfied exactly, try to reformulate your -problem in such manner that all constraints will become boundary ones -(this kind of constraints is always satisfied exactly, both in the final -solution and in all intermediate points). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetlc(minbleicstate* state, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - double v; - - - n = state->nmain; - - /* - * First, check for errors in the inputs - */ - ae_assert(k>=0, "MinBLEICSetLC: K<0", _state); - ae_assert(c->cols>=n+1||k==0, "MinBLEICSetLC: Cols(C)rows>=k, "MinBLEICSetLC: Rows(C)cnt>=k, "MinBLEICSetLC: Length(CT)nec = 0; - state->nic = 0; - return; - } - - /* - * Equality constraints are stored first, in the upper - * NEC rows of State.CLEIC matrix. Inequality constraints - * are stored in the next NIC rows. - * - * NOTE: we convert inequality constraints to the form - * A*x<=b before copying them. - */ - rmatrixsetlengthatleast(&state->cleic, k, n+1, _state); - state->nec = 0; - state->nic = 0; - for(i=0; i<=k-1; i++) - { - if( ct->ptr.p_int[i]==0 ) - { - ae_v_move(&state->cleic.ptr.pp_double[state->nec][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - state->nec = state->nec+1; - } - } - for(i=0; i<=k-1; i++) - { - if( ct->ptr.p_int[i]!=0 ) - { - if( ct->ptr.p_int[i]>0 ) - { - ae_v_moveneg(&state->cleic.ptr.pp_double[state->nec+state->nic][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - } - else - { - ae_v_move(&state->cleic.ptr.pp_double[state->nec+state->nic][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - } - state->nic = state->nic+1; - } - } - - /* - * Normalize rows of State.CLEIC: each row must have unit norm. - * Norm is calculated using first N elements (i.e. right part is - * not counted when we calculate norm). - */ - for(i=0; i<=k-1; i++) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->cleic.ptr.pp_double[i][j], _state); - } - if( ae_fp_eq(v,0) ) - { - continue; - } - v = 1/ae_sqrt(v, _state); - ae_v_muld(&state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n), v); - } - sassetlc(&state->sas, c, ct, k, _state); -} - - -/************************************************************************* -This function sets stopping conditions for the optimizer. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - step vector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinBLEICSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0 and EpsX=0 and MaxIts=0 (simultaneously) will lead -to automatic stopping criterion selection. - -NOTE: when SetCond() called with non-zero MaxIts, BLEIC solver may perform - slightly more than MaxIts iterations. I.e., MaxIts sets non-strict - limit on iterations count. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetcond(minbleicstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsg, _state), "MinBLEICSetCond: EpsG is not finite number", _state); - ae_assert(ae_fp_greater_eq(epsg,0), "MinBLEICSetCond: negative EpsG", _state); - ae_assert(ae_isfinite(epsf, _state), "MinBLEICSetCond: EpsF is not finite number", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "MinBLEICSetCond: negative EpsF", _state); - ae_assert(ae_isfinite(epsx, _state), "MinBLEICSetCond: EpsX is not finite number", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "MinBLEICSetCond: negative EpsX", _state); - ae_assert(maxits>=0, "MinBLEICSetCond: negative MaxIts!", _state); - if( ((ae_fp_eq(epsg,0)&&ae_fp_eq(epsf,0))&&ae_fp_eq(epsx,0))&&maxits==0 ) - { - epsx = 1.0E-6; - } - state->epsg = epsg; - state->epsf = epsf; - state->epsx = epsx; - state->maxits = maxits; -} - - -/************************************************************************* -This function sets scaling coefficients for BLEIC optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of the optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the BLEIC too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinBLEICSetPrec...() -functions. - -There is a special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetscale(minbleicstate* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(s->cnt>=state->nmain, "MinBLEICSetScale: Length(S)nmain-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "MinBLEICSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "MinBLEICSetScale: S contains zero elements", _state); - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } - sassetscale(&state->sas, s, _state); -} - - -/************************************************************************* -Modification of the preconditioner: preconditioning is turned off. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecdefault(minbleicstate* state, ae_state *_state) -{ - - - state->prectype = 0; -} - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE 1: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 2: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecdiag(minbleicstate* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(d->cnt>=state->nmain, "MinBLEICSetPrecDiag: D is too short", _state); - for(i=0; i<=state->nmain-1; i++) - { - ae_assert(ae_isfinite(d->ptr.p_double[i], _state), "MinBLEICSetPrecDiag: D contains infinite or NAN elements", _state); - ae_assert(ae_fp_greater(d->ptr.p_double[i],0), "MinBLEICSetPrecDiag: D contains non-positive elements", _state); - } - rvectorsetlengthatleast(&state->diagh, state->nmain, _state); - state->prectype = 2; - for(i=0; i<=state->nmain-1; i++) - { - state->diagh.ptr.p_double[i] = d->ptr.p_double[i]; - } -} - - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinBLEICSetScale() -call (before or after MinBLEICSetPrecScale() call). Without knowledge of -the scale of your variables scale-based preconditioner will be just unit -matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecscale(minbleicstate* state, ae_state *_state) -{ - - - state->prectype = 3; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinBLEICOptimize(). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetxrep(minbleicstate* state, - ae_bool needxrep, - ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function turns on/off line search reports. -These reports are described in more details in developer-only comments on -MinBLEICState object. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedDRep- whether line search reports are needed or not - -This function is intended for private use only. Turning it on artificially -may cause program failure. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetdrep(minbleicstate* state, - ae_bool needdrep, - ae_state *_state) -{ - - - state->drep = needdrep; -} - - -/************************************************************************* -This function sets maximum step length - -IMPORTANT: this feature is hard to combine with preconditioning. You can't -set upper limit on step length, when you solve optimization problem with -linear (non-boundary) constraints AND preconditioner turned on. - -When non-boundary constraints are present, you have to either a) use -preconditioner, or b) use upper limit on step length. YOU CAN'T USE BOTH! -In this case algorithm will terminate with appropriate error code. - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which lead to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetstpmax(minbleicstate* state, - double stpmax, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(stpmax, _state), "MinBLEICSetStpMax: StpMax is not finite!", _state); - ae_assert(ae_fp_greater_eq(stpmax,0), "MinBLEICSetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* -NOTES: - -1. This function has two different implementations: one which uses exact - (analytical) user-supplied gradient, and one which uses function value - only and numerically differentiates function in order to obtain - gradient. - - Depending on the specific function used to create optimizer object - (either MinBLEICCreate() for analytical gradient or MinBLEICCreateF() - for numerical differentiation) you should choose appropriate variant of - MinBLEICOptimize() - one which accepts function AND gradient or one - which accepts function ONLY. - - Be careful to choose variant of MinBLEICOptimize() which corresponds to - your optimization scheme! Table below lists different combinations of - callback (function/gradient) passed to MinBLEICOptimize() and specific - function used to create optimizer. - - - | USER PASSED TO MinBLEICOptimize() - CREATED WITH | function only | function and gradient - ------------------------------------------------------------ - MinBLEICCreateF() | work FAIL - MinBLEICCreate() | FAIL work - - Here "FAIL" denotes inappropriate combinations of optimizer creation - function and MinBLEICOptimize() version. Attemps to use such - combination (for example, to create optimizer with MinBLEICCreateF() - and to pass gradient information to MinCGOptimize()) will lead to - exception being thrown. Either you did not pass gradient when it WAS - needed or you passed gradient when it was NOT needed. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -ae_bool minbleiciteration(minbleicstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t m; - ae_int_t i; - ae_int_t j; - double v; - double vv; - ae_int_t badbfgsits; - ae_bool b; - ae_int_t nextaction; - ae_int_t mcinfo; - ae_int_t actstatus; - ae_int_t ic; - double penalty; - double ginit; - double gdecay; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - i = state->rstate.ia.ptr.p_int[2]; - j = state->rstate.ia.ptr.p_int[3]; - badbfgsits = state->rstate.ia.ptr.p_int[4]; - nextaction = state->rstate.ia.ptr.p_int[5]; - mcinfo = state->rstate.ia.ptr.p_int[6]; - actstatus = state->rstate.ia.ptr.p_int[7]; - ic = state->rstate.ia.ptr.p_int[8]; - b = state->rstate.ba.ptr.p_bool[0]; - v = state->rstate.ra.ptr.p_double[0]; - vv = state->rstate.ra.ptr.p_double[1]; - penalty = state->rstate.ra.ptr.p_double[2]; - ginit = state->rstate.ra.ptr.p_double[3]; - gdecay = state->rstate.ra.ptr.p_double[4]; - } - else - { - n = -983; - m = -989; - i = -834; - j = 900; - badbfgsits = -287; - nextaction = 364; - mcinfo = 214; - actstatus = -338; - ic = -686; - b = ae_false; - v = 585; - vv = 497; - penalty = -271; - ginit = -581; - gdecay = 745; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - if( state->rstate.stage==8 ) - { - goto lbl_8; - } - if( state->rstate.stage==9 ) - { - goto lbl_9; - } - if( state->rstate.stage==10 ) - { - goto lbl_10; - } - if( state->rstate.stage==11 ) - { - goto lbl_11; - } - if( state->rstate.stage==12 ) - { - goto lbl_12; - } - if( state->rstate.stage==13 ) - { - goto lbl_13; - } - if( state->rstate.stage==14 ) - { - goto lbl_14; - } - if( state->rstate.stage==15 ) - { - goto lbl_15; - } - if( state->rstate.stage==16 ) - { - goto lbl_16; - } - if( state->rstate.stage==17 ) - { - goto lbl_17; - } - if( state->rstate.stage==18 ) - { - goto lbl_18; - } - if( state->rstate.stage==19 ) - { - goto lbl_19; - } - if( state->rstate.stage==20 ) - { - goto lbl_20; - } - if( state->rstate.stage==21 ) - { - goto lbl_21; - } - if( state->rstate.stage==22 ) - { - goto lbl_22; - } - if( state->rstate.stage==23 ) - { - goto lbl_23; - } - if( state->rstate.stage==24 ) - { - goto lbl_24; - } - if( state->rstate.stage==25 ) - { - goto lbl_25; - } - if( state->rstate.stage==26 ) - { - goto lbl_26; - } - if( state->rstate.stage==27 ) - { - goto lbl_27; - } - if( state->rstate.stage==28 ) - { - goto lbl_28; - } - if( state->rstate.stage==29 ) - { - goto lbl_29; - } - if( state->rstate.stage==30 ) - { - goto lbl_30; - } - if( state->rstate.stage==31 ) - { - goto lbl_31; - } - if( state->rstate.stage==32 ) - { - goto lbl_32; - } - if( state->rstate.stage==33 ) - { - goto lbl_33; - } - if( state->rstate.stage==34 ) - { - goto lbl_34; - } - if( state->rstate.stage==35 ) - { - goto lbl_35; - } - if( state->rstate.stage==36 ) - { - goto lbl_36; - } - if( state->rstate.stage==37 ) - { - goto lbl_37; - } - if( state->rstate.stage==38 ) - { - goto lbl_38; - } - if( state->rstate.stage==39 ) - { - goto lbl_39; - } - if( state->rstate.stage==40 ) - { - goto lbl_40; - } - if( state->rstate.stage==41 ) - { - goto lbl_41; - } - - /* - * Routine body - */ - - /* - * Algorithm parameters: - * * M number of L-BFGS corrections. - * This coefficient remains fixed during iterations. - * * GDecay desired decrease of constrained gradient during L-BFGS iterations. - * This coefficient is decreased after each L-BFGS round until - * it reaches minimum decay. - */ - m = ae_minint(5, state->nmain, _state); - gdecay = minbleic_initialdecay; - - /* - * Init - */ - n = state->nmain; - state->repterminationtype = 0; - state->repinneriterationscount = 0; - state->repouteriterationscount = 0; - state->repnfev = 0; - state->repvaridx = -1; - state->repdebugeqerr = 0.0; - state->repdebugfs = _state->v_nan; - state->repdebugff = _state->v_nan; - state->repdebugdx = _state->v_nan; - if( ae_fp_neq(state->stpmax,0)&&state->prectype!=0 ) - { - state->repterminationtype = -10; - result = ae_false; - return result; - } - rvectorsetlengthatleast(&state->rho, m, _state); - rvectorsetlengthatleast(&state->theta, m, _state); - rmatrixsetlengthatleast(&state->yk, m, n, _state); - rmatrixsetlengthatleast(&state->sk, m, n, _state); - - /* - * Fill TmpPrec with current preconditioner - */ - rvectorsetlengthatleast(&state->tmpprec, n, _state); - for(i=0; i<=n-1; i++) - { - if( state->prectype==2 ) - { - state->tmpprec.ptr.p_double[i] = state->diagh.ptr.p_double[i]; - continue; - } - if( state->prectype==3 ) - { - state->tmpprec.ptr.p_double[i] = 1/ae_sqr(state->s.ptr.p_double[i], _state); - continue; - } - state->tmpprec.ptr.p_double[i] = 1; - } - sassetprecdiag(&state->sas, &state->tmpprec, _state); - - /* - * Start optimization - */ - if( !sasstartoptimization(&state->sas, &state->xstart, _state) ) - { - state->repterminationtype = -3; - result = ae_false; - return result; - } - - /* - * Check correctness of user-supplied gradient - */ - if( !(ae_fp_eq(state->diffstep,0)&&ae_fp_greater(state->teststep,0)) ) - { - goto lbl_42; - } - minbleic_clearrequestfields(state, _state); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->needfg = ae_true; - i = 0; -lbl_44: - if( i>n-1 ) - { - goto lbl_46; - } - ae_assert(!state->hasbndl.ptr.p_bool[i]||ae_fp_greater_eq(state->sas.xc.ptr.p_double[i],state->bndl.ptr.p_double[i]), "MinBLEICIteration: internal error(State.X is out of bounds)", _state); - ae_assert(!state->hasbndu.ptr.p_bool[i]||ae_fp_less_eq(state->sas.xc.ptr.p_double[i],state->bndu.ptr.p_double[i]), "MinBLEICIteration: internal error(State.X is out of bounds)", _state); - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->teststep*state->s.ptr.p_double[i]; - if( state->hasbndl.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_maxreal(state->x.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - state->xm1 = state->x.ptr.p_double[i]; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->fm1 = state->f; - state->gm1 = state->g.ptr.p_double[i]; - state->x.ptr.p_double[i] = v+state->teststep*state->s.ptr.p_double[i]; - if( state->hasbndu.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_minreal(state->x.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - state->xp1 = state->x.ptr.p_double[i]; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->fp1 = state->f; - state->gp1 = state->g.ptr.p_double[i]; - state->x.ptr.p_double[i] = (state->xm1+state->xp1)/2; - if( state->hasbndl.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_maxreal(state->x.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - if( state->hasbndu.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_minreal(state->x.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->x.ptr.p_double[i] = v; - if( !derivativecheck(state->fm1, state->gm1, state->fp1, state->gp1, state->f, state->g.ptr.p_double[i], state->xp1-state->xm1, _state) ) - { - state->repvaridx = i; - state->repterminationtype = -7; - sasstopoptimization(&state->sas, _state); - result = ae_false; - return result; - } - i = i+1; - goto lbl_44; -lbl_46: - state->needfg = ae_false; -lbl_42: - - /* - * Main cycle of BLEIC-PG algorithm - */ - state->repterminationtype = 4; - badbfgsits = 0; - state->lastgoodstep = 0; - state->lastscaledgoodstep = 0; - state->maxscaledgrad = 0; - state->nonmonotoniccnt = n+state->nic; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minbleic_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_47; - } - state->needfg = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needfg = ae_false; - goto lbl_48; -lbl_47: - state->needf = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->needf = ae_false; -lbl_48: - state->fc = state->f; - trimprepare(state->f, &state->trimthreshold, _state); - state->repnfev = state->repnfev+1; - if( !state->xrep ) - { - goto lbl_49; - } - - /* - * Report current point - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fc; - state->xupdated = ae_true; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->xupdated = ae_false; -lbl_49: -lbl_51: - if( ae_false ) - { - goto lbl_52; - } - - /* - * Phase 1 - * - * (a) calculate unconstrained gradient - * (b) determine active set - * (c) update MaxScaledGrad - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minbleic_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_53; - } - - /* - * Analytic gradient - */ - state->needfg = ae_true; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->needfg = ae_false; - goto lbl_54; -lbl_53: - - /* - * Numerical differentiation - */ - state->needf = ae_true; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->fbase = state->f; - i = 0; -lbl_55: - if( i>n-1 ) - { - goto lbl_57; - } - v = state->x.ptr.p_double[i]; - b = ae_false; - if( state->hasbndl.ptr.p_bool[i] ) - { - b = b||ae_fp_less(v-state->diffstep*state->s.ptr.p_double[i],state->bndl.ptr.p_double[i]); - } - if( state->hasbndu.ptr.p_bool[i] ) - { - b = b||ae_fp_greater(v+state->diffstep*state->s.ptr.p_double[i],state->bndu.ptr.p_double[i]); - } - if( b ) - { - goto lbl_58; - } - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 8; - goto lbl_rcomm; -lbl_8: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 9; - goto lbl_rcomm; -lbl_9: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 10; - goto lbl_rcomm; -lbl_10: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 11; - goto lbl_rcomm; -lbl_11: - state->fp2 = state->f; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - goto lbl_59; -lbl_58: - state->xm1 = v-state->diffstep*state->s.ptr.p_double[i]; - state->xp1 = v+state->diffstep*state->s.ptr.p_double[i]; - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(state->xm1,state->bndl.ptr.p_double[i]) ) - { - state->xm1 = state->bndl.ptr.p_double[i]; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(state->xp1,state->bndu.ptr.p_double[i]) ) - { - state->xp1 = state->bndu.ptr.p_double[i]; - } - state->x.ptr.p_double[i] = state->xm1; - state->rstate.stage = 12; - goto lbl_rcomm; -lbl_12: - state->fm1 = state->f; - state->x.ptr.p_double[i] = state->xp1; - state->rstate.stage = 13; - goto lbl_rcomm; -lbl_13: - state->fp1 = state->f; - if( ae_fp_neq(state->xm1,state->xp1) ) - { - state->g.ptr.p_double[i] = (state->fp1-state->fm1)/(state->xp1-state->xm1); - } - else - { - state->g.ptr.p_double[i] = 0; - } -lbl_59: - state->x.ptr.p_double[i] = v; - i = i+1; - goto lbl_55; -lbl_57: - state->f = state->fbase; - state->needf = ae_false; -lbl_54: - state->fc = state->f; - ae_v_move(&state->gc.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - sasreactivateconstraintsprec(&state->sas, &state->gc, _state); - v = 0.0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->gc.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - state->maxscaledgrad = ae_maxreal(state->maxscaledgrad, ae_sqrt(v, _state), _state); - - /* - * Phase 2: perform steepest descent step. - * - * NextAction control variable is set on exit from this loop: - * * NextAction>0 in case we have to proceed to Phase 3 (L-BFGS step) - * * NextAction<0 in case we have to proceed to Phase 1 (recalculate active set) - * * NextAction=0 in case we found solution (step size or function change are small enough) - */ - nextaction = 0; -lbl_60: - if( ae_false ) - { - goto lbl_61; - } - - /* - * Check gradient-based stopping criteria - */ - if( ae_fp_less_eq(sasscaledconstrainednorm(&state->sas, &state->gc, _state),state->epsg) ) - { - - /* - * Gradient is small enough, stop iterations - */ - state->repterminationtype = 4; - nextaction = 0; - goto lbl_61; - } - - /* - * Calculate normalized constrained descent direction, store to D. - * Try to use previous scaled step length as initial estimate for new step. - * - * NOTE: D can be exactly zero, in this case Stp is set to 1.0 - */ - sasconstraineddescentprec(&state->sas, &state->gc, &state->d, _state); - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->d.ptr.p_double[i]/state->s.ptr.p_double[i], _state); - } - v = ae_sqrt(v, _state); - if( ae_fp_greater(state->lastscaledgoodstep,0)&&ae_fp_greater(v,0) ) - { - state->stp = state->lastscaledgoodstep/v; - } - else - { - state->stp = 1.0; - } - - /* - * Calculate bound on step length. - * Enforce user-supplied limit on step length. - */ - sasexploredirection(&state->sas, &state->d, &state->curstpmax, &state->cidx, &state->cval, _state); - state->activationstep = state->curstpmax; - if( state->cidx>=0&&ae_fp_eq(state->activationstep,0) ) - { - sasimmediateactivation(&state->sas, state->cidx, state->cval, _state); - goto lbl_60; - } - if( ae_fp_greater(state->stpmax,0) ) - { - state->curstpmax = ae_minreal(state->curstpmax, state->stpmax, _state); - } - - /* - * Report beginning of line search (if requested by caller). - * See description of the MinBLEICState for more information - * about fields accessible to caller. - * - * Caller may do following: - * * change State.Stp and load better initial estimate of - * the step length. - */ - if( !state->drep ) - { - goto lbl_62; - } - minbleic_clearrequestfields(state, _state); - state->lsstart = ae_true; - state->lbfgssearch = ae_false; - state->boundedstep = state->cidx>=0; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->g.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fc; - state->rstate.stage = 14; - goto lbl_rcomm; -lbl_14: - state->lsstart = ae_false; -lbl_62: - - /* - * Perform optimization of F along XC+alpha*D. - */ - state->mcstage = 0; - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->gn.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fn = state->fc; - mcsrch(n, &state->xn, &state->fn, &state->gn, &state->d, &state->stp, state->curstpmax, minbleic_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); -lbl_64: - if( state->mcstage==0 ) - { - goto lbl_65; - } - - /* - * Enforce constraints (correction) in XN. - * Copy current point from XN to X. - */ - sascorrection(&state->sas, &state->xn, &penalty, _state); - for(i=0; i<=n-1; i++) - { - state->x.ptr.p_double[i] = state->xn.ptr.p_double[i]; - } - - /* - * Gradient, either user-provided or numerical differentiation - */ - minbleic_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_66; - } - - /* - * Analytic gradient - */ - state->needfg = ae_true; - state->rstate.stage = 15; - goto lbl_rcomm; -lbl_15: - state->needfg = ae_false; - state->repnfev = state->repnfev+1; - goto lbl_67; -lbl_66: - - /* - * Numerical differentiation - */ - state->needf = ae_true; - state->rstate.stage = 16; - goto lbl_rcomm; -lbl_16: - state->fbase = state->f; - i = 0; -lbl_68: - if( i>n-1 ) - { - goto lbl_70; - } - v = state->x.ptr.p_double[i]; - b = ae_false; - if( state->hasbndl.ptr.p_bool[i] ) - { - b = b||ae_fp_less(v-state->diffstep*state->s.ptr.p_double[i],state->bndl.ptr.p_double[i]); - } - if( state->hasbndu.ptr.p_bool[i] ) - { - b = b||ae_fp_greater(v+state->diffstep*state->s.ptr.p_double[i],state->bndu.ptr.p_double[i]); - } - if( b ) - { - goto lbl_71; - } - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 17; - goto lbl_rcomm; -lbl_17: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 18; - goto lbl_rcomm; -lbl_18: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 19; - goto lbl_rcomm; -lbl_19: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 20; - goto lbl_rcomm; -lbl_20: - state->fp2 = state->f; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - state->repnfev = state->repnfev+4; - goto lbl_72; -lbl_71: - state->xm1 = v-state->diffstep*state->s.ptr.p_double[i]; - state->xp1 = v+state->diffstep*state->s.ptr.p_double[i]; - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(state->xm1,state->bndl.ptr.p_double[i]) ) - { - state->xm1 = state->bndl.ptr.p_double[i]; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(state->xp1,state->bndu.ptr.p_double[i]) ) - { - state->xp1 = state->bndu.ptr.p_double[i]; - } - state->x.ptr.p_double[i] = state->xm1; - state->rstate.stage = 21; - goto lbl_rcomm; -lbl_21: - state->fm1 = state->f; - state->x.ptr.p_double[i] = state->xp1; - state->rstate.stage = 22; - goto lbl_rcomm; -lbl_22: - state->fp1 = state->f; - if( ae_fp_neq(state->xm1,state->xp1) ) - { - state->g.ptr.p_double[i] = (state->fp1-state->fm1)/(state->xp1-state->xm1); - } - else - { - state->g.ptr.p_double[i] = 0; - } - state->repnfev = state->repnfev+2; -lbl_72: - state->x.ptr.p_double[i] = v; - i = i+1; - goto lbl_68; -lbl_70: - state->f = state->fbase; - state->needf = ae_false; -lbl_67: - - /* - * Back to MCSRCH - * - * NOTE: penalty term from correction is added to FN in order - * to penalize increase in infeasibility. - */ - state->fn = state->f+minbleic_penaltyfactor*state->maxscaledgrad*penalty; - ae_v_move(&state->gn.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - trimfunction(&state->fn, &state->gn, n, state->trimthreshold, _state); - mcsrch(n, &state->xn, &state->fn, &state->gn, &state->d, &state->stp, state->curstpmax, minbleic_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); - goto lbl_64; -lbl_65: - - /* - * Handle possible failure of the line search - */ - if( mcinfo!=1&&mcinfo!=5 ) - { - - /* - * We can not find step which decreases function value. We have - * two possibilities: - * (a) numerical properties of the function do not allow us to - * find good solution. - * (b) we are close to activation of some constraint, and it is - * so close that step which activates it leads to change in - * target function which is smaller than numerical noise. - * - * Optimization algorithm must be able to handle case (b), because - * inability to handle it will cause failure when algorithm - * started very close to boundary of the feasible area. - * - * In order to correctly handle such cases we allow limited amount - * of small steps which increase function value. - */ - v = 0.0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->d.ptr.p_double[i]*state->curstpmax/state->s.ptr.p_double[i], _state); - } - v = ae_sqrt(v, _state); - if( (state->cidx>=0&&ae_fp_less_eq(v,minbleic_maxnonmonotoniclen))&&state->nonmonotoniccnt>0 ) - { - - /* - * We enforce non-monotonic step: - * * Stp := CurStpMax - * * MCINFO := 5 - * * XN := XC+CurStpMax*D - * * non-monotonic counter is decreased - */ - state->stp = state->curstpmax; - mcinfo = 5; - v = state->curstpmax; - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->xn.ptr.p_double[0], 1, &state->d.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - state->nonmonotoniccnt = state->nonmonotoniccnt-1; - } - else - { - - /* - * Numerical properties of the function does not allow us to solve problem - */ - state->repterminationtype = 7; - nextaction = 0; - goto lbl_61; - } - } - - /* - * Current point is updated. - */ - ae_v_move(&state->xp.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->gp.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fp = state->fc; - actstatus = sasmoveto(&state->sas, &state->xn, state->cidx>=0&&ae_fp_greater_eq(state->stp,state->activationstep), state->cidx, state->cval, _state); - ae_v_move(&state->gc.ptr.p_double[0], 1, &state->gn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fc = state->fn; - state->repinneriterationscount = state->repinneriterationscount+1; - if( !state->xrep ) - { - goto lbl_73; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minbleic_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 23; - goto lbl_rcomm; -lbl_23: - state->xupdated = ae_false; -lbl_73: - - /* - * Check for stopping. - * - * Step, gradient and function-based stopping criteria are tested only - * for steps which satisfy Wolfe conditions. - * - * MaxIts-based stopping condition is checked for all steps - */ - if( mcinfo==1 ) - { - - /* - * Step is small enough - */ - v = 0; - vv = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr((state->sas.xc.ptr.p_double[i]-state->xp.ptr.p_double[i])/state->s.ptr.p_double[i], _state); - vv = vv+ae_sqr(state->sas.xc.ptr.p_double[i]-state->xp.ptr.p_double[i], _state); - } - v = ae_sqrt(v, _state); - vv = ae_sqrt(vv, _state); - if( ae_fp_less_eq(v,state->epsx) ) - { - state->repterminationtype = 2; - nextaction = 0; - goto lbl_61; - } - state->lastgoodstep = vv; - minbleic_updateestimateofgoodstep(&state->lastscaledgoodstep, v, _state); - - /* - * Function change is small enough - */ - if( ae_fp_less_eq(ae_fabs(state->fp-state->fc, _state),state->epsf*ae_maxreal(ae_fabs(state->fc, _state), ae_maxreal(ae_fabs(state->fp, _state), 1.0, _state), _state)) ) - { - - /* - * Function change is small enough - */ - state->repterminationtype = 1; - nextaction = 0; - goto lbl_61; - } - } - if( state->maxits>0&&state->repinneriterationscount>=state->maxits ) - { - - /* - * Required number of iterations was performed - */ - state->repterminationtype = 5; - nextaction = 0; - goto lbl_61; - } - - /* - * Decide where to move: - * * in case only "candidate" constraints were activated, repeat stage 2 - * * in case no constraints was activated, move to stage 3 - * * otherwise, move to stage 1 (re-evaluation of the active set) - */ - if( actstatus==0 ) - { - goto lbl_60; - } - if( actstatus<0 ) - { - nextaction = 1; - } - else - { - nextaction = -1; - } - goto lbl_61; - goto lbl_60; -lbl_61: - if( nextaction<0 ) - { - goto lbl_51; - } - if( nextaction==0 ) - { - goto lbl_52; - } - - /* - * Phase 3: L-BFGS step - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minbleic_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_75; - } - - /* - * Analytic gradient - */ - state->needfg = ae_true; - state->rstate.stage = 24; - goto lbl_rcomm; -lbl_24: - state->needfg = ae_false; - state->repnfev = state->repnfev+1; - goto lbl_76; -lbl_75: - - /* - * Numerical differentiation - */ - state->needf = ae_true; - state->rstate.stage = 25; - goto lbl_rcomm; -lbl_25: - state->fbase = state->f; - i = 0; -lbl_77: - if( i>n-1 ) - { - goto lbl_79; - } - v = state->x.ptr.p_double[i]; - b = ae_false; - if( state->hasbndl.ptr.p_bool[i] ) - { - b = b||ae_fp_less(v-state->diffstep*state->s.ptr.p_double[i],state->bndl.ptr.p_double[i]); - } - if( state->hasbndu.ptr.p_bool[i] ) - { - b = b||ae_fp_greater(v+state->diffstep*state->s.ptr.p_double[i],state->bndu.ptr.p_double[i]); - } - if( b ) - { - goto lbl_80; - } - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 26; - goto lbl_rcomm; -lbl_26: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 27; - goto lbl_rcomm; -lbl_27: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 28; - goto lbl_rcomm; -lbl_28: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 29; - goto lbl_rcomm; -lbl_29: - state->fp2 = state->f; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - state->repnfev = state->repnfev+4; - goto lbl_81; -lbl_80: - state->xm1 = v-state->diffstep*state->s.ptr.p_double[i]; - state->xp1 = v+state->diffstep*state->s.ptr.p_double[i]; - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(state->xm1,state->bndl.ptr.p_double[i]) ) - { - state->xm1 = state->bndl.ptr.p_double[i]; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(state->xp1,state->bndu.ptr.p_double[i]) ) - { - state->xp1 = state->bndu.ptr.p_double[i]; - } - state->x.ptr.p_double[i] = state->xm1; - state->rstate.stage = 30; - goto lbl_rcomm; -lbl_30: - state->fm1 = state->f; - state->x.ptr.p_double[i] = state->xp1; - state->rstate.stage = 31; - goto lbl_rcomm; -lbl_31: - state->fp1 = state->f; - if( ae_fp_neq(state->xm1,state->xp1) ) - { - state->g.ptr.p_double[i] = (state->fp1-state->fm1)/(state->xp1-state->xm1); - } - else - { - state->g.ptr.p_double[i] = 0; - } - state->repnfev = state->repnfev+2; -lbl_81: - state->x.ptr.p_double[i] = v; - i = i+1; - goto lbl_77; -lbl_79: - state->f = state->fbase; - state->needf = ae_false; -lbl_76: - state->fc = state->f; - trimprepare(state->fc, &state->trimthreshold, _state); - ae_v_move(&state->gc.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_moveneg(&state->d.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - sasconstraineddirection(&state->sas, &state->gc, _state); - sasconstraineddirectionprec(&state->sas, &state->d, _state); - ginit = 0.0; - for(i=0; i<=n-1; i++) - { - ginit = ginit+ae_sqr(state->gc.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - ginit = ae_sqrt(ginit, _state); - state->k = 0; -lbl_82: - if( state->k>n ) - { - goto lbl_83; - } - - /* - * Main cycle: prepare to 1-D line search - */ - state->p = state->k%m; - state->q = ae_minint(state->k, m-1, _state); - - /* - * Store X[k], G[k] - */ - ae_v_moveneg(&state->sk.ptr.pp_double[state->p][0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_moveneg(&state->yk.ptr.pp_double[state->p][0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Try to use previous scaled step length as initial estimate for new step. - */ - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->d.ptr.p_double[i]/state->s.ptr.p_double[i], _state); - } - v = ae_sqrt(v, _state); - if( ae_fp_greater(state->lastscaledgoodstep,0)&&ae_fp_greater(v,0) ) - { - state->stp = state->lastscaledgoodstep/v; - } - else - { - state->stp = 1.0; - } - - /* - * Calculate bound on step length - */ - sasexploredirection(&state->sas, &state->d, &state->curstpmax, &state->cidx, &state->cval, _state); - state->activationstep = state->curstpmax; - if( state->cidx>=0&&ae_fp_eq(state->activationstep,0) ) - { - goto lbl_83; - } - if( ae_fp_greater(state->stpmax,0) ) - { - v = ae_v_dotproduct(&state->d.ptr.p_double[0], 1, &state->d.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = ae_sqrt(v, _state); - if( ae_fp_greater(v,0) ) - { - state->curstpmax = ae_minreal(state->curstpmax, state->stpmax/v, _state); - } - } - - /* - * Report beginning of line search (if requested by caller). - * See description of the MinBLEICState for more information - * about fields accessible to caller. - * - * Caller may do following: - * * change State.Stp and load better initial estimate of - * the step length. - * Caller may not terminate algorithm. - */ - if( !state->drep ) - { - goto lbl_84; - } - minbleic_clearrequestfields(state, _state); - state->lsstart = ae_true; - state->lbfgssearch = ae_true; - state->boundedstep = state->cidx>=0; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->rstate.stage = 32; - goto lbl_rcomm; -lbl_32: - state->lsstart = ae_false; -lbl_84: - - /* - * Minimize F(x+alpha*d) - */ - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->gn.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fn = state->fc; - state->mcstage = 0; - mcsrch(n, &state->xn, &state->fn, &state->gn, &state->d, &state->stp, state->curstpmax, minbleic_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); -lbl_86: - if( state->mcstage==0 ) - { - goto lbl_87; - } - - /* - * Perform correction (constraints are enforced) - * Copy XN to X - */ - sascorrection(&state->sas, &state->xn, &penalty, _state); - for(i=0; i<=n-1; i++) - { - state->x.ptr.p_double[i] = state->xn.ptr.p_double[i]; - } - - /* - * Gradient, either user-provided or numerical differentiation - */ - minbleic_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_88; - } - - /* - * Analytic gradient - */ - state->needfg = ae_true; - state->rstate.stage = 33; - goto lbl_rcomm; -lbl_33: - state->needfg = ae_false; - state->repnfev = state->repnfev+1; - goto lbl_89; -lbl_88: - - /* - * Numerical differentiation - */ - state->needf = ae_true; - state->rstate.stage = 34; - goto lbl_rcomm; -lbl_34: - state->fbase = state->f; - i = 0; -lbl_90: - if( i>n-1 ) - { - goto lbl_92; - } - v = state->x.ptr.p_double[i]; - b = ae_false; - if( state->hasbndl.ptr.p_bool[i] ) - { - b = b||ae_fp_less(v-state->diffstep*state->s.ptr.p_double[i],state->bndl.ptr.p_double[i]); - } - if( state->hasbndu.ptr.p_bool[i] ) - { - b = b||ae_fp_greater(v+state->diffstep*state->s.ptr.p_double[i],state->bndu.ptr.p_double[i]); - } - if( b ) - { - goto lbl_93; - } - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 35; - goto lbl_rcomm; -lbl_35: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 36; - goto lbl_rcomm; -lbl_36: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 37; - goto lbl_rcomm; -lbl_37: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 38; - goto lbl_rcomm; -lbl_38: - state->fp2 = state->f; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - state->repnfev = state->repnfev+4; - goto lbl_94; -lbl_93: - state->xm1 = v-state->diffstep*state->s.ptr.p_double[i]; - state->xp1 = v+state->diffstep*state->s.ptr.p_double[i]; - if( state->hasbndl.ptr.p_bool[i]&&ae_fp_less(state->xm1,state->bndl.ptr.p_double[i]) ) - { - state->xm1 = state->bndl.ptr.p_double[i]; - } - if( state->hasbndu.ptr.p_bool[i]&&ae_fp_greater(state->xp1,state->bndu.ptr.p_double[i]) ) - { - state->xp1 = state->bndu.ptr.p_double[i]; - } - state->x.ptr.p_double[i] = state->xm1; - state->rstate.stage = 39; - goto lbl_rcomm; -lbl_39: - state->fm1 = state->f; - state->x.ptr.p_double[i] = state->xp1; - state->rstate.stage = 40; - goto lbl_rcomm; -lbl_40: - state->fp1 = state->f; - if( ae_fp_neq(state->xm1,state->xp1) ) - { - state->g.ptr.p_double[i] = (state->fp1-state->fm1)/(state->xp1-state->xm1); - } - else - { - state->g.ptr.p_double[i] = 0; - } - state->repnfev = state->repnfev+2; -lbl_94: - state->x.ptr.p_double[i] = v; - i = i+1; - goto lbl_90; -lbl_92: - state->f = state->fbase; - state->needf = ae_false; -lbl_89: - - /* - * Back to MCSRCH - * - * NOTE: penalty term from correction is added to FN in order - * to penalize increase in infeasibility. - */ - state->fn = state->f+minbleic_penaltyfactor*state->maxscaledgrad*penalty; - ae_v_move(&state->gn.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - sasconstraineddirection(&state->sas, &state->gn, _state); - trimfunction(&state->fn, &state->gn, n, state->trimthreshold, _state); - mcsrch(n, &state->xn, &state->fn, &state->gn, &state->d, &state->stp, state->curstpmax, minbleic_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); - goto lbl_86; -lbl_87: - ae_v_add(&state->sk.ptr.pp_double[state->p][0], 1, &state->xn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_add(&state->yk.ptr.pp_double[state->p][0], 1, &state->gn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Handle possible failure of the line search - */ - if( mcinfo!=1&&mcinfo!=5 ) - { - goto lbl_83; - } - - /* - * Current point is updated. - */ - ae_v_move(&state->xp.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->gp.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fp = state->fc; - actstatus = sasmoveto(&state->sas, &state->xn, state->cidx>=0&&ae_fp_greater_eq(state->stp,state->activationstep), state->cidx, state->cval, _state); - ae_v_move(&state->gc.ptr.p_double[0], 1, &state->gn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fc = state->fn; - if( !state->xrep ) - { - goto lbl_95; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minbleic_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 41; - goto lbl_rcomm; -lbl_41: - state->xupdated = ae_false; -lbl_95: - state->repinneriterationscount = state->repinneriterationscount+1; - - /* - * Update length of the good step - */ - if( mcinfo==1 ) - { - v = 0; - vv = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr((state->sas.xc.ptr.p_double[i]-state->xp.ptr.p_double[i])/state->s.ptr.p_double[i], _state); - vv = vv+ae_sqr(state->sas.xc.ptr.p_double[i]-state->xp.ptr.p_double[i], _state); - } - state->lastgoodstep = ae_sqrt(vv, _state); - minbleic_updateestimateofgoodstep(&state->lastscaledgoodstep, ae_sqrt(v, _state), _state); - } - - /* - * Termination of the L-BFGS algorithm: - * a) line search was performed with activation of constraint - * b) scaled gradient decreased below GDecay - * c) iterations counter >= MaxIts - */ - if( actstatus>=0 ) - { - goto lbl_83; - } - v = 0.0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->gc.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - if( ae_fp_less(ae_sqrt(v, _state),gdecay*ginit) ) - { - goto lbl_83; - } - if( state->maxits>0&&state->repinneriterationscount>=state->maxits ) - { - goto lbl_83; - } - - /* - * Update L-BFGS model: - * * calculate Rho[k] - * * calculate d(k+1) = -H(k+1)*g(k+1) - * (use constrained preconditioner to perform multiplication) - */ - v = ae_v_dotproduct(&state->yk.ptr.pp_double[state->p][0], 1, &state->sk.ptr.pp_double[state->p][0], 1, ae_v_len(0,n-1)); - vv = ae_v_dotproduct(&state->yk.ptr.pp_double[state->p][0], 1, &state->yk.ptr.pp_double[state->p][0], 1, ae_v_len(0,n-1)); - if( ae_fp_eq(v,0)||ae_fp_eq(vv,0) ) - { - goto lbl_83; - } - state->rho.ptr.p_double[state->p] = 1/v; - ae_v_move(&state->work.ptr.p_double[0], 1, &state->gn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=state->k; i>=state->k-state->q; i--) - { - ic = i%m; - v = ae_v_dotproduct(&state->sk.ptr.pp_double[ic][0], 1, &state->work.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->theta.ptr.p_double[ic] = v; - vv = v*state->rho.ptr.p_double[ic]; - ae_v_subd(&state->work.ptr.p_double[0], 1, &state->yk.ptr.pp_double[ic][0], 1, ae_v_len(0,n-1), vv); - } - sasconstraineddirectionprec(&state->sas, &state->work, _state); - for(i=state->k-state->q; i<=state->k; i++) - { - ic = i%m; - v = ae_v_dotproduct(&state->yk.ptr.pp_double[ic][0], 1, &state->work.ptr.p_double[0], 1, ae_v_len(0,n-1)); - vv = state->rho.ptr.p_double[ic]*(-v+state->theta.ptr.p_double[ic]); - ae_v_addd(&state->work.ptr.p_double[0], 1, &state->sk.ptr.pp_double[ic][0], 1, ae_v_len(0,n-1), vv); - } - ae_v_moveneg(&state->d.ptr.p_double[0], 1, &state->work.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->k = state->k+1; - goto lbl_82; -lbl_83: - - /* - * Decrease decay coefficient. Subsequent L-BFGS stages will - * have more stringent stopping criteria. - */ - gdecay = ae_maxreal(gdecay*minbleic_decaycorrection, minbleic_mindecay, _state); - goto lbl_51; -lbl_52: - sasstopoptimization(&state->sas, _state); - state->repouteriterationscount = 1; - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = i; - state->rstate.ia.ptr.p_int[3] = j; - state->rstate.ia.ptr.p_int[4] = badbfgsits; - state->rstate.ia.ptr.p_int[5] = nextaction; - state->rstate.ia.ptr.p_int[6] = mcinfo; - state->rstate.ia.ptr.p_int[7] = actstatus; - state->rstate.ia.ptr.p_int[8] = ic; - state->rstate.ba.ptr.p_bool[0] = b; - state->rstate.ra.ptr.p_double[0] = v; - state->rstate.ra.ptr.p_double[1] = vv; - state->rstate.ra.ptr.p_double[2] = penalty; - state->rstate.ra.ptr.p_double[3] = ginit; - state->rstate.ra.ptr.p_double[4] = gdecay; - return result; -} - - -/************************************************************************* -BLEIC results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report. You should check Rep.TerminationType - in order to distinguish successful termination from - unsuccessful one: - * -7 gradient verification failed. - See MinBLEICSetGradientCheck() for more information. - * -3 inconsistent constraints. Feasible point is - either nonexistent or too hard to find. Try to - restart optimizer with better initial approximation - * 1 relative function improvement is no more than EpsF. - * 2 scaled step is no more than EpsX. - * 4 scaled gradient norm is no more than EpsG. - * 5 MaxIts steps was taken - More information about fields of this structure can be - found in the comments on MinBLEICReport datatype. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicresults(minbleicstate* state, - /* Real */ ae_vector* x, - minbleicreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _minbleicreport_clear(rep); - - minbleicresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -BLEIC results - -Buffered implementation of MinBLEICResults() which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicresultsbuf(minbleicstate* state, - /* Real */ ae_vector* x, - minbleicreport* rep, - ae_state *_state) -{ - ae_int_t i; - - - if( x->cntnmain ) - { - ae_vector_set_length(x, state->nmain, _state); - } - rep->iterationscount = state->repinneriterationscount; - rep->inneriterationscount = state->repinneriterationscount; - rep->outeriterationscount = state->repouteriterationscount; - rep->nfev = state->repnfev; - rep->varidx = state->repvaridx; - rep->terminationtype = state->repterminationtype; - if( state->repterminationtype>0 ) - { - ae_v_move(&x->ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,state->nmain-1)); - } - else - { - for(i=0; i<=state->nmain-1; i++) - { - x->ptr.p_double[i] = _state->v_nan; - } - } - rep->debugeqerr = state->repdebugeqerr; - rep->debugfs = state->repdebugfs; - rep->debugff = state->repdebugff; - rep->debugdx = state->repdebugdx; - rep->debugfeasqpits = state->repdebugfeasqpits; - rep->debugfeasgpaits = state->repdebugfeasgpaits; -} - - -/************************************************************************* -This subroutine restarts algorithm from new point. -All optimization parameters (including constraints) are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - X - new starting point. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicrestartfrom(minbleicstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - - - n = state->nmain; - - /* - * First, check for errors in the inputs - */ - ae_assert(x->cnt>=n, "MinBLEICRestartFrom: Length(X)xstart.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * prepare RComm facilities - */ - ae_vector_set_length(&state->rstate.ia, 8+1, _state); - ae_vector_set_length(&state->rstate.ba, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 4+1, _state); - state->rstate.stage = -1; - minbleic_clearrequestfields(state, _state); - sasstopoptimization(&state->sas, _state); -} - - -/************************************************************************* -This subroutine finalizes internal structures after emergency termination -from State.LSStart report (see comments on MinBLEICState for more information). - -INPUT PARAMETERS: - State - structure after exit from LSStart report - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicemergencytermination(minbleicstate* state, ae_state *_state) -{ - - - sasstopoptimization(&state->sas, _state); -} - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinBLEICOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinBLEICSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetgradientcheck(minbleicstate* state, - double teststep, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(teststep, _state), "MinBLEICSetGradientCheck: TestStep contains NaN or Infinite", _state); - ae_assert(ae_fp_greater_eq(teststep,0), "MinBLEICSetGradientCheck: invalid argument TestStep(TestStep<0)", _state); - state->teststep = teststep; -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forget to clear something) -*************************************************************************/ -static void minbleic_clearrequestfields(minbleicstate* state, - ae_state *_state) -{ - - - state->needf = ae_false; - state->needfg = ae_false; - state->xupdated = ae_false; - state->lsstart = ae_false; -} - - -/************************************************************************* -Internal initialization subroutine -*************************************************************************/ -static void minbleic_minbleicinitinternal(ae_int_t n, - /* Real */ ae_vector* x, - double diffstep, - minbleicstate* state, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_matrix c; - ae_vector ct; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init(&c, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ct, 0, DT_INT, _state, ae_true); - - - /* - * Initialize - */ - state->teststep = 0; - state->nmain = n; - state->diffstep = diffstep; - sasinit(n, &state->sas, _state); - ae_vector_set_length(&state->bndl, n, _state); - ae_vector_set_length(&state->hasbndl, n, _state); - ae_vector_set_length(&state->bndu, n, _state); - ae_vector_set_length(&state->hasbndu, n, _state); - ae_vector_set_length(&state->xstart, n, _state); - ae_vector_set_length(&state->gc, n, _state); - ae_vector_set_length(&state->xn, n, _state); - ae_vector_set_length(&state->gn, n, _state); - ae_vector_set_length(&state->xp, n, _state); - ae_vector_set_length(&state->gp, n, _state); - ae_vector_set_length(&state->d, n, _state); - ae_vector_set_length(&state->s, n, _state); - ae_vector_set_length(&state->x, n, _state); - ae_vector_set_length(&state->g, n, _state); - ae_vector_set_length(&state->work, n, _state); - for(i=0; i<=n-1; i++) - { - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->hasbndl.ptr.p_bool[i] = ae_false; - state->bndu.ptr.p_double[i] = _state->v_posinf; - state->hasbndu.ptr.p_bool[i] = ae_false; - state->s.ptr.p_double[i] = 1.0; - } - minbleicsetlc(state, &c, &ct, 0, _state); - minbleicsetcond(state, 0.0, 0.0, 0.0, 0, _state); - minbleicsetxrep(state, ae_false, _state); - minbleicsetdrep(state, ae_false, _state); - minbleicsetstpmax(state, 0.0, _state); - minbleicsetprecdefault(state, _state); - minbleicrestartfrom(state, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -This subroutine updates estimate of the good step length given: -1) previous estimate -2) new length of the good step - -It makes sure that estimate does not change too rapidly - ratio of new and -old estimates will be at least 0.01, at most 100.0 - -In case previous estimate of good step is zero (no estimate), new estimate -is used unconditionally. - - -- ALGLIB -- - Copyright 16.01.2013 by Bochkanov Sergey -*************************************************************************/ -static void minbleic_updateestimateofgoodstep(double* estimate, - double newstep, - ae_state *_state) -{ - - - if( ae_fp_eq(*estimate,0) ) - { - *estimate = newstep; - return; - } - if( ae_fp_less(newstep,*estimate*0.01) ) - { - *estimate = *estimate*0.01; - return; - } - if( ae_fp_greater(newstep,*estimate*100) ) - { - *estimate = *estimate*100; - return; - } - *estimate = newstep; -} - - -ae_bool _minbleicstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minbleicstate *p = (minbleicstate*)_p; - ae_touch_ptr((void*)p); - if( !_sactiveset_init(&p->sas, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->diagh, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gp, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->cleic, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hasbndl, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->hasbndu, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xstart, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_snnlssolver_init(&p->solver, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpprec, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->work, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_linminstate_init(&p->lstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rho, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->yk, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->sk, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->theta, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _minbleicstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minbleicstate *dst = (minbleicstate*)_dst; - minbleicstate *src = (minbleicstate*)_src; - dst->nmain = src->nmain; - dst->nslack = src->nslack; - dst->epsg = src->epsg; - dst->epsf = src->epsf; - dst->epsx = src->epsx; - dst->maxits = src->maxits; - dst->xrep = src->xrep; - dst->drep = src->drep; - dst->stpmax = src->stpmax; - dst->diffstep = src->diffstep; - if( !_sactiveset_init_copy(&dst->sas, &src->sas, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - dst->prectype = src->prectype; - if( !ae_vector_init_copy(&dst->diagh, &src->diagh, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - dst->needf = src->needf; - dst->needfg = src->needfg; - dst->xupdated = src->xupdated; - dst->lsstart = src->lsstart; - dst->lbfgssearch = src->lbfgssearch; - dst->boundedstep = src->boundedstep; - dst->teststep = src->teststep; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gc, &src->gc, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xn, &src->xn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gn, &src->gn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xp, &src->xp, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gp, &src->gp, _state, make_automatic) ) - return ae_false; - dst->fc = src->fc; - dst->fn = src->fn; - dst->fp = src->fp; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->cleic, &src->cleic, _state, make_automatic) ) - return ae_false; - dst->nec = src->nec; - dst->nic = src->nic; - dst->lastgoodstep = src->lastgoodstep; - dst->lastscaledgoodstep = src->lastscaledgoodstep; - dst->maxscaledgrad = src->maxscaledgrad; - if( !ae_vector_init_copy(&dst->hasbndl, &src->hasbndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->hasbndu, &src->hasbndu, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - dst->repinneriterationscount = src->repinneriterationscount; - dst->repouteriterationscount = src->repouteriterationscount; - dst->repnfev = src->repnfev; - dst->repvaridx = src->repvaridx; - dst->repterminationtype = src->repterminationtype; - dst->repdebugeqerr = src->repdebugeqerr; - dst->repdebugfs = src->repdebugfs; - dst->repdebugff = src->repdebugff; - dst->repdebugdx = src->repdebugdx; - dst->repdebugfeasqpits = src->repdebugfeasqpits; - dst->repdebugfeasgpaits = src->repdebugfeasgpaits; - if( !ae_vector_init_copy(&dst->xstart, &src->xstart, _state, make_automatic) ) - return ae_false; - if( !_snnlssolver_init_copy(&dst->solver, &src->solver, _state, make_automatic) ) - return ae_false; - dst->fbase = src->fbase; - dst->fm2 = src->fm2; - dst->fm1 = src->fm1; - dst->fp1 = src->fp1; - dst->fp2 = src->fp2; - dst->xm1 = src->xm1; - dst->xp1 = src->xp1; - dst->gm1 = src->gm1; - dst->gp1 = src->gp1; - dst->cidx = src->cidx; - dst->cval = src->cval; - if( !ae_vector_init_copy(&dst->tmpprec, &src->tmpprec, _state, make_automatic) ) - return ae_false; - dst->nfev = src->nfev; - dst->mcstage = src->mcstage; - dst->stp = src->stp; - dst->curstpmax = src->curstpmax; - dst->activationstep = src->activationstep; - if( !ae_vector_init_copy(&dst->work, &src->work, _state, make_automatic) ) - return ae_false; - if( !_linminstate_init_copy(&dst->lstate, &src->lstate, _state, make_automatic) ) - return ae_false; - dst->trimthreshold = src->trimthreshold; - dst->nonmonotoniccnt = src->nonmonotoniccnt; - dst->k = src->k; - dst->q = src->q; - dst->p = src->p; - if( !ae_vector_init_copy(&dst->rho, &src->rho, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->yk, &src->yk, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->sk, &src->sk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->theta, &src->theta, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _minbleicstate_clear(void* _p) -{ - minbleicstate *p = (minbleicstate*)_p; - ae_touch_ptr((void*)p); - _sactiveset_clear(&p->sas); - ae_vector_clear(&p->s); - ae_vector_clear(&p->diagh); - ae_vector_clear(&p->x); - ae_vector_clear(&p->g); - _rcommstate_clear(&p->rstate); - ae_vector_clear(&p->gc); - ae_vector_clear(&p->xn); - ae_vector_clear(&p->gn); - ae_vector_clear(&p->xp); - ae_vector_clear(&p->gp); - ae_vector_clear(&p->d); - ae_matrix_clear(&p->cleic); - ae_vector_clear(&p->hasbndl); - ae_vector_clear(&p->hasbndu); - ae_vector_clear(&p->bndl); - ae_vector_clear(&p->bndu); - ae_vector_clear(&p->xstart); - _snnlssolver_clear(&p->solver); - ae_vector_clear(&p->tmpprec); - ae_vector_clear(&p->work); - _linminstate_clear(&p->lstate); - ae_vector_clear(&p->rho); - ae_matrix_clear(&p->yk); - ae_matrix_clear(&p->sk); - ae_vector_clear(&p->theta); -} - - -void _minbleicstate_destroy(void* _p) -{ - minbleicstate *p = (minbleicstate*)_p; - ae_touch_ptr((void*)p); - _sactiveset_destroy(&p->sas); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->diagh); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->g); - _rcommstate_destroy(&p->rstate); - ae_vector_destroy(&p->gc); - ae_vector_destroy(&p->xn); - ae_vector_destroy(&p->gn); - ae_vector_destroy(&p->xp); - ae_vector_destroy(&p->gp); - ae_vector_destroy(&p->d); - ae_matrix_destroy(&p->cleic); - ae_vector_destroy(&p->hasbndl); - ae_vector_destroy(&p->hasbndu); - ae_vector_destroy(&p->bndl); - ae_vector_destroy(&p->bndu); - ae_vector_destroy(&p->xstart); - _snnlssolver_destroy(&p->solver); - ae_vector_destroy(&p->tmpprec); - ae_vector_destroy(&p->work); - _linminstate_destroy(&p->lstate); - ae_vector_destroy(&p->rho); - ae_matrix_destroy(&p->yk); - ae_matrix_destroy(&p->sk); - ae_vector_destroy(&p->theta); -} - - -ae_bool _minbleicreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minbleicreport *p = (minbleicreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _minbleicreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minbleicreport *dst = (minbleicreport*)_dst; - minbleicreport *src = (minbleicreport*)_src; - dst->iterationscount = src->iterationscount; - dst->nfev = src->nfev; - dst->varidx = src->varidx; - dst->terminationtype = src->terminationtype; - dst->debugeqerr = src->debugeqerr; - dst->debugfs = src->debugfs; - dst->debugff = src->debugff; - dst->debugdx = src->debugdx; - dst->debugfeasqpits = src->debugfeasqpits; - dst->debugfeasgpaits = src->debugfeasgpaits; - dst->inneriterationscount = src->inneriterationscount; - dst->outeriterationscount = src->outeriterationscount; - return ae_true; -} - - -void _minbleicreport_clear(void* _p) -{ - minbleicreport *p = (minbleicreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _minbleicreport_destroy(void* _p) -{ - minbleicreport *p = (minbleicreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* - LIMITED MEMORY BFGS METHOD FOR LARGE SCALE OPTIMIZATION - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using a quasi- -Newton method (LBFGS scheme) which is optimized to use a minimum amount -of memory. -The subroutine generates the approximation of an inverse Hessian matrix by -using information about the last M steps of the algorithm (instead of N). -It lessens a required amount of memory from a value of order N^2 to a -value of order 2*N*M. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinLBFGSCreate() call -2. User tunes solver parameters with MinLBFGSSetCond() MinLBFGSSetStpMax() - and other functions -3. User calls MinLBFGSOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinLBFGSResults() to get solution -5. Optionally user may call MinLBFGSRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLBFGSRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension. N>0 - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - initial solution approximation, array[0..N-1]. - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with MinLBFGSSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLBFGSSetStpMax() function to bound algorithm's steps. However, - L-BFGS rarely needs such a tuning. - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreate(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlbfgsstate* state, - ae_state *_state) -{ - - _minlbfgsstate_clear(state); - - ae_assert(n>=1, "MinLBFGSCreate: N<1!", _state); - ae_assert(m>=1, "MinLBFGSCreate: M<1", _state); - ae_assert(m<=n, "MinLBFGSCreate: M>N", _state); - ae_assert(x->cnt>=n, "MinLBFGSCreate: Length(X)0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinLBFGSSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. LBFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreatef(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - double diffstep, - minlbfgsstate* state, - ae_state *_state) -{ - - _minlbfgsstate_clear(state); - - ae_assert(n>=1, "MinLBFGSCreateF: N too small!", _state); - ae_assert(m>=1, "MinLBFGSCreateF: M<1", _state); - ae_assert(m<=n, "MinLBFGSCreateF: M>N", _state); - ae_assert(x->cnt>=n, "MinLBFGSCreateF: Length(X)=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinLBFGSSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetcond(minlbfgsstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsg, _state), "MinLBFGSSetCond: EpsG is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsg,0), "MinLBFGSSetCond: negative EpsG!", _state); - ae_assert(ae_isfinite(epsf, _state), "MinLBFGSSetCond: EpsF is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "MinLBFGSSetCond: negative EpsF!", _state); - ae_assert(ae_isfinite(epsx, _state), "MinLBFGSSetCond: EpsX is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "MinLBFGSSetCond: negative EpsX!", _state); - ae_assert(maxits>=0, "MinLBFGSSetCond: negative MaxIts!", _state); - if( ((ae_fp_eq(epsg,0)&&ae_fp_eq(epsf,0))&&ae_fp_eq(epsx,0))&&maxits==0 ) - { - epsx = 1.0E-6; - } - state->epsg = epsg; - state->epsf = epsf; - state->epsx = epsx; - state->maxits = maxits; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinLBFGSOptimize(). - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetxrep(minlbfgsstate* state, - ae_bool needxrep, - ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0 (default), if - you don't want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetstpmax(minlbfgsstate* state, - double stpmax, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(stpmax, _state), "MinLBFGSSetStpMax: StpMax is not finite!", _state); - ae_assert(ae_fp_greater_eq(stpmax,0), "MinLBFGSSetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* -This function sets scaling coefficients for LBFGS optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of the optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the LBFGS too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinLBFGSSetPrec...() -functions. - -There is special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetscale(minlbfgsstate* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(s->cnt>=state->n, "MinLBFGSSetScale: Length(S)n-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "MinLBFGSSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "MinLBFGSSetScale: S contains zero elements", _state); - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -Extended subroutine for internal use only. - -Accepts additional parameters: - - Flags - additional settings: - * Flags = 0 means no additional settings - * Flags = 1 "do not allocate memory". used when solving - a many subsequent tasks with same N/M values. - First call MUST be without this flag bit set, - subsequent calls of MinLBFGS with same - MinLBFGSState structure can set Flags to 1. - DiffStep - numerical differentiation step - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreatex(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - ae_int_t flags, - double diffstep, - minlbfgsstate* state, - ae_state *_state) -{ - ae_bool allocatemem; - ae_int_t i; - - - ae_assert(n>=1, "MinLBFGS: N too small!", _state); - ae_assert(m>=1, "MinLBFGS: M too small!", _state); - ae_assert(m<=n, "MinLBFGS: M too large!", _state); - - /* - * Initialize - */ - state->teststep = 0; - state->diffstep = diffstep; - state->n = n; - state->m = m; - allocatemem = flags%2==0; - flags = flags/2; - if( allocatemem ) - { - ae_vector_set_length(&state->rho, m, _state); - ae_vector_set_length(&state->theta, m, _state); - ae_matrix_set_length(&state->yk, m, n, _state); - ae_matrix_set_length(&state->sk, m, n, _state); - ae_vector_set_length(&state->d, n, _state); - ae_vector_set_length(&state->x, n, _state); - ae_vector_set_length(&state->s, n, _state); - ae_vector_set_length(&state->g, n, _state); - ae_vector_set_length(&state->work, n, _state); - } - minlbfgssetcond(state, 0, 0, 0, 0, _state); - minlbfgssetxrep(state, ae_false, _state); - minlbfgssetstpmax(state, 0, _state); - minlbfgsrestartfrom(state, x, _state); - for(i=0; i<=n-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - } - state->prectype = 0; -} - - -/************************************************************************* -Modification of the preconditioner: default preconditioner (simple -scaling, same for all elements of X) is used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecdefault(minlbfgsstate* state, ae_state *_state) -{ - - - state->prectype = 0; -} - - -/************************************************************************* -Modification of the preconditioner: Cholesky factorization of approximate -Hessian is used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - P - triangular preconditioner, Cholesky factorization of - the approximate Hessian. array[0..N-1,0..N-1], - (if larger, only leading N elements are used). - IsUpper - whether upper or lower triangle of P is given - (other triangle is not referenced) - -After call to this function preconditioner is changed to P (P is copied -into the internal buffer). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: P should be nonsingular. Exception will be thrown otherwise. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetpreccholesky(minlbfgsstate* state, - /* Real */ ae_matrix* p, - ae_bool isupper, - ae_state *_state) -{ - ae_int_t i; - double mx; - - - ae_assert(isfinitertrmatrix(p, state->n, isupper, _state), "MinLBFGSSetPrecCholesky: P contains infinite or NAN values!", _state); - mx = 0; - for(i=0; i<=state->n-1; i++) - { - mx = ae_maxreal(mx, ae_fabs(p->ptr.pp_double[i][i], _state), _state); - } - ae_assert(ae_fp_greater(mx,0), "MinLBFGSSetPrecCholesky: P is strictly singular!", _state); - if( state->denseh.rowsn||state->denseh.colsn ) - { - ae_matrix_set_length(&state->denseh, state->n, state->n, _state); - } - state->prectype = 1; - if( isupper ) - { - rmatrixcopy(state->n, state->n, p, 0, 0, &state->denseh, 0, 0, _state); - } - else - { - rmatrixtranspose(state->n, state->n, p, 0, 0, &state->denseh, 0, 0, _state); - } -} - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 3: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecdiag(minlbfgsstate* state, - /* Real */ ae_vector* d, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(d->cnt>=state->n, "MinLBFGSSetPrecDiag: D is too short", _state); - for(i=0; i<=state->n-1; i++) - { - ae_assert(ae_isfinite(d->ptr.p_double[i], _state), "MinLBFGSSetPrecDiag: D contains infinite or NAN elements", _state); - ae_assert(ae_fp_greater(d->ptr.p_double[i],0), "MinLBFGSSetPrecDiag: D contains non-positive elements", _state); - } - rvectorsetlengthatleast(&state->diagh, state->n, _state); - state->prectype = 2; - for(i=0; i<=state->n-1; i++) - { - state->diagh.ptr.p_double[i] = d->ptr.p_double[i]; - } -} - - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinLBFGSSetScale() -call (before or after MinLBFGSSetPrecScale() call). Without knowledge of -the scale of your variables scale-based preconditioner will be just unit -matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecscale(minlbfgsstate* state, ae_state *_state) -{ - - - state->prectype = 3; -} - - -/************************************************************************* -NOTES: - -1. This function has two different implementations: one which uses exact - (analytical) user-supplied gradient, and one which uses function value - only and numerically differentiates function in order to obtain - gradient. - - Depending on the specific function used to create optimizer object - (either MinLBFGSCreate() for analytical gradient or MinLBFGSCreateF() - for numerical differentiation) you should choose appropriate variant of - MinLBFGSOptimize() - one which accepts function AND gradient or one - which accepts function ONLY. - - Be careful to choose variant of MinLBFGSOptimize() which corresponds to - your optimization scheme! Table below lists different combinations of - callback (function/gradient) passed to MinLBFGSOptimize() and specific - function used to create optimizer. - - - | USER PASSED TO MinLBFGSOptimize() - CREATED WITH | function only | function and gradient - ------------------------------------------------------------ - MinLBFGSCreateF() | work FAIL - MinLBFGSCreate() | FAIL work - - Here "FAIL" denotes inappropriate combinations of optimizer creation - function and MinLBFGSOptimize() version. Attemps to use such - combination (for example, to create optimizer with MinLBFGSCreateF() and - to pass gradient information to MinCGOptimize()) will lead to exception - being thrown. Either you did not pass gradient when it WAS needed or - you passed gradient when it was NOT needed. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool minlbfgsiteration(minlbfgsstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t m; - ae_int_t i; - ae_int_t j; - ae_int_t ic; - ae_int_t mcinfo; - double v; - double vv; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - i = state->rstate.ia.ptr.p_int[2]; - j = state->rstate.ia.ptr.p_int[3]; - ic = state->rstate.ia.ptr.p_int[4]; - mcinfo = state->rstate.ia.ptr.p_int[5]; - v = state->rstate.ra.ptr.p_double[0]; - vv = state->rstate.ra.ptr.p_double[1]; - } - else - { - n = -983; - m = -989; - i = -834; - j = 900; - ic = -287; - mcinfo = 364; - v = 214; - vv = -338; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - if( state->rstate.stage==8 ) - { - goto lbl_8; - } - if( state->rstate.stage==9 ) - { - goto lbl_9; - } - if( state->rstate.stage==10 ) - { - goto lbl_10; - } - if( state->rstate.stage==11 ) - { - goto lbl_11; - } - if( state->rstate.stage==12 ) - { - goto lbl_12; - } - if( state->rstate.stage==13 ) - { - goto lbl_13; - } - if( state->rstate.stage==14 ) - { - goto lbl_14; - } - if( state->rstate.stage==15 ) - { - goto lbl_15; - } - if( state->rstate.stage==16 ) - { - goto lbl_16; - } - - /* - * Routine body - */ - - /* - * Unload frequently used variables from State structure - * (just for typing convinience) - */ - n = state->n; - m = state->m; - state->repterminationtype = 0; - state->repiterationscount = 0; - state->repvaridx = -1; - state->repnfev = 0; - - /* - * Check, that transferred derivative value is right - */ - minlbfgs_clearrequestfields(state, _state); - if( !(ae_fp_eq(state->diffstep,0)&&ae_fp_greater(state->teststep,0)) ) - { - goto lbl_17; - } - state->needfg = ae_true; - i = 0; -lbl_19: - if( i>n-1 ) - { - goto lbl_21; - } - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->teststep*state->s.ptr.p_double[i]; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->fm1 = state->f; - state->fp1 = state->g.ptr.p_double[i]; - state->x.ptr.p_double[i] = v+state->teststep*state->s.ptr.p_double[i]; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->fm2 = state->f; - state->fp2 = state->g.ptr.p_double[i]; - state->x.ptr.p_double[i] = v; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - - /* - * 2*State.TestStep - scale parameter - * width of segment [Xi-TestStep;Xi+TestStep] - */ - if( !derivativecheck(state->fm1, state->fp1, state->fm2, state->fp2, state->f, state->g.ptr.p_double[i], 2*state->teststep, _state) ) - { - state->repvaridx = i; - state->repterminationtype = -7; - result = ae_false; - return result; - } - i = i+1; - goto lbl_19; -lbl_21: - state->needfg = ae_false; -lbl_17: - - /* - * Calculate F/G at the initial point - */ - minlbfgs_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_22; - } - state->needfg = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needfg = ae_false; - goto lbl_23; -lbl_22: - state->needf = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->fbase = state->f; - i = 0; -lbl_24: - if( i>n-1 ) - { - goto lbl_26; - } - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 8; - goto lbl_rcomm; -lbl_8: - state->fp2 = state->f; - state->x.ptr.p_double[i] = v; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - i = i+1; - goto lbl_24; -lbl_26: - state->f = state->fbase; - state->needf = ae_false; -lbl_23: - trimprepare(state->f, &state->trimthreshold, _state); - if( !state->xrep ) - { - goto lbl_27; - } - minlbfgs_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 9; - goto lbl_rcomm; -lbl_9: - state->xupdated = ae_false; -lbl_27: - state->repnfev = 1; - state->fold = state->f; - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->g.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - if( ae_fp_less_eq(ae_sqrt(v, _state),state->epsg) ) - { - state->repterminationtype = 4; - result = ae_false; - return result; - } - - /* - * Choose initial step and direction. - * Apply preconditioner, if we have something other than default. - */ - ae_v_moveneg(&state->d.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( state->prectype==0 ) - { - - /* - * Default preconditioner is used, but we can't use it before iterations will start - */ - v = ae_v_dotproduct(&state->g.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = ae_sqrt(v, _state); - if( ae_fp_eq(state->stpmax,0) ) - { - state->stp = ae_minreal(1.0/v, 1, _state); - } - else - { - state->stp = ae_minreal(1.0/v, state->stpmax, _state); - } - } - if( state->prectype==1 ) - { - - /* - * Cholesky preconditioner is used - */ - fblscholeskysolve(&state->denseh, 1.0, n, ae_true, &state->d, &state->autobuf, _state); - state->stp = 1; - } - if( state->prectype==2 ) - { - - /* - * diagonal approximation is used - */ - for(i=0; i<=n-1; i++) - { - state->d.ptr.p_double[i] = state->d.ptr.p_double[i]/state->diagh.ptr.p_double[i]; - } - state->stp = 1; - } - if( state->prectype==3 ) - { - - /* - * scale-based preconditioner is used - */ - for(i=0; i<=n-1; i++) - { - state->d.ptr.p_double[i] = state->d.ptr.p_double[i]*state->s.ptr.p_double[i]*state->s.ptr.p_double[i]; - } - state->stp = 1; - } - - /* - * Main cycle - */ - state->k = 0; -lbl_29: - if( ae_false ) - { - goto lbl_30; - } - - /* - * Main cycle: prepare to 1-D line search - */ - state->p = state->k%m; - state->q = ae_minint(state->k, m-1, _state); - - /* - * Store X[k], G[k] - */ - ae_v_moveneg(&state->sk.ptr.pp_double[state->p][0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_moveneg(&state->yk.ptr.pp_double[state->p][0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Minimize F(x+alpha*d) - * Calculate S[k], Y[k] - */ - state->mcstage = 0; - if( state->k!=0 ) - { - state->stp = 1.0; - } - linminnormalized(&state->d, &state->stp, n, _state); - mcsrch(n, &state->x, &state->f, &state->g, &state->d, &state->stp, state->stpmax, minlbfgs_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); -lbl_31: - if( state->mcstage==0 ) - { - goto lbl_32; - } - minlbfgs_clearrequestfields(state, _state); - if( ae_fp_neq(state->diffstep,0) ) - { - goto lbl_33; - } - state->needfg = ae_true; - state->rstate.stage = 10; - goto lbl_rcomm; -lbl_10: - state->needfg = ae_false; - goto lbl_34; -lbl_33: - state->needf = ae_true; - state->rstate.stage = 11; - goto lbl_rcomm; -lbl_11: - state->fbase = state->f; - i = 0; -lbl_35: - if( i>n-1 ) - { - goto lbl_37; - } - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 12; - goto lbl_rcomm; -lbl_12: - state->fm2 = state->f; - state->x.ptr.p_double[i] = v-0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 13; - goto lbl_rcomm; -lbl_13: - state->fm1 = state->f; - state->x.ptr.p_double[i] = v+0.5*state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 14; - goto lbl_rcomm; -lbl_14: - state->fp1 = state->f; - state->x.ptr.p_double[i] = v+state->diffstep*state->s.ptr.p_double[i]; - state->rstate.stage = 15; - goto lbl_rcomm; -lbl_15: - state->fp2 = state->f; - state->x.ptr.p_double[i] = v; - state->g.ptr.p_double[i] = (8*(state->fp1-state->fm1)-(state->fp2-state->fm2))/(6*state->diffstep*state->s.ptr.p_double[i]); - i = i+1; - goto lbl_35; -lbl_37: - state->f = state->fbase; - state->needf = ae_false; -lbl_34: - trimfunction(&state->f, &state->g, n, state->trimthreshold, _state); - mcsrch(n, &state->x, &state->f, &state->g, &state->d, &state->stp, state->stpmax, minlbfgs_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); - goto lbl_31; -lbl_32: - if( !state->xrep ) - { - goto lbl_38; - } - - /* - * report - */ - minlbfgs_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 16; - goto lbl_rcomm; -lbl_16: - state->xupdated = ae_false; -lbl_38: - state->repnfev = state->repnfev+state->nfev; - state->repiterationscount = state->repiterationscount+1; - ae_v_add(&state->sk.ptr.pp_double[state->p][0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_add(&state->yk.ptr.pp_double[state->p][0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Stopping conditions - */ - if( state->repiterationscount>=state->maxits&&state->maxits>0 ) - { - - /* - * Too many iterations - */ - state->repterminationtype = 5; - result = ae_false; - return result; - } - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->g.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - if( ae_fp_less_eq(ae_sqrt(v, _state),state->epsg) ) - { - - /* - * Gradient is small enough - */ - state->repterminationtype = 4; - result = ae_false; - return result; - } - if( ae_fp_less_eq(state->fold-state->f,state->epsf*ae_maxreal(ae_fabs(state->fold, _state), ae_maxreal(ae_fabs(state->f, _state), 1.0, _state), _state)) ) - { - - /* - * F(k+1)-F(k) is small enough - */ - state->repterminationtype = 1; - result = ae_false; - return result; - } - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->sk.ptr.pp_double[state->p][i]/state->s.ptr.p_double[i], _state); - } - if( ae_fp_less_eq(ae_sqrt(v, _state),state->epsx) ) - { - - /* - * X(k+1)-X(k) is small enough - */ - state->repterminationtype = 2; - result = ae_false; - return result; - } - - /* - * If Wolfe conditions are satisfied, we can update - * limited memory model. - * - * However, if conditions are not satisfied (NFEV limit is met, - * function is too wild, ...), we'll skip L-BFGS update - */ - if( mcinfo!=1 ) - { - - /* - * Skip update. - * - * In such cases we'll initialize search direction by - * antigradient vector, because it leads to more - * transparent code with less number of special cases - */ - state->fold = state->f; - ae_v_moveneg(&state->d.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - } - else - { - - /* - * Calculate Rho[k], GammaK - */ - v = ae_v_dotproduct(&state->yk.ptr.pp_double[state->p][0], 1, &state->sk.ptr.pp_double[state->p][0], 1, ae_v_len(0,n-1)); - vv = ae_v_dotproduct(&state->yk.ptr.pp_double[state->p][0], 1, &state->yk.ptr.pp_double[state->p][0], 1, ae_v_len(0,n-1)); - if( ae_fp_eq(v,0)||ae_fp_eq(vv,0) ) - { - - /* - * Rounding errors make further iterations impossible. - */ - state->repterminationtype = -2; - result = ae_false; - return result; - } - state->rho.ptr.p_double[state->p] = 1/v; - state->gammak = v/vv; - - /* - * Calculate d(k+1) = -H(k+1)*g(k+1) - * - * for I:=K downto K-Q do - * V = s(i)^T * work(iteration:I) - * theta(i) = V - * work(iteration:I+1) = work(iteration:I) - V*Rho(i)*y(i) - * work(last iteration) = H0*work(last iteration) - preconditioner - * for I:=K-Q to K do - * V = y(i)^T*work(iteration:I) - * work(iteration:I+1) = work(iteration:I) +(-V+theta(i))*Rho(i)*s(i) - * - * NOW WORK CONTAINS d(k+1) - */ - ae_v_move(&state->work.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=state->k; i>=state->k-state->q; i--) - { - ic = i%m; - v = ae_v_dotproduct(&state->sk.ptr.pp_double[ic][0], 1, &state->work.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->theta.ptr.p_double[ic] = v; - vv = v*state->rho.ptr.p_double[ic]; - ae_v_subd(&state->work.ptr.p_double[0], 1, &state->yk.ptr.pp_double[ic][0], 1, ae_v_len(0,n-1), vv); - } - if( state->prectype==0 ) - { - - /* - * Simple preconditioner is used - */ - v = state->gammak; - ae_v_muld(&state->work.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - } - if( state->prectype==1 ) - { - - /* - * Cholesky preconditioner is used - */ - fblscholeskysolve(&state->denseh, 1, n, ae_true, &state->work, &state->autobuf, _state); - } - if( state->prectype==2 ) - { - - /* - * diagonal approximation is used - */ - for(i=0; i<=n-1; i++) - { - state->work.ptr.p_double[i] = state->work.ptr.p_double[i]/state->diagh.ptr.p_double[i]; - } - } - if( state->prectype==3 ) - { - - /* - * scale-based preconditioner is used - */ - for(i=0; i<=n-1; i++) - { - state->work.ptr.p_double[i] = state->work.ptr.p_double[i]*state->s.ptr.p_double[i]*state->s.ptr.p_double[i]; - } - } - for(i=state->k-state->q; i<=state->k; i++) - { - ic = i%m; - v = ae_v_dotproduct(&state->yk.ptr.pp_double[ic][0], 1, &state->work.ptr.p_double[0], 1, ae_v_len(0,n-1)); - vv = state->rho.ptr.p_double[ic]*(-v+state->theta.ptr.p_double[ic]); - ae_v_addd(&state->work.ptr.p_double[0], 1, &state->sk.ptr.pp_double[ic][0], 1, ae_v_len(0,n-1), vv); - } - ae_v_moveneg(&state->d.ptr.p_double[0], 1, &state->work.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Next step - */ - state->fold = state->f; - state->k = state->k+1; - } - goto lbl_29; -lbl_30: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = i; - state->rstate.ia.ptr.p_int[3] = j; - state->rstate.ia.ptr.p_int[4] = ic; - state->rstate.ia.ptr.p_int[5] = mcinfo; - state->rstate.ra.ptr.p_double[0] = v; - state->rstate.ra.ptr.p_double[1] = vv; - return result; -} - - -/************************************************************************* -L-BFGS algorithm results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -7 gradient verification failed. - See MinLBFGSSetGradientCheck() for more information. - * -2 rounding errors prevent further improvement. - X contains best point found. - * -1 incorrect parameters were specified - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsresults(minlbfgsstate* state, - /* Real */ ae_vector* x, - minlbfgsreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _minlbfgsreport_clear(rep); - - minlbfgsresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -L-BFGS algorithm results - -Buffered implementation of MinLBFGSResults which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsresultsbuf(minlbfgsstate* state, - /* Real */ ae_vector* x, - minlbfgsreport* rep, - ae_state *_state) -{ - - - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->nfev = state->repnfev; - rep->varidx = state->repvaridx; - rep->terminationtype = state->repterminationtype; -} - - -/************************************************************************* -This subroutine restarts LBFGS algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used to store algorithm state - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsrestartfrom(minlbfgsstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - - - ae_assert(x->cnt>=state->n, "MinLBFGSRestartFrom: Length(X)n, _state), "MinLBFGSRestartFrom: X contains infinite or NaN values!", _state); - ae_v_move(&state->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_vector_set_length(&state->rstate.ia, 5+1, _state); - ae_vector_set_length(&state->rstate.ra, 1+1, _state); - state->rstate.stage = -1; - minlbfgs_clearrequestfields(state, _state); -} - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinLBFGSOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinLBFGSSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 24.05.2012 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetgradientcheck(minlbfgsstate* state, - double teststep, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(teststep, _state), "MinLBFGSSetGradientCheck: TestStep contains NaN or Infinite", _state); - ae_assert(ae_fp_greater_eq(teststep,0), "MinLBFGSSetGradientCheck: invalid argument TestStep(TestStep<0)", _state); - state->teststep = teststep; -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void minlbfgs_clearrequestfields(minlbfgsstate* state, - ae_state *_state) -{ - - - state->needf = ae_false; - state->needfg = ae_false; - state->xupdated = ae_false; -} - - -ae_bool _minlbfgsstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minlbfgsstate *p = (minlbfgsstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rho, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->yk, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->sk, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->theta, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->work, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->denseh, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->diagh, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->autobuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !_linminstate_init(&p->lstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _minlbfgsstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minlbfgsstate *dst = (minlbfgsstate*)_dst; - minlbfgsstate *src = (minlbfgsstate*)_src; - dst->n = src->n; - dst->m = src->m; - dst->epsg = src->epsg; - dst->epsf = src->epsf; - dst->epsx = src->epsx; - dst->maxits = src->maxits; - dst->xrep = src->xrep; - dst->stpmax = src->stpmax; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - dst->diffstep = src->diffstep; - dst->nfev = src->nfev; - dst->mcstage = src->mcstage; - dst->k = src->k; - dst->q = src->q; - dst->p = src->p; - if( !ae_vector_init_copy(&dst->rho, &src->rho, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->yk, &src->yk, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->sk, &src->sk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->theta, &src->theta, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - dst->stp = src->stp; - if( !ae_vector_init_copy(&dst->work, &src->work, _state, make_automatic) ) - return ae_false; - dst->fold = src->fold; - dst->trimthreshold = src->trimthreshold; - dst->prectype = src->prectype; - dst->gammak = src->gammak; - if( !ae_matrix_init_copy(&dst->denseh, &src->denseh, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->diagh, &src->diagh, _state, make_automatic) ) - return ae_false; - dst->fbase = src->fbase; - dst->fm2 = src->fm2; - dst->fm1 = src->fm1; - dst->fp1 = src->fp1; - dst->fp2 = src->fp2; - if( !ae_vector_init_copy(&dst->autobuf, &src->autobuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - dst->needf = src->needf; - dst->needfg = src->needfg; - dst->xupdated = src->xupdated; - dst->teststep = src->teststep; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - dst->repiterationscount = src->repiterationscount; - dst->repnfev = src->repnfev; - dst->repvaridx = src->repvaridx; - dst->repterminationtype = src->repterminationtype; - if( !_linminstate_init_copy(&dst->lstate, &src->lstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _minlbfgsstate_clear(void* _p) -{ - minlbfgsstate *p = (minlbfgsstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->s); - ae_vector_clear(&p->rho); - ae_matrix_clear(&p->yk); - ae_matrix_clear(&p->sk); - ae_vector_clear(&p->theta); - ae_vector_clear(&p->d); - ae_vector_clear(&p->work); - ae_matrix_clear(&p->denseh); - ae_vector_clear(&p->diagh); - ae_vector_clear(&p->autobuf); - ae_vector_clear(&p->x); - ae_vector_clear(&p->g); - _rcommstate_clear(&p->rstate); - _linminstate_clear(&p->lstate); -} - - -void _minlbfgsstate_destroy(void* _p) -{ - minlbfgsstate *p = (minlbfgsstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->rho); - ae_matrix_destroy(&p->yk); - ae_matrix_destroy(&p->sk); - ae_vector_destroy(&p->theta); - ae_vector_destroy(&p->d); - ae_vector_destroy(&p->work); - ae_matrix_destroy(&p->denseh); - ae_vector_destroy(&p->diagh); - ae_vector_destroy(&p->autobuf); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->g); - _rcommstate_destroy(&p->rstate); - _linminstate_destroy(&p->lstate); -} - - -ae_bool _minlbfgsreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minlbfgsreport *p = (minlbfgsreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _minlbfgsreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minlbfgsreport *dst = (minlbfgsreport*)_dst; - minlbfgsreport *src = (minlbfgsreport*)_src; - dst->iterationscount = src->iterationscount; - dst->nfev = src->nfev; - dst->varidx = src->varidx; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _minlbfgsreport_clear(void* _p) -{ - minlbfgsreport *p = (minlbfgsreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _minlbfgsreport_destroy(void* _p) -{ - minlbfgsreport *p = (minlbfgsreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* - CONSTRAINED QUADRATIC PROGRAMMING - -The subroutine creates QP optimizer. After initial creation, it contains -default optimization problem with zero quadratic and linear terms and no -constraints. You should set quadratic/linear terms with calls to functions -provided by MinQP subpackage. - -INPUT PARAMETERS: - N - problem size - -OUTPUT PARAMETERS: - State - optimizer with zero quadratic/linear terms - and no constraints - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpcreate(ae_int_t n, minqpstate* state, ae_state *_state) -{ - ae_int_t i; - - _minqpstate_clear(state); - - ae_assert(n>=1, "MinQPCreate: N<1", _state); - - /* - * initialize QP solver - */ - state->n = n; - state->nec = 0; - state->nic = 0; - state->repterminationtype = 0; - state->anorm = 1; - state->akind = 0; - cqminit(n, &state->a, _state); - sasinit(n, &state->sas, _state); - ae_vector_set_length(&state->b, n, _state); - ae_vector_set_length(&state->bndl, n, _state); - ae_vector_set_length(&state->bndu, n, _state); - ae_vector_set_length(&state->workbndl, n, _state); - ae_vector_set_length(&state->workbndu, n, _state); - ae_vector_set_length(&state->havebndl, n, _state); - ae_vector_set_length(&state->havebndu, n, _state); - ae_vector_set_length(&state->s, n, _state); - ae_vector_set_length(&state->startx, n, _state); - ae_vector_set_length(&state->xorigin, n, _state); - ae_vector_set_length(&state->xs, n, _state); - ae_vector_set_length(&state->xn, n, _state); - ae_vector_set_length(&state->gc, n, _state); - ae_vector_set_length(&state->pg, n, _state); - for(i=0; i<=n-1; i++) - { - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->bndu.ptr.p_double[i] = _state->v_posinf; - state->havebndl.ptr.p_bool[i] = ae_false; - state->havebndu.ptr.p_bool[i] = ae_false; - state->b.ptr.p_double[i] = 0.0; - state->startx.ptr.p_double[i] = 0.0; - state->xorigin.ptr.p_double[i] = 0.0; - state->s.ptr.p_double[i] = 1.0; - } - state->havex = ae_false; - minqpsetalgocholesky(state, _state); - normestimatorcreate(n, n, 5, 5, &state->estimator, _state); - minbleiccreate(n, &state->startx, &state->solver, _state); -} - - -/************************************************************************* -This function sets linear term for QP solver. - -By default, linear term is zero. - -INPUT PARAMETERS: - State - structure which stores algorithm state - B - linear term, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlinearterm(minqpstate* state, - /* Real */ ae_vector* b, - ae_state *_state) -{ - ae_int_t n; - - - n = state->n; - ae_assert(b->cnt>=n, "MinQPSetLinearTerm: Length(B)n; - ae_assert(a->rows>=n, "MinQPSetQuadraticTerm: Rows(A)cols>=n, "MinQPSetQuadraticTerm: Cols(A)n; - ae_assert(sparsegetnrows(a, _state)>=n, "MinQPSetQuadraticTermSparse: Rows(A)=n, "MinQPSetQuadraticTermSparse: Cols(A)sparsea, _state); - state->sparseaupper = isupper; - state->akind = 1; -} - - -/************************************************************************* -This function sets starting point for QP solver. It is useful to have -good initial approximation to the solution, because it will increase -speed of convergence and identification of active constraints. - -INPUT PARAMETERS: - State - structure which stores algorithm state - X - starting point, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetstartingpoint(minqpstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - - - n = state->n; - ae_assert(x->cnt>=n, "MinQPSetStartingPoint: Length(B)n; - ae_assert(xorigin->cnt>=n, "MinQPSetOrigin: Length(B)cnt>=state->n, "MinQPSetScale: Length(S)n-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "MinQPSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "MinQPSetScale: S contains zero elements", _state); - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -This function tells solver to use Cholesky-based algorithm. This algorithm -is active by default. - -DESCRIPTION: - -Cholesky-based algorithm can be used only for problems which: -* have dense quadratic term, set by MinQPSetQuadraticTerm(), sparse or - structured problems are not supported. -* are strictly convex, i.e. quadratic term is symmetric positive definite, - indefinite or semidefinite problems are not supported by this algorithm. - -If anything of what listed above is violated, you may use BLEIC-based QP -algorithm which can be activated by MinQPSetAlgoBLEIC(). - -BENEFITS AND DRAWBACKS: - -This algorithm gives best precision amongst all QP solvers provided by -ALGLIB (Newton iterations have much higher precision than any other -optimization algorithm). This solver also gracefully handles problems with -very large amount of constraints. - -Performance of the algorithm is good because internally it uses Level 3 -Dense BLAS for its performance-critical parts. - - -From the other side, algorithm has O(N^3) complexity for unconstrained -problems and up to orders of magnitude slower on constrained problems -(these additional iterations are needed to identify active constraints). -So, its running time depends on number of constraints active at solution. - -Furthermore, this algorithm can not solve problems with sparse matrices or -problems with semidefinite/indefinite matrices of any kind (dense/sparse). - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetalgocholesky(minqpstate* state, ae_state *_state) -{ - - - state->algokind = 1; -} - - -/************************************************************************* -This function tells solver to use BLEIC-based algorithm and sets stopping -criteria for the algorithm. - -DESCRIPTION: - -BLEIC-based QP algorithm can be used for any kind of QP problems: -* problems with both dense and sparse quadratic terms -* problems with positive definite, semidefinite, indefinite terms - -BLEIC-based algorithm can solve even indefinite problems - as long as they -are bounded from below on the feasible set. Of course, global minimum is -found only for positive definite and semidefinite problems. As for -indefinite ones - only local minimum is found. - -BENEFITS AND DRAWBACKS: - -This algorithm can be used to solve both convex and indefinite QP problems -and it can utilize sparsity of the quadratic term (algorithm calculates -matrix-vector products, which can be performed efficiently in case of -sparse matrix). - -Algorithm has iteration cost, which (assuming fixed amount of non-boundary -linear constraints) linearly depends on problem size. Boundary constraints -does not significantly change iteration cost. - -Thus, it outperforms Cholesky-based QP algorithm (CQP) on high-dimensional -sparse problems with moderate amount of constraints. - - -From the other side, unlike CQP solver, this algorithm does NOT make use -of Level 3 Dense BLAS. Thus, its performance on dense problems is inferior -to that of CQP solver. - -Its precision is also inferior to that of CQP. CQP performs Newton steps -which are know to achieve very good precision. In many cases Newton step -leads us exactly to the solution. BLEIC-QP performs LBFGS steps, which are -good at detecting neighborhood of the solution, buy need many iterations -to find solution with 6 digits of precision. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if exploratory steepest - descent step on k+1-th iteration satisfies following - condition: |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - EpsX - >=0 - The subroutine finishes its work if exploratory steepest - descent step on k+1-th iteration satisfies following - condition: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - step vector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinQPSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0 and EpsX=0 and MaxIts=0 (simultaneously) will lead -to automatic stopping criterion selection (presently it is small step -length, but it may change in the future versions of ALGLIB). - -IT IS VERY IMPORTANT THAT YOU CALL MinQPSetScale() WHEN YOU USE THIS ALGO! - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetalgobleic(minqpstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsg, _state), "MinQPSetAlgoBLEIC: EpsG is not finite number", _state); - ae_assert(ae_fp_greater_eq(epsg,0), "MinQPSetAlgoBLEIC: negative EpsG", _state); - ae_assert(ae_isfinite(epsf, _state), "MinQPSetAlgoBLEIC: EpsF is not finite number", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "MinQPSetAlgoBLEIC: negative EpsF", _state); - ae_assert(ae_isfinite(epsx, _state), "MinQPSetAlgoBLEIC: EpsX is not finite number", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "MinQPSetAlgoBLEIC: negative EpsX", _state); - ae_assert(maxits>=0, "MinQPSetAlgoBLEIC: negative MaxIts!", _state); - state->algokind = 2; - if( ((ae_fp_eq(epsg,0)&&ae_fp_eq(epsf,0))&&ae_fp_eq(epsx,0))&&maxits==0 ) - { - epsx = 1.0E-6; - } - state->bleicepsg = epsg; - state->bleicepsf = epsf; - state->bleicepsx = epsx; - state->bleicmaxits = maxits; -} - - -/************************************************************************* -This function sets boundary constraints for QP solver - -Boundary constraints are inactive by default (after initial creation). -After being set, they are preserved until explicitly turned off with -another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetbc(minqpstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - - - n = state->n; - ae_assert(bndl->cnt>=n, "MinQPSetBC: Length(BndL)cnt>=n, "MinQPSetBC: Length(BndU)ptr.p_double[i], _state)||ae_isneginf(bndl->ptr.p_double[i], _state), "MinQPSetBC: BndL contains NAN or +INF", _state); - ae_assert(ae_isfinite(bndu->ptr.p_double[i], _state)||ae_isposinf(bndu->ptr.p_double[i], _state), "MinQPSetBC: BndU contains NAN or -INF", _state); - state->bndl.ptr.p_double[i] = bndl->ptr.p_double[i]; - state->havebndl.ptr.p_bool[i] = ae_isfinite(bndl->ptr.p_double[i], _state); - state->bndu.ptr.p_double[i] = bndu->ptr.p_double[i]; - state->havebndu.ptr.p_bool[i] = ae_isfinite(bndu->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -This function sets linear constraints for QP optimizer. - -Linear constraints are inactive by default (after initial creation). - -INPUT PARAMETERS: - State - structure previously allocated with MinQPCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately - - there always exists some minor violation (about 10^-10...10^-13) - due to numerical errors. - - -- ALGLIB -- - Copyright 19.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlc(minqpstate* state, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - double v; - - - n = state->n; - - /* - * First, check for errors in the inputs - */ - ae_assert(k>=0, "MinQPSetLC: K<0", _state); - ae_assert(c->cols>=n+1||k==0, "MinQPSetLC: Cols(C)rows>=k, "MinQPSetLC: Rows(C)cnt>=k, "MinQPSetLC: Length(CT)nec = 0; - state->nic = 0; - return; - } - - /* - * Equality constraints are stored first, in the upper - * NEC rows of State.CLEIC matrix. Inequality constraints - * are stored in the next NIC rows. - * - * NOTE: we convert inequality constraints to the form - * A*x<=b before copying them. - */ - rmatrixsetlengthatleast(&state->cleic, k, n+1, _state); - state->nec = 0; - state->nic = 0; - for(i=0; i<=k-1; i++) - { - if( ct->ptr.p_int[i]==0 ) - { - ae_v_move(&state->cleic.ptr.pp_double[state->nec][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - state->nec = state->nec+1; - } - } - for(i=0; i<=k-1; i++) - { - if( ct->ptr.p_int[i]!=0 ) - { - if( ct->ptr.p_int[i]>0 ) - { - ae_v_moveneg(&state->cleic.ptr.pp_double[state->nec+state->nic][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - } - else - { - ae_v_move(&state->cleic.ptr.pp_double[state->nec+state->nic][0], 1, &c->ptr.pp_double[i][0], 1, ae_v_len(0,n)); - } - state->nic = state->nic+1; - } - } - - /* - * Normalize rows of State.CLEIC: each row must have unit norm. - * Norm is calculated using first N elements (i.e. right part is - * not counted when we calculate norm). - */ - for(i=0; i<=k-1; i++) - { - v = 0; - for(j=0; j<=n-1; j++) - { - v = v+ae_sqr(state->cleic.ptr.pp_double[i][j], _state); - } - if( ae_fp_eq(v,0) ) - { - continue; - } - v = 1/ae_sqrt(v, _state); - ae_v_muld(&state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n), v); - } -} - - -/************************************************************************* -This function solves quadratic programming problem. -You should call it after setting solver options with MinQPSet...() calls. - -INPUT PARAMETERS: - State - algorithm state - -You should use MinQPResults() function to access results after calls -to this function. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey. - Special thanks to Elvira Illarionova for important suggestions on - the linearly constrained QP algorithm. -*************************************************************************/ -void minqpoptimize(minqpstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t nbc; - double v0; - double v1; - double v; - double d2; - double d1; - double d0; - double noisetolerance; - double fprev; - double fcand; - double fcur; - ae_int_t nextaction; - ae_int_t actstatus; - double noiselevel; - ae_int_t badnewtonits; - double maxscaledgrad; - - - noisetolerance = 10; - n = state->n; - state->repterminationtype = -5; - state->repinneriterationscount = 0; - state->repouteriterationscount = 0; - state->repncholesky = 0; - state->repnmv = 0; - state->debugphase1flops = 0; - state->debugphase2flops = 0; - state->debugphase3flops = 0; - rvectorsetlengthatleast(&state->rctmpg, n, _state); - - /* - * check correctness of constraints - */ - for(i=0; i<=n-1; i++) - { - if( state->havebndl.ptr.p_bool[i]&&state->havebndu.ptr.p_bool[i] ) - { - if( ae_fp_greater(state->bndl.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->repterminationtype = -3; - return; - } - } - } - - /* - * count number of bound and linear constraints - */ - nbc = 0; - for(i=0; i<=n-1; i++) - { - if( state->havebndl.ptr.p_bool[i] ) - { - nbc = nbc+1; - } - if( state->havebndu.ptr.p_bool[i] ) - { - nbc = nbc+1; - } - } - - /* - * Initial point: - * * if we have starting point in StartX, we just have to bound it - * * if we do not have StartX, deduce initial point from boundary constraints - */ - if( state->havex ) - { - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->startx.ptr.p_double[i]; - if( state->havebndl.ptr.p_bool[i]&&ae_fp_less(state->xs.ptr.p_double[i],state->bndl.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->bndl.ptr.p_double[i]; - } - if( state->havebndu.ptr.p_bool[i]&&ae_fp_greater(state->xs.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->bndu.ptr.p_double[i]; - } - } - } - else - { - for(i=0; i<=n-1; i++) - { - if( state->havebndl.ptr.p_bool[i]&&state->havebndu.ptr.p_bool[i] ) - { - state->xs.ptr.p_double[i] = 0.5*(state->bndl.ptr.p_double[i]+state->bndu.ptr.p_double[i]); - continue; - } - if( state->havebndl.ptr.p_bool[i] ) - { - state->xs.ptr.p_double[i] = state->bndl.ptr.p_double[i]; - continue; - } - if( state->havebndu.ptr.p_bool[i] ) - { - state->xs.ptr.p_double[i] = state->bndu.ptr.p_double[i]; - continue; - } - state->xs.ptr.p_double[i] = 0; - } - } - - /* - * Cholesky solver. - */ - if( state->algokind==1 ) - { - - /* - * Check matrix type. - * Cholesky solver supports only dense matrices. - */ - if( state->akind!=0 ) - { - state->repterminationtype = -5; - return; - } - - /* - * Our formulation of quadratic problem includes origin point, - * i.e. we have F(x-x_origin) which is minimized subject to - * constraints on x, instead of having simply F(x). - * - * Here we make transition from non-zero origin to zero one. - * In order to make such transition we have to: - * 1. subtract x_origin from x_start - * 2. modify constraints - * 3. solve problem - * 4. add x_origin to solution - * - * There is alternate solution - to modify quadratic function - * by expansion of multipliers containing (x-x_origin), but - * we prefer to modify constraints, because it is a) more precise - * and b) easier to to. - * - * Parts (1)-(2) are done here. After this block is over, - * we have: - * * XS, which stores shifted XStart (if we don't have XStart, - * value of XS will be ignored later) - * * WorkBndL, WorkBndU, which store modified boundary constraints. - */ - for(i=0; i<=n-1; i++) - { - if( state->havebndl.ptr.p_bool[i] ) - { - state->workbndl.ptr.p_double[i] = state->bndl.ptr.p_double[i]-state->xorigin.ptr.p_double[i]; - } - else - { - state->workbndl.ptr.p_double[i] = _state->v_neginf; - } - if( state->havebndu.ptr.p_bool[i] ) - { - state->workbndu.ptr.p_double[i] = state->bndu.ptr.p_double[i]-state->xorigin.ptr.p_double[i]; - } - else - { - state->workbndu.ptr.p_double[i] = _state->v_posinf; - } - } - rmatrixsetlengthatleast(&state->workcleic, state->nec+state->nic, n+1, _state); - for(i=0; i<=state->nec+state->nic-1; i++) - { - v = ae_v_dotproduct(&state->cleic.ptr.pp_double[i][0], 1, &state->xorigin.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->workcleic.ptr.pp_double[i][0], 1, &state->cleic.ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - state->workcleic.ptr.pp_double[i][n] = state->cleic.ptr.pp_double[i][n]-v; - } - - /* - * Starting point XS - */ - if( state->havex ) - { - - /* - * We have starting point in StartX, so we just have to shift and bound it - */ - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->startx.ptr.p_double[i]-state->xorigin.ptr.p_double[i]; - if( state->havebndl.ptr.p_bool[i] ) - { - if( ae_fp_less(state->xs.ptr.p_double[i],state->workbndl.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->workbndl.ptr.p_double[i]; - } - } - if( state->havebndu.ptr.p_bool[i] ) - { - if( ae_fp_greater(state->xs.ptr.p_double[i],state->workbndu.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->workbndu.ptr.p_double[i]; - } - } - } - } - else - { - - /* - * We don't have starting point, so we deduce it from - * constraints (if they are present). - * - * NOTE: XS contains some meaningless values from previous block - * which are ignored by code below. - */ - for(i=0; i<=n-1; i++) - { - if( state->havebndl.ptr.p_bool[i]&&state->havebndu.ptr.p_bool[i] ) - { - state->xs.ptr.p_double[i] = 0.5*(state->workbndl.ptr.p_double[i]+state->workbndu.ptr.p_double[i]); - if( ae_fp_less(state->xs.ptr.p_double[i],state->workbndl.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->workbndl.ptr.p_double[i]; - } - if( ae_fp_greater(state->xs.ptr.p_double[i],state->workbndu.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->workbndu.ptr.p_double[i]; - } - continue; - } - if( state->havebndl.ptr.p_bool[i] ) - { - state->xs.ptr.p_double[i] = state->workbndl.ptr.p_double[i]; - continue; - } - if( state->havebndu.ptr.p_bool[i] ) - { - state->xs.ptr.p_double[i] = state->workbndu.ptr.p_double[i]; - continue; - } - state->xs.ptr.p_double[i] = 0; - } - } - - /* - * Handle special case - no constraints - */ - if( nbc==0&&state->nec+state->nic==0 ) - { - - /* - * "Simple" unconstrained Cholesky - */ - bvectorsetlengthatleast(&state->tmpb, n, _state); - for(i=0; i<=n-1; i++) - { - state->tmpb.ptr.p_bool[i] = ae_false; - } - state->repncholesky = state->repncholesky+1; - cqmsetb(&state->a, &state->b, _state); - cqmsetactiveset(&state->a, &state->xs, &state->tmpb, _state); - if( !cqmconstrainedoptimum(&state->a, &state->xn, _state) ) - { - state->repterminationtype = -5; - return; - } - ae_v_move(&state->xs.ptr.p_double[0], 1, &state->xn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_add(&state->xs.ptr.p_double[0], 1, &state->xorigin.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->repinneriterationscount = 1; - state->repouteriterationscount = 1; - state->repterminationtype = 4; - return; - } - - /* - * Prepare "active set" structure - */ - sassetbc(&state->sas, &state->workbndl, &state->workbndu, _state); - sassetlcx(&state->sas, &state->workcleic, state->nec, state->nic, _state); - sassetscale(&state->sas, &state->s, _state); - if( !sasstartoptimization(&state->sas, &state->xs, _state) ) - { - state->repterminationtype = -3; - return; - } - - /* - * Main cycle of CQP algorithm - */ - state->repterminationtype = 4; - badnewtonits = 0; - maxscaledgrad = 0.0; - for(;;) - { - - /* - * Update iterations count - */ - inc(&state->repouteriterationscount, _state); - inc(&state->repinneriterationscount, _state); - - /* - * Phase 1. - * - * Determine active set. - * Update MaxScaledGrad. - */ - cqmadx(&state->a, &state->sas.xc, &state->rctmpg, _state); - ae_v_add(&state->rctmpg.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - sasreactivateconstraints(&state->sas, &state->rctmpg, _state); - v = 0.0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->rctmpg.ptr.p_double[i]*state->s.ptr.p_double[i], _state); - } - maxscaledgrad = ae_maxreal(maxscaledgrad, ae_sqrt(v, _state), _state); - - /* - * Phase 2: perform penalized steepest descent step. - * - * NextAction control variable is set on exit from this loop: - * * NextAction>0 in case we have to proceed to Phase 3 (Newton step) - * * NextAction<0 in case we have to proceed to Phase 1 (recalculate active set) - * * NextAction=0 in case we found solution (step along projected gradient is small enough) - */ - for(;;) - { - - /* - * Calculate constrained descent direction, store to PG. - * Successful termination if PG is zero. - */ - cqmadx(&state->a, &state->sas.xc, &state->gc, _state); - ae_v_add(&state->gc.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - sasconstraineddescent(&state->sas, &state->gc, &state->pg, _state); - state->debugphase2flops = state->debugphase2flops+4*(state->nec+state->nic)*n; - v0 = ae_v_dotproduct(&state->pg.ptr.p_double[0], 1, &state->pg.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_eq(v0,0) ) - { - - /* - * Constrained derivative is zero. - * Solution found. - */ - nextaction = 0; - break; - } - - /* - * Build quadratic model of F along descent direction: - * F(xc+alpha*pg) = D2*alpha^2 + D1*alpha + D0 - * Store noise level in the XC (noise level is used to classify - * step as singificant or insignificant). - * - * In case function curvature is negative or product of descent - * direction and gradient is non-negative, iterations are terminated. - * - * NOTE: D0 is not actually used, but we prefer to maintain it. - */ - fprev = minqp_minqpmodelvalue(&state->a, &state->b, &state->sas.xc, n, &state->tmp0, _state); - fprev = fprev+minqp_penaltyfactor*maxscaledgrad*sasactivelcpenalty1(&state->sas, &state->sas.xc, _state); - cqmevalx(&state->a, &state->sas.xc, &v, &noiselevel, _state); - v0 = cqmxtadx2(&state->a, &state->pg, _state); - state->debugphase2flops = state->debugphase2flops+3*2*n*n; - d2 = v0; - v1 = ae_v_dotproduct(&state->pg.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - d1 = v1; - d0 = fprev; - if( ae_fp_less_eq(d2,0) ) - { - - /* - * Second derivative is non-positive, function is non-convex. - */ - state->repterminationtype = -5; - nextaction = 0; - break; - } - if( ae_fp_greater_eq(d1,0) ) - { - - /* - * Second derivative is positive, first derivative is non-negative. - * Solution found. - */ - nextaction = 0; - break; - } - - /* - * Modify quadratic model - add penalty for violation of the active - * constraints. - * - * Boundary constraints are always satisfied exactly, so we do not - * add penalty term for them. General equality constraint of the - * form a'*(xc+alpha*d)=b adds penalty term: - * P(alpha) = (a'*(xc+alpha*d)-b)^2 - * = (alpha*(a'*d) + (a'*xc-b))^2 - * = alpha^2*(a'*d)^2 + alpha*2*(a'*d)*(a'*xc-b) + (a'*xc-b)^2 - * Each penalty term is multiplied by 100*Anorm before adding it to - * the 1-dimensional quadratic model. - * - * Penalization of the quadratic model improves behavior of the - * algorithm in the presense of the multiple degenerate constraints. - * In particular, it prevents algorithm from making large steps in - * directions which violate equality constraints. - */ - for(i=0; i<=state->nec+state->nic-1; i++) - { - if( state->sas.activeset.ptr.p_int[n+i]>0 ) - { - v0 = ae_v_dotproduct(&state->workcleic.ptr.pp_double[i][0], 1, &state->pg.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v1 = ae_v_dotproduct(&state->workcleic.ptr.pp_double[i][0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v1 = v1-state->workcleic.ptr.pp_double[i][n]; - v = 100*state->anorm; - d2 = d2+v*ae_sqr(v0, _state); - d1 = d1+v*2*v0*v1; - d0 = d0+v*ae_sqr(v1, _state); - } - } - state->debugphase2flops = state->debugphase2flops+2*2*(state->nec+state->nic)*n; - - /* - * Try unbounded step. - * In case function change is dominated by noise or function actually increased - * instead of decreasing, we terminate iterations. - */ - v = -d1/(2*d2); - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->xn.ptr.p_double[0], 1, &state->pg.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - fcand = minqp_minqpmodelvalue(&state->a, &state->b, &state->xn, n, &state->tmp0, _state); - fcand = fcand+minqp_penaltyfactor*maxscaledgrad*sasactivelcpenalty1(&state->sas, &state->xn, _state); - state->debugphase2flops = state->debugphase2flops+2*n*n; - if( ae_fp_greater_eq(fcand,fprev-noiselevel*noisetolerance) ) - { - nextaction = 0; - break; - } - - /* - * Save active set - * Perform bounded step with (possible) activation - */ - actstatus = minqp_minqpboundedstepandactivation(state, &state->xn, &state->tmp0, _state); - fcur = minqp_minqpmodelvalue(&state->a, &state->b, &state->sas.xc, n, &state->tmp0, _state); - state->debugphase2flops = state->debugphase2flops+2*n*n; - - /* - * Depending on results, decide what to do: - * 1. In case step was performed without activation of constraints, - * we proceed to Newton method - * 2. In case there was activated at least one constraint with ActiveSet[I]<0, - * we proceed to Phase 1 and re-evaluate active set. - * 3. Otherwise (activation of the constraints with ActiveSet[I]=0) - * we try Phase 2 one more time. - */ - if( actstatus<0 ) - { - - /* - * Step without activation, proceed to Newton - */ - nextaction = 1; - break; - } - if( actstatus==0 ) - { - - /* - * No new constraints added during last activation - only - * ones which were at the boundary (ActiveSet[I]=0), but - * inactive due to numerical noise. - * - * Now, these constraints are added to the active set, and - * we try to perform steepest descent (Phase 2) one more time. - */ - continue; - } - else - { - - /* - * Last step activated at least one significantly new - * constraint (ActiveSet[I]<0), we have to re-evaluate - * active set (Phase 1). - */ - nextaction = -1; - break; - } - } - if( nextaction<0 ) - { - continue; - } - if( nextaction==0 ) - { - break; - } - - /* - * Phase 3: fast equality-constrained solver - * - * NOTE: this solver uses Augmented Lagrangian algorithm to solve - * equality-constrained subproblems. This algorithm may - * perform steps which increase function values instead of - * decreasing it (in hard cases, like overconstrained problems). - * - * Such non-monononic steps may create a loop, when Augmented - * Lagrangian algorithm performs uphill step, and steepest - * descent algorithm (Phase 2) performs downhill step in the - * opposite direction. - * - * In order to prevent iterations to continue forever we - * count iterations when AL algorithm increased function - * value instead of decreasing it. When number of such "bad" - * iterations will increase beyong MaxBadNewtonIts, we will - * terminate algorithm. - */ - fprev = minqp_minqpmodelvalue(&state->a, &state->b, &state->sas.xc, n, &state->tmp0, _state); - for(;;) - { - - /* - * Calculate optimum subject to presently active constraints - */ - state->repncholesky = state->repncholesky+1; - state->debugphase3flops = state->debugphase3flops+ae_pow(n, 3, _state)/3; - if( !minqp_minqpconstrainedoptimum(state, &state->a, state->anorm, &state->b, &state->xn, &state->tmp0, &state->tmpb, &state->tmp1, _state) ) - { - state->repterminationtype = -5; - sasstopoptimization(&state->sas, _state); - return; - } - - /* - * Add constraints. - * If no constraints was added, accept candidate point XN and move to next phase. - */ - if( minqp_minqpboundedstepandactivation(state, &state->xn, &state->tmp0, _state)<0 ) - { - break; - } - } - fcur = minqp_minqpmodelvalue(&state->a, &state->b, &state->sas.xc, n, &state->tmp0, _state); - if( ae_fp_greater_eq(fcur,fprev) ) - { - badnewtonits = badnewtonits+1; - } - if( badnewtonits>=minqp_maxbadnewtonits ) - { - - /* - * Algorithm found solution, but keeps iterating because Newton - * algorithm performs uphill steps (noise in the Augmented Lagrangian - * algorithm). We terminate algorithm; it is considered normal - * termination. - */ - break; - } - } - sasstopoptimization(&state->sas, _state); - - /* - * Post-process: add XOrigin to XC - */ - for(i=0; i<=n-1; i++) - { - if( state->havebndl.ptr.p_bool[i]&&ae_fp_eq(state->sas.xc.ptr.p_double[i],state->workbndl.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->bndl.ptr.p_double[i]; - continue; - } - if( state->havebndu.ptr.p_bool[i]&&ae_fp_eq(state->sas.xc.ptr.p_double[i],state->workbndu.ptr.p_double[i]) ) - { - state->xs.ptr.p_double[i] = state->bndu.ptr.p_double[i]; - continue; - } - state->xs.ptr.p_double[i] = boundval(state->sas.xc.ptr.p_double[i]+state->xorigin.ptr.p_double[i], state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - return; - } - - /* - * BLEIC solver - */ - if( state->algokind==2 ) - { - ae_assert(state->akind==0||state->akind==1, "MinQPOptimize: unexpected AKind", _state); - ivectorsetlengthatleast(&state->tmpi, state->nec+state->nic, _state); - rvectorsetlengthatleast(&state->tmp0, n, _state); - rvectorsetlengthatleast(&state->tmp1, n, _state); - for(i=0; i<=state->nec-1; i++) - { - state->tmpi.ptr.p_int[i] = 0; - } - for(i=0; i<=state->nic-1; i++) - { - state->tmpi.ptr.p_int[state->nec+i] = -1; - } - minbleicsetlc(&state->solver, &state->cleic, &state->tmpi, state->nec+state->nic, _state); - minbleicsetbc(&state->solver, &state->bndl, &state->bndu, _state); - minbleicsetdrep(&state->solver, ae_true, _state); - minbleicsetcond(&state->solver, ae_minrealnumber, 0.0, 0.0, state->bleicmaxits, _state); - minbleicsetscale(&state->solver, &state->s, _state); - minbleicsetprecscale(&state->solver, _state); - minbleicrestartfrom(&state->solver, &state->xs, _state); - state->repterminationtype = 0; - while(minbleiciteration(&state->solver, _state)) - { - - /* - * Line search started - */ - if( state->solver.lsstart ) - { - - /* - * Iteration counters: - * * inner iterations count is increased on every line search - * * outer iterations count is increased only at steepest descent line search - */ - inc(&state->repinneriterationscount, _state); - if( !state->solver.lbfgssearch ) - { - inc(&state->repouteriterationscount, _state); - } - - /* - * Build quadratic model of F along descent direction: - * F(x+alpha*d) = D2*alpha^2 + D1*alpha + D0 - */ - d0 = state->solver.f; - d1 = ae_v_dotproduct(&state->solver.d.ptr.p_double[0], 1, &state->solver.g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - d2 = 0; - if( state->akind==0 ) - { - d2 = cqmxtadx2(&state->a, &state->solver.d, _state); - } - if( state->akind==1 ) - { - sparsesmv(&state->sparsea, state->sparseaupper, &state->solver.d, &state->tmp0, _state); - d2 = 0.0; - for(i=0; i<=n-1; i++) - { - d2 = d2+state->solver.d.ptr.p_double[i]*state->tmp0.ptr.p_double[i]; - } - d2 = 0.5*d2; - } - - /* - * Suggest new step - */ - if( ae_fp_less(d1,0)&&ae_fp_greater(d2,0) ) - { - state->solver.stp = safeminposrv(-d1, 2*d2, state->solver.curstpmax, _state); - } - - /* - * This line search may be started from steepest descent - * stage (stage 2) or from L-BFGS stage (stage 3) of the - * BLEIC algorithm. Depending on stage type, different - * checks are performed. - * - * Say, L-BFGS stage is an equality-constrained refinement - * stage of BLEIC. This stage refines current iterate - * under "frozen" equality constraints. We can terminate - * iterations at this stage only when we encounter - * unconstrained direction of negative curvature. In all - * other cases (say, when constrained gradient is zero) - * we should not terminate algorithm because everything may - * change after de-activating presently active constraints. - * - * At steepest descent stage of BLEIC we can terminate algorithm - * because it found minimum (steepest descent step is zero - * or too short). We also perform check for direction of - * negative curvature. - */ - if( (ae_fp_less(d2,0)||(ae_fp_eq(d2,0)&&ae_fp_less(d1,0)))&&!state->solver.boundedstep ) - { - - /* - * Function is unbounded from below: - * * function will decrease along D, i.e. either: - * * D2<0 - * * D2=0 and D1<0 - * * step is unconstrained - * - * If these conditions are true, we abnormally terminate QP - * algorithm with return code -4 (we can do so at any stage - * of BLEIC - whether it is L-BFGS or steepest descent one). - */ - state->repterminationtype = -4; - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->solver.x.ptr.p_double[i]; - } - break; - } - if( !state->solver.lbfgssearch&&ae_fp_greater_eq(d2,0) ) - { - - /* - * Tests for "normal" convergence. - * - * These tests are performed only at "steepest descent" stage - * of the BLEIC algorithm, and only when function is non-concave - * (D2>=0) along direction D. - * - * NOTE: we do not test iteration count (MaxIts) here, because - * this stopping condition is tested by BLEIC itself. - */ - if( ae_fp_greater_eq(d1,0) ) - { - - /* - * "Emergency" stopping condition: D is non-descent direction. - * Sometimes it is possible because of numerical noise in the - * target function. - */ - state->repterminationtype = 4; - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->solver.x.ptr.p_double[i]; - } - break; - } - if( ae_fp_greater(d2,0) ) - { - - /* - * Stopping condition #4 - gradient norm is small: - * - * 1. rescale State.Solver.D and State.Solver.G according to - * current scaling, store results to Tmp0 and Tmp1. - * 2. Normalize Tmp0 (scaled direction vector). - * 3. compute directional derivative (in scaled variables), - * which is equal to DOTPRODUCT(Tmp0,Tmp1). - */ - v = 0; - for(i=0; i<=n-1; i++) - { - state->tmp0.ptr.p_double[i] = state->solver.d.ptr.p_double[i]/state->s.ptr.p_double[i]; - state->tmp1.ptr.p_double[i] = state->solver.g.ptr.p_double[i]*state->s.ptr.p_double[i]; - v = v+ae_sqr(state->tmp0.ptr.p_double[i], _state); - } - ae_assert(ae_fp_greater(v,0), "MinQPOptimize: inernal errror (scaled direction is zero)", _state); - v = 1/ae_sqrt(v, _state); - ae_v_muld(&state->tmp0.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - v = ae_v_dotproduct(&state->tmp0.ptr.p_double[0], 1, &state->tmp1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_less_eq(ae_fabs(v, _state),state->bleicepsg) ) - { - state->repterminationtype = 4; - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->solver.x.ptr.p_double[i]; - } - break; - } - - /* - * Stopping condition #1 - relative function improvement is small: - * - * 1. calculate steepest descent step: V = -D1/(2*D2) - * 2. calculate function change: V1= D2*V^2 + D1*V - * 3. stop if function change is small enough - */ - v = -d1/(2*d2); - v1 = d2*v*v+d1*v; - if( ae_fp_less_eq(ae_fabs(v1, _state),state->bleicepsf*ae_maxreal(d0, 1.0, _state)) ) - { - state->repterminationtype = 1; - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->solver.x.ptr.p_double[i]; - } - break; - } - - /* - * Stopping condition #2 - scaled step is small: - * - * 1. calculate step multiplier V0 (step itself is D*V0) - * 2. calculate scaled step length V - * 3. stop if step is small enough - */ - v0 = -d1/(2*d2); - v = 0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(v0*state->solver.d.ptr.p_double[i]/state->s.ptr.p_double[i], _state); - } - if( ae_fp_less_eq(ae_sqrt(v, _state),state->bleicepsx) ) - { - state->repterminationtype = 2; - for(i=0; i<=n-1; i++) - { - state->xs.ptr.p_double[i] = state->solver.x.ptr.p_double[i]; - } - break; - } - } - } - } - - /* - * Gradient evaluation - */ - if( state->solver.needfg ) - { - for(i=0; i<=n-1; i++) - { - state->tmp0.ptr.p_double[i] = state->solver.x.ptr.p_double[i]-state->xorigin.ptr.p_double[i]; - } - if( state->akind==0 ) - { - cqmadx(&state->a, &state->tmp0, &state->tmp1, _state); - } - if( state->akind==1 ) - { - sparsesmv(&state->sparsea, state->sparseaupper, &state->tmp0, &state->tmp1, _state); - } - v0 = ae_v_dotproduct(&state->tmp0.ptr.p_double[0], 1, &state->tmp1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v1 = ae_v_dotproduct(&state->tmp0.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->solver.f = 0.5*v0+v1; - ae_v_move(&state->solver.g.ptr.p_double[0], 1, &state->tmp1.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_add(&state->solver.g.ptr.p_double[0], 1, &state->b.ptr.p_double[0], 1, ae_v_len(0,n-1)); - } - } - if( state->repterminationtype==0 ) - { - - /* - * BLEIC optimizer was terminated by one of its inner stopping - * conditions. Usually it is iteration counter (if such - * stopping condition was specified by user). - */ - minbleicresults(&state->solver, &state->xs, &state->solverrep, _state); - state->repterminationtype = state->solverrep.terminationtype; - } - else - { - - /* - * BLEIC optimizer was terminated in "emergency" mode by QP - * solver. - * - * NOTE: such termination is "emergency" only when viewed from - * BLEIC's position. QP solver sees such termination as - * routine one, triggered by QP's stopping criteria. - */ - minbleicemergencytermination(&state->solver, _state); - } - return; - } -} - - -/************************************************************************* -QP solver results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution. - This array is allocated and initialized only when - Rep.TerminationType parameter is positive (success). - Rep - optimization report. You should check Rep.TerminationType, - which contains completion code, and you may check another - fields which contain another information about algorithm - functioning. - - Failure codes returned by algorithm are: - * -5 inappropriate solver was used: - * Cholesky solver for (semi)indefinite problems - * Cholesky solver for problems with sparse matrix - * -4 BLEIC-QP algorithm found unconstrained direction - of negative curvature (function is unbounded from - below even under constraints), no meaningful - minimum can be found. - * -3 inconsistent constraints (or maybe feasible point - is too hard to find). If you are sure that - constraints are feasible, try to restart optimizer - with better initial approximation. - - Completion codes specific for Cholesky algorithm: - * 4 successful completion - - Completion codes specific for BLEIC-based algorithm: - * 1 relative function improvement is no more than EpsF. - * 2 scaled step is no more than EpsX. - * 4 scaled gradient norm is no more than EpsG. - * 5 MaxIts steps was taken - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpresults(minqpstate* state, - /* Real */ ae_vector* x, - minqpreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _minqpreport_clear(rep); - - minqpresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -QP results - -Buffered implementation of MinQPResults() which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpresultsbuf(minqpstate* state, - /* Real */ ae_vector* x, - minqpreport* rep, - ae_state *_state) -{ - - - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->xs.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->inneriterationscount = state->repinneriterationscount; - rep->outeriterationscount = state->repouteriterationscount; - rep->nmv = state->repnmv; - rep->ncholesky = state->repncholesky; - rep->terminationtype = state->repterminationtype; -} - - -/************************************************************************* -Fast version of MinQPSetLinearTerm(), which doesn't check its arguments. -For internal use only. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlineartermfast(minqpstate* state, - /* Real */ ae_vector* b, - ae_state *_state) -{ - - - ae_v_move(&state->b.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); -} - - -/************************************************************************* -Fast version of MinQPSetQuadraticTerm(), which doesn't check its arguments. - -It accepts additional parameter - shift S, which allows to "shift" matrix -A by adding s*I to A. S must be positive (although it is not checked). - -For internal use only. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetquadratictermfast(minqpstate* state, - /* Real */ ae_matrix* a, - ae_bool isupper, - double s, - ae_state *_state) -{ - ae_int_t i; - ae_int_t j; - ae_int_t n; - - - n = state->n; - state->akind = 0; - cqmseta(&state->a, a, isupper, 1.0, _state); - if( ae_fp_greater(s,0) ) - { - rvectorsetlengthatleast(&state->tmp0, n, _state); - for(i=0; i<=n-1; i++) - { - state->tmp0.ptr.p_double[i] = a->ptr.pp_double[i][i]+s; - } - cqmrewritedensediagonal(&state->a, &state->tmp0, _state); - } - - /* - * Estimate norm of A - * (it will be used later in the quadratic penalty function) - */ - state->anorm = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - for(j=i; j<=n-1; j++) - { - state->anorm = ae_maxreal(state->anorm, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - else - { - for(j=0; j<=i; j++) - { - state->anorm = ae_maxreal(state->anorm, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - } - state->anorm = state->anorm*n; -} - - -/************************************************************************* -Internal function which allows to rewrite diagonal of quadratic term. -For internal use only. - -This function can be used only when you have dense A and already made -MinQPSetQuadraticTerm(Fast) call. - - -- ALGLIB -- - Copyright 16.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqprewritediagonal(minqpstate* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - - - cqmrewritedensediagonal(&state->a, s, _state); -} - - -/************************************************************************* -Fast version of MinQPSetStartingPoint(), which doesn't check its arguments. -For internal use only. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetstartingpointfast(minqpstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_int_t n; - - - n = state->n; - ae_v_move(&state->startx.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->havex = ae_true; -} - - -/************************************************************************* -Fast version of MinQPSetOrigin(), which doesn't check its arguments. -For internal use only. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetoriginfast(minqpstate* state, - /* Real */ ae_vector* xorigin, - ae_state *_state) -{ - ae_int_t n; - - - n = state->n; - ae_v_move(&state->xorigin.ptr.p_double[0], 1, &xorigin->ptr.p_double[0], 1, ae_v_len(0,n-1)); -} - - -/************************************************************************* -Having feasible current point XC and possibly infeasible candidate point -XN, this function performs longest step from XC to XN which retains -feasibility. In case XN is found to be infeasible, at least one constraint -is activated. - -For example, if we have: - XC=0.5 - XN=1.2 - x>=0, x<=1 -then this function will move us to X=1.0 and activate constraint "x<=1". - -INPUT PARAMETERS: - State - MinQP state. - XC - current point, must be feasible with respect to - all constraints - XN - candidate point, can be infeasible with respect to some - constraints. Must be located in the subspace of current - active set, i.e. it is feasible with respect to already - active constraints. - Buf - temporary buffer, automatically resized if needed - -OUTPUT PARAMETERS: - State - this function changes following fields of State: - * State.ActiveSet - * State.ActiveC - active linear constraints - XC - new position - -RESULT: - >0, in case at least one inactive non-candidate constraint was activated - =0, in case only "candidate" constraints were activated - <0, in case no constraints were activated by the step - - - -- ALGLIB -- - Copyright 29.02.2012 by Bochkanov Sergey -*************************************************************************/ -static ae_int_t minqp_minqpboundedstepandactivation(minqpstate* state, - /* Real */ ae_vector* xn, - /* Real */ ae_vector* buf, - ae_state *_state) -{ - ae_int_t n; - double stpmax; - ae_int_t cidx; - double cval; - ae_bool needact; - double v; - ae_int_t result; - - - n = state->n; - rvectorsetlengthatleast(buf, n, _state); - ae_v_move(&buf->ptr.p_double[0], 1, &xn->ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_sub(&buf->ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - sasexploredirection(&state->sas, buf, &stpmax, &cidx, &cval, _state); - needact = ae_fp_less_eq(stpmax,1); - v = ae_minreal(stpmax, 1.0, _state); - ae_v_muld(&buf->ptr.p_double[0], 1, ae_v_len(0,n-1), v); - ae_v_add(&buf->ptr.p_double[0], 1, &state->sas.xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - result = sasmoveto(&state->sas, buf, needact, cidx, cval, _state); - return result; -} - - -/************************************************************************* -Model value: f = 0.5*x'*A*x + b'*x - -INPUT PARAMETERS: - A - convex quadratic model; only main quadratic term is used, - other parts of the model (D/Q/linear term) are ignored. - This function does not modify model state. - B - right part - XC - evaluation point - Tmp - temporary buffer, automatically resized if needed - - -- ALGLIB -- - Copyright 20.06.2012 by Bochkanov Sergey -*************************************************************************/ -static double minqp_minqpmodelvalue(convexquadraticmodel* a, - /* Real */ ae_vector* b, - /* Real */ ae_vector* xc, - ae_int_t n, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - double v0; - double v1; - double result; - - - rvectorsetlengthatleast(tmp, n, _state); - cqmadx(a, xc, tmp, _state); - v0 = ae_v_dotproduct(&xc->ptr.p_double[0], 1, &tmp->ptr.p_double[0], 1, ae_v_len(0,n-1)); - v1 = ae_v_dotproduct(&xc->ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - result = 0.5*v0+v1; - return result; -} - - -/************************************************************************* -Optimum of A subject to: -a) active boundary constraints (given by ActiveSet[] and corresponding - elements of XC) -b) active linear constraints (given by C, R, LagrangeC) - -INPUT PARAMETERS: - A - main quadratic term of the model; - although structure may store linear and rank-K terms, - these terms are ignored and rewritten by this function. - ANorm - estimate of ||A|| (2-norm is used) - B - array[N], linear term of the model - XN - possibly preallocated buffer - Tmp - temporary buffer (automatically resized) - Tmp1 - temporary buffer (automatically resized) - -OUTPUT PARAMETERS: - A - modified quadratic model (this function changes rank-K - term and linear term of the model) - LagrangeC- current estimate of the Lagrange coefficients - XN - solution - -RESULT: - True on success, False on failure (non-SPD model) - - -- ALGLIB -- - Copyright 20.06.2012 by Bochkanov Sergey -*************************************************************************/ -static ae_bool minqp_minqpconstrainedoptimum(minqpstate* state, - convexquadraticmodel* a, - double anorm, - /* Real */ ae_vector* b, - /* Real */ ae_vector* xn, - /* Real */ ae_vector* tmp, - /* Boolean */ ae_vector* tmpb, - /* Real */ ae_vector* lagrangec, - ae_state *_state) -{ - ae_int_t itidx; - ae_int_t i; - double v; - double feaserrold; - double feaserrnew; - double theta; - ae_int_t n; - ae_bool result; - - - n = state->n; - - /* - * Rebuild basis accroding to current active set. - * We call SASRebuildBasis() to make sure that fields of SAS - * store up to date values. - */ - sasrebuildbasis(&state->sas, _state); - - /* - * Allocate temporaries. - */ - rvectorsetlengthatleast(tmp, ae_maxint(n, state->sas.basissize, _state), _state); - bvectorsetlengthatleast(tmpb, n, _state); - rvectorsetlengthatleast(lagrangec, state->sas.basissize, _state); - - /* - * Prepare model - */ - for(i=0; i<=state->sas.basissize-1; i++) - { - tmp->ptr.p_double[i] = state->sas.pbasis.ptr.pp_double[i][n]; - } - theta = 100.0*anorm; - for(i=0; i<=n-1; i++) - { - if( state->sas.activeset.ptr.p_int[i]>0 ) - { - tmpb->ptr.p_bool[i] = ae_true; - } - else - { - tmpb->ptr.p_bool[i] = ae_false; - } - } - cqmsetactiveset(a, &state->sas.xc, tmpb, _state); - cqmsetq(a, &state->sas.pbasis, tmp, state->sas.basissize, theta, _state); - - /* - * Iterate until optimal values of Lagrange multipliers are found - */ - for(i=0; i<=state->sas.basissize-1; i++) - { - lagrangec->ptr.p_double[i] = 0; - } - feaserrnew = ae_maxrealnumber; - result = ae_true; - for(itidx=1; itidx<=minqp_maxlagrangeits; itidx++) - { - - /* - * Generate right part B using linear term and current - * estimate of the Lagrange multipliers. - */ - ae_v_move(&tmp->ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=state->sas.basissize-1; i++) - { - v = lagrangec->ptr.p_double[i]; - ae_v_subd(&tmp->ptr.p_double[0], 1, &state->sas.pbasis.ptr.pp_double[i][0], 1, ae_v_len(0,n-1), v); - } - cqmsetb(a, tmp, _state); - - /* - * Solve - */ - result = cqmconstrainedoptimum(a, xn, _state); - if( !result ) - { - return result; - } - - /* - * Compare feasibility errors. - * Terminate if error decreased too slowly. - */ - feaserrold = feaserrnew; - feaserrnew = 0; - for(i=0; i<=state->sas.basissize-1; i++) - { - v = ae_v_dotproduct(&state->sas.pbasis.ptr.pp_double[i][0], 1, &xn->ptr.p_double[0], 1, ae_v_len(0,n-1)); - feaserrnew = feaserrnew+ae_sqr(v-state->sas.pbasis.ptr.pp_double[i][n], _state); - } - feaserrnew = ae_sqrt(feaserrnew, _state); - if( ae_fp_greater_eq(feaserrnew,0.2*feaserrold) ) - { - break; - } - - /* - * Update Lagrange multipliers - */ - for(i=0; i<=state->sas.basissize-1; i++) - { - v = ae_v_dotproduct(&state->sas.pbasis.ptr.pp_double[i][0], 1, &xn->ptr.p_double[0], 1, ae_v_len(0,n-1)); - lagrangec->ptr.p_double[i] = lagrangec->ptr.p_double[i]-theta*(v-state->sas.pbasis.ptr.pp_double[i][n]); - } - } - return result; -} - - -ae_bool _minqpstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minqpstate *p = (minqpstate*)_p; - ae_touch_ptr((void*)p); - if( !_convexquadraticmodel_init(&p->a, _state, make_automatic) ) - return ae_false; - if( !_sparsematrix_init(&p->sparsea, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->b, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->havebndl, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->havebndu, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xorigin, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->startx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->cleic, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_sactiveset_init(&p->sas, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->pg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->workbndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->workbndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->workcleic, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xs, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpb, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rctmpg, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpi, 0, DT_INT, _state, make_automatic) ) - return ae_false; - if( !_normestimatorstate_init(&p->estimator, _state, make_automatic) ) - return ae_false; - if( !_minbleicstate_init(&p->solver, _state, make_automatic) ) - return ae_false; - if( !_minbleicreport_init(&p->solverrep, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _minqpstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minqpstate *dst = (minqpstate*)_dst; - minqpstate *src = (minqpstate*)_src; - dst->n = src->n; - dst->algokind = src->algokind; - dst->akind = src->akind; - if( !_convexquadraticmodel_init_copy(&dst->a, &src->a, _state, make_automatic) ) - return ae_false; - if( !_sparsematrix_init_copy(&dst->sparsea, &src->sparsea, _state, make_automatic) ) - return ae_false; - dst->sparseaupper = src->sparseaupper; - dst->anorm = src->anorm; - if( !ae_vector_init_copy(&dst->b, &src->b, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->havebndl, &src->havebndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->havebndu, &src->havebndu, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xorigin, &src->xorigin, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->startx, &src->startx, _state, make_automatic) ) - return ae_false; - dst->havex = src->havex; - if( !ae_matrix_init_copy(&dst->cleic, &src->cleic, _state, make_automatic) ) - return ae_false; - dst->nec = src->nec; - dst->nic = src->nic; - dst->bleicepsg = src->bleicepsg; - dst->bleicepsf = src->bleicepsf; - dst->bleicepsx = src->bleicepsx; - dst->bleicmaxits = src->bleicmaxits; - if( !_sactiveset_init_copy(&dst->sas, &src->sas, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gc, &src->gc, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xn, &src->xn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->pg, &src->pg, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->workbndl, &src->workbndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->workbndu, &src->workbndu, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->workcleic, &src->workcleic, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xs, &src->xs, _state, make_automatic) ) - return ae_false; - dst->repinneriterationscount = src->repinneriterationscount; - dst->repouteriterationscount = src->repouteriterationscount; - dst->repncholesky = src->repncholesky; - dst->repnmv = src->repnmv; - dst->repterminationtype = src->repterminationtype; - dst->debugphase1flops = src->debugphase1flops; - dst->debugphase2flops = src->debugphase2flops; - dst->debugphase3flops = src->debugphase3flops; - if( !ae_vector_init_copy(&dst->tmp0, &src->tmp0, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp1, &src->tmp1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpb, &src->tmpb, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rctmpg, &src->rctmpg, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpi, &src->tmpi, _state, make_automatic) ) - return ae_false; - if( !_normestimatorstate_init_copy(&dst->estimator, &src->estimator, _state, make_automatic) ) - return ae_false; - if( !_minbleicstate_init_copy(&dst->solver, &src->solver, _state, make_automatic) ) - return ae_false; - if( !_minbleicreport_init_copy(&dst->solverrep, &src->solverrep, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _minqpstate_clear(void* _p) -{ - minqpstate *p = (minqpstate*)_p; - ae_touch_ptr((void*)p); - _convexquadraticmodel_clear(&p->a); - _sparsematrix_clear(&p->sparsea); - ae_vector_clear(&p->b); - ae_vector_clear(&p->bndl); - ae_vector_clear(&p->bndu); - ae_vector_clear(&p->s); - ae_vector_clear(&p->havebndl); - ae_vector_clear(&p->havebndu); - ae_vector_clear(&p->xorigin); - ae_vector_clear(&p->startx); - ae_matrix_clear(&p->cleic); - _sactiveset_clear(&p->sas); - ae_vector_clear(&p->gc); - ae_vector_clear(&p->xn); - ae_vector_clear(&p->pg); - ae_vector_clear(&p->workbndl); - ae_vector_clear(&p->workbndu); - ae_matrix_clear(&p->workcleic); - ae_vector_clear(&p->xs); - ae_vector_clear(&p->tmp0); - ae_vector_clear(&p->tmp1); - ae_vector_clear(&p->tmpb); - ae_vector_clear(&p->rctmpg); - ae_vector_clear(&p->tmpi); - _normestimatorstate_clear(&p->estimator); - _minbleicstate_clear(&p->solver); - _minbleicreport_clear(&p->solverrep); -} - - -void _minqpstate_destroy(void* _p) -{ - minqpstate *p = (minqpstate*)_p; - ae_touch_ptr((void*)p); - _convexquadraticmodel_destroy(&p->a); - _sparsematrix_destroy(&p->sparsea); - ae_vector_destroy(&p->b); - ae_vector_destroy(&p->bndl); - ae_vector_destroy(&p->bndu); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->havebndl); - ae_vector_destroy(&p->havebndu); - ae_vector_destroy(&p->xorigin); - ae_vector_destroy(&p->startx); - ae_matrix_destroy(&p->cleic); - _sactiveset_destroy(&p->sas); - ae_vector_destroy(&p->gc); - ae_vector_destroy(&p->xn); - ae_vector_destroy(&p->pg); - ae_vector_destroy(&p->workbndl); - ae_vector_destroy(&p->workbndu); - ae_matrix_destroy(&p->workcleic); - ae_vector_destroy(&p->xs); - ae_vector_destroy(&p->tmp0); - ae_vector_destroy(&p->tmp1); - ae_vector_destroy(&p->tmpb); - ae_vector_destroy(&p->rctmpg); - ae_vector_destroy(&p->tmpi); - _normestimatorstate_destroy(&p->estimator); - _minbleicstate_destroy(&p->solver); - _minbleicreport_destroy(&p->solverrep); -} - - -ae_bool _minqpreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minqpreport *p = (minqpreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _minqpreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minqpreport *dst = (minqpreport*)_dst; - minqpreport *src = (minqpreport*)_src; - dst->inneriterationscount = src->inneriterationscount; - dst->outeriterationscount = src->outeriterationscount; - dst->nmv = src->nmv; - dst->ncholesky = src->ncholesky; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _minqpreport_clear(void* _p) -{ - minqpreport *p = (minqpreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _minqpreport_destroy(void* _p) -{ - minqpreport *p = (minqpreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] and Jacobian of f[]. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function vector f[] at given point X -* function vector f[] and Jacobian of f[] (simultaneously) at given point - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() and jac() callbacks. -First one is used to calculate f[] at given point, second one calculates -f[] and Jacobian df[i]/dx[j]. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not provide Jacobian), but it -will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateVJ() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state) -{ - - _minlmstate_clear(state); - - ae_assert(n>=1, "MinLMCreateVJ: N<1!", _state); - ae_assert(m>=1, "MinLMCreateVJ: M<1!", _state); - ae_assert(x->cnt>=n, "MinLMCreateVJ: Length(X)teststep = 0; - state->n = n; - state->m = m; - state->algomode = 1; - state->hasf = ae_false; - state->hasfi = ae_true; - state->hasg = ae_false; - - /* - * second stage of initialization - */ - minlm_lmprepare(n, m, ae_false, state, _state); - minlmsetacctype(state, 0, _state); - minlmsetcond(state, 0, 0, 0, 0, _state); - minlmsetxrep(state, ae_false, _state); - minlmsetstpmax(state, 0, _state); - minlmrestartfrom(state, x, _state); -} - - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] only. Finite differences are used to -calculate Jacobian. - - -REQUIREMENTS: -This algorithm will request following information during its operation: -* function vector f[] at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() callback. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not accept function vector), but -it will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateV() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also MinLMIteration, MinLMResults. - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatev(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - double diffstep, - minlmstate* state, - ae_state *_state) -{ - - _minlmstate_clear(state); - - ae_assert(ae_isfinite(diffstep, _state), "MinLMCreateV: DiffStep is not finite!", _state); - ae_assert(ae_fp_greater(diffstep,0), "MinLMCreateV: DiffStep<=0!", _state); - ae_assert(n>=1, "MinLMCreateV: N<1!", _state); - ae_assert(m>=1, "MinLMCreateV: M<1!", _state); - ae_assert(x->cnt>=n, "MinLMCreateV: Length(X)teststep = 0; - state->n = n; - state->m = m; - state->algomode = 0; - state->hasf = ae_false; - state->hasfi = ae_true; - state->hasg = ae_false; - state->diffstep = diffstep; - - /* - * Second stage of initialization - */ - minlm_lmprepare(n, m, ae_false, state, _state); - minlmsetacctype(state, 1, _state); - minlmsetcond(state, 0, 0, 0, 0, _state); - minlmsetxrep(state, ae_false, _state); - minlmsetstpmax(state, 0, _state); - minlmrestartfrom(state, x, _state); -} - - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE METHOD FOR NON-LINEAR OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of general form (not "sum-of- --squares") function - F = F(x[0], ..., x[n-1]) -using its gradient and Hessian. Levenberg-Marquardt modification with -L-BFGS pre-optimization and internal pre-conditioned L-BFGS optimization -after each Levenberg-Marquardt step is used. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function value F at given point X -* F and gradient G (simultaneously) at given point X -* F, G and Hessian H (simultaneously) at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts func(), grad() and hess() -function pointers. First pointer is used to calculate F at given point, -second one calculates F(x) and grad F(x), third one calculates F(x), -grad F(x), hess F(x). - -You can try to initialize MinLMState structure with FGH-function and then -use incorrect version of MinLMOptimize() (for example, version which does -not provide Hessian matrix), but it will lead to exception being thrown -after first attempt to calculate Hessian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateFGH() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - pointers (delegates, etc.) to callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgh(ae_int_t n, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state) -{ - - _minlmstate_clear(state); - - ae_assert(n>=1, "MinLMCreateFGH: N<1!", _state); - ae_assert(x->cnt>=n, "MinLMCreateFGH: Length(X)teststep = 0; - state->n = n; - state->m = 0; - state->algomode = 2; - state->hasf = ae_true; - state->hasfi = ae_false; - state->hasg = ae_true; - - /* - * init2 - */ - minlm_lmprepare(n, 0, ae_true, state, _state); - minlmsetacctype(state, 2, _state); - minlmsetcond(state, 0, 0, 0, 0, _state); - minlmsetxrep(state, ae_false, _state); - minlmsetstpmax(state, 0, _state); - minlmrestartfrom(state, x, _state); -} - - -/************************************************************************* -This function sets stopping conditions for Levenberg-Marquardt optimization -algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinLMSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. Only Levenberg-Marquardt - iterations are counted (L-BFGS/CG iterations are NOT - counted because their cost is very low compared to that of - LM). - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetcond(minlmstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsg, _state), "MinLMSetCond: EpsG is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsg,0), "MinLMSetCond: negative EpsG!", _state); - ae_assert(ae_isfinite(epsf, _state), "MinLMSetCond: EpsF is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "MinLMSetCond: negative EpsF!", _state); - ae_assert(ae_isfinite(epsx, _state), "MinLMSetCond: EpsX is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "MinLMSetCond: negative EpsX!", _state); - ae_assert(maxits>=0, "MinLMSetCond: negative MaxIts!", _state); - if( ((ae_fp_eq(epsg,0)&&ae_fp_eq(epsf,0))&&ae_fp_eq(epsx,0))&&maxits==0 ) - { - epsx = 1.0E-6; - } - state->epsg = epsg; - state->epsf = epsf; - state->epsx = epsx; - state->maxits = maxits; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinLMOptimize(). Both Levenberg-Marquardt and internal L-BFGS -iterations are reported. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetxrep(minlmstate* state, ae_bool needxrep, ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - -NOTE: non-zero StpMax leads to moderate performance degradation because -intermediate step of preconditioned L-BFGS optimization is incompatible -with limits on step size. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetstpmax(minlmstate* state, double stpmax, ae_state *_state) -{ - - - ae_assert(ae_isfinite(stpmax, _state), "MinLMSetStpMax: StpMax is not finite!", _state); - ae_assert(ae_fp_greater_eq(stpmax,0), "MinLMSetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* -This function sets scaling coefficients for LM optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Generally, scale is NOT considered to be a form of preconditioner. But LM -optimizer is unique in that it uses scaling matrix both in the stopping -condition tests and as Marquardt damping factor. - -Proper scaling is very important for the algorithm performance. It is less -important for the quality of results, but still has some influence (it is -easier to converge when variables are properly scaled, so premature -stopping is possible when very badly scalled variables are combined with -relaxed stopping conditions). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlmsetscale(minlmstate* state, - /* Real */ ae_vector* s, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(s->cnt>=state->n, "MinLMSetScale: Length(S)n-1; i++) - { - ae_assert(ae_isfinite(s->ptr.p_double[i], _state), "MinLMSetScale: S contains infinite or NAN elements", _state); - ae_assert(ae_fp_neq(s->ptr.p_double[i],0), "MinLMSetScale: S contains zero elements", _state); - state->s.ptr.p_double[i] = ae_fabs(s->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -This function sets boundary constraints for LM optimizer - -Boundary constraints are inactive by default (after initial creation). -They are preserved until explicitly turned off with another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: this solver has following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints - or at its boundary - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlmsetbc(minlmstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state) -{ - ae_int_t i; - ae_int_t n; - - - n = state->n; - ae_assert(bndl->cnt>=n, "MinLMSetBC: Length(BndL)cnt>=n, "MinLMSetBC: Length(BndU)ptr.p_double[i], _state)||ae_isneginf(bndl->ptr.p_double[i], _state), "MinLMSetBC: BndL contains NAN or +INF", _state); - ae_assert(ae_isfinite(bndu->ptr.p_double[i], _state)||ae_isposinf(bndu->ptr.p_double[i], _state), "MinLMSetBC: BndU contains NAN or -INF", _state); - state->bndl.ptr.p_double[i] = bndl->ptr.p_double[i]; - state->havebndl.ptr.p_bool[i] = ae_isfinite(bndl->ptr.p_double[i], _state); - state->bndu.ptr.p_double[i] = bndu->ptr.p_double[i]; - state->havebndu.ptr.p_bool[i] = ae_isfinite(bndu->ptr.p_double[i], _state); - } -} - - -/************************************************************************* -This function is used to change acceleration settings - -You can choose between three acceleration strategies: -* AccType=0, no acceleration. -* AccType=1, secant updates are used to update quadratic model after each - iteration. After fixed number of iterations (or after model breakdown) - we recalculate quadratic model using analytic Jacobian or finite - differences. Number of secant-based iterations depends on optimization - settings: about 3 iterations - when we have analytic Jacobian, up to 2*N - iterations - when we use finite differences to calculate Jacobian. - -AccType=1 is recommended when Jacobian calculation cost is prohibitive -high (several Mx1 function vector calculations followed by several NxN -Cholesky factorizations are faster than calculation of one M*N Jacobian). -It should also be used when we have no Jacobian, because finite difference -approximation takes too much time to compute. - -Table below list optimization protocols (XYZ protocol corresponds to -MinLMCreateXYZ) and acceleration types they support (and use by default). - -ACCELERATION TYPES SUPPORTED BY OPTIMIZATION PROTOCOLS: - -protocol 0 1 comment -V + + -VJ + + -FGH + - -DAFAULT VALUES: - -protocol 0 1 comment -V x without acceleration it is so slooooooooow -VJ x -FGH x - -NOTE: this function should be called before optimization. Attempt to call -it during algorithm iterations may result in unexpected behavior. - -NOTE: attempt to call this function with unsupported protocol/acceleration -combination will result in exception being thrown. - - -- ALGLIB -- - Copyright 14.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetacctype(minlmstate* state, - ae_int_t acctype, - ae_state *_state) -{ - - - ae_assert((acctype==0||acctype==1)||acctype==2, "MinLMSetAccType: incorrect AccType!", _state); - if( acctype==2 ) - { - acctype = 0; - } - if( acctype==0 ) - { - state->maxmodelage = 0; - state->makeadditers = ae_false; - return; - } - if( acctype==1 ) - { - ae_assert(state->hasfi, "MinLMSetAccType: AccType=1 is incompatible with current protocol!", _state); - if( state->algomode==0 ) - { - state->maxmodelage = 2*state->n; - } - else - { - state->maxmodelage = minlm_smallmodelage; - } - state->makeadditers = ae_false; - return; - } -} - - -/************************************************************************* -NOTES: - -1. Depending on function used to create state structure, this algorithm - may accept Jacobian and/or Hessian and/or gradient. According to the - said above, there ase several versions of this function, which accept - different sets of callbacks. - - This flexibility opens way to subtle errors - you may create state with - MinLMCreateFGH() (optimization using Hessian), but call function which - does not accept Hessian. So when algorithm will request Hessian, there - will be no callback to call. In this case exception will be thrown. - - Be careful to avoid such errors because there is no way to find them at - compile time - you can see them at runtime only. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool minlmiteration(minlmstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t m; - ae_bool bflag; - ae_int_t iflag; - double v; - double s; - double t; - ae_int_t i; - ae_int_t k; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - iflag = state->rstate.ia.ptr.p_int[2]; - i = state->rstate.ia.ptr.p_int[3]; - k = state->rstate.ia.ptr.p_int[4]; - bflag = state->rstate.ba.ptr.p_bool[0]; - v = state->rstate.ra.ptr.p_double[0]; - s = state->rstate.ra.ptr.p_double[1]; - t = state->rstate.ra.ptr.p_double[2]; - } - else - { - n = -983; - m = -989; - iflag = -834; - i = 900; - k = -287; - bflag = ae_false; - v = 214; - s = -338; - t = -686; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - if( state->rstate.stage==8 ) - { - goto lbl_8; - } - if( state->rstate.stage==9 ) - { - goto lbl_9; - } - if( state->rstate.stage==10 ) - { - goto lbl_10; - } - if( state->rstate.stage==11 ) - { - goto lbl_11; - } - if( state->rstate.stage==12 ) - { - goto lbl_12; - } - if( state->rstate.stage==13 ) - { - goto lbl_13; - } - if( state->rstate.stage==14 ) - { - goto lbl_14; - } - if( state->rstate.stage==15 ) - { - goto lbl_15; - } - if( state->rstate.stage==16 ) - { - goto lbl_16; - } - if( state->rstate.stage==17 ) - { - goto lbl_17; - } - if( state->rstate.stage==18 ) - { - goto lbl_18; - } - - /* - * Routine body - */ - - /* - * prepare - */ - n = state->n; - m = state->m; - state->repiterationscount = 0; - state->repterminationtype = 0; - state->repfuncidx = -1; - state->repvaridx = -1; - state->repnfunc = 0; - state->repnjac = 0; - state->repngrad = 0; - state->repnhess = 0; - state->repncholesky = 0; - - /* - * check consistency of constraints, - * enforce feasibility of the solution - * set constraints - */ - if( !enforceboundaryconstraints(&state->xbase, &state->bndl, &state->havebndl, &state->bndu, &state->havebndu, n, 0, _state) ) - { - state->repterminationtype = -3; - result = ae_false; - return result; - } - minqpsetbc(&state->qpstate, &state->bndl, &state->bndu, _state); - - /* - * Check, that transferred derivative value is right - */ - minlm_clearrequestfields(state, _state); - if( !(state->algomode==1&&ae_fp_greater(state->teststep,0)) ) - { - goto lbl_19; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->needfij = ae_true; - i = 0; -lbl_21: - if( i>n-1 ) - { - goto lbl_23; - } - ae_assert((state->havebndl.ptr.p_bool[i]&&ae_fp_less_eq(state->bndl.ptr.p_double[i],state->x.ptr.p_double[i]))||!state->havebndl.ptr.p_bool[i], "MinLM: internal error(State.X is out of bounds)", _state); - ae_assert((state->havebndu.ptr.p_bool[i]&&ae_fp_less_eq(state->x.ptr.p_double[i],state->bndu.ptr.p_double[i]))||!state->havebndu.ptr.p_bool[i], "MinLMIteration: internal error(State.X is out of bounds)", _state); - v = state->x.ptr.p_double[i]; - state->x.ptr.p_double[i] = v-state->teststep*state->s.ptr.p_double[i]; - if( state->havebndl.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_maxreal(state->x.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - state->xm1 = state->x.ptr.p_double[i]; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - ae_v_move(&state->fm1.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_v_move(&state->gm1.ptr.p_double[0], 1, &state->j.ptr.pp_double[0][i], state->j.stride, ae_v_len(0,m-1)); - state->x.ptr.p_double[i] = v+state->teststep*state->s.ptr.p_double[i]; - if( state->havebndu.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_minreal(state->x.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - state->xp1 = state->x.ptr.p_double[i]; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - ae_v_move(&state->fp1.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_v_move(&state->gp1.ptr.p_double[0], 1, &state->j.ptr.pp_double[0][i], state->j.stride, ae_v_len(0,m-1)); - state->x.ptr.p_double[i] = (state->xm1+state->xp1)/2; - if( state->havebndl.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_maxreal(state->x.ptr.p_double[i], state->bndl.ptr.p_double[i], _state); - } - if( state->havebndu.ptr.p_bool[i] ) - { - state->x.ptr.p_double[i] = ae_minreal(state->x.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - ae_v_move(&state->fc1.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_v_move(&state->gc1.ptr.p_double[0], 1, &state->j.ptr.pp_double[0][i], state->j.stride, ae_v_len(0,m-1)); - state->x.ptr.p_double[i] = v; - for(k=0; k<=m-1; k++) - { - if( !derivativecheck(state->fm1.ptr.p_double[k], state->gm1.ptr.p_double[k], state->fp1.ptr.p_double[k], state->gp1.ptr.p_double[k], state->fc1.ptr.p_double[k], state->gc1.ptr.p_double[k], state->xp1-state->xm1, _state) ) - { - state->repfuncidx = k; - state->repvaridx = i; - state->repterminationtype = -7; - result = ae_false; - return result; - } - } - i = i+1; - goto lbl_21; -lbl_23: - state->needfij = ae_false; -lbl_19: - - /* - * Initial report of current point - * - * Note 1: we rewrite State.X twice because - * user may accidentally change it after first call. - * - * Note 2: we set NeedF or NeedFI depending on what - * information about function we have. - */ - if( !state->xrep ) - { - goto lbl_24; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - if( !state->hasf ) - { - goto lbl_26; - } - state->needf = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needf = ae_false; - goto lbl_27; -lbl_26: - ae_assert(state->hasfi, "MinLM: internal error 2!", _state); - state->needfi = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->needfi = ae_false; - v = ae_v_dotproduct(&state->fi.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - state->f = v; -lbl_27: - state->repnfunc = state->repnfunc+1; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->xupdated = ae_false; -lbl_24: - - /* - * Prepare control variables - */ - state->nu = 1; - state->lambdav = -ae_maxrealnumber; - state->modelage = state->maxmodelage+1; - state->deltaxready = ae_false; - state->deltafready = ae_false; - - /* - * Main cycle. - * - * We move through it until either: - * * one of the stopping conditions is met - * * we decide that stopping conditions are too stringent - * and break from cycle - * - */ -lbl_28: - if( ae_false ) - { - goto lbl_29; - } - - /* - * First, we have to prepare quadratic model for our function. - * We use BFlag to ensure that model is prepared; - * if it is false at the end of this block, something went wrong. - * - * We may either calculate brand new model or update old one. - * - * Before this block we have: - * * State.XBase - current position. - * * State.DeltaX - if DeltaXReady is True - * * State.DeltaF - if DeltaFReady is True - * - * After this block is over, we will have: - * * State.XBase - base point (unchanged) - * * State.FBase - F(XBase) - * * State.GBase - linear term - * * State.QuadraticModel - quadratic term - * * State.LambdaV - current estimate for lambda - * - * We also clear DeltaXReady/DeltaFReady flags - * after initialization is done. - */ - bflag = ae_false; - if( !(state->algomode==0||state->algomode==1) ) - { - goto lbl_30; - } - - /* - * Calculate f[] and Jacobian - */ - if( !(state->modelage>state->maxmodelage||!(state->deltaxready&&state->deltafready)) ) - { - goto lbl_32; - } - - /* - * Refresh model (using either finite differences or analytic Jacobian) - */ - if( state->algomode!=0 ) - { - goto lbl_34; - } - - /* - * Optimization using F values only. - * Use finite differences to estimate Jacobian. - */ - ae_assert(state->hasfi, "MinLMIteration: internal error when estimating Jacobian (no f[])", _state); - k = 0; -lbl_36: - if( k>n-1 ) - { - goto lbl_38; - } - - /* - * We guard X[k] from leaving [BndL,BndU]. - * In case BndL=BndU, we assume that derivative in this direction is zero. - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->x.ptr.p_double[k] = state->x.ptr.p_double[k]-state->s.ptr.p_double[k]*state->diffstep; - if( state->havebndl.ptr.p_bool[k] ) - { - state->x.ptr.p_double[k] = ae_maxreal(state->x.ptr.p_double[k], state->bndl.ptr.p_double[k], _state); - } - if( state->havebndu.ptr.p_bool[k] ) - { - state->x.ptr.p_double[k] = ae_minreal(state->x.ptr.p_double[k], state->bndu.ptr.p_double[k], _state); - } - state->xm1 = state->x.ptr.p_double[k]; - minlm_clearrequestfields(state, _state); - state->needfi = ae_true; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->repnfunc = state->repnfunc+1; - ae_v_move(&state->fm1.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->x.ptr.p_double[k] = state->x.ptr.p_double[k]+state->s.ptr.p_double[k]*state->diffstep; - if( state->havebndl.ptr.p_bool[k] ) - { - state->x.ptr.p_double[k] = ae_maxreal(state->x.ptr.p_double[k], state->bndl.ptr.p_double[k], _state); - } - if( state->havebndu.ptr.p_bool[k] ) - { - state->x.ptr.p_double[k] = ae_minreal(state->x.ptr.p_double[k], state->bndu.ptr.p_double[k], _state); - } - state->xp1 = state->x.ptr.p_double[k]; - minlm_clearrequestfields(state, _state); - state->needfi = ae_true; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->repnfunc = state->repnfunc+1; - ae_v_move(&state->fp1.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - v = state->xp1-state->xm1; - if( ae_fp_neq(v,0) ) - { - v = 1/v; - ae_v_moved(&state->j.ptr.pp_double[0][k], state->j.stride, &state->fp1.ptr.p_double[0], 1, ae_v_len(0,m-1), v); - ae_v_subd(&state->j.ptr.pp_double[0][k], state->j.stride, &state->fm1.ptr.p_double[0], 1, ae_v_len(0,m-1), v); - } - else - { - for(i=0; i<=m-1; i++) - { - state->j.ptr.pp_double[i][k] = 0; - } - } - k = k+1; - goto lbl_36; -lbl_38: - - /* - * Calculate F(XBase) - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - state->needfi = ae_true; - state->rstate.stage = 8; - goto lbl_rcomm; -lbl_8: - state->needfi = ae_false; - state->repnfunc = state->repnfunc+1; - state->repnjac = state->repnjac+1; - - /* - * New model - */ - state->modelage = 0; - goto lbl_35; -lbl_34: - - /* - * Obtain f[] and Jacobian - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - state->needfij = ae_true; - state->rstate.stage = 9; - goto lbl_rcomm; -lbl_9: - state->needfij = ae_false; - state->repnfunc = state->repnfunc+1; - state->repnjac = state->repnjac+1; - - /* - * New model - */ - state->modelage = 0; -lbl_35: - goto lbl_33; -lbl_32: - - /* - * State.J contains Jacobian or its current approximation; - * refresh it using secant updates: - * - * f(x0+dx) = f(x0) + J*dx, - * J_new = J_old + u*h' - * h = x_new-x_old - * u = (f_new - f_old - J_old*h)/(h'h) - * - * We can explicitly generate h and u, but it is - * preferential to do in-place calculations. Only - * I-th row of J_old is needed to calculate u[I], - * so we can update J row by row in one pass. - * - * NOTE: we expect that State.XBase contains new point, - * State.FBase contains old point, State.DeltaX and - * State.DeltaY contain updates from last step. - */ - ae_assert(state->deltaxready&&state->deltafready, "MinLMIteration: uninitialized DeltaX/DeltaF", _state); - t = ae_v_dotproduct(&state->deltax.ptr.p_double[0], 1, &state->deltax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_assert(ae_fp_neq(t,0), "MinLM: internal error (T=0)", _state); - for(i=0; i<=m-1; i++) - { - v = ae_v_dotproduct(&state->j.ptr.pp_double[i][0], 1, &state->deltax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = (state->deltaf.ptr.p_double[i]-v)/t; - ae_v_addd(&state->j.ptr.pp_double[i][0], 1, &state->deltax.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - } - ae_v_move(&state->fi.ptr.p_double[0], 1, &state->fibase.ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_v_add(&state->fi.ptr.p_double[0], 1, &state->deltaf.ptr.p_double[0], 1, ae_v_len(0,m-1)); - - /* - * Increase model age - */ - state->modelage = state->modelage+1; -lbl_33: - - /* - * Generate quadratic model: - * f(xbase+dx) = - * = (f0 + J*dx)'(f0 + J*dx) - * = f0^2 + dx'J'f0 + f0*J*dx + dx'J'J*dx - * = f0^2 + 2*f0*J*dx + dx'J'J*dx - * - * Note that we calculate 2*(J'J) instead of J'J because - * our quadratic model is based on Tailor decomposition, - * i.e. it has 0.5 before quadratic term. - */ - rmatrixgemm(n, n, m, 2.0, &state->j, 0, 0, 1, &state->j, 0, 0, 0, 0.0, &state->quadraticmodel, 0, 0, _state); - rmatrixmv(n, m, &state->j, 0, 0, 1, &state->fi, 0, &state->gbase, 0, _state); - ae_v_muld(&state->gbase.ptr.p_double[0], 1, ae_v_len(0,n-1), 2); - v = ae_v_dotproduct(&state->fi.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - state->fbase = v; - ae_v_move(&state->fibase.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - - /* - * set control variables - */ - bflag = ae_true; -lbl_30: - if( state->algomode!=2 ) - { - goto lbl_39; - } - ae_assert(!state->hasfi, "MinLMIteration: internal error (HasFI is True in Hessian-based mode)", _state); - - /* - * Obtain F, G, H - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - state->needfgh = ae_true; - state->rstate.stage = 10; - goto lbl_rcomm; -lbl_10: - state->needfgh = ae_false; - state->repnfunc = state->repnfunc+1; - state->repngrad = state->repngrad+1; - state->repnhess = state->repnhess+1; - rmatrixcopy(n, n, &state->h, 0, 0, &state->quadraticmodel, 0, 0, _state); - ae_v_move(&state->gbase.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fbase = state->f; - - /* - * set control variables - */ - bflag = ae_true; - state->modelage = 0; -lbl_39: - ae_assert(bflag, "MinLM: internal integrity check failed!", _state); - state->deltaxready = ae_false; - state->deltafready = ae_false; - - /* - * If Lambda is not initialized, initialize it using quadratic model - */ - if( ae_fp_less(state->lambdav,0) ) - { - state->lambdav = 0; - for(i=0; i<=n-1; i++) - { - state->lambdav = ae_maxreal(state->lambdav, ae_fabs(state->quadraticmodel.ptr.pp_double[i][i], _state)*ae_sqr(state->s.ptr.p_double[i], _state), _state); - } - state->lambdav = 0.001*state->lambdav; - if( ae_fp_eq(state->lambdav,0) ) - { - state->lambdav = 1; - } - } - - /* - * Test stopping conditions for function gradient - */ - if( ae_fp_greater(minlm_boundedscaledantigradnorm(state, &state->xbase, &state->gbase, _state),state->epsg) ) - { - goto lbl_41; - } - if( state->modelage!=0 ) - { - goto lbl_43; - } - - /* - * Model is fresh, we can rely on it and terminate algorithm - */ - state->repterminationtype = 4; - if( !state->xrep ) - { - goto lbl_45; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fbase; - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 11; - goto lbl_rcomm; -lbl_11: - state->xupdated = ae_false; -lbl_45: - result = ae_false; - return result; - goto lbl_44; -lbl_43: - - /* - * Model is not fresh, we should refresh it and test - * conditions once more - */ - state->modelage = state->maxmodelage+1; - goto lbl_28; -lbl_44: -lbl_41: - - /* - * Find value of Levenberg-Marquardt damping parameter which: - * * leads to positive definite damped model - * * within bounds specified by StpMax - * * generates step which decreases function value - * - * After this block IFlag is set to: - * * -3, if constraints are infeasible - * * -2, if model update is needed (either Lambda growth is too large - * or step is too short, but we can't rely on model and stop iterations) - * * -1, if model is fresh, Lambda have grown too large, termination is needed - * * 0, if everything is OK, continue iterations - * - * State.Nu can have any value on enter, but after exit it is set to 1.0 - */ - iflag = -99; -lbl_47: - if( ae_false ) - { - goto lbl_48; - } - - /* - * Do we need model update? - */ - if( state->modelage>0&&ae_fp_greater_eq(state->nu,minlm_suspiciousnu) ) - { - iflag = -2; - goto lbl_48; - } - - /* - * Setup quadratic solver and solve quadratic programming problem. - * After problem is solved we'll try to bound step by StpMax - * (Lambda will be increased if step size is too large). - * - * We use BFlag variable to indicate that we have to increase Lambda. - * If it is False, we will try to increase Lambda and move to new iteration. - */ - bflag = ae_true; - minqpsetstartingpointfast(&state->qpstate, &state->xbase, _state); - minqpsetoriginfast(&state->qpstate, &state->xbase, _state); - minqpsetlineartermfast(&state->qpstate, &state->gbase, _state); - minqpsetquadratictermfast(&state->qpstate, &state->quadraticmodel, ae_true, 0.0, _state); - for(i=0; i<=n-1; i++) - { - state->tmp0.ptr.p_double[i] = state->quadraticmodel.ptr.pp_double[i][i]+state->lambdav/ae_sqr(state->s.ptr.p_double[i], _state); - } - minqprewritediagonal(&state->qpstate, &state->tmp0, _state); - minqpoptimize(&state->qpstate, _state); - minqpresultsbuf(&state->qpstate, &state->xdir, &state->qprep, _state); - if( state->qprep.terminationtype>0 ) - { - - /* - * successful solution of QP problem - */ - ae_v_sub(&state->xdir.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = ae_v_dotproduct(&state->xdir.ptr.p_double[0], 1, &state->xdir.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_isfinite(v, _state) ) - { - v = ae_sqrt(v, _state); - if( ae_fp_greater(state->stpmax,0)&&ae_fp_greater(v,state->stpmax) ) - { - bflag = ae_false; - } - } - else - { - bflag = ae_false; - } - } - else - { - - /* - * Either problem is non-convex (increase LambdaV) or constraints are inconsistent - */ - ae_assert(state->qprep.terminationtype==-3||state->qprep.terminationtype==-5, "MinLM: unexpected completion code from QP solver", _state); - if( state->qprep.terminationtype==-3 ) - { - iflag = -3; - goto lbl_48; - } - bflag = ae_false; - } - if( !bflag ) - { - - /* - * Solution failed: - * try to increase lambda to make matrix positive definite and continue. - */ - if( !minlm_increaselambda(&state->lambdav, &state->nu, _state) ) - { - iflag = -1; - goto lbl_48; - } - goto lbl_47; - } - - /* - * Step in State.XDir and it is bounded by StpMax. - * - * We should check stopping conditions on step size here. - * DeltaX, which is used for secant updates, is initialized here. - * - * This code is a bit tricky because sometimes XDir<>0, but - * it is so small that XDir+XBase==XBase (in finite precision - * arithmetics). So we set DeltaX to XBase, then - * add XDir, and then subtract XBase to get exact value of - * DeltaX. - * - * Step length is estimated using DeltaX. - * - * NOTE: stopping conditions are tested - * for fresh models only (ModelAge=0) - */ - ae_v_move(&state->deltax.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_add(&state->deltax.ptr.p_double[0], 1, &state->xdir.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_sub(&state->deltax.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->deltaxready = ae_true; - v = 0.0; - for(i=0; i<=n-1; i++) - { - v = v+ae_sqr(state->deltax.ptr.p_double[i]/state->s.ptr.p_double[i], _state); - } - v = ae_sqrt(v, _state); - if( ae_fp_greater(v,state->epsx) ) - { - goto lbl_49; - } - if( state->modelage!=0 ) - { - goto lbl_51; - } - - /* - * Step is too short, model is fresh and we can rely on it. - * Terminating. - */ - state->repterminationtype = 2; - if( !state->xrep ) - { - goto lbl_53; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fbase; - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 12; - goto lbl_rcomm; -lbl_12: - state->xupdated = ae_false; -lbl_53: - result = ae_false; - return result; - goto lbl_52; -lbl_51: - - /* - * Step is suspiciously short, but model is not fresh - * and we can't rely on it. - */ - iflag = -2; - goto lbl_48; -lbl_52: -lbl_49: - - /* - * Let's evaluate new step: - * a) if we have Fi vector, we evaluate it using rcomm, and - * then we manually calculate State.F as sum of squares of Fi[] - * b) if we have F value, we just evaluate it through rcomm interface - * - * We prefer (a) because we may need Fi vector for additional - * iterations - */ - ae_assert(state->hasfi||state->hasf, "MinLM: internal error 2!", _state); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_add(&state->x.ptr.p_double[0], 1, &state->xdir.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - if( !state->hasfi ) - { - goto lbl_55; - } - state->needfi = ae_true; - state->rstate.stage = 13; - goto lbl_rcomm; -lbl_13: - state->needfi = ae_false; - v = ae_v_dotproduct(&state->fi.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - state->f = v; - ae_v_move(&state->deltaf.ptr.p_double[0], 1, &state->fi.ptr.p_double[0], 1, ae_v_len(0,m-1)); - ae_v_sub(&state->deltaf.ptr.p_double[0], 1, &state->fibase.ptr.p_double[0], 1, ae_v_len(0,m-1)); - state->deltafready = ae_true; - goto lbl_56; -lbl_55: - state->needf = ae_true; - state->rstate.stage = 14; - goto lbl_rcomm; -lbl_14: - state->needf = ae_false; -lbl_56: - state->repnfunc = state->repnfunc+1; - if( ae_fp_greater_eq(state->f,state->fbase) ) - { - - /* - * Increase lambda and continue - */ - if( !minlm_increaselambda(&state->lambdav, &state->nu, _state) ) - { - iflag = -1; - goto lbl_48; - } - goto lbl_47; - } - - /* - * We've found our step! - */ - iflag = 0; - goto lbl_48; - goto lbl_47; -lbl_48: - state->nu = 1; - ae_assert(iflag>=-3&&iflag<=0, "MinLM: internal integrity check failed!", _state); - if( iflag==-3 ) - { - state->repterminationtype = -3; - result = ae_false; - return result; - } - if( iflag==-2 ) - { - state->modelage = state->maxmodelage+1; - goto lbl_28; - } - if( iflag==-1 ) - { - goto lbl_29; - } - - /* - * Levenberg-Marquardt step is ready. - * Compare predicted vs. actual decrease and decide what to do with lambda. - * - * NOTE: we expect that State.DeltaX contains direction of step, - * State.F contains function value at new point. - */ - ae_assert(state->deltaxready, "MinLM: deltaX is not ready", _state); - t = 0; - for(i=0; i<=n-1; i++) - { - v = ae_v_dotproduct(&state->quadraticmodel.ptr.pp_double[i][0], 1, &state->deltax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - t = t+state->deltax.ptr.p_double[i]*state->gbase.ptr.p_double[i]+0.5*state->deltax.ptr.p_double[i]*v; - } - state->predicteddecrease = -t; - state->actualdecrease = -(state->f-state->fbase); - if( ae_fp_less_eq(state->predicteddecrease,0) ) - { - goto lbl_29; - } - v = state->actualdecrease/state->predicteddecrease; - if( ae_fp_greater_eq(v,0.1) ) - { - goto lbl_57; - } - if( minlm_increaselambda(&state->lambdav, &state->nu, _state) ) - { - goto lbl_59; - } - - /* - * Lambda is too large, we have to break iterations. - */ - state->repterminationtype = 7; - if( !state->xrep ) - { - goto lbl_61; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fbase; - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 15; - goto lbl_rcomm; -lbl_15: - state->xupdated = ae_false; -lbl_61: - result = ae_false; - return result; -lbl_59: -lbl_57: - if( ae_fp_greater(v,0.5) ) - { - minlm_decreaselambda(&state->lambdav, &state->nu, _state); - } - - /* - * Accept step, report it and - * test stopping conditions on iterations count and function decrease. - * - * NOTE: we expect that State.DeltaX contains direction of step, - * State.F contains function value at new point. - * - * NOTE2: we should update XBase ONLY. In the beginning of the next - * iteration we expect that State.FIBase is NOT updated and - * contains old value of a function vector. - */ - ae_v_add(&state->xbase.ptr.p_double[0], 1, &state->deltax.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( !state->xrep ) - { - goto lbl_63; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 16; - goto lbl_rcomm; -lbl_16: - state->xupdated = ae_false; -lbl_63: - state->repiterationscount = state->repiterationscount+1; - if( state->repiterationscount>=state->maxits&&state->maxits>0 ) - { - state->repterminationtype = 5; - } - if( state->modelage==0 ) - { - if( ae_fp_less_eq(ae_fabs(state->f-state->fbase, _state),state->epsf*ae_maxreal(1, ae_maxreal(ae_fabs(state->f, _state), ae_fabs(state->fbase, _state), _state), _state)) ) - { - state->repterminationtype = 1; - } - } - if( state->repterminationtype<=0 ) - { - goto lbl_65; - } - if( !state->xrep ) - { - goto lbl_67; - } - - /* - * Report: XBase contains new point, F contains function value at new point - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 17; - goto lbl_rcomm; -lbl_17: - state->xupdated = ae_false; -lbl_67: - result = ae_false; - return result; -lbl_65: - state->modelage = state->modelage+1; - goto lbl_28; -lbl_29: - - /* - * Lambda is too large, we have to break iterations. - */ - state->repterminationtype = 7; - if( !state->xrep ) - { - goto lbl_69; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fbase; - minlm_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 18; - goto lbl_rcomm; -lbl_18: - state->xupdated = ae_false; -lbl_69: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = iflag; - state->rstate.ia.ptr.p_int[3] = i; - state->rstate.ia.ptr.p_int[4] = k; - state->rstate.ba.ptr.p_bool[0] = bflag; - state->rstate.ra.ptr.p_double[0] = v; - state->rstate.ra.ptr.p_double[1] = s; - state->rstate.ra.ptr.p_double[2] = t; - return result; -} - - -/************************************************************************* -Levenberg-Marquardt algorithm results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report; - see comments for this structure for more info. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmresults(minlmstate* state, - /* Real */ ae_vector* x, - minlmreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _minlmreport_clear(rep); - - minlmresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -Levenberg-Marquardt algorithm results - -Buffered implementation of MinLMResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmresultsbuf(minlmstate* state, - /* Real */ ae_vector* x, - minlmreport* rep, - ae_state *_state) -{ - - - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->terminationtype = state->repterminationtype; - rep->funcidx = state->repfuncidx; - rep->varidx = state->repvaridx; - rep->nfunc = state->repnfunc; - rep->njac = state->repnjac; - rep->ngrad = state->repngrad; - rep->nhess = state->repnhess; - rep->ncholesky = state->repncholesky; -} - - -/************************************************************************* -This subroutine restarts LM algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used for reverse communication previously - allocated with MinLMCreateXXX call. - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmrestartfrom(minlmstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - - - ae_assert(x->cnt>=state->n, "MinLMRestartFrom: Length(X)n, _state), "MinLMRestartFrom: X contains infinite or NaN values!", _state); - ae_v_move(&state->xbase.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_vector_set_length(&state->rstate.ia, 4+1, _state); - ae_vector_set_length(&state->rstate.ba, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; - minlm_clearrequestfields(state, _state); -} - - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateVJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevgj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state) -{ - - _minlmstate_clear(state); - - minlmcreatevj(n, m, x, state, _state); -} - - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateFJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state) -{ - - _minlmstate_clear(state); - - minlmcreatefj(n, m, x, state, _state); -} - - -/************************************************************************* -This function is considered obsolete since ALGLIB 3.1.0 and is present for -backward compatibility only. We recommend to use MinLMCreateVJ, which -provides similar, but more consistent and feature-rich interface. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state) -{ - - _minlmstate_clear(state); - - ae_assert(n>=1, "MinLMCreateFJ: N<1!", _state); - ae_assert(m>=1, "MinLMCreateFJ: M<1!", _state); - ae_assert(x->cnt>=n, "MinLMCreateFJ: Length(X)teststep = 0; - state->n = n; - state->m = m; - state->algomode = 1; - state->hasf = ae_true; - state->hasfi = ae_false; - state->hasg = ae_false; - - /* - * init 2 - */ - minlm_lmprepare(n, m, ae_true, state, _state); - minlmsetacctype(state, 0, _state); - minlmsetcond(state, 0, 0, 0, 0, _state); - minlmsetxrep(state, ae_false, _state); - minlmsetstpmax(state, 0, _state); - minlmrestartfrom(state, x, _state); -} - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinLMOptimize() is called -* prior to actual optimization, for each function Fi and each component - of parameters being optimized X[j] algorithm performs following steps: - * two trial steps are made to X[j]-TestStep*S[j] and X[j]+TestStep*S[j], - where X[j] is j-th parameter and S[j] is a scale of j-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * Fi(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative, - Rep.FuncIdx is set to index of the function. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) Jacobian evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided - by some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinLMSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minlmsetgradientcheck(minlmstate* state, - double teststep, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(teststep, _state), "MinLMSetGradientCheck: TestStep contains NaN or Infinite", _state); - ae_assert(ae_fp_greater_eq(teststep,0), "MinLMSetGradientCheck: invalid argument TestStep(TestStep<0)", _state); - state->teststep = teststep; -} - - -/************************************************************************* -Prepare internal structures (except for RComm). - -Note: M must be zero for FGH mode, non-zero for V/VJ/FJ/FGJ mode. -*************************************************************************/ -static void minlm_lmprepare(ae_int_t n, - ae_int_t m, - ae_bool havegrad, - minlmstate* state, - ae_state *_state) -{ - ae_int_t i; - - - if( n<=0||m<0 ) - { - return; - } - if( havegrad ) - { - ae_vector_set_length(&state->g, n, _state); - } - if( m!=0 ) - { - ae_matrix_set_length(&state->j, m, n, _state); - ae_vector_set_length(&state->fi, m, _state); - ae_vector_set_length(&state->fibase, m, _state); - ae_vector_set_length(&state->deltaf, m, _state); - ae_vector_set_length(&state->fm1, m, _state); - ae_vector_set_length(&state->fp1, m, _state); - ae_vector_set_length(&state->fc1, m, _state); - ae_vector_set_length(&state->gm1, m, _state); - ae_vector_set_length(&state->gp1, m, _state); - ae_vector_set_length(&state->gc1, m, _state); - } - else - { - ae_matrix_set_length(&state->h, n, n, _state); - } - ae_vector_set_length(&state->x, n, _state); - ae_vector_set_length(&state->deltax, n, _state); - ae_matrix_set_length(&state->quadraticmodel, n, n, _state); - ae_vector_set_length(&state->xbase, n, _state); - ae_vector_set_length(&state->gbase, n, _state); - ae_vector_set_length(&state->xdir, n, _state); - ae_vector_set_length(&state->tmp0, n, _state); - - /* - * prepare internal L-BFGS - */ - for(i=0; i<=n-1; i++) - { - state->x.ptr.p_double[i] = 0; - } - minlbfgscreate(n, ae_minint(minlm_additers, n, _state), &state->x, &state->internalstate, _state); - minlbfgssetcond(&state->internalstate, 0.0, 0.0, 0.0, ae_minint(minlm_additers, n, _state), _state); - - /* - * Prepare internal QP solver - */ - minqpcreate(n, &state->qpstate, _state); - minqpsetalgocholesky(&state->qpstate, _state); - - /* - * Prepare boundary constraints - */ - ae_vector_set_length(&state->bndl, n, _state); - ae_vector_set_length(&state->bndu, n, _state); - ae_vector_set_length(&state->havebndl, n, _state); - ae_vector_set_length(&state->havebndu, n, _state); - for(i=0; i<=n-1; i++) - { - state->bndl.ptr.p_double[i] = _state->v_neginf; - state->havebndl.ptr.p_bool[i] = ae_false; - state->bndu.ptr.p_double[i] = _state->v_posinf; - state->havebndu.ptr.p_bool[i] = ae_false; - } - - /* - * Prepare scaling matrix - */ - ae_vector_set_length(&state->s, n, _state); - for(i=0; i<=n-1; i++) - { - state->s.ptr.p_double[i] = 1.0; - } -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void minlm_clearrequestfields(minlmstate* state, ae_state *_state) -{ - - - state->needf = ae_false; - state->needfg = ae_false; - state->needfgh = ae_false; - state->needfij = ae_false; - state->needfi = ae_false; - state->xupdated = ae_false; -} - - -/************************************************************************* -Increases lambda, returns False when there is a danger of overflow -*************************************************************************/ -static ae_bool minlm_increaselambda(double* lambdav, - double* nu, - ae_state *_state) -{ - double lnlambda; - double lnnu; - double lnlambdaup; - double lnmax; - ae_bool result; - - - result = ae_false; - lnlambda = ae_log(*lambdav, _state); - lnlambdaup = ae_log(minlm_lambdaup, _state); - lnnu = ae_log(*nu, _state); - lnmax = ae_log(ae_maxrealnumber, _state); - if( ae_fp_greater(lnlambda+lnlambdaup+lnnu,0.25*lnmax) ) - { - return result; - } - if( ae_fp_greater(lnnu+ae_log(2, _state),lnmax) ) - { - return result; - } - *lambdav = *lambdav*minlm_lambdaup*(*nu); - *nu = *nu*2; - result = ae_true; - return result; -} - - -/************************************************************************* -Decreases lambda, but leaves it unchanged when there is danger of underflow. -*************************************************************************/ -static void minlm_decreaselambda(double* lambdav, - double* nu, - ae_state *_state) -{ - - - *nu = 1; - if( ae_fp_less(ae_log(*lambdav, _state)+ae_log(minlm_lambdadown, _state),ae_log(ae_minrealnumber, _state)) ) - { - *lambdav = ae_minrealnumber; - } - else - { - *lambdav = *lambdav*minlm_lambdadown; - } -} - - -/************************************************************************* -Returns norm of bounded scaled anti-gradient. - -Bounded antigradient is a vector obtained from anti-gradient by zeroing -components which point outwards: - result = norm(v) - v[i]=0 if ((-g[i]<0)and(x[i]=bndl[i])) or - ((-g[i]>0)and(x[i]=bndu[i])) - v[i]=-g[i]*s[i] otherwise, where s[i] is a scale for I-th variable - -This function may be used to check a stopping criterion. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -static double minlm_boundedscaledantigradnorm(minlmstate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* g, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double v; - double result; - - - result = 0; - n = state->n; - for(i=0; i<=n-1; i++) - { - v = -g->ptr.p_double[i]*state->s.ptr.p_double[i]; - if( state->havebndl.ptr.p_bool[i] ) - { - if( ae_fp_less_eq(x->ptr.p_double[i],state->bndl.ptr.p_double[i])&&ae_fp_less(-g->ptr.p_double[i],0) ) - { - v = 0; - } - } - if( state->havebndu.ptr.p_bool[i] ) - { - if( ae_fp_greater_eq(x->ptr.p_double[i],state->bndu.ptr.p_double[i])&&ae_fp_greater(-g->ptr.p_double[i],0) ) - { - v = 0; - } - } - result = result+ae_sqr(v, _state); - } - result = ae_sqrt(result, _state); - return result; -} - - -ae_bool _minlmstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minlmstate *p = (minlmstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->fi, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->j, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->h, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xbase, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->fibase, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gbase, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->quadraticmodel, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->havebndl, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->havebndu, 0, DT_BOOL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->s, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xdir, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->deltax, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->deltaf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->choleskybuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmp0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->fm1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->fp1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->fc1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gm1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gp1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gc1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsstate_init(&p->internalstate, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsreport_init(&p->internalrep, _state, make_automatic) ) - return ae_false; - if( !_minqpstate_init(&p->qpstate, _state, make_automatic) ) - return ae_false; - if( !_minqpreport_init(&p->qprep, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _minlmstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minlmstate *dst = (minlmstate*)_dst; - minlmstate *src = (minlmstate*)_src; - dst->n = src->n; - dst->m = src->m; - dst->diffstep = src->diffstep; - dst->epsg = src->epsg; - dst->epsf = src->epsf; - dst->epsx = src->epsx; - dst->maxits = src->maxits; - dst->xrep = src->xrep; - dst->stpmax = src->stpmax; - dst->maxmodelage = src->maxmodelage; - dst->makeadditers = src->makeadditers; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->fi, &src->fi, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->j, &src->j, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->h, &src->h, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - dst->needf = src->needf; - dst->needfg = src->needfg; - dst->needfgh = src->needfgh; - dst->needfij = src->needfij; - dst->needfi = src->needfi; - dst->xupdated = src->xupdated; - dst->algomode = src->algomode; - dst->hasf = src->hasf; - dst->hasfi = src->hasfi; - dst->hasg = src->hasg; - if( !ae_vector_init_copy(&dst->xbase, &src->xbase, _state, make_automatic) ) - return ae_false; - dst->fbase = src->fbase; - if( !ae_vector_init_copy(&dst->fibase, &src->fibase, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gbase, &src->gbase, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->quadraticmodel, &src->quadraticmodel, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->havebndl, &src->havebndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->havebndu, &src->havebndu, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->s, &src->s, _state, make_automatic) ) - return ae_false; - dst->lambdav = src->lambdav; - dst->nu = src->nu; - dst->modelage = src->modelage; - if( !ae_vector_init_copy(&dst->xdir, &src->xdir, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->deltax, &src->deltax, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->deltaf, &src->deltaf, _state, make_automatic) ) - return ae_false; - dst->deltaxready = src->deltaxready; - dst->deltafready = src->deltafready; - dst->teststep = src->teststep; - dst->repiterationscount = src->repiterationscount; - dst->repterminationtype = src->repterminationtype; - dst->repfuncidx = src->repfuncidx; - dst->repvaridx = src->repvaridx; - dst->repnfunc = src->repnfunc; - dst->repnjac = src->repnjac; - dst->repngrad = src->repngrad; - dst->repnhess = src->repnhess; - dst->repncholesky = src->repncholesky; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->choleskybuf, &src->choleskybuf, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmp0, &src->tmp0, _state, make_automatic) ) - return ae_false; - dst->actualdecrease = src->actualdecrease; - dst->predicteddecrease = src->predicteddecrease; - dst->xm1 = src->xm1; - dst->xp1 = src->xp1; - if( !ae_vector_init_copy(&dst->fm1, &src->fm1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->fp1, &src->fp1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->fc1, &src->fc1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gm1, &src->gm1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gp1, &src->gp1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gc1, &src->gc1, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsstate_init_copy(&dst->internalstate, &src->internalstate, _state, make_automatic) ) - return ae_false; - if( !_minlbfgsreport_init_copy(&dst->internalrep, &src->internalrep, _state, make_automatic) ) - return ae_false; - if( !_minqpstate_init_copy(&dst->qpstate, &src->qpstate, _state, make_automatic) ) - return ae_false; - if( !_minqpreport_init_copy(&dst->qprep, &src->qprep, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _minlmstate_clear(void* _p) -{ - minlmstate *p = (minlmstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->fi); - ae_matrix_clear(&p->j); - ae_matrix_clear(&p->h); - ae_vector_clear(&p->g); - ae_vector_clear(&p->xbase); - ae_vector_clear(&p->fibase); - ae_vector_clear(&p->gbase); - ae_matrix_clear(&p->quadraticmodel); - ae_vector_clear(&p->bndl); - ae_vector_clear(&p->bndu); - ae_vector_clear(&p->havebndl); - ae_vector_clear(&p->havebndu); - ae_vector_clear(&p->s); - ae_vector_clear(&p->xdir); - ae_vector_clear(&p->deltax); - ae_vector_clear(&p->deltaf); - _rcommstate_clear(&p->rstate); - ae_vector_clear(&p->choleskybuf); - ae_vector_clear(&p->tmp0); - ae_vector_clear(&p->fm1); - ae_vector_clear(&p->fp1); - ae_vector_clear(&p->fc1); - ae_vector_clear(&p->gm1); - ae_vector_clear(&p->gp1); - ae_vector_clear(&p->gc1); - _minlbfgsstate_clear(&p->internalstate); - _minlbfgsreport_clear(&p->internalrep); - _minqpstate_clear(&p->qpstate); - _minqpreport_clear(&p->qprep); -} - - -void _minlmstate_destroy(void* _p) -{ - minlmstate *p = (minlmstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->fi); - ae_matrix_destroy(&p->j); - ae_matrix_destroy(&p->h); - ae_vector_destroy(&p->g); - ae_vector_destroy(&p->xbase); - ae_vector_destroy(&p->fibase); - ae_vector_destroy(&p->gbase); - ae_matrix_destroy(&p->quadraticmodel); - ae_vector_destroy(&p->bndl); - ae_vector_destroy(&p->bndu); - ae_vector_destroy(&p->havebndl); - ae_vector_destroy(&p->havebndu); - ae_vector_destroy(&p->s); - ae_vector_destroy(&p->xdir); - ae_vector_destroy(&p->deltax); - ae_vector_destroy(&p->deltaf); - _rcommstate_destroy(&p->rstate); - ae_vector_destroy(&p->choleskybuf); - ae_vector_destroy(&p->tmp0); - ae_vector_destroy(&p->fm1); - ae_vector_destroy(&p->fp1); - ae_vector_destroy(&p->fc1); - ae_vector_destroy(&p->gm1); - ae_vector_destroy(&p->gp1); - ae_vector_destroy(&p->gc1); - _minlbfgsstate_destroy(&p->internalstate); - _minlbfgsreport_destroy(&p->internalrep); - _minqpstate_destroy(&p->qpstate); - _minqpreport_destroy(&p->qprep); -} - - -ae_bool _minlmreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minlmreport *p = (minlmreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _minlmreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minlmreport *dst = (minlmreport*)_dst; - minlmreport *src = (minlmreport*)_src; - dst->iterationscount = src->iterationscount; - dst->terminationtype = src->terminationtype; - dst->funcidx = src->funcidx; - dst->varidx = src->varidx; - dst->nfunc = src->nfunc; - dst->njac = src->njac; - dst->ngrad = src->ngrad; - dst->nhess = src->nhess; - dst->ncholesky = src->ncholesky; - return ae_true; -} - - -void _minlmreport_clear(void* _p) -{ - minlmreport *p = (minlmreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _minlmreport_destroy(void* _p) -{ - minlmreport *p = (minlmreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -Obsolete function, use MinLBFGSSetPrecDefault() instead. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetdefaultpreconditioner(minlbfgsstate* state, - ae_state *_state) -{ - - - minlbfgssetprecdefault(state, _state); -} - - -/************************************************************************* -Obsolete function, use MinLBFGSSetCholeskyPreconditioner() instead. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetcholeskypreconditioner(minlbfgsstate* state, - /* Real */ ae_matrix* p, - ae_bool isupper, - ae_state *_state) -{ - - - minlbfgssetpreccholesky(state, p, isupper, _state); -} - - -/************************************************************************* -This is obsolete function which was used by previous version of the BLEIC -optimizer. It does nothing in the current version of BLEIC. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbarrierwidth(minbleicstate* state, - double mu, - ae_state *_state) -{ - - -} - - -/************************************************************************* -This is obsolete function which was used by previous version of the BLEIC -optimizer. It does nothing in the current version of BLEIC. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbarrierdecay(minbleicstate* state, - double mudecay, - ae_state *_state) -{ - - -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void minasacreate(ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - minasastate* state, - ae_state *_state) -{ - ae_int_t i; - - _minasastate_clear(state); - - ae_assert(n>=1, "MinASA: N too small!", _state); - ae_assert(x->cnt>=n, "MinCGCreate: Length(X)cnt>=n, "MinCGCreate: Length(BndL)cnt>=n, "MinCGCreate: Length(BndU)ptr.p_double[i],bndu->ptr.p_double[i]), "MinASA: inconsistent bounds!", _state); - ae_assert(ae_fp_less_eq(bndl->ptr.p_double[i],x->ptr.p_double[i]), "MinASA: infeasible X!", _state); - ae_assert(ae_fp_less_eq(x->ptr.p_double[i],bndu->ptr.p_double[i]), "MinASA: infeasible X!", _state); - } - - /* - * Initialize - */ - state->n = n; - minasasetcond(state, 0, 0, 0, 0, _state); - minasasetxrep(state, ae_false, _state); - minasasetstpmax(state, 0, _state); - minasasetalgorithm(state, -1, _state); - ae_vector_set_length(&state->bndl, n, _state); - ae_vector_set_length(&state->bndu, n, _state); - ae_vector_set_length(&state->ak, n, _state); - ae_vector_set_length(&state->xk, n, _state); - ae_vector_set_length(&state->dk, n, _state); - ae_vector_set_length(&state->an, n, _state); - ae_vector_set_length(&state->xn, n, _state); - ae_vector_set_length(&state->dn, n, _state); - ae_vector_set_length(&state->x, n, _state); - ae_vector_set_length(&state->d, n, _state); - ae_vector_set_length(&state->g, n, _state); - ae_vector_set_length(&state->gc, n, _state); - ae_vector_set_length(&state->work, n, _state); - ae_vector_set_length(&state->yk, n, _state); - minasarestartfrom(state, x, bndl, bndu, _state); -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetcond(minasastate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsg, _state), "MinASASetCond: EpsG is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsg,0), "MinASASetCond: negative EpsG!", _state); - ae_assert(ae_isfinite(epsf, _state), "MinASASetCond: EpsF is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "MinASASetCond: negative EpsF!", _state); - ae_assert(ae_isfinite(epsx, _state), "MinASASetCond: EpsX is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsx,0), "MinASASetCond: negative EpsX!", _state); - ae_assert(maxits>=0, "MinASASetCond: negative MaxIts!", _state); - if( ((ae_fp_eq(epsg,0)&&ae_fp_eq(epsf,0))&&ae_fp_eq(epsx,0))&&maxits==0 ) - { - epsx = 1.0E-6; - } - state->epsg = epsg; - state->epsf = epsf; - state->epsx = epsx; - state->maxits = maxits; -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetxrep(minasastate* state, ae_bool needxrep, ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetalgorithm(minasastate* state, - ae_int_t algotype, - ae_state *_state) -{ - - - ae_assert(algotype>=-1&&algotype<=1, "MinASASetAlgorithm: incorrect AlgoType!", _state); - if( algotype==-1 ) - { - algotype = 1; - } - state->cgtype = algotype; -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetstpmax(minasastate* state, double stpmax, ae_state *_state) -{ - - - ae_assert(ae_isfinite(stpmax, _state), "MinASASetStpMax: StpMax is not finite!", _state); - ae_assert(ae_fp_greater_eq(stpmax,0), "MinASASetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool minasaiteration(minasastate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double betak; - double v; - double vv; - ae_int_t mcinfo; - ae_bool b; - ae_bool stepfound; - ae_int_t diffcnt; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - i = state->rstate.ia.ptr.p_int[1]; - mcinfo = state->rstate.ia.ptr.p_int[2]; - diffcnt = state->rstate.ia.ptr.p_int[3]; - b = state->rstate.ba.ptr.p_bool[0]; - stepfound = state->rstate.ba.ptr.p_bool[1]; - betak = state->rstate.ra.ptr.p_double[0]; - v = state->rstate.ra.ptr.p_double[1]; - vv = state->rstate.ra.ptr.p_double[2]; - } - else - { - n = -983; - i = -989; - mcinfo = -834; - diffcnt = 900; - b = ae_true; - stepfound = ae_false; - betak = 214; - v = -338; - vv = -686; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - if( state->rstate.stage==8 ) - { - goto lbl_8; - } - if( state->rstate.stage==9 ) - { - goto lbl_9; - } - if( state->rstate.stage==10 ) - { - goto lbl_10; - } - if( state->rstate.stage==11 ) - { - goto lbl_11; - } - if( state->rstate.stage==12 ) - { - goto lbl_12; - } - if( state->rstate.stage==13 ) - { - goto lbl_13; - } - if( state->rstate.stage==14 ) - { - goto lbl_14; - } - - /* - * Routine body - */ - - /* - * Prepare - */ - n = state->n; - state->repterminationtype = 0; - state->repiterationscount = 0; - state->repnfev = 0; - state->debugrestartscount = 0; - state->cgtype = 1; - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - if( ae_fp_eq(state->xk.ptr.p_double[i],state->bndl.ptr.p_double[i])||ae_fp_eq(state->xk.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->ak.ptr.p_double[i] = 0; - } - else - { - state->ak.ptr.p_double[i] = 1; - } - } - state->mu = 0.1; - state->curalgo = 0; - - /* - * Calculate F/G, initialize algorithm - */ - mincomp_clearrequestfields(state, _state); - state->needfg = ae_true; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->needfg = ae_false; - if( !state->xrep ) - { - goto lbl_15; - } - - /* - * progress report - */ - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->xupdated = ae_false; -lbl_15: - if( ae_fp_less_eq(mincomp_asaboundedantigradnorm(state, _state),state->epsg) ) - { - state->repterminationtype = 4; - result = ae_false; - return result; - } - state->repnfev = state->repnfev+1; - - /* - * Main cycle - * - * At the beginning of new iteration: - * * CurAlgo stores current algorithm selector - * * State.XK, State.F and State.G store current X/F/G - * * State.AK stores current set of active constraints - */ -lbl_17: - if( ae_false ) - { - goto lbl_18; - } - - /* - * GPA algorithm - */ - if( state->curalgo!=0 ) - { - goto lbl_19; - } - state->k = 0; - state->acount = 0; -lbl_21: - if( ae_false ) - { - goto lbl_22; - } - - /* - * Determine Dk = proj(xk - gk)-xk - */ - for(i=0; i<=n-1; i++) - { - state->d.ptr.p_double[i] = boundval(state->xk.ptr.p_double[i]-state->g.ptr.p_double[i], state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i], _state)-state->xk.ptr.p_double[i]; - } - - /* - * Armijo line search. - * * exact search with alpha=1 is tried first, - * 'exact' means that we evaluate f() EXACTLY at - * bound(x-g,bndl,bndu), without intermediate floating - * point operations. - * * alpha<1 are tried if explicit search wasn't successful - * Result is placed into XN. - * - * Two types of search are needed because we can't - * just use second type with alpha=1 because in finite - * precision arithmetics (x1-x0)+x0 may differ from x1. - * So while x1 is correctly bounded (it lie EXACTLY on - * boundary, if it is active), (x1-x0)+x0 may be - * not bounded. - */ - v = ae_v_dotproduct(&state->d.ptr.p_double[0], 1, &state->g.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->dginit = v; - state->finit = state->f; - if( !(ae_fp_less_eq(mincomp_asad1norm(state, _state),state->stpmax)||ae_fp_eq(state->stpmax,0)) ) - { - goto lbl_23; - } - - /* - * Try alpha=1 step first - */ - for(i=0; i<=n-1; i++) - { - state->x.ptr.p_double[i] = boundval(state->xk.ptr.p_double[i]-state->g.ptr.p_double[i], state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - mincomp_clearrequestfields(state, _state); - state->needfg = ae_true; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->needfg = ae_false; - state->repnfev = state->repnfev+1; - stepfound = ae_fp_less_eq(state->f,state->finit+mincomp_gpaftol*state->dginit); - goto lbl_24; -lbl_23: - stepfound = ae_false; -lbl_24: - if( !stepfound ) - { - goto lbl_25; - } - - /* - * we are at the boundary(ies) - */ - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->stp = 1; - goto lbl_26; -lbl_25: - - /* - * alpha=1 is too large, try smaller values - */ - state->stp = 1; - linminnormalized(&state->d, &state->stp, n, _state); - state->dginit = state->dginit/state->stp; - state->stp = mincomp_gpadecay*state->stp; - if( ae_fp_greater(state->stpmax,0) ) - { - state->stp = ae_minreal(state->stp, state->stpmax, _state); - } -lbl_27: - if( ae_false ) - { - goto lbl_28; - } - v = state->stp; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->x.ptr.p_double[0], 1, &state->d.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - mincomp_clearrequestfields(state, _state); - state->needfg = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needfg = ae_false; - state->repnfev = state->repnfev+1; - if( ae_fp_less_eq(state->stp,mincomp_stpmin) ) - { - goto lbl_28; - } - if( ae_fp_less_eq(state->f,state->finit+state->stp*mincomp_gpaftol*state->dginit) ) - { - goto lbl_28; - } - state->stp = state->stp*mincomp_gpadecay; - goto lbl_27; -lbl_28: - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); -lbl_26: - state->repiterationscount = state->repiterationscount+1; - if( !state->xrep ) - { - goto lbl_29; - } - - /* - * progress report - */ - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->xupdated = ae_false; -lbl_29: - - /* - * Calculate new set of active constraints. - * Reset counter if active set was changed. - * Prepare for the new iteration - */ - for(i=0; i<=n-1; i++) - { - if( ae_fp_eq(state->xn.ptr.p_double[i],state->bndl.ptr.p_double[i])||ae_fp_eq(state->xn.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->an.ptr.p_double[i] = 0; - } - else - { - state->an.ptr.p_double[i] = 1; - } - } - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(state->ak.ptr.p_double[i],state->an.ptr.p_double[i]) ) - { - state->acount = -1; - break; - } - } - state->acount = state->acount+1; - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->xn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->ak.ptr.p_double[0], 1, &state->an.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * Stopping conditions - */ - if( !(state->repiterationscount>=state->maxits&&state->maxits>0) ) - { - goto lbl_31; - } - - /* - * Too many iterations - */ - state->repterminationtype = 5; - if( !state->xrep ) - { - goto lbl_33; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->xupdated = ae_false; -lbl_33: - result = ae_false; - return result; -lbl_31: - if( ae_fp_greater(mincomp_asaboundedantigradnorm(state, _state),state->epsg) ) - { - goto lbl_35; - } - - /* - * Gradient is small enough - */ - state->repterminationtype = 4; - if( !state->xrep ) - { - goto lbl_37; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->xupdated = ae_false; -lbl_37: - result = ae_false; - return result; -lbl_35: - v = ae_v_dotproduct(&state->d.ptr.p_double[0], 1, &state->d.ptr.p_double[0], 1, ae_v_len(0,n-1)); - if( ae_fp_greater(ae_sqrt(v, _state)*state->stp,state->epsx) ) - { - goto lbl_39; - } - - /* - * Step size is too small, no further improvement is - * possible - */ - state->repterminationtype = 2; - if( !state->xrep ) - { - goto lbl_41; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->xupdated = ae_false; -lbl_41: - result = ae_false; - return result; -lbl_39: - if( ae_fp_greater(state->finit-state->f,state->epsf*ae_maxreal(ae_fabs(state->finit, _state), ae_maxreal(ae_fabs(state->f, _state), 1.0, _state), _state)) ) - { - goto lbl_43; - } - - /* - * F(k+1)-F(k) is small enough - */ - state->repterminationtype = 1; - if( !state->xrep ) - { - goto lbl_45; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 8; - goto lbl_rcomm; -lbl_8: - state->xupdated = ae_false; -lbl_45: - result = ae_false; - return result; -lbl_43: - - /* - * Decide - should we switch algorithm or not - */ - if( mincomp_asauisempty(state, _state) ) - { - if( ae_fp_greater_eq(mincomp_asaginorm(state, _state),state->mu*mincomp_asad1norm(state, _state)) ) - { - state->curalgo = 1; - goto lbl_22; - } - else - { - state->mu = state->mu*mincomp_asarho; - } - } - else - { - if( state->acount==mincomp_n1 ) - { - if( ae_fp_greater_eq(mincomp_asaginorm(state, _state),state->mu*mincomp_asad1norm(state, _state)) ) - { - state->curalgo = 1; - goto lbl_22; - } - } - } - - /* - * Next iteration - */ - state->k = state->k+1; - goto lbl_21; -lbl_22: -lbl_19: - - /* - * CG algorithm - */ - if( state->curalgo!=1 ) - { - goto lbl_47; - } - - /* - * first, check that there are non-active constraints. - * move to GPA algorithm, if all constraints are active - */ - b = ae_true; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(state->ak.ptr.p_double[i],0) ) - { - b = ae_false; - break; - } - } - if( b ) - { - state->curalgo = 0; - goto lbl_17; - } - - /* - * CG iterations - */ - state->fold = state->f; - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - state->dk.ptr.p_double[i] = -state->g.ptr.p_double[i]*state->ak.ptr.p_double[i]; - state->gc.ptr.p_double[i] = state->g.ptr.p_double[i]*state->ak.ptr.p_double[i]; - } -lbl_49: - if( ae_false ) - { - goto lbl_50; - } - - /* - * Store G[k] for later calculation of Y[k] - */ - for(i=0; i<=n-1; i++) - { - state->yk.ptr.p_double[i] = -state->gc.ptr.p_double[i]; - } - - /* - * Make a CG step in direction given by DK[]: - * * calculate step. Step projection into feasible set - * is used. It has several benefits: a) step may be - * found with usual line search, b) multiple constraints - * may be activated with one step, c) activated constraints - * are detected in a natural way - just compare x[i] with - * bounds - * * update active set, set B to True, if there - * were changes in the set. - */ - ae_v_move(&state->d.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_move(&state->xn.ptr.p_double[0], 1, &state->xk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->mcstage = 0; - state->stp = 1; - linminnormalized(&state->d, &state->stp, n, _state); - if( ae_fp_neq(state->laststep,0) ) - { - state->stp = state->laststep; - } - mcsrch(n, &state->xn, &state->f, &state->gc, &state->d, &state->stp, state->stpmax, mincomp_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); -lbl_51: - if( state->mcstage==0 ) - { - goto lbl_52; - } - - /* - * preprocess data: bound State.XN so it belongs to the - * feasible set and store it in the State.X - */ - for(i=0; i<=n-1; i++) - { - state->x.ptr.p_double[i] = boundval(state->xn.ptr.p_double[i], state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - } - - /* - * RComm - */ - mincomp_clearrequestfields(state, _state); - state->needfg = ae_true; - state->rstate.stage = 9; - goto lbl_rcomm; -lbl_9: - state->needfg = ae_false; - - /* - * postprocess data: zero components of G corresponding to - * the active constraints - */ - for(i=0; i<=n-1; i++) - { - if( ae_fp_eq(state->x.ptr.p_double[i],state->bndl.ptr.p_double[i])||ae_fp_eq(state->x.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->gc.ptr.p_double[i] = 0; - } - else - { - state->gc.ptr.p_double[i] = state->g.ptr.p_double[i]; - } - } - mcsrch(n, &state->xn, &state->f, &state->gc, &state->d, &state->stp, state->stpmax, mincomp_gtol, &mcinfo, &state->nfev, &state->work, &state->lstate, &state->mcstage, _state); - goto lbl_51; -lbl_52: - diffcnt = 0; - for(i=0; i<=n-1; i++) - { - - /* - * XN contains unprojected result, project it, - * save copy to X (will be used for progress reporting) - */ - state->xn.ptr.p_double[i] = boundval(state->xn.ptr.p_double[i], state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i], _state); - - /* - * update active set - */ - if( ae_fp_eq(state->xn.ptr.p_double[i],state->bndl.ptr.p_double[i])||ae_fp_eq(state->xn.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - state->an.ptr.p_double[i] = 0; - } - else - { - state->an.ptr.p_double[i] = 1; - } - if( ae_fp_neq(state->an.ptr.p_double[i],state->ak.ptr.p_double[i]) ) - { - diffcnt = diffcnt+1; - } - state->ak.ptr.p_double[i] = state->an.ptr.p_double[i]; - } - ae_v_move(&state->xk.ptr.p_double[0], 1, &state->xn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->repnfev = state->repnfev+state->nfev; - state->repiterationscount = state->repiterationscount+1; - if( !state->xrep ) - { - goto lbl_53; - } - - /* - * progress report - */ - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 10; - goto lbl_rcomm; -lbl_10: - state->xupdated = ae_false; -lbl_53: - - /* - * Update info about step length - */ - v = ae_v_dotproduct(&state->d.ptr.p_double[0], 1, &state->d.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->laststep = ae_sqrt(v, _state)*state->stp; - - /* - * Check stopping conditions. - */ - if( ae_fp_greater(mincomp_asaboundedantigradnorm(state, _state),state->epsg) ) - { - goto lbl_55; - } - - /* - * Gradient is small enough - */ - state->repterminationtype = 4; - if( !state->xrep ) - { - goto lbl_57; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 11; - goto lbl_rcomm; -lbl_11: - state->xupdated = ae_false; -lbl_57: - result = ae_false; - return result; -lbl_55: - if( !(state->repiterationscount>=state->maxits&&state->maxits>0) ) - { - goto lbl_59; - } - - /* - * Too many iterations - */ - state->repterminationtype = 5; - if( !state->xrep ) - { - goto lbl_61; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 12; - goto lbl_rcomm; -lbl_12: - state->xupdated = ae_false; -lbl_61: - result = ae_false; - return result; -lbl_59: - if( !(ae_fp_greater_eq(mincomp_asaginorm(state, _state),state->mu*mincomp_asad1norm(state, _state))&&diffcnt==0) ) - { - goto lbl_63; - } - - /* - * These conditions (EpsF/EpsX) are explicitly or implicitly - * related to the current step size and influenced - * by changes in the active constraints. - * - * For these reasons they are checked only when we don't - * want to 'unstick' at the end of the iteration and there - * were no changes in the active set. - * - * NOTE: consition |G|>=Mu*|D1| must be exactly opposite - * to the condition used to switch back to GPA. At least - * one inequality must be strict, otherwise infinite cycle - * may occur when |G|=Mu*|D1| (we DON'T test stopping - * conditions and we DON'T switch to GPA, so we cycle - * indefinitely). - */ - if( ae_fp_greater(state->fold-state->f,state->epsf*ae_maxreal(ae_fabs(state->fold, _state), ae_maxreal(ae_fabs(state->f, _state), 1.0, _state), _state)) ) - { - goto lbl_65; - } - - /* - * F(k+1)-F(k) is small enough - */ - state->repterminationtype = 1; - if( !state->xrep ) - { - goto lbl_67; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 13; - goto lbl_rcomm; -lbl_13: - state->xupdated = ae_false; -lbl_67: - result = ae_false; - return result; -lbl_65: - if( ae_fp_greater(state->laststep,state->epsx) ) - { - goto lbl_69; - } - - /* - * X(k+1)-X(k) is small enough - */ - state->repterminationtype = 2; - if( !state->xrep ) - { - goto lbl_71; - } - mincomp_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 14; - goto lbl_rcomm; -lbl_14: - state->xupdated = ae_false; -lbl_71: - result = ae_false; - return result; -lbl_69: -lbl_63: - - /* - * Check conditions for switching - */ - if( ae_fp_less(mincomp_asaginorm(state, _state),state->mu*mincomp_asad1norm(state, _state)) ) - { - state->curalgo = 0; - goto lbl_50; - } - if( diffcnt>0 ) - { - if( mincomp_asauisempty(state, _state)||diffcnt>=mincomp_n2 ) - { - state->curalgo = 1; - } - else - { - state->curalgo = 0; - } - goto lbl_50; - } - - /* - * Calculate D(k+1) - * - * Line search may result in: - * * maximum feasible step being taken (already processed) - * * point satisfying Wolfe conditions - * * some kind of error (CG is restarted by assigning 0.0 to Beta) - */ - if( mcinfo==1 ) - { - - /* - * Standard Wolfe conditions are satisfied: - * * calculate Y[K] and BetaK - */ - ae_v_add(&state->yk.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - vv = ae_v_dotproduct(&state->yk.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - v = ae_v_dotproduct(&state->gc.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->betady = v/vv; - v = ae_v_dotproduct(&state->gc.ptr.p_double[0], 1, &state->yk.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->betahs = v/vv; - if( state->cgtype==0 ) - { - betak = state->betady; - } - if( state->cgtype==1 ) - { - betak = ae_maxreal(0, ae_minreal(state->betady, state->betahs, _state), _state); - } - } - else - { - - /* - * Something is wrong (may be function is too wild or too flat). - * - * We'll set BetaK=0, which will restart CG algorithm. - * We can stop later (during normal checks) if stopping conditions are met. - */ - betak = 0; - state->debugrestartscount = state->debugrestartscount+1; - } - ae_v_moveneg(&state->dn.ptr.p_double[0], 1, &state->gc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->dn.ptr.p_double[0], 1, &state->dk.ptr.p_double[0], 1, ae_v_len(0,n-1), betak); - ae_v_move(&state->dk.ptr.p_double[0], 1, &state->dn.ptr.p_double[0], 1, ae_v_len(0,n-1)); - - /* - * update other information - */ - state->fold = state->f; - state->k = state->k+1; - goto lbl_49; -lbl_50: -lbl_47: - goto lbl_17; -lbl_18: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = i; - state->rstate.ia.ptr.p_int[2] = mcinfo; - state->rstate.ia.ptr.p_int[3] = diffcnt; - state->rstate.ba.ptr.p_bool[0] = b; - state->rstate.ba.ptr.p_bool[1] = stepfound; - state->rstate.ra.ptr.p_double[0] = betak; - state->rstate.ra.ptr.p_double[1] = v; - state->rstate.ra.ptr.p_double[2] = vv; - return result; -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minasaresults(minasastate* state, - /* Real */ ae_vector* x, - minasareport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _minasareport_clear(rep); - - minasaresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minasaresultsbuf(minasastate* state, - /* Real */ ae_vector* x, - minasareport* rep, - ae_state *_state) -{ - ae_int_t i; - - - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->nfev = state->repnfev; - rep->terminationtype = state->repterminationtype; - rep->activeconstraints = 0; - for(i=0; i<=state->n-1; i++) - { - if( ae_fp_eq(state->ak.ptr.p_double[i],0) ) - { - rep->activeconstraints = rep->activeconstraints+1; - } - } -} - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minasarestartfrom(minasastate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state) -{ - - - ae_assert(x->cnt>=state->n, "MinASARestartFrom: Length(X)n, _state), "MinASARestartFrom: X contains infinite or NaN values!", _state); - ae_assert(bndl->cnt>=state->n, "MinASARestartFrom: Length(BndL)n, _state), "MinASARestartFrom: BndL contains infinite or NaN values!", _state); - ae_assert(bndu->cnt>=state->n, "MinASARestartFrom: Length(BndU)n, _state), "MinASARestartFrom: BndU contains infinite or NaN values!", _state); - ae_v_move(&state->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_v_move(&state->bndl.ptr.p_double[0], 1, &bndl->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_v_move(&state->bndu.ptr.p_double[0], 1, &bndu->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->laststep = 0; - ae_vector_set_length(&state->rstate.ia, 3+1, _state); - ae_vector_set_length(&state->rstate.ba, 1+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; - mincomp_clearrequestfields(state, _state); -} - - -/************************************************************************* -Returns norm of bounded anti-gradient. - -Bounded antigradient is a vector obtained from anti-gradient by zeroing -components which point outwards: - result = norm(v) - v[i]=0 if ((-g[i]<0)and(x[i]=bndl[i])) or - ((-g[i]>0)and(x[i]=bndu[i])) - v[i]=-g[i] otherwise - -This function may be used to check a stopping criterion. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -static double mincomp_asaboundedantigradnorm(minasastate* state, - ae_state *_state) -{ - ae_int_t i; - double v; - double result; - - - result = 0; - for(i=0; i<=state->n-1; i++) - { - v = -state->g.ptr.p_double[i]; - if( ae_fp_eq(state->x.ptr.p_double[i],state->bndl.ptr.p_double[i])&&ae_fp_less(-state->g.ptr.p_double[i],0) ) - { - v = 0; - } - if( ae_fp_eq(state->x.ptr.p_double[i],state->bndu.ptr.p_double[i])&&ae_fp_greater(-state->g.ptr.p_double[i],0) ) - { - v = 0; - } - result = result+ae_sqr(v, _state); - } - result = ae_sqrt(result, _state); - return result; -} - - -/************************************************************************* -Returns norm of GI(x). - -GI(x) is a gradient vector whose components associated with active -constraints are zeroed. It differs from bounded anti-gradient because -components of GI(x) are zeroed independently of sign(g[i]), and -anti-gradient's components are zeroed with respect to both constraint and -sign. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -static double mincomp_asaginorm(minasastate* state, ae_state *_state) -{ - ae_int_t i; - double result; - - - result = 0; - for(i=0; i<=state->n-1; i++) - { - if( ae_fp_neq(state->x.ptr.p_double[i],state->bndl.ptr.p_double[i])&&ae_fp_neq(state->x.ptr.p_double[i],state->bndu.ptr.p_double[i]) ) - { - result = result+ae_sqr(state->g.ptr.p_double[i], _state); - } - } - result = ae_sqrt(result, _state); - return result; -} - - -/************************************************************************* -Returns norm(D1(State.X)) - -For a meaning of D1 see 'NEW ACTIVE SET ALGORITHM FOR BOX CONSTRAINED -OPTIMIZATION' by WILLIAM W. HAGER AND HONGCHAO ZHANG. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -static double mincomp_asad1norm(minasastate* state, ae_state *_state) -{ - ae_int_t i; - double result; - - - result = 0; - for(i=0; i<=state->n-1; i++) - { - result = result+ae_sqr(boundval(state->x.ptr.p_double[i]-state->g.ptr.p_double[i], state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i], _state)-state->x.ptr.p_double[i], _state); - } - result = ae_sqrt(result, _state); - return result; -} - - -/************************************************************************* -Returns True, if U set is empty. - -* State.X is used as point, -* State.G - as gradient, -* D is calculated within function (because State.D may have different - meaning depending on current optimization algorithm) - -For a meaning of U see 'NEW ACTIVE SET ALGORITHM FOR BOX CONSTRAINED -OPTIMIZATION' by WILLIAM W. HAGER AND HONGCHAO ZHANG. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -static ae_bool mincomp_asauisempty(minasastate* state, ae_state *_state) -{ - ae_int_t i; - double d; - double d2; - double d32; - ae_bool result; - - - d = mincomp_asad1norm(state, _state); - d2 = ae_sqrt(d, _state); - d32 = d*d2; - result = ae_true; - for(i=0; i<=state->n-1; i++) - { - if( ae_fp_greater_eq(ae_fabs(state->g.ptr.p_double[i], _state),d2)&&ae_fp_greater_eq(ae_minreal(state->x.ptr.p_double[i]-state->bndl.ptr.p_double[i], state->bndu.ptr.p_double[i]-state->x.ptr.p_double[i], _state),d32) ) - { - result = ae_false; - return result; - } - } - return result; -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void mincomp_clearrequestfields(minasastate* state, - ae_state *_state) -{ - - - state->needfg = ae_false; - state->xupdated = ae_false; -} - - -ae_bool _minasastate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minasastate *p = (minasastate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->bndl, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->bndu, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ak, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->an, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->dn, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->work, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->yk, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->gc, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->g, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !_linminstate_init(&p->lstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _minasastate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minasastate *dst = (minasastate*)_dst; - minasastate *src = (minasastate*)_src; - dst->n = src->n; - dst->epsg = src->epsg; - dst->epsf = src->epsf; - dst->epsx = src->epsx; - dst->maxits = src->maxits; - dst->xrep = src->xrep; - dst->stpmax = src->stpmax; - dst->cgtype = src->cgtype; - dst->k = src->k; - dst->nfev = src->nfev; - dst->mcstage = src->mcstage; - if( !ae_vector_init_copy(&dst->bndl, &src->bndl, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->bndu, &src->bndu, _state, make_automatic) ) - return ae_false; - dst->curalgo = src->curalgo; - dst->acount = src->acount; - dst->mu = src->mu; - dst->finit = src->finit; - dst->dginit = src->dginit; - if( !ae_vector_init_copy(&dst->ak, &src->ak, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xk, &src->xk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dk, &src->dk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->an, &src->an, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->xn, &src->xn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->dn, &src->dn, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - dst->fold = src->fold; - dst->stp = src->stp; - if( !ae_vector_init_copy(&dst->work, &src->work, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->yk, &src->yk, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->gc, &src->gc, _state, make_automatic) ) - return ae_false; - dst->laststep = src->laststep; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->g, &src->g, _state, make_automatic) ) - return ae_false; - dst->needfg = src->needfg; - dst->xupdated = src->xupdated; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - dst->repiterationscount = src->repiterationscount; - dst->repnfev = src->repnfev; - dst->repterminationtype = src->repterminationtype; - dst->debugrestartscount = src->debugrestartscount; - if( !_linminstate_init_copy(&dst->lstate, &src->lstate, _state, make_automatic) ) - return ae_false; - dst->betahs = src->betahs; - dst->betady = src->betady; - return ae_true; -} - - -void _minasastate_clear(void* _p) -{ - minasastate *p = (minasastate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->bndl); - ae_vector_clear(&p->bndu); - ae_vector_clear(&p->ak); - ae_vector_clear(&p->xk); - ae_vector_clear(&p->dk); - ae_vector_clear(&p->an); - ae_vector_clear(&p->xn); - ae_vector_clear(&p->dn); - ae_vector_clear(&p->d); - ae_vector_clear(&p->work); - ae_vector_clear(&p->yk); - ae_vector_clear(&p->gc); - ae_vector_clear(&p->x); - ae_vector_clear(&p->g); - _rcommstate_clear(&p->rstate); - _linminstate_clear(&p->lstate); -} - - -void _minasastate_destroy(void* _p) -{ - minasastate *p = (minasastate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->bndl); - ae_vector_destroy(&p->bndu); - ae_vector_destroy(&p->ak); - ae_vector_destroy(&p->xk); - ae_vector_destroy(&p->dk); - ae_vector_destroy(&p->an); - ae_vector_destroy(&p->xn); - ae_vector_destroy(&p->dn); - ae_vector_destroy(&p->d); - ae_vector_destroy(&p->work); - ae_vector_destroy(&p->yk); - ae_vector_destroy(&p->gc); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->g); - _rcommstate_destroy(&p->rstate); - _linminstate_destroy(&p->lstate); -} - - -ae_bool _minasareport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - minasareport *p = (minasareport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _minasareport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - minasareport *dst = (minasareport*)_dst; - minasareport *src = (minasareport*)_src; - dst->iterationscount = src->iterationscount; - dst->nfev = src->nfev; - dst->terminationtype = src->terminationtype; - dst->activeconstraints = src->activeconstraints; - return ae_true; -} - - -void _minasareport_clear(void* _p) -{ - minasareport *p = (minasareport*)_p; - ae_touch_ptr((void*)p); -} - - -void _minasareport_destroy(void* _p) -{ - minasareport *p = (minasareport*)_p; - ae_touch_ptr((void*)p); -} - - - -} - diff --git a/src/inc/alglib/optimization.h b/src/inc/alglib/optimization.h deleted file mode 100644 index eb62e95..0000000 --- a/src/inc/alglib/optimization.h +++ /dev/null @@ -1,4379 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _optimization_pkg_h -#define _optimization_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "linalg.h" -#include "alglibmisc.h" -#include "solvers.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - ae_int_t n; - ae_int_t k; - double alpha; - double tau; - double theta; - ae_matrix a; - ae_matrix q; - ae_vector b; - ae_vector r; - ae_vector xc; - ae_vector d; - ae_vector activeset; - ae_matrix tq2dense; - ae_matrix tk2; - ae_vector tq2diag; - ae_vector tq1; - ae_vector tk1; - double tq0; - double tk0; - ae_vector txc; - ae_vector tb; - ae_int_t nfree; - ae_int_t ecakind; - ae_matrix ecadense; - ae_matrix eq; - ae_matrix eccm; - ae_vector ecadiag; - ae_vector eb; - double ec; - ae_vector tmp0; - ae_vector tmp1; - ae_vector tmpg; - ae_matrix tmp2; - ae_bool ismaintermchanged; - ae_bool issecondarytermchanged; - ae_bool islineartermchanged; - ae_bool isactivesetchanged; -} convexquadraticmodel; -typedef struct -{ - ae_int_t ns; - ae_int_t nd; - ae_int_t nr; - ae_matrix densea; - ae_vector b; - ae_vector nnc; - ae_int_t refinementits; - double debugflops; - ae_int_t debugmaxnewton; - ae_vector xn; - ae_matrix tmpz; - ae_matrix tmpca; - ae_vector g; - ae_vector d; - ae_vector dx; - ae_vector diagaa; - ae_vector cb; - ae_vector cx; - ae_vector cborg; - ae_vector columnmap; - ae_vector rowmap; - ae_vector tmpcholesky; - ae_vector r; -} snnlssolver; -typedef struct -{ - ae_int_t n; - ae_int_t algostate; - ae_vector xc; - ae_bool hasxc; - ae_vector s; - ae_vector h; - ae_vector activeset; - ae_bool basisisready; - ae_matrix sbasis; - ae_matrix pbasis; - ae_matrix ibasis; - ae_int_t basissize; - ae_bool constraintschanged; - ae_vector hasbndl; - ae_vector hasbndu; - ae_vector bndl; - ae_vector bndu; - ae_matrix cleic; - ae_int_t nec; - ae_int_t nic; - ae_vector mtx; - ae_vector mtas; - ae_vector cdtmp; - ae_vector corrtmp; - ae_vector unitdiagonal; - snnlssolver solver; - ae_vector scntmp; - ae_vector tmp0; - ae_vector tmpfeas; - ae_matrix tmpm0; - ae_vector rctmps; - ae_vector rctmpg; - ae_vector rctmprightpart; - ae_matrix rctmpdense0; - ae_matrix rctmpdense1; - ae_vector rctmpisequality; - ae_vector rctmpconstraintidx; - ae_vector rctmplambdas; - ae_matrix tmpbasis; -} sactiveset; -typedef struct -{ - ae_int_t n; - double epsg; - double epsf; - double epsx; - ae_int_t maxits; - double stpmax; - double suggestedstep; - ae_bool xrep; - ae_bool drep; - ae_int_t cgtype; - ae_int_t prectype; - ae_vector diagh; - ae_vector diaghl2; - ae_matrix vcorr; - ae_int_t vcnt; - ae_vector s; - double diffstep; - ae_int_t nfev; - ae_int_t mcstage; - ae_int_t k; - ae_vector xk; - ae_vector dk; - ae_vector xn; - ae_vector dn; - ae_vector d; - double fold; - double stp; - double curstpmax; - ae_vector yk; - double lastgoodstep; - double lastscaledstep; - ae_int_t mcinfo; - ae_bool innerresetneeded; - ae_bool terminationneeded; - double trimthreshold; - ae_int_t rstimer; - ae_vector x; - double f; - ae_vector g; - ae_bool needf; - ae_bool needfg; - ae_bool xupdated; - ae_bool algpowerup; - ae_bool lsstart; - ae_bool lsend; - double teststep; - rcommstate rstate; - ae_int_t repiterationscount; - ae_int_t repnfev; - ae_int_t repvaridx; - ae_int_t repterminationtype; - ae_int_t debugrestartscount; - linminstate lstate; - double fbase; - double fm2; - double fm1; - double fp1; - double fp2; - double betahs; - double betady; - ae_vector work0; - ae_vector work1; -} mincgstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nfev; - ae_int_t varidx; - ae_int_t terminationtype; -} mincgreport; -typedef struct -{ - ae_int_t nmain; - ae_int_t nslack; - double epsg; - double epsf; - double epsx; - ae_int_t maxits; - ae_bool xrep; - ae_bool drep; - double stpmax; - double diffstep; - sactiveset sas; - ae_vector s; - ae_int_t prectype; - ae_vector diagh; - ae_vector x; - double f; - ae_vector g; - ae_bool needf; - ae_bool needfg; - ae_bool xupdated; - ae_bool lsstart; - ae_bool lbfgssearch; - ae_bool boundedstep; - double teststep; - rcommstate rstate; - ae_vector gc; - ae_vector xn; - ae_vector gn; - ae_vector xp; - ae_vector gp; - double fc; - double fn; - double fp; - ae_vector d; - ae_matrix cleic; - ae_int_t nec; - ae_int_t nic; - double lastgoodstep; - double lastscaledgoodstep; - double maxscaledgrad; - ae_vector hasbndl; - ae_vector hasbndu; - ae_vector bndl; - ae_vector bndu; - ae_int_t repinneriterationscount; - ae_int_t repouteriterationscount; - ae_int_t repnfev; - ae_int_t repvaridx; - ae_int_t repterminationtype; - double repdebugeqerr; - double repdebugfs; - double repdebugff; - double repdebugdx; - ae_int_t repdebugfeasqpits; - ae_int_t repdebugfeasgpaits; - ae_vector xstart; - snnlssolver solver; - double fbase; - double fm2; - double fm1; - double fp1; - double fp2; - double xm1; - double xp1; - double gm1; - double gp1; - ae_int_t cidx; - double cval; - ae_vector tmpprec; - ae_int_t nfev; - ae_int_t mcstage; - double stp; - double curstpmax; - double activationstep; - ae_vector work; - linminstate lstate; - double trimthreshold; - ae_int_t nonmonotoniccnt; - ae_int_t k; - ae_int_t q; - ae_int_t p; - ae_vector rho; - ae_matrix yk; - ae_matrix sk; - ae_vector theta; -} minbleicstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nfev; - ae_int_t varidx; - ae_int_t terminationtype; - double debugeqerr; - double debugfs; - double debugff; - double debugdx; - ae_int_t debugfeasqpits; - ae_int_t debugfeasgpaits; - ae_int_t inneriterationscount; - ae_int_t outeriterationscount; -} minbleicreport; -typedef struct -{ - ae_int_t n; - ae_int_t m; - double epsg; - double epsf; - double epsx; - ae_int_t maxits; - ae_bool xrep; - double stpmax; - ae_vector s; - double diffstep; - ae_int_t nfev; - ae_int_t mcstage; - ae_int_t k; - ae_int_t q; - ae_int_t p; - ae_vector rho; - ae_matrix yk; - ae_matrix sk; - ae_vector theta; - ae_vector d; - double stp; - ae_vector work; - double fold; - double trimthreshold; - ae_int_t prectype; - double gammak; - ae_matrix denseh; - ae_vector diagh; - double fbase; - double fm2; - double fm1; - double fp1; - double fp2; - ae_vector autobuf; - ae_vector x; - double f; - ae_vector g; - ae_bool needf; - ae_bool needfg; - ae_bool xupdated; - double teststep; - rcommstate rstate; - ae_int_t repiterationscount; - ae_int_t repnfev; - ae_int_t repvaridx; - ae_int_t repterminationtype; - linminstate lstate; -} minlbfgsstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nfev; - ae_int_t varidx; - ae_int_t terminationtype; -} minlbfgsreport; -typedef struct -{ - ae_int_t n; - ae_int_t algokind; - ae_int_t akind; - convexquadraticmodel a; - sparsematrix sparsea; - ae_bool sparseaupper; - double anorm; - ae_vector b; - ae_vector bndl; - ae_vector bndu; - ae_vector s; - ae_vector havebndl; - ae_vector havebndu; - ae_vector xorigin; - ae_vector startx; - ae_bool havex; - ae_matrix cleic; - ae_int_t nec; - ae_int_t nic; - double bleicepsg; - double bleicepsf; - double bleicepsx; - ae_int_t bleicmaxits; - sactiveset sas; - ae_vector gc; - ae_vector xn; - ae_vector pg; - ae_vector workbndl; - ae_vector workbndu; - ae_matrix workcleic; - ae_vector xs; - ae_int_t repinneriterationscount; - ae_int_t repouteriterationscount; - ae_int_t repncholesky; - ae_int_t repnmv; - ae_int_t repterminationtype; - double debugphase1flops; - double debugphase2flops; - double debugphase3flops; - ae_vector tmp0; - ae_vector tmp1; - ae_vector tmpb; - ae_vector rctmpg; - ae_vector tmpi; - normestimatorstate estimator; - minbleicstate solver; - minbleicreport solverrep; -} minqpstate; -typedef struct -{ - ae_int_t inneriterationscount; - ae_int_t outeriterationscount; - ae_int_t nmv; - ae_int_t ncholesky; - ae_int_t terminationtype; -} minqpreport; -typedef struct -{ - ae_int_t n; - ae_int_t m; - double diffstep; - double epsg; - double epsf; - double epsx; - ae_int_t maxits; - ae_bool xrep; - double stpmax; - ae_int_t maxmodelage; - ae_bool makeadditers; - ae_vector x; - double f; - ae_vector fi; - ae_matrix j; - ae_matrix h; - ae_vector g; - ae_bool needf; - ae_bool needfg; - ae_bool needfgh; - ae_bool needfij; - ae_bool needfi; - ae_bool xupdated; - ae_int_t algomode; - ae_bool hasf; - ae_bool hasfi; - ae_bool hasg; - ae_vector xbase; - double fbase; - ae_vector fibase; - ae_vector gbase; - ae_matrix quadraticmodel; - ae_vector bndl; - ae_vector bndu; - ae_vector havebndl; - ae_vector havebndu; - ae_vector s; - double lambdav; - double nu; - ae_int_t modelage; - ae_vector xdir; - ae_vector deltax; - ae_vector deltaf; - ae_bool deltaxready; - ae_bool deltafready; - double teststep; - ae_int_t repiterationscount; - ae_int_t repterminationtype; - ae_int_t repfuncidx; - ae_int_t repvaridx; - ae_int_t repnfunc; - ae_int_t repnjac; - ae_int_t repngrad; - ae_int_t repnhess; - ae_int_t repncholesky; - rcommstate rstate; - ae_vector choleskybuf; - ae_vector tmp0; - double actualdecrease; - double predicteddecrease; - double xm1; - double xp1; - ae_vector fm1; - ae_vector fp1; - ae_vector fc1; - ae_vector gm1; - ae_vector gp1; - ae_vector gc1; - minlbfgsstate internalstate; - minlbfgsreport internalrep; - minqpstate qpstate; - minqpreport qprep; -} minlmstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t terminationtype; - ae_int_t funcidx; - ae_int_t varidx; - ae_int_t nfunc; - ae_int_t njac; - ae_int_t ngrad; - ae_int_t nhess; - ae_int_t ncholesky; -} minlmreport; -typedef struct -{ - ae_int_t n; - double epsg; - double epsf; - double epsx; - ae_int_t maxits; - ae_bool xrep; - double stpmax; - ae_int_t cgtype; - ae_int_t k; - ae_int_t nfev; - ae_int_t mcstage; - ae_vector bndl; - ae_vector bndu; - ae_int_t curalgo; - ae_int_t acount; - double mu; - double finit; - double dginit; - ae_vector ak; - ae_vector xk; - ae_vector dk; - ae_vector an; - ae_vector xn; - ae_vector dn; - ae_vector d; - double fold; - double stp; - ae_vector work; - ae_vector yk; - ae_vector gc; - double laststep; - ae_vector x; - double f; - ae_vector g; - ae_bool needfg; - ae_bool xupdated; - rcommstate rstate; - ae_int_t repiterationscount; - ae_int_t repnfev; - ae_int_t repterminationtype; - ae_int_t debugrestartscount; - linminstate lstate; - double betahs; - double betady; -} minasastate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nfev; - ae_int_t terminationtype; - ae_int_t activeconstraints; -} minasareport; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - - - - - - - - -/************************************************************************* -This object stores state of the nonlinear CG optimizer. - -You should use ALGLIB functions to work with this object. -*************************************************************************/ -class _mincgstate_owner -{ -public: - _mincgstate_owner(); - _mincgstate_owner(const _mincgstate_owner &rhs); - _mincgstate_owner& operator=(const _mincgstate_owner &rhs); - virtual ~_mincgstate_owner(); - alglib_impl::mincgstate* c_ptr(); - alglib_impl::mincgstate* c_ptr() const; -protected: - alglib_impl::mincgstate *p_struct; -}; -class mincgstate : public _mincgstate_owner -{ -public: - mincgstate(); - mincgstate(const mincgstate &rhs); - mincgstate& operator=(const mincgstate &rhs); - virtual ~mincgstate(); - ae_bool &needf; - ae_bool &needfg; - ae_bool &xupdated; - double &f; - real_1d_array g; - real_1d_array x; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _mincgreport_owner -{ -public: - _mincgreport_owner(); - _mincgreport_owner(const _mincgreport_owner &rhs); - _mincgreport_owner& operator=(const _mincgreport_owner &rhs); - virtual ~_mincgreport_owner(); - alglib_impl::mincgreport* c_ptr(); - alglib_impl::mincgreport* c_ptr() const; -protected: - alglib_impl::mincgreport *p_struct; -}; -class mincgreport : public _mincgreport_owner -{ -public: - mincgreport(); - mincgreport(const mincgreport &rhs); - mincgreport& operator=(const mincgreport &rhs); - virtual ~mincgreport(); - ae_int_t &iterationscount; - ae_int_t &nfev; - ae_int_t &varidx; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -This object stores nonlinear optimizer state. -You should use functions provided by MinBLEIC subpackage to work with this -object -*************************************************************************/ -class _minbleicstate_owner -{ -public: - _minbleicstate_owner(); - _minbleicstate_owner(const _minbleicstate_owner &rhs); - _minbleicstate_owner& operator=(const _minbleicstate_owner &rhs); - virtual ~_minbleicstate_owner(); - alglib_impl::minbleicstate* c_ptr(); - alglib_impl::minbleicstate* c_ptr() const; -protected: - alglib_impl::minbleicstate *p_struct; -}; -class minbleicstate : public _minbleicstate_owner -{ -public: - minbleicstate(); - minbleicstate(const minbleicstate &rhs); - minbleicstate& operator=(const minbleicstate &rhs); - virtual ~minbleicstate(); - ae_bool &needf; - ae_bool &needfg; - ae_bool &xupdated; - double &f; - real_1d_array g; - real_1d_array x; - -}; - - -/************************************************************************* -This structure stores optimization report: -* IterationsCount number of iterations -* NFEV number of gradient evaluations -* TerminationType termination type (see below) - -TERMINATION CODES - -TerminationType field contains completion code, which can be: - -7 gradient verification failed. - See MinBLEICSetGradientCheck() for more information. - -3 inconsistent constraints. Feasible point is - either nonexistent or too hard to find. Try to - restart optimizer with better initial approximation - 1 relative function improvement is no more than EpsF. - 2 relative step is no more than EpsX. - 4 gradient norm is no more than EpsG - 5 MaxIts steps was taken - 7 stopping conditions are too stringent, - further improvement is impossible, - X contains best point found so far. - -ADDITIONAL FIELDS - -There are additional fields which can be used for debugging: -* DebugEqErr error in the equality constraints (2-norm) -* DebugFS f, calculated at projection of initial point - to the feasible set -* DebugFF f, calculated at the final point -* DebugDX |X_start-X_final| -*************************************************************************/ -class _minbleicreport_owner -{ -public: - _minbleicreport_owner(); - _minbleicreport_owner(const _minbleicreport_owner &rhs); - _minbleicreport_owner& operator=(const _minbleicreport_owner &rhs); - virtual ~_minbleicreport_owner(); - alglib_impl::minbleicreport* c_ptr(); - alglib_impl::minbleicreport* c_ptr() const; -protected: - alglib_impl::minbleicreport *p_struct; -}; -class minbleicreport : public _minbleicreport_owner -{ -public: - minbleicreport(); - minbleicreport(const minbleicreport &rhs); - minbleicreport& operator=(const minbleicreport &rhs); - virtual ~minbleicreport(); - ae_int_t &iterationscount; - ae_int_t &nfev; - ae_int_t &varidx; - ae_int_t &terminationtype; - double &debugeqerr; - double &debugfs; - double &debugff; - double &debugdx; - ae_int_t &debugfeasqpits; - ae_int_t &debugfeasgpaits; - ae_int_t &inneriterationscount; - ae_int_t &outeriterationscount; - -}; - -/************************************************************************* - -*************************************************************************/ -class _minlbfgsstate_owner -{ -public: - _minlbfgsstate_owner(); - _minlbfgsstate_owner(const _minlbfgsstate_owner &rhs); - _minlbfgsstate_owner& operator=(const _minlbfgsstate_owner &rhs); - virtual ~_minlbfgsstate_owner(); - alglib_impl::minlbfgsstate* c_ptr(); - alglib_impl::minlbfgsstate* c_ptr() const; -protected: - alglib_impl::minlbfgsstate *p_struct; -}; -class minlbfgsstate : public _minlbfgsstate_owner -{ -public: - minlbfgsstate(); - minlbfgsstate(const minlbfgsstate &rhs); - minlbfgsstate& operator=(const minlbfgsstate &rhs); - virtual ~minlbfgsstate(); - ae_bool &needf; - ae_bool &needfg; - ae_bool &xupdated; - double &f; - real_1d_array g; - real_1d_array x; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _minlbfgsreport_owner -{ -public: - _minlbfgsreport_owner(); - _minlbfgsreport_owner(const _minlbfgsreport_owner &rhs); - _minlbfgsreport_owner& operator=(const _minlbfgsreport_owner &rhs); - virtual ~_minlbfgsreport_owner(); - alglib_impl::minlbfgsreport* c_ptr(); - alglib_impl::minlbfgsreport* c_ptr() const; -protected: - alglib_impl::minlbfgsreport *p_struct; -}; -class minlbfgsreport : public _minlbfgsreport_owner -{ -public: - minlbfgsreport(); - minlbfgsreport(const minlbfgsreport &rhs); - minlbfgsreport& operator=(const minlbfgsreport &rhs); - virtual ~minlbfgsreport(); - ae_int_t &iterationscount; - ae_int_t &nfev; - ae_int_t &varidx; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -This object stores nonlinear optimizer state. -You should use functions provided by MinQP subpackage to work with this -object -*************************************************************************/ -class _minqpstate_owner -{ -public: - _minqpstate_owner(); - _minqpstate_owner(const _minqpstate_owner &rhs); - _minqpstate_owner& operator=(const _minqpstate_owner &rhs); - virtual ~_minqpstate_owner(); - alglib_impl::minqpstate* c_ptr(); - alglib_impl::minqpstate* c_ptr() const; -protected: - alglib_impl::minqpstate *p_struct; -}; -class minqpstate : public _minqpstate_owner -{ -public: - minqpstate(); - minqpstate(const minqpstate &rhs); - minqpstate& operator=(const minqpstate &rhs); - virtual ~minqpstate(); - -}; - - -/************************************************************************* -This structure stores optimization report: -* InnerIterationsCount number of inner iterations -* OuterIterationsCount number of outer iterations -* NCholesky number of Cholesky decomposition -* NMV number of matrix-vector products - (only products calculated as part of iterative - process are counted) -* TerminationType completion code (see below) - -Completion codes: -* -5 inappropriate solver was used: - * Cholesky solver for semidefinite or indefinite problems - * Cholesky solver for problems with non-boundary constraints -* -4 BLEIC-QP algorithm found unconstrained direction - of negative curvature (function is unbounded from - below even under constraints), no meaningful - minimum can be found. -* -3 inconsistent constraints (or, maybe, feasible point is - too hard to find). If you are sure that constraints are feasible, - try to restart optimizer with better initial approximation. -* -1 solver error -* 4 successful completion -* 5 MaxIts steps was taken -* 7 stopping conditions are too stringent, - further improvement is impossible, - X contains best point found so far. -*************************************************************************/ -class _minqpreport_owner -{ -public: - _minqpreport_owner(); - _minqpreport_owner(const _minqpreport_owner &rhs); - _minqpreport_owner& operator=(const _minqpreport_owner &rhs); - virtual ~_minqpreport_owner(); - alglib_impl::minqpreport* c_ptr(); - alglib_impl::minqpreport* c_ptr() const; -protected: - alglib_impl::minqpreport *p_struct; -}; -class minqpreport : public _minqpreport_owner -{ -public: - minqpreport(); - minqpreport(const minqpreport &rhs); - minqpreport& operator=(const minqpreport &rhs); - virtual ~minqpreport(); - ae_int_t &inneriterationscount; - ae_int_t &outeriterationscount; - ae_int_t &nmv; - ae_int_t &ncholesky; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -Levenberg-Marquardt optimizer. - -This structure should be created using one of the MinLMCreate???() -functions. You should not access its fields directly; use ALGLIB functions -to work with it. -*************************************************************************/ -class _minlmstate_owner -{ -public: - _minlmstate_owner(); - _minlmstate_owner(const _minlmstate_owner &rhs); - _minlmstate_owner& operator=(const _minlmstate_owner &rhs); - virtual ~_minlmstate_owner(); - alglib_impl::minlmstate* c_ptr(); - alglib_impl::minlmstate* c_ptr() const; -protected: - alglib_impl::minlmstate *p_struct; -}; -class minlmstate : public _minlmstate_owner -{ -public: - minlmstate(); - minlmstate(const minlmstate &rhs); - minlmstate& operator=(const minlmstate &rhs); - virtual ~minlmstate(); - ae_bool &needf; - ae_bool &needfg; - ae_bool &needfgh; - ae_bool &needfi; - ae_bool &needfij; - ae_bool &xupdated; - double &f; - real_1d_array fi; - real_1d_array g; - real_2d_array h; - real_2d_array j; - real_1d_array x; - -}; - - -/************************************************************************* -Optimization report, filled by MinLMResults() function - -FIELDS: -* TerminationType, completetion code: - * -7 derivative correctness check failed; - see Rep.WrongNum, Rep.WrongI, Rep.WrongJ for - more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient is no more than EpsG. - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible -* IterationsCount, contains iterations count -* NFunc, number of function calculations -* NJac, number of Jacobi matrix calculations -* NGrad, number of gradient calculations -* NHess, number of Hessian calculations -* NCholesky, number of Cholesky decomposition calculations -*************************************************************************/ -class _minlmreport_owner -{ -public: - _minlmreport_owner(); - _minlmreport_owner(const _minlmreport_owner &rhs); - _minlmreport_owner& operator=(const _minlmreport_owner &rhs); - virtual ~_minlmreport_owner(); - alglib_impl::minlmreport* c_ptr(); - alglib_impl::minlmreport* c_ptr() const; -protected: - alglib_impl::minlmreport *p_struct; -}; -class minlmreport : public _minlmreport_owner -{ -public: - minlmreport(); - minlmreport(const minlmreport &rhs); - minlmreport& operator=(const minlmreport &rhs); - virtual ~minlmreport(); - ae_int_t &iterationscount; - ae_int_t &terminationtype; - ae_int_t &funcidx; - ae_int_t &varidx; - ae_int_t &nfunc; - ae_int_t &njac; - ae_int_t &ngrad; - ae_int_t &nhess; - ae_int_t &ncholesky; - -}; - -/************************************************************************* - -*************************************************************************/ -class _minasastate_owner -{ -public: - _minasastate_owner(); - _minasastate_owner(const _minasastate_owner &rhs); - _minasastate_owner& operator=(const _minasastate_owner &rhs); - virtual ~_minasastate_owner(); - alglib_impl::minasastate* c_ptr(); - alglib_impl::minasastate* c_ptr() const; -protected: - alglib_impl::minasastate *p_struct; -}; -class minasastate : public _minasastate_owner -{ -public: - minasastate(); - minasastate(const minasastate &rhs); - minasastate& operator=(const minasastate &rhs); - virtual ~minasastate(); - ae_bool &needfg; - ae_bool &xupdated; - double &f; - real_1d_array g; - real_1d_array x; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _minasareport_owner -{ -public: - _minasareport_owner(); - _minasareport_owner(const _minasareport_owner &rhs); - _minasareport_owner& operator=(const _minasareport_owner &rhs); - virtual ~_minasareport_owner(); - alglib_impl::minasareport* c_ptr(); - alglib_impl::minasareport* c_ptr() const; -protected: - alglib_impl::minasareport *p_struct; -}; -class minasareport : public _minasareport_owner -{ -public: - minasareport(); - minasareport(const minasareport &rhs); - minasareport& operator=(const minasareport &rhs); - virtual ~minasareport(); - ae_int_t &iterationscount; - ae_int_t &nfev; - ae_int_t &terminationtype; - ae_int_t &activeconstraints; - -}; - - - - - - - - - -/************************************************************************* - NONLINEAR CONJUGATE GRADIENT METHOD - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using one of the -nonlinear conjugate gradient methods. - -These CG methods are globally convergent (even on non-convex functions) as -long as grad(f) is Lipschitz continuous in a some neighborhood of the -L = { x : f(x)<=f(x0) }. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinCGCreate() call -2. User tunes solver parameters with MinCGSetCond(), MinCGSetStpMax() and - other functions -3. User calls MinCGOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinCGResults() to get solution -5. Optionally, user may call MinCGRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinCGRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgcreate(const ae_int_t n, const real_1d_array &x, mincgstate &state); -void mincgcreate(const real_1d_array &x, mincgstate &state); - - -/************************************************************************* -The subroutine is finite difference variant of MinCGCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinCGCreate() in order to get more -information about creation of CG optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinCGSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. L-BFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgcreatef(const ae_int_t n, const real_1d_array &x, const double diffstep, mincgstate &state); -void mincgcreatef(const real_1d_array &x, const double diffstep, mincgstate &state); - - -/************************************************************************* -This function sets stopping conditions for CG optimization algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinCGSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetcond(const mincgstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -This function sets scaling coefficients for CG optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of CG optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the CG too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinCGSetPrec...() functions. - -There is special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void mincgsetscale(const mincgstate &state, const real_1d_array &s); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetxrep(const mincgstate &state, const bool needxrep); - - -/************************************************************************* -This function sets CG algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - CGType - algorithm type: - * -1 automatic selection of the best algorithm - * 0 DY (Dai and Yuan) algorithm - * 1 Hybrid DY-HS algorithm - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetcgtype(const mincgstate &state, const ae_int_t cgtype); - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetstpmax(const mincgstate &state, const double stpmax); - - -/************************************************************************* -This function allows to suggest initial step length to the CG algorithm. - -Suggested step length is used as starting point for the line search. It -can be useful when you have badly scaled problem, i.e. when ||grad|| -(which is used as initial estimate for the first step) is many orders of -magnitude different from the desired step. - -Line search may fail on such problems without good estimate of initial -step length. Imagine, for example, problem with ||grad||=10^50 and desired -step equal to 0.1 Line search function will use 10^50 as initial step, -then it will decrease step length by 2 (up to 20 attempts) and will get -10^44, which is still too large. - -This function allows us to tell than line search should be started from -some moderate step length, like 1.0, so algorithm will be able to detect -desired step length in a several searches. - -Default behavior (when no step is suggested) is to use preconditioner, if -it is available, to generate initial estimate of step length. - -This function influences only first iteration of algorithm. It should be -called between MinCGCreate/MinCGRestartFrom() call and MinCGOptimize call. -Suggested step is ignored if you have preconditioner. - -INPUT PARAMETERS: - State - structure used to store algorithm state. - Stp - initial estimate of the step length. - Can be zero (no estimate). - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsuggeststep(const mincgstate &state, const double stp); - - -/************************************************************************* -Modification of the preconditioner: preconditioning is turned off. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdefault(const mincgstate &state); - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 3: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecdiag(const mincgstate &state, const real_1d_array &d); - - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinCGSetScale() call -(before or after MinCGSetPrecScale() call). Without knowledge of the scale -of your variables scale-based preconditioner will be just unit matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgsetprecscale(const mincgstate &state); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool mincgiteration(const mincgstate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear optimizer - -These functions accept following parameters: - state - algorithm state - func - callback which calculates function (or merit function) - value func at given point x - grad - callback which calculates function (or merit function) - value func and gradient grad at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - -NOTES: - -1. This function has two different implementations: one which uses exact - (analytical) user-supplied gradient, and one which uses function value - only and numerically differentiates function in order to obtain - gradient. - - Depending on the specific function used to create optimizer object - (either MinCGCreate() for analytical gradient or MinCGCreateF() for - numerical differentiation) you should choose appropriate variant of - MinCGOptimize() - one which accepts function AND gradient or one which - accepts function ONLY. - - Be careful to choose variant of MinCGOptimize() which corresponds to - your optimization scheme! Table below lists different combinations of - callback (function/gradient) passed to MinCGOptimize() and specific - function used to create optimizer. - - - | USER PASSED TO MinCGOptimize() - CREATED WITH | function only | function and gradient - ------------------------------------------------------------ - MinCGCreateF() | work FAIL - MinCGCreate() | FAIL work - - Here "FAIL" denotes inappropriate combinations of optimizer creation - function and MinCGOptimize() version. Attemps to use such combination - (for example, to create optimizer with MinCGCreateF() and to pass - gradient information to MinCGOptimize()) will lead to exception being - thrown. Either you did not pass gradient when it WAS needed or you - passed gradient when it was NOT needed. - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey - -*************************************************************************/ -void mincgoptimize(mincgstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void mincgoptimize(mincgstate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -Conjugate gradient results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -7 gradient verification failed. - See MinCGSetGradientCheck() for more information. - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible, - we return best X found so far - * 8 terminated by user - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -void mincgresults(const mincgstate &state, real_1d_array &x, mincgreport &rep); - - -/************************************************************************* -Conjugate gradient results - -Buffered implementation of MinCGResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.04.2009 by Bochkanov Sergey -*************************************************************************/ -void mincgresultsbuf(const mincgstate &state, real_1d_array &x, mincgreport &rep); - - -/************************************************************************* -This subroutine restarts CG algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used to store algorithm state. - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void mincgrestartfrom(const mincgstate &state, const real_1d_array &x); - - -/************************************************************************* - -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinCGOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinCGSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 31.05.2012 by Bochkanov Sergey -*************************************************************************/ -void mincgsetgradientcheck(const mincgstate &state, const double teststep); - -/************************************************************************* - BOUND CONSTRAINED OPTIMIZATION - WITH ADDITIONAL LINEAR EQUALITY AND INEQUALITY CONSTRAINTS - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments subject to any -combination of: -* bound constraints -* linear inequality constraints -* linear equality constraints - -REQUIREMENTS: -* user must provide function value and gradient -* starting point X0 must be feasible or - not too far away from the feasible set -* grad(f) must be Lipschitz continuous on a level set: - L = { x : f(x)<=f(x0) } -* function must be defined everywhere on the feasible set F - -USAGE: - -Constrained optimization if far more complex than the unconstrained one. -Here we give very brief outline of the BLEIC optimizer. We strongly recommend -you to read examples in the ALGLIB Reference Manual and to read ALGLIB User Guide -on optimization, which is available at http://www.alglib.net/optimization/ - -1. User initializes algorithm state with MinBLEICCreate() call - -2. USer adds boundary and/or linear constraints by calling - MinBLEICSetBC() and MinBLEICSetLC() functions. - -3. User sets stopping conditions with MinBLEICSetCond(). - -4. User calls MinBLEICOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. - -5. User calls MinBLEICResults() to get solution - -6. Optionally user may call MinBLEICRestartFrom() to solve another problem - with same N but another starting point. - MinBLEICRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size ofX - X - starting point, array[N]: - * it is better to set X to a feasible point - * but X can be infeasible, in which case algorithm will try - to find feasible point first, using X as initial - approximation. - -OUTPUT PARAMETERS: - State - structure stores algorithm state - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreate(const ae_int_t n, const real_1d_array &x, minbleicstate &state); -void minbleiccreate(const real_1d_array &x, minbleicstate &state); - - -/************************************************************************* -The subroutine is finite difference variant of MinBLEICCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinBLEICCreate() in order to get -more information about creation of BLEIC optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinBLEICSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. CG needs exact gradient values. Imprecise - gradient may slow down convergence, especially on highly nonlinear - problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleiccreatef(const ae_int_t n, const real_1d_array &x, const double diffstep, minbleicstate &state); -void minbleiccreatef(const real_1d_array &x, const double diffstep, minbleicstate &state); - - -/************************************************************************* -This function sets boundary constraints for BLEIC optimizer. - -Boundary constraints are inactive by default (after initial creation). -They are preserved after algorithm restart with MinBLEICRestartFrom(). - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF. - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF. - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: this solver has following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints, - even when numerical differentiation is used (algorithm adjusts nodes - according to boundary constraints) - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbc(const minbleicstate &state, const real_1d_array &bndl, const real_1d_array &bndu); - - -/************************************************************************* -This function sets linear constraints for BLEIC optimizer. - -Linear constraints are inactive by default (after initial creation). -They are preserved after algorithm restart with MinBLEICRestartFrom(). - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately: -* there always exists some minor violation (about Epsilon in magnitude) - due to rounding errors -* numerical differentiation, if used, may lead to function evaluations - outside of the feasible area, because algorithm does NOT change - numerical differentiation formula according to linear constraints. -If you want constraints to be satisfied exactly, try to reformulate your -problem in such manner that all constraints will become boundary ones -(this kind of constraints is always satisfied exactly, both in the final -solution and in all intermediate points). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetlc(const minbleicstate &state, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k); -void minbleicsetlc(const minbleicstate &state, const real_2d_array &c, const integer_1d_array &ct); - - -/************************************************************************* -This function sets stopping conditions for the optimizer. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - step vector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinBLEICSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0 and EpsX=0 and MaxIts=0 (simultaneously) will lead -to automatic stopping criterion selection. - -NOTE: when SetCond() called with non-zero MaxIts, BLEIC solver may perform - slightly more than MaxIts iterations. I.e., MaxIts sets non-strict - limit on iterations count. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetcond(const minbleicstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -This function sets scaling coefficients for BLEIC optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of the optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the BLEIC too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinBLEICSetPrec...() -functions. - -There is a special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetscale(const minbleicstate &state, const real_1d_array &s); - - -/************************************************************************* -Modification of the preconditioner: preconditioning is turned off. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecdefault(const minbleicstate &state); - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE 1: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 2: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecdiag(const minbleicstate &state, const real_1d_array &d); - - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinBLEICSetScale() -call (before or after MinBLEICSetPrecScale() call). Without knowledge of -the scale of your variables scale-based preconditioner will be just unit -matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetprecscale(const minbleicstate &state); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinBLEICOptimize(). - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetxrep(const minbleicstate &state, const bool needxrep); - - -/************************************************************************* -This function sets maximum step length - -IMPORTANT: this feature is hard to combine with preconditioning. You can't -set upper limit on step length, when you solve optimization problem with -linear (non-boundary) constraints AND preconditioner turned on. - -When non-boundary constraints are present, you have to either a) use -preconditioner, or b) use upper limit on step length. YOU CAN'T USE BOTH! -In this case algorithm will terminate with appropriate error code. - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which lead to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetstpmax(const minbleicstate &state, const double stpmax); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minbleiciteration(const minbleicstate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear optimizer - -These functions accept following parameters: - state - algorithm state - func - callback which calculates function (or merit function) - value func at given point x - grad - callback which calculates function (or merit function) - value func and gradient grad at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - -NOTES: - -1. This function has two different implementations: one which uses exact - (analytical) user-supplied gradient, and one which uses function value - only and numerically differentiates function in order to obtain - gradient. - - Depending on the specific function used to create optimizer object - (either MinBLEICCreate() for analytical gradient or MinBLEICCreateF() - for numerical differentiation) you should choose appropriate variant of - MinBLEICOptimize() - one which accepts function AND gradient or one - which accepts function ONLY. - - Be careful to choose variant of MinBLEICOptimize() which corresponds to - your optimization scheme! Table below lists different combinations of - callback (function/gradient) passed to MinBLEICOptimize() and specific - function used to create optimizer. - - - | USER PASSED TO MinBLEICOptimize() - CREATED WITH | function only | function and gradient - ------------------------------------------------------------ - MinBLEICCreateF() | work FAIL - MinBLEICCreate() | FAIL work - - Here "FAIL" denotes inappropriate combinations of optimizer creation - function and MinBLEICOptimize() version. Attemps to use such - combination (for example, to create optimizer with MinBLEICCreateF() - and to pass gradient information to MinCGOptimize()) will lead to - exception being thrown. Either you did not pass gradient when it WAS - needed or you passed gradient when it was NOT needed. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey - -*************************************************************************/ -void minbleicoptimize(minbleicstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void minbleicoptimize(minbleicstate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -BLEIC results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report. You should check Rep.TerminationType - in order to distinguish successful termination from - unsuccessful one: - * -7 gradient verification failed. - See MinBLEICSetGradientCheck() for more information. - * -3 inconsistent constraints. Feasible point is - either nonexistent or too hard to find. Try to - restart optimizer with better initial approximation - * 1 relative function improvement is no more than EpsF. - * 2 scaled step is no more than EpsX. - * 4 scaled gradient norm is no more than EpsG. - * 5 MaxIts steps was taken - More information about fields of this structure can be - found in the comments on MinBLEICReport datatype. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicresults(const minbleicstate &state, real_1d_array &x, minbleicreport &rep); - - -/************************************************************************* -BLEIC results - -Buffered implementation of MinBLEICResults() which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicresultsbuf(const minbleicstate &state, real_1d_array &x, minbleicreport &rep); - - -/************************************************************************* -This subroutine restarts algorithm from new point. -All optimization parameters (including constraints) are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure previously allocated with MinBLEICCreate call. - X - new starting point. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicrestartfrom(const minbleicstate &state, const real_1d_array &x); - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinBLEICOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinBLEICSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetgradientcheck(const minbleicstate &state, const double teststep); - -/************************************************************************* - LIMITED MEMORY BFGS METHOD FOR LARGE SCALE OPTIMIZATION - -DESCRIPTION: -The subroutine minimizes function F(x) of N arguments by using a quasi- -Newton method (LBFGS scheme) which is optimized to use a minimum amount -of memory. -The subroutine generates the approximation of an inverse Hessian matrix by -using information about the last M steps of the algorithm (instead of N). -It lessens a required amount of memory from a value of order N^2 to a -value of order 2*N*M. - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function value F and its gradient G (simultaneously) at given point X - - -USAGE: -1. User initializes algorithm state with MinLBFGSCreate() call -2. User tunes solver parameters with MinLBFGSSetCond() MinLBFGSSetStpMax() - and other functions -3. User calls MinLBFGSOptimize() function which takes algorithm state and - pointer (delegate, etc.) to callback function which calculates F/G. -4. User calls MinLBFGSResults() to get solution -5. Optionally user may call MinLBFGSRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLBFGSRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - problem dimension. N>0 - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - initial solution approximation, array[0..N-1]. - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with MinLBFGSSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLBFGSSetStpMax() function to bound algorithm's steps. However, - L-BFGS rarely needs such a tuning. - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreate(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlbfgsstate &state); -void minlbfgscreate(const ae_int_t m, const real_1d_array &x, minlbfgsstate &state); - - -/************************************************************************* -The subroutine is finite difference variant of MinLBFGSCreate(). It uses -finite differences in order to differentiate target function. - -Description below contains information which is specific to this function -only. We recommend to read comments on MinLBFGSCreate() in order to get -more information about creation of LBFGS optimizer. - -INPUT PARAMETERS: - N - problem dimension, N>0: - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of corrections in the BFGS scheme of Hessian - approximation update. Recommended value: 3<=M<=7. The smaller - value causes worse convergence, the bigger will not cause a - considerably better convergence, but will cause a fall in the - performance. M<=N. - X - starting point, array[0..N-1]. - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. algorithm uses 4-point central formula for differentiation. -2. differentiation step along I-th axis is equal to DiffStep*S[I] where - S[] is scaling vector which can be set by MinLBFGSSetScale() call. -3. we recommend you to use moderate values of differentiation step. Too - large step will result in too large truncation errors, while too small - step will result in too large numerical errors. 1.0E-6 can be good - value to start with. -4. Numerical differentiation is very inefficient - one gradient - calculation needs 4*N function evaluations. This function will work for - any N - either small (1...10), moderate (10...100) or large (100...). - However, performance penalty will be too severe for any N's except for - small ones. - We should also say that code which relies on numerical differentiation - is less robust and precise. LBFGS needs exact gradient values. - Imprecise gradient may slow down convergence, especially on highly - nonlinear problems. - Thus we recommend to use this function for fast prototyping on small- - dimensional problems only, and to implement analytical gradient as soon - as possible. - - -- ALGLIB -- - Copyright 16.05.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgscreatef(const ae_int_t n, const ae_int_t m, const real_1d_array &x, const double diffstep, minlbfgsstate &state); -void minlbfgscreatef(const ae_int_t m, const real_1d_array &x, const double diffstep, minlbfgsstate &state); - - -/************************************************************************* -This function sets stopping conditions for L-BFGS optimization algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinLBFGSSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetcond(const minlbfgsstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinLBFGSOptimize(). - - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetxrep(const minlbfgsstate &state, const bool needxrep); - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0 (default), if - you don't want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetstpmax(const minlbfgsstate &state, const double stpmax); - - -/************************************************************************* -This function sets scaling coefficients for LBFGS optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Scaling is also used by finite difference variant of the optimizer - step -along I-th axis is equal to DiffStep*S[I]. - -In most optimizers (and in the LBFGS too) scaling is NOT a form of -preconditioning. It just affects stopping conditions. You should set -preconditioner by separate call to one of the MinLBFGSSetPrec...() -functions. - -There is special preconditioning mode, however, which uses scaling -coefficients to form diagonal preconditioning matrix. You can turn this -mode on, if you want. But you should understand that scaling is not the -same thing as preconditioning - these are two different, although related -forms of tuning solver. - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetscale(const minlbfgsstate &state, const real_1d_array &s); - - -/************************************************************************* -Modification of the preconditioner: default preconditioner (simple -scaling, same for all elements of X) is used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecdefault(const minlbfgsstate &state); - - -/************************************************************************* -Modification of the preconditioner: Cholesky factorization of approximate -Hessian is used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - P - triangular preconditioner, Cholesky factorization of - the approximate Hessian. array[0..N-1,0..N-1], - (if larger, only leading N elements are used). - IsUpper - whether upper or lower triangle of P is given - (other triangle is not referenced) - -After call to this function preconditioner is changed to P (P is copied -into the internal buffer). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: P should be nonsingular. Exception will be thrown otherwise. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetpreccholesky(const minlbfgsstate &state, const real_2d_array &p, const bool isupper); - - -/************************************************************************* -Modification of the preconditioner: diagonal of approximate Hessian is -used. - -INPUT PARAMETERS: - State - structure which stores algorithm state - D - diagonal of the approximate Hessian, array[0..N-1], - (if larger, only leading N elements are used). - -NOTE: you can change preconditioner "on the fly", during algorithm -iterations. - -NOTE 2: D[i] should be positive. Exception will be thrown otherwise. - -NOTE 3: you should pass diagonal of approximate Hessian - NOT ITS INVERSE. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecdiag(const minlbfgsstate &state, const real_1d_array &d); - - -/************************************************************************* -Modification of the preconditioner: scale-based diagonal preconditioning. - -This preconditioning mode can be useful when you don't have approximate -diagonal of Hessian, but you know that your variables are badly scaled -(for example, one variable is in [1,10], and another in [1000,100000]), -and most part of the ill-conditioning comes from different scales of vars. - -In this case simple scale-based preconditioner, with H[i] = 1/(s[i]^2), -can greatly improve convergence. - -IMPRTANT: you should set scale of your variables with MinLBFGSSetScale() -call (before or after MinLBFGSSetPrecScale() call). Without knowledge of -the scale of your variables scale-based preconditioner will be just unit -matrix. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetprecscale(const minlbfgsstate &state); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minlbfgsiteration(const minlbfgsstate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear optimizer - -These functions accept following parameters: - state - algorithm state - func - callback which calculates function (or merit function) - value func at given point x - grad - callback which calculates function (or merit function) - value func and gradient grad at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - -NOTES: - -1. This function has two different implementations: one which uses exact - (analytical) user-supplied gradient, and one which uses function value - only and numerically differentiates function in order to obtain - gradient. - - Depending on the specific function used to create optimizer object - (either MinLBFGSCreate() for analytical gradient or MinLBFGSCreateF() - for numerical differentiation) you should choose appropriate variant of - MinLBFGSOptimize() - one which accepts function AND gradient or one - which accepts function ONLY. - - Be careful to choose variant of MinLBFGSOptimize() which corresponds to - your optimization scheme! Table below lists different combinations of - callback (function/gradient) passed to MinLBFGSOptimize() and specific - function used to create optimizer. - - - | USER PASSED TO MinLBFGSOptimize() - CREATED WITH | function only | function and gradient - ------------------------------------------------------------ - MinLBFGSCreateF() | work FAIL - MinLBFGSCreate() | FAIL work - - Here "FAIL" denotes inappropriate combinations of optimizer creation - function and MinLBFGSOptimize() version. Attemps to use such - combination (for example, to create optimizer with MinLBFGSCreateF() and - to pass gradient information to MinCGOptimize()) will lead to exception - being thrown. Either you did not pass gradient when it WAS needed or - you passed gradient when it was NOT needed. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey - -*************************************************************************/ -void minlbfgsoptimize(minlbfgsstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void minlbfgsoptimize(minlbfgsstate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -L-BFGS algorithm results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -7 gradient verification failed. - See MinLBFGSSetGradientCheck() for more information. - * -2 rounding errors prevent further improvement. - X contains best point found. - * -1 incorrect parameters were specified - * 1 relative function improvement is no more than - EpsF. - * 2 relative step is no more than EpsX. - * 4 gradient norm is no more than EpsG - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsresults(const minlbfgsstate &state, real_1d_array &x, minlbfgsreport &rep); - - -/************************************************************************* -L-BFGS algorithm results - -Buffered implementation of MinLBFGSResults which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsresultsbuf(const minlbfgsstate &state, real_1d_array &x, minlbfgsreport &rep); - - -/************************************************************************* -This subroutine restarts LBFGS algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used to store algorithm state - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgsrestartfrom(const minlbfgsstate &state, const real_1d_array &x); - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinLBFGSOptimize() is called -* prior to actual optimization, for each component of parameters being - optimized X[i] algorithm performs following steps: - * two trial steps are made to X[i]-TestStep*S[i] and X[i]+TestStep*S[i], - where X[i] is i-th component of the initial point and S[i] is a scale - of i-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * F(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) gradient evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided by - some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinLBFGSSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 24.05.2012 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetgradientcheck(const minlbfgsstate &state, const double teststep); - -/************************************************************************* - CONSTRAINED QUADRATIC PROGRAMMING - -The subroutine creates QP optimizer. After initial creation, it contains -default optimization problem with zero quadratic and linear terms and no -constraints. You should set quadratic/linear terms with calls to functions -provided by MinQP subpackage. - -INPUT PARAMETERS: - N - problem size - -OUTPUT PARAMETERS: - State - optimizer with zero quadratic/linear terms - and no constraints - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpcreate(const ae_int_t n, minqpstate &state); - - -/************************************************************************* -This function sets linear term for QP solver. - -By default, linear term is zero. - -INPUT PARAMETERS: - State - structure which stores algorithm state - B - linear term, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlinearterm(const minqpstate &state, const real_1d_array &b); - - -/************************************************************************* -This function sets dense quadratic term for QP solver. By default, -quadratic term is zero. - -SUPPORT BY ALGLIB QP ALGORITHMS: - -Dense quadratic term can be handled by any of the QP algorithms supported -by ALGLIB QP Solver. - -IMPORTANT: - -This solver minimizes following function: - f(x) = 0.5*x'*A*x + b'*x. -Note that quadratic term has 0.5 before it. So if you want to minimize - f(x) = x^2 + x -you should rewrite your problem as follows: - f(x) = 0.5*(2*x^2) + x -and your matrix A will be equal to [[2.0]], not to [[1.0]] - -INPUT PARAMETERS: - State - structure which stores algorithm state - A - matrix, array[N,N] - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used - * if not given, both lower and upper triangles must be - filled. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetquadraticterm(const minqpstate &state, const real_2d_array &a, const bool isupper); -void minqpsetquadraticterm(const minqpstate &state, const real_2d_array &a); - - -/************************************************************************* -This function sets sparse quadratic term for QP solver. By default, -quadratic term is zero. - -SUPPORT BY ALGLIB QP ALGORITHMS: - -Sparse quadratic term is supported only by BLEIC-based QP algorithm (one -which is activated by MinQPSetAlgoBLEIC function). Cholesky-based QP algo -won't be able to deal with sparse quadratic term and will terminate -abnormally. - -IF YOU CALLED THIS FUNCTION, YOU MUST SWITCH TO BLEIC-BASED QP ALGORITHM -BEFORE CALLING MINQPOPTIMIZE() FUNCTION. - -IMPORTANT: - -This solver minimizes following function: - f(x) = 0.5*x'*A*x + b'*x. -Note that quadratic term has 0.5 before it. So if you want to minimize - f(x) = x^2 + x -you should rewrite your problem as follows: - f(x) = 0.5*(2*x^2) + x -and your matrix A will be equal to [[2.0]], not to [[1.0]] - -INPUT PARAMETERS: - State - structure which stores algorithm state - A - matrix, array[N,N] - IsUpper - (optional) storage type: - * if True, symmetric matrix A is given by its upper - triangle, and the lower triangle isn’t used - * if False, symmetric matrix A is given by its lower - triangle, and the upper triangle isn’t used - * if not given, both lower and upper triangles must be - filled. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetquadratictermsparse(const minqpstate &state, const sparsematrix &a, const bool isupper); - - -/************************************************************************* -This function sets starting point for QP solver. It is useful to have -good initial approximation to the solution, because it will increase -speed of convergence and identification of active constraints. - -INPUT PARAMETERS: - State - structure which stores algorithm state - X - starting point, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetstartingpoint(const minqpstate &state, const real_1d_array &x); - - -/************************************************************************* -This function sets origin for QP solver. By default, following QP program -is solved: - - min(0.5*x'*A*x+b'*x) - -This function allows to solve different problem: - - min(0.5*(x-x_origin)'*A*(x-x_origin)+b'*(x-x_origin)) - -INPUT PARAMETERS: - State - structure which stores algorithm state - XOrigin - origin, array[N]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetorigin(const minqpstate &state, const real_1d_array &xorigin); - - -/************************************************************************* -This function sets scaling coefficients. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -BLEIC-based QP solver uses scale for two purposes: -* to evaluate stopping conditions -* for preconditioning of the underlying BLEIC solver - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetscale(const minqpstate &state, const real_1d_array &s); - - -/************************************************************************* -This function tells solver to use Cholesky-based algorithm. This algorithm -is active by default. - -DESCRIPTION: - -Cholesky-based algorithm can be used only for problems which: -* have dense quadratic term, set by MinQPSetQuadraticTerm(), sparse or - structured problems are not supported. -* are strictly convex, i.e. quadratic term is symmetric positive definite, - indefinite or semidefinite problems are not supported by this algorithm. - -If anything of what listed above is violated, you may use BLEIC-based QP -algorithm which can be activated by MinQPSetAlgoBLEIC(). - -BENEFITS AND DRAWBACKS: - -This algorithm gives best precision amongst all QP solvers provided by -ALGLIB (Newton iterations have much higher precision than any other -optimization algorithm). This solver also gracefully handles problems with -very large amount of constraints. - -Performance of the algorithm is good because internally it uses Level 3 -Dense BLAS for its performance-critical parts. - - -From the other side, algorithm has O(N^3) complexity for unconstrained -problems and up to orders of magnitude slower on constrained problems -(these additional iterations are needed to identify active constraints). -So, its running time depends on number of constraints active at solution. - -Furthermore, this algorithm can not solve problems with sparse matrices or -problems with semidefinite/indefinite matrices of any kind (dense/sparse). - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetalgocholesky(const minqpstate &state); - - -/************************************************************************* -This function tells solver to use BLEIC-based algorithm and sets stopping -criteria for the algorithm. - -DESCRIPTION: - -BLEIC-based QP algorithm can be used for any kind of QP problems: -* problems with both dense and sparse quadratic terms -* problems with positive definite, semidefinite, indefinite terms - -BLEIC-based algorithm can solve even indefinite problems - as long as they -are bounded from below on the feasible set. Of course, global minimum is -found only for positive definite and semidefinite problems. As for -indefinite ones - only local minimum is found. - -BENEFITS AND DRAWBACKS: - -This algorithm can be used to solve both convex and indefinite QP problems -and it can utilize sparsity of the quadratic term (algorithm calculates -matrix-vector products, which can be performed efficiently in case of -sparse matrix). - -Algorithm has iteration cost, which (assuming fixed amount of non-boundary -linear constraints) linearly depends on problem size. Boundary constraints -does not significantly change iteration cost. - -Thus, it outperforms Cholesky-based QP algorithm (CQP) on high-dimensional -sparse problems with moderate amount of constraints. - - -From the other side, unlike CQP solver, this algorithm does NOT make use -of Level 3 Dense BLAS. Thus, its performance on dense problems is inferior -to that of CQP solver. - -Its precision is also inferior to that of CQP. CQP performs Newton steps -which are know to achieve very good precision. In many cases Newton step -leads us exactly to the solution. BLEIC-QP performs LBFGS steps, which are -good at detecting neighborhood of the solution, buy need many iterations -to find solution with 6 digits of precision. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if exploratory steepest - descent step on k+1-th iteration satisfies following - condition: |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - EpsX - >=0 - The subroutine finishes its work if exploratory steepest - descent step on k+1-th iteration satisfies following - condition: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - step vector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinQPSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsG=0, EpsF=0 and EpsX=0 and MaxIts=0 (simultaneously) will lead -to automatic stopping criterion selection (presently it is small step -length, but it may change in the future versions of ALGLIB). - -IT IS VERY IMPORTANT THAT YOU CALL MinQPSetScale() WHEN YOU USE THIS ALGO! - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetalgobleic(const minqpstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -This function sets boundary constraints for QP solver - -Boundary constraints are inactive by default (after initial creation). -After being set, they are preserved until explicitly turned off with -another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpsetbc(const minqpstate &state, const real_1d_array &bndl, const real_1d_array &bndu); - - -/************************************************************************* -This function sets linear constraints for QP optimizer. - -Linear constraints are inactive by default (after initial creation). - -INPUT PARAMETERS: - State - structure previously allocated with MinQPCreate call. - C - linear constraints, array[K,N+1]. - Each row of C represents one constraint, either equality - or inequality (see below): - * first N elements correspond to coefficients, - * last element corresponds to the right part. - All elements of C (including right part) must be finite. - CT - type of constraints, array[K]: - * if CT[i]>0, then I-th constraint is C[i,*]*x >= C[i,n+1] - * if CT[i]=0, then I-th constraint is C[i,*]*x = C[i,n+1] - * if CT[i]<0, then I-th constraint is C[i,*]*x <= C[i,n+1] - K - number of equality/inequality constraints, K>=0: - * if given, only leading K elements of C/CT are used - * if not given, automatically determined from sizes of C/CT - -NOTE 1: linear (non-bound) constraints are satisfied only approximately - - there always exists some minor violation (about 10^-10...10^-13) - due to numerical errors. - - -- ALGLIB -- - Copyright 19.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minqpsetlc(const minqpstate &state, const real_2d_array &c, const integer_1d_array &ct, const ae_int_t k); -void minqpsetlc(const minqpstate &state, const real_2d_array &c, const integer_1d_array &ct); - - -/************************************************************************* -This function solves quadratic programming problem. -You should call it after setting solver options with MinQPSet...() calls. - -INPUT PARAMETERS: - State - algorithm state - -You should use MinQPResults() function to access results after calls -to this function. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey. - Special thanks to Elvira Illarionova for important suggestions on - the linearly constrained QP algorithm. -*************************************************************************/ -void minqpoptimize(const minqpstate &state); - - -/************************************************************************* -QP solver results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution. - This array is allocated and initialized only when - Rep.TerminationType parameter is positive (success). - Rep - optimization report. You should check Rep.TerminationType, - which contains completion code, and you may check another - fields which contain another information about algorithm - functioning. - - Failure codes returned by algorithm are: - * -5 inappropriate solver was used: - * Cholesky solver for (semi)indefinite problems - * Cholesky solver for problems with sparse matrix - * -4 BLEIC-QP algorithm found unconstrained direction - of negative curvature (function is unbounded from - below even under constraints), no meaningful - minimum can be found. - * -3 inconsistent constraints (or maybe feasible point - is too hard to find). If you are sure that - constraints are feasible, try to restart optimizer - with better initial approximation. - - Completion codes specific for Cholesky algorithm: - * 4 successful completion - - Completion codes specific for BLEIC-based algorithm: - * 1 relative function improvement is no more than EpsF. - * 2 scaled step is no more than EpsX. - * 4 scaled gradient norm is no more than EpsG. - * 5 MaxIts steps was taken - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpresults(const minqpstate &state, real_1d_array &x, minqpreport &rep); - - -/************************************************************************* -QP results - -Buffered implementation of MinQPResults() which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 11.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minqpresultsbuf(const minqpstate &state, real_1d_array &x, minqpreport &rep); - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] and Jacobian of f[]. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function vector f[] at given point X -* function vector f[] and Jacobian of f[] (simultaneously) at given point - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() and jac() callbacks. -First one is used to calculate f[] at given point, second one calculates -f[] and Jacobian df[i]/dx[j]. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not provide Jacobian), but it -will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateVJ() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state); -void minlmcreatevj(const ae_int_t m, const real_1d_array &x, minlmstate &state); - - -/************************************************************************* - IMPROVED LEVENBERG-MARQUARDT METHOD FOR - NON-LINEAR LEAST SQUARES OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of function which is represented as -sum of squares: - F(x) = f[0]^2(x[0],...,x[n-1]) + ... + f[m-1]^2(x[0],...,x[n-1]) -using value of function vector f[] only. Finite differences are used to -calculate Jacobian. - - -REQUIREMENTS: -This algorithm will request following information during its operation: -* function vector f[] at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts fvec() callback. - -You can try to initialize MinLMState structure with VJ function and then -use incorrect version of MinLMOptimize() (for example, version which -works with general form function and does not accept function vector), but -it will lead to exception being thrown after first attempt to calculate -Jacobian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateV() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N/M but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - M - number of functions f[i] - X - initial solution, array[0..N-1] - DiffStep- differentiation step, >0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -See also MinLMIteration, MinLMResults. - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatev(const ae_int_t n, const ae_int_t m, const real_1d_array &x, const double diffstep, minlmstate &state); -void minlmcreatev(const ae_int_t m, const real_1d_array &x, const double diffstep, minlmstate &state); - - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE METHOD FOR NON-LINEAR OPTIMIZATION - -DESCRIPTION: -This function is used to find minimum of general form (not "sum-of- --squares") function - F = F(x[0], ..., x[n-1]) -using its gradient and Hessian. Levenberg-Marquardt modification with -L-BFGS pre-optimization and internal pre-conditioned L-BFGS optimization -after each Levenberg-Marquardt step is used. - - -REQUIREMENTS: -This algorithm will request following information during its operation: - -* function value F at given point X -* F and gradient G (simultaneously) at given point X -* F, G and Hessian H (simultaneously) at given point X - -There are several overloaded versions of MinLMOptimize() function which -correspond to different LM-like optimization algorithms provided by this -unit. You should choose version which accepts func(), grad() and hess() -function pointers. First pointer is used to calculate F at given point, -second one calculates F(x) and grad F(x), third one calculates F(x), -grad F(x), hess F(x). - -You can try to initialize MinLMState structure with FGH-function and then -use incorrect version of MinLMOptimize() (for example, version which does -not provide Hessian matrix), but it will lead to exception being thrown -after first attempt to calculate Hessian. - - -USAGE: -1. User initializes algorithm state with MinLMCreateFGH() call -2. User tunes solver parameters with MinLMSetCond(), MinLMSetStpMax() and - other functions -3. User calls MinLMOptimize() function which takes algorithm state and - pointers (delegates, etc.) to callback functions. -4. User calls MinLMResults() to get solution -5. Optionally, user may call MinLMRestartFrom() to solve another problem - with same N but another starting point and/or another function. - MinLMRestartFrom() allows to reuse already initialized structure. - - -INPUT PARAMETERS: - N - dimension, N>1 - * if given, only leading N elements of X are used - * if not given, automatically determined from size of X - X - initial solution, array[0..N-1] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -1. you may tune stopping conditions with MinLMSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use MinLMSetStpMax() function to bound algorithm's steps. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgh(const ae_int_t n, const real_1d_array &x, minlmstate &state); -void minlmcreatefgh(const real_1d_array &x, minlmstate &state); - - -/************************************************************************* -This function sets stopping conditions for Levenberg-Marquardt optimization -algorithm. - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsG - >=0 - The subroutine finishes its work if the condition - |v|=0 - The subroutine finishes its work if on k+1-th iteration - the condition |F(k+1)-F(k)|<=EpsF*max{|F(k)|,|F(k+1)|,1} - is satisfied. - EpsX - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition |v|<=EpsX is fulfilled, where: - * |.| means Euclidian norm - * v - scaled step vector, v[i]=dx[i]/s[i] - * dx - ste pvector, dx=X(k+1)-X(k) - * s - scaling coefficients set by MinLMSetScale() - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. Only Levenberg-Marquardt - iterations are counted (L-BFGS/CG iterations are NOT - counted because their cost is very low compared to that of - LM). - -Passing EpsG=0, EpsF=0, EpsX=0 and MaxIts=0 (simultaneously) will lead to -automatic stopping criterion selection (small EpsX). - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetcond(const minlmstate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinLMOptimize(). Both Levenberg-Marquardt and internal L-BFGS -iterations are reported. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetxrep(const minlmstate &state, const bool needxrep); - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when you optimize target function which contains exp() -or other fast growing functions, and optimization algorithm makes too -large steps which leads to overflow. This function allows us to reject -steps that are too large (and therefore expose us to the possible -overflow) without actually calculating function value at the x+stp*d. - -NOTE: non-zero StpMax leads to moderate performance degradation because -intermediate step of preconditioned L-BFGS optimization is incompatible -with limits on step size. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetstpmax(const minlmstate &state, const double stpmax); - - -/************************************************************************* -This function sets scaling coefficients for LM optimizer. - -ALGLIB optimizers use scaling matrices to test stopping conditions (step -size and gradient are scaled before comparison with tolerances). Scale of -the I-th variable is a translation invariant measure of: -a) "how large" the variable is -b) how large the step should be to make significant changes in the function - -Generally, scale is NOT considered to be a form of preconditioner. But LM -optimizer is unique in that it uses scaling matrix both in the stopping -condition tests and as Marquardt damping factor. - -Proper scaling is very important for the algorithm performance. It is less -important for the quality of results, but still has some influence (it is -easier to converge when variables are properly scaled, so premature -stopping is possible when very badly scalled variables are combined with -relaxed stopping conditions). - -INPUT PARAMETERS: - State - structure stores algorithm state - S - array[N], non-zero scaling coefficients - S[i] may be negative, sign doesn't matter. - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlmsetscale(const minlmstate &state, const real_1d_array &s); - - -/************************************************************************* -This function sets boundary constraints for LM optimizer - -Boundary constraints are inactive by default (after initial creation). -They are preserved until explicitly turned off with another SetBC() call. - -INPUT PARAMETERS: - State - structure stores algorithm state - BndL - lower bounds, array[N]. - If some (all) variables are unbounded, you may specify - very small number or -INF (latter is recommended because - it will allow solver to use better algorithm). - BndU - upper bounds, array[N]. - If some (all) variables are unbounded, you may specify - very large number or +INF (latter is recommended because - it will allow solver to use better algorithm). - -NOTE 1: it is possible to specify BndL[i]=BndU[i]. In this case I-th -variable will be "frozen" at X[i]=BndL[i]=BndU[i]. - -NOTE 2: this solver has following useful properties: -* bound constraints are always satisfied exactly -* function is evaluated only INSIDE area specified by bound constraints - or at its boundary - - -- ALGLIB -- - Copyright 14.01.2011 by Bochkanov Sergey -*************************************************************************/ -void minlmsetbc(const minlmstate &state, const real_1d_array &bndl, const real_1d_array &bndu); - - -/************************************************************************* -This function is used to change acceleration settings - -You can choose between three acceleration strategies: -* AccType=0, no acceleration. -* AccType=1, secant updates are used to update quadratic model after each - iteration. After fixed number of iterations (or after model breakdown) - we recalculate quadratic model using analytic Jacobian or finite - differences. Number of secant-based iterations depends on optimization - settings: about 3 iterations - when we have analytic Jacobian, up to 2*N - iterations - when we use finite differences to calculate Jacobian. - -AccType=1 is recommended when Jacobian calculation cost is prohibitive -high (several Mx1 function vector calculations followed by several NxN -Cholesky factorizations are faster than calculation of one M*N Jacobian). -It should also be used when we have no Jacobian, because finite difference -approximation takes too much time to compute. - -Table below list optimization protocols (XYZ protocol corresponds to -MinLMCreateXYZ) and acceleration types they support (and use by default). - -ACCELERATION TYPES SUPPORTED BY OPTIMIZATION PROTOCOLS: - -protocol 0 1 comment -V + + -VJ + + -FGH + - -DAFAULT VALUES: - -protocol 0 1 comment -V x without acceleration it is so slooooooooow -VJ x -FGH x - -NOTE: this function should be called before optimization. Attempt to call -it during algorithm iterations may result in unexpected behavior. - -NOTE: attempt to call this function with unsupported protocol/acceleration -combination will result in exception being thrown. - - -- ALGLIB -- - Copyright 14.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmsetacctype(const minlmstate &state, const ae_int_t acctype); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minlmiteration(const minlmstate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear optimizer - -These functions accept following parameters: - state - algorithm state - func - callback which calculates function (or merit function) - value func at given point x - grad - callback which calculates function (or merit function) - value func and gradient grad at given point x - hess - callback which calculates function (or merit function) - value func, gradient grad and Hessian hess at given point x - fvec - callback which calculates function vector fi[] - at given point x - jac - callback which calculates function vector fi[] - and Jacobian jac at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - -NOTES: - -1. Depending on function used to create state structure, this algorithm - may accept Jacobian and/or Hessian and/or gradient. According to the - said above, there ase several versions of this function, which accept - different sets of callbacks. - - This flexibility opens way to subtle errors - you may create state with - MinLMCreateFGH() (optimization using Hessian), but call function which - does not accept Hessian. So when algorithm will request Hessian, there - will be no callback to call. In this case exception will be thrown. - - Be careful to avoid such errors because there is no way to find them at - compile time - you can see them at runtime only. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey - -*************************************************************************/ -void minlmoptimize(minlmstate &state, - void (*fvec)(const real_1d_array &x, real_1d_array &fi, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void minlmoptimize(minlmstate &state, - void (*fvec)(const real_1d_array &x, real_1d_array &fi, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void minlmoptimize(minlmstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*hess)(const real_1d_array &x, double &func, real_1d_array &grad, real_2d_array &hess, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void minlmoptimize(minlmstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); -void minlmoptimize(minlmstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -Levenberg-Marquardt algorithm results - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report; - see comments for this structure for more info. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmresults(const minlmstate &state, real_1d_array &x, minlmreport &rep); - - -/************************************************************************* -Levenberg-Marquardt algorithm results - -Buffered implementation of MinLMResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 10.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmresultsbuf(const minlmstate &state, real_1d_array &x, minlmreport &rep); - - -/************************************************************************* -This subroutine restarts LM algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used for reverse communication previously - allocated with MinLMCreateXXX call. - X - new starting point. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minlmrestartfrom(const minlmstate &state, const real_1d_array &x); - - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateVJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatevgj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state); -void minlmcreatevgj(const ae_int_t m, const real_1d_array &x, minlmstate &state); - - -/************************************************************************* -This is obsolete function. - -Since ALGLIB 3.3 it is equivalent to MinLMCreateFJ(). - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefgj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state); -void minlmcreatefgj(const ae_int_t m, const real_1d_array &x, minlmstate &state); - - -/************************************************************************* -This function is considered obsolete since ALGLIB 3.1.0 and is present for -backward compatibility only. We recommend to use MinLMCreateVJ, which -provides similar, but more consistent and feature-rich interface. - - -- ALGLIB -- - Copyright 30.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minlmcreatefj(const ae_int_t n, const ae_int_t m, const real_1d_array &x, minlmstate &state); -void minlmcreatefj(const ae_int_t m, const real_1d_array &x, minlmstate &state); - - -/************************************************************************* -This subroutine turns on verification of the user-supplied analytic -gradient: -* user calls this subroutine before optimization begins -* MinLMOptimize() is called -* prior to actual optimization, for each function Fi and each component - of parameters being optimized X[j] algorithm performs following steps: - * two trial steps are made to X[j]-TestStep*S[j] and X[j]+TestStep*S[j], - where X[j] is j-th parameter and S[j] is a scale of j-th parameter - * if needed, steps are bounded with respect to constraints on X[] - * Fi(X) is evaluated at these trial points - * we perform one more evaluation in the middle point of the interval - * we build cubic model using function values and derivatives at trial - points and we compare its prediction with actual value in the middle - point - * in case difference between prediction and actual value is higher than - some predetermined threshold, algorithm stops with completion code -7; - Rep.VarIdx is set to index of the parameter with incorrect derivative, - Rep.FuncIdx is set to index of the function. -* after verification is over, algorithm proceeds to the actual optimization. - -NOTE 1: verification needs N (parameters count) Jacobian evaluations. It - is very costly and you should use it only for low dimensional - problems, when you want to be sure that you've correctly - calculated analytic derivatives. You should not use it in the - production code (unless you want to check derivatives provided - by some third party). - -NOTE 2: you should carefully choose TestStep. Value which is too large - (so large that function behaviour is significantly non-cubic) will - lead to false alarms. You may use different step for different - parameters by means of setting scale with MinLMSetScale(). - -NOTE 3: this function may lead to false positives. In case it reports that - I-th derivative was calculated incorrectly, you may decrease test - step and try one more time - maybe your function changes too - sharply and your step is too large for such rapidly chanding - function. - -INPUT PARAMETERS: - State - structure used to store algorithm state - TestStep - verification step: - * TestStep=0 turns verification off - * TestStep>0 activates verification - - -- ALGLIB -- - Copyright 15.06.2012 by Bochkanov Sergey -*************************************************************************/ -void minlmsetgradientcheck(const minlmstate &state, const double teststep); - -/************************************************************************* -Obsolete function, use MinLBFGSSetPrecDefault() instead. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetdefaultpreconditioner(const minlbfgsstate &state); - - -/************************************************************************* -Obsolete function, use MinLBFGSSetCholeskyPreconditioner() instead. - - -- ALGLIB -- - Copyright 13.10.2010 by Bochkanov Sergey -*************************************************************************/ -void minlbfgssetcholeskypreconditioner(const minlbfgsstate &state, const real_2d_array &p, const bool isupper); - - -/************************************************************************* -This is obsolete function which was used by previous version of the BLEIC -optimizer. It does nothing in the current version of BLEIC. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbarrierwidth(const minbleicstate &state, const double mu); - - -/************************************************************************* -This is obsolete function which was used by previous version of the BLEIC -optimizer. It does nothing in the current version of BLEIC. - - -- ALGLIB -- - Copyright 28.11.2010 by Bochkanov Sergey -*************************************************************************/ -void minbleicsetbarrierdecay(const minbleicstate &state, const double mudecay); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 25.03.2010 by Bochkanov Sergey -*************************************************************************/ -void minasacreate(const ae_int_t n, const real_1d_array &x, const real_1d_array &bndl, const real_1d_array &bndu, minasastate &state); -void minasacreate(const real_1d_array &x, const real_1d_array &bndl, const real_1d_array &bndu, minasastate &state); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetcond(const minasastate &state, const double epsg, const double epsf, const double epsx, const ae_int_t maxits); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetxrep(const minasastate &state, const bool needxrep); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetalgorithm(const minasastate &state, const ae_int_t algotype); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 02.04.2010 by Bochkanov Sergey -*************************************************************************/ -void minasasetstpmax(const minasastate &state, const double stpmax); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool minasaiteration(const minasastate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear optimizer - -These functions accept following parameters: - state - algorithm state - grad - callback which calculates function (or merit function) - value func and gradient grad at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey - -*************************************************************************/ -void minasaoptimize(minasastate &state, - void (*grad)(const real_1d_array &x, double &func, real_1d_array &grad, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minasaresults(const minasastate &state, real_1d_array &x, minasareport &rep); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -void minasaresultsbuf(const minasastate &state, real_1d_array &x, minasareport &rep); - - -/************************************************************************* -Obsolete optimization algorithm. -Was replaced by MinBLEIC subpackage. - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void minasarestartfrom(const minasastate &state, const real_1d_array &x, const real_1d_array &bndl, const real_1d_array &bndu); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void trimprepare(double f, double* threshold, ae_state *_state); -void trimfunction(double* f, - /* Real */ ae_vector* g, - ae_int_t n, - double threshold, - ae_state *_state); -ae_bool enforceboundaryconstraints(/* Real */ ae_vector* x, - /* Real */ ae_vector* bl, - /* Boolean */ ae_vector* havebl, - /* Real */ ae_vector* bu, - /* Boolean */ ae_vector* havebu, - ae_int_t nmain, - ae_int_t nslack, - ae_state *_state); -void projectgradientintobc(/* Real */ ae_vector* x, - /* Real */ ae_vector* g, - /* Real */ ae_vector* bl, - /* Boolean */ ae_vector* havebl, - /* Real */ ae_vector* bu, - /* Boolean */ ae_vector* havebu, - ae_int_t nmain, - ae_int_t nslack, - ae_state *_state); -void calculatestepbound(/* Real */ ae_vector* x, - /* Real */ ae_vector* d, - double alpha, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - ae_int_t* variabletofreeze, - double* valuetofreeze, - double* maxsteplen, - ae_state *_state); -ae_int_t postprocessboundedstep(/* Real */ ae_vector* x, - /* Real */ ae_vector* xprev, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - ae_int_t variabletofreeze, - double valuetofreeze, - double steptaken, - double maxsteplen, - ae_state *_state); -void filterdirection(/* Real */ ae_vector* d, - /* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - /* Real */ ae_vector* s, - ae_int_t nmain, - ae_int_t nslack, - double droptol, - ae_state *_state); -ae_int_t numberofchangedconstraints(/* Real */ ae_vector* x, - /* Real */ ae_vector* xprev, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - ae_state *_state); -ae_bool findfeasiblepoint(/* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Boolean */ ae_vector* havebndl, - /* Real */ ae_vector* bndu, - /* Boolean */ ae_vector* havebndu, - ae_int_t nmain, - ae_int_t nslack, - /* Real */ ae_matrix* ce, - ae_int_t k, - double epsi, - ae_int_t* qpits, - ae_int_t* gpaits, - ae_state *_state); -ae_bool derivativecheck(double f0, - double df0, - double f1, - double df1, - double f, - double df, - double width, - ae_state *_state); -void cqminit(ae_int_t n, convexquadraticmodel* s, ae_state *_state); -void cqmseta(convexquadraticmodel* s, - /* Real */ ae_matrix* a, - ae_bool isupper, - double alpha, - ae_state *_state); -void cqmrewritedensediagonal(convexquadraticmodel* s, - /* Real */ ae_vector* z, - ae_state *_state); -void cqmsetd(convexquadraticmodel* s, - /* Real */ ae_vector* d, - double tau, - ae_state *_state); -void cqmdropa(convexquadraticmodel* s, ae_state *_state); -void cqmsetb(convexquadraticmodel* s, - /* Real */ ae_vector* b, - ae_state *_state); -void cqmsetq(convexquadraticmodel* s, - /* Real */ ae_matrix* q, - /* Real */ ae_vector* r, - ae_int_t k, - double theta, - ae_state *_state); -void cqmsetactiveset(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Boolean */ ae_vector* activeset, - ae_state *_state); -double cqmeval(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state); -void cqmevalx(convexquadraticmodel* s, - /* Real */ ae_vector* x, - double* r, - double* noise, - ae_state *_state); -void cqmgradunconstrained(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* g, - ae_state *_state); -double cqmxtadx2(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state); -void cqmadx(convexquadraticmodel* s, - /* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_state *_state); -ae_bool cqmconstrainedoptimum(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state); -void cqmscalevector(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state); -double cqmdebugconstrainedevalt(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state); -double cqmdebugconstrainedevale(convexquadraticmodel* s, - /* Real */ ae_vector* x, - ae_state *_state); -ae_bool _convexquadraticmodel_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _convexquadraticmodel_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _convexquadraticmodel_clear(void* _p); -void _convexquadraticmodel_destroy(void* _p); -void snnlsinit(ae_int_t nsmax, - ae_int_t ndmax, - ae_int_t nrmax, - snnlssolver* s, - ae_state *_state); -void snnlssetproblem(snnlssolver* s, - /* Real */ ae_matrix* a, - /* Real */ ae_vector* b, - ae_int_t ns, - ae_int_t nd, - ae_int_t nr, - ae_state *_state); -void snnlsdropnnc(snnlssolver* s, ae_int_t idx, ae_state *_state); -void snnlssolve(snnlssolver* s, - /* Real */ ae_vector* x, - ae_state *_state); -ae_bool _snnlssolver_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _snnlssolver_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _snnlssolver_clear(void* _p); -void _snnlssolver_destroy(void* _p); -void sasinit(ae_int_t n, sactiveset* s, ae_state *_state); -void sassetscale(sactiveset* state, - /* Real */ ae_vector* s, - ae_state *_state); -void sassetprecdiag(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state); -void sassetbc(sactiveset* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state); -void sassetlc(sactiveset* state, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state); -void sassetlcx(sactiveset* state, - /* Real */ ae_matrix* cleic, - ae_int_t nec, - ae_int_t nic, - ae_state *_state); -ae_bool sasstartoptimization(sactiveset* state, - /* Real */ ae_vector* x, - ae_state *_state); -void sasexploredirection(sactiveset* state, - /* Real */ ae_vector* d, - double* stpmax, - ae_int_t* cidx, - double* vval, - ae_state *_state); -ae_int_t sasmoveto(sactiveset* state, - /* Real */ ae_vector* xn, - ae_bool needact, - ae_int_t cidx, - double cval, - ae_state *_state); -void sasimmediateactivation(sactiveset* state, - ae_int_t cidx, - double cval, - ae_state *_state); -void sasconstraineddescent(sactiveset* state, - /* Real */ ae_vector* g, - /* Real */ ae_vector* d, - ae_state *_state); -void sasconstraineddescentprec(sactiveset* state, - /* Real */ ae_vector* g, - /* Real */ ae_vector* d, - ae_state *_state); -void sasconstraineddirection(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state); -void sasconstraineddirectionprec(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state); -void sascorrection(sactiveset* state, - /* Real */ ae_vector* x, - double* penalty, - ae_state *_state); -double sasactivelcpenalty1(sactiveset* state, - /* Real */ ae_vector* x, - ae_state *_state); -double sasscaledconstrainednorm(sactiveset* state, - /* Real */ ae_vector* d, - ae_state *_state); -void sasstopoptimization(sactiveset* state, ae_state *_state); -void sasreactivateconstraints(sactiveset* state, - /* Real */ ae_vector* gc, - ae_state *_state); -void sasreactivateconstraintsprec(sactiveset* state, - /* Real */ ae_vector* gc, - ae_state *_state); -void sasrebuildbasis(sactiveset* state, ae_state *_state); -ae_bool _sactiveset_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _sactiveset_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _sactiveset_clear(void* _p); -void _sactiveset_destroy(void* _p); -void mincgcreate(ae_int_t n, - /* Real */ ae_vector* x, - mincgstate* state, - ae_state *_state); -void mincgcreatef(ae_int_t n, - /* Real */ ae_vector* x, - double diffstep, - mincgstate* state, - ae_state *_state); -void mincgsetcond(mincgstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void mincgsetscale(mincgstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void mincgsetxrep(mincgstate* state, ae_bool needxrep, ae_state *_state); -void mincgsetdrep(mincgstate* state, ae_bool needdrep, ae_state *_state); -void mincgsetcgtype(mincgstate* state, ae_int_t cgtype, ae_state *_state); -void mincgsetstpmax(mincgstate* state, double stpmax, ae_state *_state); -void mincgsuggeststep(mincgstate* state, double stp, ae_state *_state); -void mincgsetprecdefault(mincgstate* state, ae_state *_state); -void mincgsetprecdiag(mincgstate* state, - /* Real */ ae_vector* d, - ae_state *_state); -void mincgsetprecscale(mincgstate* state, ae_state *_state); -ae_bool mincgiteration(mincgstate* state, ae_state *_state); -void mincgresults(mincgstate* state, - /* Real */ ae_vector* x, - mincgreport* rep, - ae_state *_state); -void mincgresultsbuf(mincgstate* state, - /* Real */ ae_vector* x, - mincgreport* rep, - ae_state *_state); -void mincgrestartfrom(mincgstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void mincgsetprecdiagfast(mincgstate* state, - /* Real */ ae_vector* d, - ae_state *_state); -void mincgsetpreclowrankfast(mincgstate* state, - /* Real */ ae_vector* d1, - /* Real */ ae_vector* c, - /* Real */ ae_matrix* v, - ae_int_t vcnt, - ae_state *_state); -void mincgsetprecvarpart(mincgstate* state, - /* Real */ ae_vector* d2, - ae_state *_state); -void mincgsetgradientcheck(mincgstate* state, - double teststep, - ae_state *_state); -ae_bool _mincgstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mincgstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mincgstate_clear(void* _p); -void _mincgstate_destroy(void* _p); -ae_bool _mincgreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _mincgreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _mincgreport_clear(void* _p); -void _mincgreport_destroy(void* _p); -void minbleiccreate(ae_int_t n, - /* Real */ ae_vector* x, - minbleicstate* state, - ae_state *_state); -void minbleiccreatef(ae_int_t n, - /* Real */ ae_vector* x, - double diffstep, - minbleicstate* state, - ae_state *_state); -void minbleicsetbc(minbleicstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state); -void minbleicsetlc(minbleicstate* state, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state); -void minbleicsetcond(minbleicstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void minbleicsetscale(minbleicstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void minbleicsetprecdefault(minbleicstate* state, ae_state *_state); -void minbleicsetprecdiag(minbleicstate* state, - /* Real */ ae_vector* d, - ae_state *_state); -void minbleicsetprecscale(minbleicstate* state, ae_state *_state); -void minbleicsetxrep(minbleicstate* state, - ae_bool needxrep, - ae_state *_state); -void minbleicsetdrep(minbleicstate* state, - ae_bool needdrep, - ae_state *_state); -void minbleicsetstpmax(minbleicstate* state, - double stpmax, - ae_state *_state); -ae_bool minbleiciteration(minbleicstate* state, ae_state *_state); -void minbleicresults(minbleicstate* state, - /* Real */ ae_vector* x, - minbleicreport* rep, - ae_state *_state); -void minbleicresultsbuf(minbleicstate* state, - /* Real */ ae_vector* x, - minbleicreport* rep, - ae_state *_state); -void minbleicrestartfrom(minbleicstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void minbleicemergencytermination(minbleicstate* state, ae_state *_state); -void minbleicsetgradientcheck(minbleicstate* state, - double teststep, - ae_state *_state); -ae_bool _minbleicstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minbleicstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minbleicstate_clear(void* _p); -void _minbleicstate_destroy(void* _p); -ae_bool _minbleicreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minbleicreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minbleicreport_clear(void* _p); -void _minbleicreport_destroy(void* _p); -void minlbfgscreate(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlbfgsstate* state, - ae_state *_state); -void minlbfgscreatef(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - double diffstep, - minlbfgsstate* state, - ae_state *_state); -void minlbfgssetcond(minlbfgsstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void minlbfgssetxrep(minlbfgsstate* state, - ae_bool needxrep, - ae_state *_state); -void minlbfgssetstpmax(minlbfgsstate* state, - double stpmax, - ae_state *_state); -void minlbfgssetscale(minlbfgsstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void minlbfgscreatex(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - ae_int_t flags, - double diffstep, - minlbfgsstate* state, - ae_state *_state); -void minlbfgssetprecdefault(minlbfgsstate* state, ae_state *_state); -void minlbfgssetpreccholesky(minlbfgsstate* state, - /* Real */ ae_matrix* p, - ae_bool isupper, - ae_state *_state); -void minlbfgssetprecdiag(minlbfgsstate* state, - /* Real */ ae_vector* d, - ae_state *_state); -void minlbfgssetprecscale(minlbfgsstate* state, ae_state *_state); -ae_bool minlbfgsiteration(minlbfgsstate* state, ae_state *_state); -void minlbfgsresults(minlbfgsstate* state, - /* Real */ ae_vector* x, - minlbfgsreport* rep, - ae_state *_state); -void minlbfgsresultsbuf(minlbfgsstate* state, - /* Real */ ae_vector* x, - minlbfgsreport* rep, - ae_state *_state); -void minlbfgsrestartfrom(minlbfgsstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void minlbfgssetgradientcheck(minlbfgsstate* state, - double teststep, - ae_state *_state); -ae_bool _minlbfgsstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minlbfgsstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minlbfgsstate_clear(void* _p); -void _minlbfgsstate_destroy(void* _p); -ae_bool _minlbfgsreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minlbfgsreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minlbfgsreport_clear(void* _p); -void _minlbfgsreport_destroy(void* _p); -void minqpcreate(ae_int_t n, minqpstate* state, ae_state *_state); -void minqpsetlinearterm(minqpstate* state, - /* Real */ ae_vector* b, - ae_state *_state); -void minqpsetquadraticterm(minqpstate* state, - /* Real */ ae_matrix* a, - ae_bool isupper, - ae_state *_state); -void minqpsetquadratictermsparse(minqpstate* state, - sparsematrix* a, - ae_bool isupper, - ae_state *_state); -void minqpsetstartingpoint(minqpstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void minqpsetorigin(minqpstate* state, - /* Real */ ae_vector* xorigin, - ae_state *_state); -void minqpsetscale(minqpstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void minqpsetalgocholesky(minqpstate* state, ae_state *_state); -void minqpsetalgobleic(minqpstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void minqpsetbc(minqpstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state); -void minqpsetlc(minqpstate* state, - /* Real */ ae_matrix* c, - /* Integer */ ae_vector* ct, - ae_int_t k, - ae_state *_state); -void minqpoptimize(minqpstate* state, ae_state *_state); -void minqpresults(minqpstate* state, - /* Real */ ae_vector* x, - minqpreport* rep, - ae_state *_state); -void minqpresultsbuf(minqpstate* state, - /* Real */ ae_vector* x, - minqpreport* rep, - ae_state *_state); -void minqpsetlineartermfast(minqpstate* state, - /* Real */ ae_vector* b, - ae_state *_state); -void minqpsetquadratictermfast(minqpstate* state, - /* Real */ ae_matrix* a, - ae_bool isupper, - double s, - ae_state *_state); -void minqprewritediagonal(minqpstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void minqpsetstartingpointfast(minqpstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void minqpsetoriginfast(minqpstate* state, - /* Real */ ae_vector* xorigin, - ae_state *_state); -ae_bool _minqpstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minqpstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minqpstate_clear(void* _p); -void _minqpstate_destroy(void* _p); -ae_bool _minqpreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minqpreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minqpreport_clear(void* _p); -void _minqpreport_destroy(void* _p); -void minlmcreatevj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state); -void minlmcreatev(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - double diffstep, - minlmstate* state, - ae_state *_state); -void minlmcreatefgh(ae_int_t n, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state); -void minlmsetcond(minlmstate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void minlmsetxrep(minlmstate* state, ae_bool needxrep, ae_state *_state); -void minlmsetstpmax(minlmstate* state, double stpmax, ae_state *_state); -void minlmsetscale(minlmstate* state, - /* Real */ ae_vector* s, - ae_state *_state); -void minlmsetbc(minlmstate* state, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state); -void minlmsetacctype(minlmstate* state, - ae_int_t acctype, - ae_state *_state); -ae_bool minlmiteration(minlmstate* state, ae_state *_state); -void minlmresults(minlmstate* state, - /* Real */ ae_vector* x, - minlmreport* rep, - ae_state *_state); -void minlmresultsbuf(minlmstate* state, - /* Real */ ae_vector* x, - minlmreport* rep, - ae_state *_state); -void minlmrestartfrom(minlmstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void minlmcreatevgj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state); -void minlmcreatefgj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state); -void minlmcreatefj(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - minlmstate* state, - ae_state *_state); -void minlmsetgradientcheck(minlmstate* state, - double teststep, - ae_state *_state); -ae_bool _minlmstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minlmstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minlmstate_clear(void* _p); -void _minlmstate_destroy(void* _p); -ae_bool _minlmreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minlmreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minlmreport_clear(void* _p); -void _minlmreport_destroy(void* _p); -void minlbfgssetdefaultpreconditioner(minlbfgsstate* state, - ae_state *_state); -void minlbfgssetcholeskypreconditioner(minlbfgsstate* state, - /* Real */ ae_matrix* p, - ae_bool isupper, - ae_state *_state); -void minbleicsetbarrierwidth(minbleicstate* state, - double mu, - ae_state *_state); -void minbleicsetbarrierdecay(minbleicstate* state, - double mudecay, - ae_state *_state); -void minasacreate(ae_int_t n, - /* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - minasastate* state, - ae_state *_state); -void minasasetcond(minasastate* state, - double epsg, - double epsf, - double epsx, - ae_int_t maxits, - ae_state *_state); -void minasasetxrep(minasastate* state, ae_bool needxrep, ae_state *_state); -void minasasetalgorithm(minasastate* state, - ae_int_t algotype, - ae_state *_state); -void minasasetstpmax(minasastate* state, double stpmax, ae_state *_state); -ae_bool minasaiteration(minasastate* state, ae_state *_state); -void minasaresults(minasastate* state, - /* Real */ ae_vector* x, - minasareport* rep, - ae_state *_state); -void minasaresultsbuf(minasastate* state, - /* Real */ ae_vector* x, - minasareport* rep, - ae_state *_state); -void minasarestartfrom(minasastate* state, - /* Real */ ae_vector* x, - /* Real */ ae_vector* bndl, - /* Real */ ae_vector* bndu, - ae_state *_state); -ae_bool _minasastate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minasastate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minasastate_clear(void* _p); -void _minasastate_destroy(void* _p); -ae_bool _minasareport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _minasareport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _minasareport_clear(void* _p); -void _minasareport_destroy(void* _p); - -} -#endif - diff --git a/src/inc/alglib/solvers.cpp b/src/inc/alglib/solvers.cpp deleted file mode 100644 index f1632cd..0000000 --- a/src/inc/alglib/solvers.cpp +++ /dev/null @@ -1,8709 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "solvers.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* - -*************************************************************************/ -_densesolverreport_owner::_densesolverreport_owner() -{ - p_struct = (alglib_impl::densesolverreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::densesolverreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_densesolverreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_densesolverreport_owner::_densesolverreport_owner(const _densesolverreport_owner &rhs) -{ - p_struct = (alglib_impl::densesolverreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::densesolverreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_densesolverreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_densesolverreport_owner& _densesolverreport_owner::operator=(const _densesolverreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_densesolverreport_clear(p_struct); - if( !alglib_impl::_densesolverreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_densesolverreport_owner::~_densesolverreport_owner() -{ - alglib_impl::_densesolverreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::densesolverreport* _densesolverreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::densesolverreport* _densesolverreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -densesolverreport::densesolverreport() : _densesolverreport_owner() ,r1(p_struct->r1),rinf(p_struct->rinf) -{ -} - -densesolverreport::densesolverreport(const densesolverreport &rhs):_densesolverreport_owner(rhs) ,r1(p_struct->r1),rinf(p_struct->rinf) -{ -} - -densesolverreport& densesolverreport::operator=(const densesolverreport &rhs) -{ - if( this==&rhs ) - return *this; - _densesolverreport_owner::operator=(rhs); - return *this; -} - -densesolverreport::~densesolverreport() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_densesolverlsreport_owner::_densesolverlsreport_owner() -{ - p_struct = (alglib_impl::densesolverlsreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::densesolverlsreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_densesolverlsreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_densesolverlsreport_owner::_densesolverlsreport_owner(const _densesolverlsreport_owner &rhs) -{ - p_struct = (alglib_impl::densesolverlsreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::densesolverlsreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_densesolverlsreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_densesolverlsreport_owner& _densesolverlsreport_owner::operator=(const _densesolverlsreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_densesolverlsreport_clear(p_struct); - if( !alglib_impl::_densesolverlsreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_densesolverlsreport_owner::~_densesolverlsreport_owner() -{ - alglib_impl::_densesolverlsreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::densesolverlsreport* _densesolverlsreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::densesolverlsreport* _densesolverlsreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -densesolverlsreport::densesolverlsreport() : _densesolverlsreport_owner() ,r2(p_struct->r2),cx(&p_struct->cx),n(p_struct->n),k(p_struct->k) -{ -} - -densesolverlsreport::densesolverlsreport(const densesolverlsreport &rhs):_densesolverlsreport_owner(rhs) ,r2(p_struct->r2),cx(&p_struct->cx),n(p_struct->n),k(p_struct->k) -{ -} - -densesolverlsreport& densesolverlsreport::operator=(const densesolverlsreport &rhs) -{ - if( this==&rhs ) - return *this; - _densesolverlsreport_owner::operator=(rhs); - return *this; -} - -densesolverlsreport::~densesolverlsreport() -{ -} - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*x=b, where A is NxN non-denegerate -real matrix, x and b are vectors. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - return code: - * -3 A is singular, or VERY close to singular. - X is filled by zeros in such cases. - * -1 N<=0 was passed - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - X - array[0..N-1], it contains: - * solution of A*x=b if A is non-singular (well-conditioned - or ill-conditioned, but not very close to singular) - * zeros, if A is singular or VERY close to singular - (in this case Info=-3). - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolve(const real_2d_array &a, const ae_int_t n, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixsolve(const_cast(a.c_ptr()), n, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. - -Similar to RMatrixSolve() but solves task with multiple right parts (where -b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* optional iterative refinement -* O(N^3+M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - RFS - iterative refinement switch: - * True - refinement is used. - Less performance, more precision. - * False - refinement is not used. - More performance, less precision. - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolvem(const real_2d_array &a, const ae_int_t n, const real_2d_array &b, const ae_int_t m, const bool rfs, ae_int_t &info, densesolverreport &rep, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixsolvem(const_cast(a.c_ptr()), n, const_cast(b.c_ptr()), m, rfs, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*X=B, where A is NxN non-denegerate -real matrix given by its LU decomposition, X and B are NxM real matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixlusolve(const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlusolve(const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. - -Similar to RMatrixLUSolve() but solves task with multiple right parts -(where b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixlusolvem(const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixlusolvem(const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*x=b, where BOTH ORIGINAL A AND ITS -LU DECOMPOSITION ARE KNOWN. You can use it if for some reasons you have -both A and its LU decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixmixedsolve(const real_2d_array &a, const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixmixedsolve(const_cast(a.c_ptr()), const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. - -Similar to RMatrixMixedSolve() but solves task with multiple right parts -(where b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixmixedsolvem(const real_2d_array &a, const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixmixedsolvem(const_cast(a.c_ptr()), const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3+M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - RFS - iterative refinement switch: - * True - refinement is used. - Less performance, more precision. - * False - refinement is not used. - More performance, less precision. - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixsolvem(const complex_2d_array &a, const ae_int_t n, const complex_2d_array &b, const ae_int_t m, const bool rfs, ae_int_t &info, densesolverreport &rep, complex_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixsolvem(const_cast(a.c_ptr()), n, const_cast(b.c_ptr()), m, rfs, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixsolve(const complex_2d_array &a, const ae_int_t n, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixsolve(const_cast(a.c_ptr()), n, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use CMatrixSolve or CMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixlusolvem(const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlusolvem(const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use CMatrixSolve or CMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixlusolve(const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixlusolve(const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixMixedSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixmixedsolvem(const complex_2d_array &a, const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixmixedsolvem(const_cast(a.c_ptr()), const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixMixedSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixmixedsolve(const complex_2d_array &a, const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::cmatrixmixedsolve(const_cast(a.c_ptr()), const_cast(lua.c_ptr()), const_cast(p.c_ptr()), n, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for symmetric positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3+M*N^2) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve. - Returns -3 for non-SPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixsolvem(const real_2d_array &a, const ae_int_t n, const bool isupper, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixsolvem(const_cast(a.c_ptr()), n, isupper, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for SPD matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Returns -3 for non-SPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixsolve(const real_2d_array &a, const ae_int_t n, const bool isupper, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixsolve(const_cast(a.c_ptr()), n, isupper, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for SPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of CHA - IsUpper - what half of CHA is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskysolvem(const real_2d_array &cha, const ae_int_t n, const bool isupper, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixcholeskysolvem(const_cast(cha.c_ptr()), n, isupper, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for SPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of A - IsUpper - what half of CHA is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskysolve(const real_2d_array &cha, const ae_int_t n, const bool isupper, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spdmatrixcholeskysolve(const_cast(cha.c_ptr()), n, isupper, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for Hermitian positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3+M*N^2) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve. - Returns -3 for non-HPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixsolvem(const complex_2d_array &a, const ae_int_t n, const bool isupper, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixsolvem(const_cast(a.c_ptr()), n, isupper, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for Hermitian positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Returns -3 for non-HPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixsolve(const complex_2d_array &a, const ae_int_t n, const bool isupper, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixsolve(const_cast(a.c_ptr()), n, isupper, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for HPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - HPDMatrixCholesky result - N - size of CHA - IsUpper - what half of CHA is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskysolvem(const complex_2d_array &cha, const ae_int_t n, const bool isupper, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixcholeskysolvem(const_cast(cha.c_ptr()), n, isupper, const_cast(b.c_ptr()), m, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for HPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of A - IsUpper - what half of CHA is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskysolve(const complex_2d_array &cha, const ae_int_t n, const bool isupper, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hpdmatrixcholeskysolve(const_cast(cha.c_ptr()), n, isupper, const_cast(b.c_ptr()), &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dense solver. - -This subroutine finds solution of the linear system A*X=B with non-square, -possibly degenerate A. System is solved in the least squares sense, and -general least squares solution X = X0 + CX*y which minimizes |A*X-B| is -returned. If A is non-degenerate, solution in the usual sense is returned. - -Algorithm features: -* automatic detection (and correct handling!) of degenerate cases -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..NRows-1,0..NCols-1], system matrix - NRows - vertical size of A - NCols - horizontal size of A - B - array[0..NCols-1], right part - Threshold- a number in [0,1]. Singular values beyond Threshold are - considered zero. Set it to 0.0, if you don't understand - what it means, so the solver will choose good value on its - own. - -OUTPUT PARAMETERS - Info - return code: - * -4 SVD subroutine failed - * -1 if NRows<=0 or NCols<=0 or Threshold<0 was passed - * 1 if task is solved - Rep - solver report, see below for more info - X - array[0..N-1,0..M-1], it contains: - * solution of A*X=B (even for singular A) - * zeros, if SVD subroutine failed - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R2 reciprocal of condition number: 1/cond(A), 2-norm. -* N = NCols -* K dim(Null(A)) -* CX array[0..N-1,0..K-1], kernel of A. - Columns of CX store such vectors that A*CX[i]=0. - - -- ALGLIB -- - Copyright 24.08.2009 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolvels(const real_2d_array &a, const ae_int_t nrows, const ae_int_t ncols, const real_1d_array &b, const double threshold, ae_int_t &info, densesolverlsreport &rep, real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rmatrixsolvels(const_cast(a.c_ptr()), nrows, ncols, const_cast(b.c_ptr()), threshold, &info, const_cast(rep.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This object stores state of the LinLSQR method. - -You should use ALGLIB functions to work with this object. -*************************************************************************/ -_linlsqrstate_owner::_linlsqrstate_owner() -{ - p_struct = (alglib_impl::linlsqrstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::linlsqrstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_linlsqrstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_linlsqrstate_owner::_linlsqrstate_owner(const _linlsqrstate_owner &rhs) -{ - p_struct = (alglib_impl::linlsqrstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::linlsqrstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_linlsqrstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_linlsqrstate_owner& _linlsqrstate_owner::operator=(const _linlsqrstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_linlsqrstate_clear(p_struct); - if( !alglib_impl::_linlsqrstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_linlsqrstate_owner::~_linlsqrstate_owner() -{ - alglib_impl::_linlsqrstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::linlsqrstate* _linlsqrstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::linlsqrstate* _linlsqrstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -linlsqrstate::linlsqrstate() : _linlsqrstate_owner() -{ -} - -linlsqrstate::linlsqrstate(const linlsqrstate &rhs):_linlsqrstate_owner(rhs) -{ -} - -linlsqrstate& linlsqrstate::operator=(const linlsqrstate &rhs) -{ - if( this==&rhs ) - return *this; - _linlsqrstate_owner::operator=(rhs); - return *this; -} - -linlsqrstate::~linlsqrstate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_linlsqrreport_owner::_linlsqrreport_owner() -{ - p_struct = (alglib_impl::linlsqrreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::linlsqrreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_linlsqrreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_linlsqrreport_owner::_linlsqrreport_owner(const _linlsqrreport_owner &rhs) -{ - p_struct = (alglib_impl::linlsqrreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::linlsqrreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_linlsqrreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_linlsqrreport_owner& _linlsqrreport_owner::operator=(const _linlsqrreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_linlsqrreport_clear(p_struct); - if( !alglib_impl::_linlsqrreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_linlsqrreport_owner::~_linlsqrreport_owner() -{ - alglib_impl::_linlsqrreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::linlsqrreport* _linlsqrreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::linlsqrreport* _linlsqrreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -linlsqrreport::linlsqrreport() : _linlsqrreport_owner() ,iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype) -{ -} - -linlsqrreport::linlsqrreport(const linlsqrreport &rhs):_linlsqrreport_owner(rhs) ,iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype) -{ -} - -linlsqrreport& linlsqrreport::operator=(const linlsqrreport &rhs) -{ - if( this==&rhs ) - return *this; - _linlsqrreport_owner::operator=(rhs); - return *this; -} - -linlsqrreport::~linlsqrreport() -{ -} - -/************************************************************************* -This function initializes linear LSQR Solver. This solver is used to solve -non-symmetric (and, possibly, non-square) problems. Least squares solution -is returned for non-compatible systems. - -USAGE: -1. User initializes algorithm state with LinLSQRCreate() call -2. User tunes solver parameters with LinLSQRSetCond() and other functions -3. User calls LinLSQRSolveSparse() function which takes algorithm state - and SparseMatrix object. -4. User calls LinLSQRResults() to get solution -5. Optionally, user may call LinLSQRSolveSparse() again to solve another - problem with different matrix and/or right part without reinitializing - LinLSQRState structure. - -INPUT PARAMETERS: - M - number of rows in A - N - number of variables, N>0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrcreate(const ae_int_t m, const ae_int_t n, linlsqrstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrcreate(m, n, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function changes preconditioning settings of LinLSQQSolveSparse() -function. By default, SolveSparse() uses diagonal preconditioner, but if -you want to use solver without preconditioning, you can call this function -which forces solver to use unit matrix for preconditioning. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetprecunit(const linlsqrstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrsetprecunit(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. LinCGSolveSparse() will use diagonal of the system matrix as -preconditioner. This preconditioning mode is active by default. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetprecdiag(const linlsqrstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrsetprecdiag(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets optional Tikhonov regularization coefficient. -It is zero by default. - -INPUT PARAMETERS: - LambdaI - regularization factor, LambdaI>=0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetlambdai(const linlsqrstate &state, const double lambdai) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrsetlambdai(const_cast(state.c_ptr()), lambdai, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procedure for solution of A*x=b with sparse A. - -INPUT PARAMETERS: - State - algorithm state - A - sparse M*N matrix in the CRS format (you MUST contvert it - to CRS format by calling SparseConvertToCRS() function - BEFORE you pass it to this function). - B - right part, array[M] - -RESULT: - This function returns no result. - You can get solution by calling LinCGResults() - -NOTE: this function uses lightweight preconditioning - multiplication by - inverse of diag(A). If you want, you can turn preconditioning off by - calling LinLSQRSetPrecUnit(). However, preconditioning cost is low - and preconditioner is very important for solution of badly scaled - problems. - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsolvesparse(const linlsqrstate &state, const sparsematrix &a, const real_1d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrsolvesparse(const_cast(state.c_ptr()), const_cast(a.c_ptr()), const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping criteria. - -INPUT PARAMETERS: - EpsA - algorithm will be stopped if ||A^T*Rk||/(||A||*||Rk||)<=EpsA. - EpsB - algorithm will be stopped if ||Rk||<=EpsB*||B|| - MaxIts - algorithm will be stopped if number of iterations - more than MaxIts. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: if EpsA,EpsB,EpsC and MaxIts are zero then these variables will -be setted as default values. - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetcond(const linlsqrstate &state, const double epsa, const double epsb, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrsetcond(const_cast(state.c_ptr()), epsa, epsb, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -LSQR solver: results. - -This function must be called after LinLSQRSolve - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[N], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * 1 ||Rk||<=EpsB*||B|| - * 4 ||A^T*Rk||/(||A||*||Rk||)<=EpsA - * 5 MaxIts steps was taken - * 7 rounding errors prevent further progress, - X contains best point found so far. - (sometimes returned on singular systems) - * Rep.IterationsCount contains iterations count - * NMV countains number of matrix-vector calculations - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrresults(const linlsqrstate &state, real_1d_array &x, linlsqrreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetxrep(const linlsqrstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::linlsqrsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This object stores state of the linear CG method. - -You should use ALGLIB functions to work with this object. -Never try to access its fields directly! -*************************************************************************/ -_lincgstate_owner::_lincgstate_owner() -{ - p_struct = (alglib_impl::lincgstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::lincgstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lincgstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lincgstate_owner::_lincgstate_owner(const _lincgstate_owner &rhs) -{ - p_struct = (alglib_impl::lincgstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::lincgstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lincgstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lincgstate_owner& _lincgstate_owner::operator=(const _lincgstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_lincgstate_clear(p_struct); - if( !alglib_impl::_lincgstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_lincgstate_owner::~_lincgstate_owner() -{ - alglib_impl::_lincgstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::lincgstate* _lincgstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::lincgstate* _lincgstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -lincgstate::lincgstate() : _lincgstate_owner() -{ -} - -lincgstate::lincgstate(const lincgstate &rhs):_lincgstate_owner(rhs) -{ -} - -lincgstate& lincgstate::operator=(const lincgstate &rhs) -{ - if( this==&rhs ) - return *this; - _lincgstate_owner::operator=(rhs); - return *this; -} - -lincgstate::~lincgstate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_lincgreport_owner::_lincgreport_owner() -{ - p_struct = (alglib_impl::lincgreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lincgreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lincgreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lincgreport_owner::_lincgreport_owner(const _lincgreport_owner &rhs) -{ - p_struct = (alglib_impl::lincgreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::lincgreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_lincgreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_lincgreport_owner& _lincgreport_owner::operator=(const _lincgreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_lincgreport_clear(p_struct); - if( !alglib_impl::_lincgreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_lincgreport_owner::~_lincgreport_owner() -{ - alglib_impl::_lincgreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::lincgreport* _lincgreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::lincgreport* _lincgreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -lincgreport::lincgreport() : _lincgreport_owner() ,iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype),r2(p_struct->r2) -{ -} - -lincgreport::lincgreport(const lincgreport &rhs):_lincgreport_owner(rhs) ,iterationscount(p_struct->iterationscount),nmv(p_struct->nmv),terminationtype(p_struct->terminationtype),r2(p_struct->r2) -{ -} - -lincgreport& lincgreport::operator=(const lincgreport &rhs) -{ - if( this==&rhs ) - return *this; - _lincgreport_owner::operator=(rhs); - return *this; -} - -lincgreport::~lincgreport() -{ -} - -/************************************************************************* -This function initializes linear CG Solver. This solver is used to solve -symmetric positive definite problems. If you want to solve nonsymmetric -(or non-positive definite) problem you may use LinLSQR solver provided by -ALGLIB. - -USAGE: -1. User initializes algorithm state with LinCGCreate() call -2. User tunes solver parameters with LinCGSetCond() and other functions -3. Optionally, user sets starting point with LinCGSetStartingPoint() -4. User calls LinCGSolveSparse() function which takes algorithm state and - SparseMatrix object. -5. User calls LinCGResults() to get solution -6. Optionally, user may call LinCGSolveSparse() again to solve another - problem with different matrix and/or right part without reinitializing - LinCGState structure. - -INPUT PARAMETERS: - N - problem dimension, N>0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgcreate(const ae_int_t n, lincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgcreate(n, const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets starting point. -By default, zero starting point is used. - -INPUT PARAMETERS: - X - starting point, array[N] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetstartingpoint(const lincgstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetstartingpoint(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. By default, SolveSparse() uses diagonal preconditioner, but if -you want to use solver without preconditioning, you can call this function -which forces solver to use unit matrix for preconditioning. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void lincgsetprecunit(const lincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetprecunit(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. LinCGSolveSparse() will use diagonal of the system matrix as -preconditioner. This preconditioning mode is active by default. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void lincgsetprecdiag(const lincgstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetprecdiag(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping criteria. - -INPUT PARAMETERS: - EpsF - algorithm will be stopped if norm of residual is less than - EpsF*||b||. - MaxIts - algorithm will be stopped if number of iterations is more - than MaxIts. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -If both EpsF and MaxIts are zero then small EpsF will be set to small -value. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetcond(const lincgstate &state, const double epsf, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetcond(const_cast(state.c_ptr()), epsf, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Procedure for solution of A*x=b with sparse A. - -INPUT PARAMETERS: - State - algorithm state - A - sparse matrix in the CRS format (you MUST contvert it to - CRS format by calling SparseConvertToCRS() function). - IsUpper - whether upper or lower triangle of A is used: - * IsUpper=True => only upper triangle is used and lower - triangle is not referenced at all - * IsUpper=False => only lower triangle is used and upper - triangle is not referenced at all - B - right part, array[N] - -RESULT: - This function returns no result. - You can get solution by calling LinCGResults() - -NOTE: this function uses lightweight preconditioning - multiplication by - inverse of diag(A). If you want, you can turn preconditioning off by - calling LinCGSetPrecUnit(). However, preconditioning cost is low and - preconditioner is very important for solution of badly scaled - problems. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsolvesparse(const lincgstate &state, const sparsematrix &a, const bool isupper, const real_1d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsolvesparse(const_cast(state.c_ptr()), const_cast(a.c_ptr()), isupper, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -CG-solver: results. - -This function must be called after LinCGSolve - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[N], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -5 input matrix is either not positive definite, - too large or too small - * -4 overflow/underflow during solution - (ill conditioned problem) - * 1 ||residual||<=EpsF*||b|| - * 5 MaxIts steps was taken - * 7 rounding errors prevent further progress, - best point found is returned - * Rep.IterationsCount contains iterations count - * NMV countains number of matrix-vector calculations - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgresults(const lincgstate &state, real_1d_array &x, lincgreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets restart frequency. By default, algorithm is restarted -after N subsequent iterations. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetrestartfreq(const lincgstate &state, const ae_int_t srf) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetrestartfreq(const_cast(state.c_ptr()), srf, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets frequency of residual recalculations. - -Algorithm updates residual r_k using iterative formula, but recalculates -it from scratch after each 10 iterations. It is done to avoid accumulation -of numerical errors and to stop algorithm when r_k starts to grow. - -Such low update frequence (1/10) gives very little overhead, but makes -algorithm a bit more robust against numerical errors. However, you may -change it - -INPUT PARAMETERS: - Freq - desired update frequency, Freq>=0. - Zero value means that no updates will be done. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetrupdatefreq(const lincgstate &state, const ae_int_t freq) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetrupdatefreq(const_cast(state.c_ptr()), freq, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetxrep(const lincgstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::lincgsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -_nleqstate_owner::_nleqstate_owner() -{ - p_struct = (alglib_impl::nleqstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::nleqstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_nleqstate_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_nleqstate_owner::_nleqstate_owner(const _nleqstate_owner &rhs) -{ - p_struct = (alglib_impl::nleqstate*)alglib_impl::ae_malloc(sizeof(alglib_impl::nleqstate), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_nleqstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_nleqstate_owner& _nleqstate_owner::operator=(const _nleqstate_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_nleqstate_clear(p_struct); - if( !alglib_impl::_nleqstate_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_nleqstate_owner::~_nleqstate_owner() -{ - alglib_impl::_nleqstate_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::nleqstate* _nleqstate_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::nleqstate* _nleqstate_owner::c_ptr() const -{ - return const_cast(p_struct); -} -nleqstate::nleqstate() : _nleqstate_owner() ,needf(p_struct->needf),needfij(p_struct->needfij),xupdated(p_struct->xupdated),f(p_struct->f),fi(&p_struct->fi),j(&p_struct->j),x(&p_struct->x) -{ -} - -nleqstate::nleqstate(const nleqstate &rhs):_nleqstate_owner(rhs) ,needf(p_struct->needf),needfij(p_struct->needfij),xupdated(p_struct->xupdated),f(p_struct->f),fi(&p_struct->fi),j(&p_struct->j),x(&p_struct->x) -{ -} - -nleqstate& nleqstate::operator=(const nleqstate &rhs) -{ - if( this==&rhs ) - return *this; - _nleqstate_owner::operator=(rhs); - return *this; -} - -nleqstate::~nleqstate() -{ -} - - -/************************************************************************* - -*************************************************************************/ -_nleqreport_owner::_nleqreport_owner() -{ - p_struct = (alglib_impl::nleqreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::nleqreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_nleqreport_init(p_struct, NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_nleqreport_owner::_nleqreport_owner(const _nleqreport_owner &rhs) -{ - p_struct = (alglib_impl::nleqreport*)alglib_impl::ae_malloc(sizeof(alglib_impl::nleqreport), NULL); - if( p_struct==NULL ) - throw ap_error("ALGLIB: malloc error"); - if( !alglib_impl::_nleqreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); -} - -_nleqreport_owner& _nleqreport_owner::operator=(const _nleqreport_owner &rhs) -{ - if( this==&rhs ) - return *this; - alglib_impl::_nleqreport_clear(p_struct); - if( !alglib_impl::_nleqreport_init_copy(p_struct, const_cast(rhs.p_struct), NULL, ae_false) ) - throw ap_error("ALGLIB: malloc error"); - return *this; -} - -_nleqreport_owner::~_nleqreport_owner() -{ - alglib_impl::_nleqreport_clear(p_struct); - ae_free(p_struct); -} - -alglib_impl::nleqreport* _nleqreport_owner::c_ptr() -{ - return p_struct; -} - -alglib_impl::nleqreport* _nleqreport_owner::c_ptr() const -{ - return const_cast(p_struct); -} -nleqreport::nleqreport() : _nleqreport_owner() ,iterationscount(p_struct->iterationscount),nfunc(p_struct->nfunc),njac(p_struct->njac),terminationtype(p_struct->terminationtype) -{ -} - -nleqreport::nleqreport(const nleqreport &rhs):_nleqreport_owner(rhs) ,iterationscount(p_struct->iterationscount),nfunc(p_struct->nfunc),njac(p_struct->njac),terminationtype(p_struct->terminationtype) -{ -} - -nleqreport& nleqreport::operator=(const nleqreport &rhs) -{ - if( this==&rhs ) - return *this; - _nleqreport_owner::operator=(rhs); - return *this; -} - -nleqreport::~nleqreport() -{ -} - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE NONLINEAR SOLVER - -DESCRIPTION: -This algorithm solves system of nonlinear equations - F[0](x[0], ..., x[n-1]) = 0 - F[1](x[0], ..., x[n-1]) = 0 - ... - F[M-1](x[0], ..., x[n-1]) = 0 -with M/N do not necessarily coincide. Algorithm converges quadratically -under following conditions: - * the solution set XS is nonempty - * for some xs in XS there exist such neighbourhood N(xs) that: - * vector function F(x) and its Jacobian J(x) are continuously - differentiable on N - * ||F(x)|| provides local error bound on N, i.e. there exists such - c1, that ||F(x)||>c1*distance(x,XS) -Note that these conditions are much more weaker than usual non-singularity -conditions. For example, algorithm will converge for any affine function -F (whether its Jacobian singular or not). - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function vector F[] and Jacobian matrix at given point X -* value of merit function f(x)=F[0]^2(x)+...+F[M-1]^2(x) at given point X - - -USAGE: -1. User initializes algorithm state with NLEQCreateLM() call -2. User tunes solver parameters with NLEQSetCond(), NLEQSetStpMax() and - other functions -3. User calls NLEQSolve() function which takes algorithm state and - pointers (delegates, etc.) to callback functions which calculate merit - function value and Jacobian. -4. User calls NLEQResults() to get solution -5. Optionally, user may call NLEQRestartFrom() to solve another problem - with same parameters (N/M) but another starting point and/or another - function vector. NLEQRestartFrom() allows to reuse already initialized - structure. - - -INPUT PARAMETERS: - N - space dimension, N>1: - * if provided, only leading N elements of X are used - * if not provided, determined automatically from size of X - M - system size - X - starting point - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with NLEQSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use NLEQSetStpMax() function to bound algorithm's steps. -3. this algorithm is a slightly modified implementation of the method - described in 'Levenberg-Marquardt method for constrained nonlinear - equations with strong local convergence properties' by Christian Kanzow - Nobuo Yamashita and Masao Fukushima and further developed in 'On the - convergence of a New Levenberg-Marquardt Method' by Jin-yan Fan and - Ya-Xiang Yuan. - - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqcreatelm(const ae_int_t n, const ae_int_t m, const real_1d_array &x, nleqstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqcreatelm(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE NONLINEAR SOLVER - -DESCRIPTION: -This algorithm solves system of nonlinear equations - F[0](x[0], ..., x[n-1]) = 0 - F[1](x[0], ..., x[n-1]) = 0 - ... - F[M-1](x[0], ..., x[n-1]) = 0 -with M/N do not necessarily coincide. Algorithm converges quadratically -under following conditions: - * the solution set XS is nonempty - * for some xs in XS there exist such neighbourhood N(xs) that: - * vector function F(x) and its Jacobian J(x) are continuously - differentiable on N - * ||F(x)|| provides local error bound on N, i.e. there exists such - c1, that ||F(x)||>c1*distance(x,XS) -Note that these conditions are much more weaker than usual non-singularity -conditions. For example, algorithm will converge for any affine function -F (whether its Jacobian singular or not). - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function vector F[] and Jacobian matrix at given point X -* value of merit function f(x)=F[0]^2(x)+...+F[M-1]^2(x) at given point X - - -USAGE: -1. User initializes algorithm state with NLEQCreateLM() call -2. User tunes solver parameters with NLEQSetCond(), NLEQSetStpMax() and - other functions -3. User calls NLEQSolve() function which takes algorithm state and - pointers (delegates, etc.) to callback functions which calculate merit - function value and Jacobian. -4. User calls NLEQResults() to get solution -5. Optionally, user may call NLEQRestartFrom() to solve another problem - with same parameters (N/M) but another starting point and/or another - function vector. NLEQRestartFrom() allows to reuse already initialized - structure. - - -INPUT PARAMETERS: - N - space dimension, N>1: - * if provided, only leading N elements of X are used - * if not provided, determined automatically from size of X - M - system size - X - starting point - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with NLEQSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use NLEQSetStpMax() function to bound algorithm's steps. -3. this algorithm is a slightly modified implementation of the method - described in 'Levenberg-Marquardt method for constrained nonlinear - equations with strong local convergence properties' by Christian Kanzow - Nobuo Yamashita and Masao Fukushima and further developed in 'On the - convergence of a New Levenberg-Marquardt Method' by Jin-yan Fan and - Ya-Xiang Yuan. - - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqcreatelm(const ae_int_t m, const real_1d_array &x, nleqstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqcreatelm(n, m, const_cast(x.c_ptr()), const_cast(state.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets stopping conditions for the nonlinear solver - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsF - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition ||F||<=EpsF is satisfied - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsF=0 and MaxIts=0 simultaneously will lead to automatic -stopping criterion selection (small EpsF). - -NOTES: - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetcond(const nleqstate &state, const double epsf, const ae_int_t maxits) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqsetcond(const_cast(state.c_ptr()), epsf, maxits, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to NLEQSolve(). - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetxrep(const nleqstate &state, const bool needxrep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqsetxrep(const_cast(state.c_ptr()), needxrep, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when target function contains exp() or other fast -growing functions, and algorithm makes too large steps which lead to -overflow. This function allows us to reject steps that are too large (and -therefore expose us to the possible overflow) without actually calculating -function value at the x+stp*d. - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetstpmax(const nleqstate &state, const double stpmax) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqsetstpmax(const_cast(state.c_ptr()), stpmax, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool nleqiteration(const nleqstate &state) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - ae_bool result = alglib_impl::nleqiteration(const_cast(state.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void nleqsolve(nleqstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr), - void *ptr) -{ - alglib_impl::ae_state _alglib_env_state; - if( func==NULL ) - throw ap_error("ALGLIB: error in 'nleqsolve()' (func is NULL)"); - if( jac==NULL ) - throw ap_error("ALGLIB: error in 'nleqsolve()' (jac is NULL)"); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - while( alglib_impl::nleqiteration(state.c_ptr(), &_alglib_env_state) ) - { - if( state.needf ) - { - func(state.x, state.f, ptr); - continue; - } - if( state.needfij ) - { - jac(state.x, state.fi, state.j, ptr); - continue; - } - if( state.xupdated ) - { - if( rep!=NULL ) - rep(state.x, state.f, ptr); - continue; - } - throw ap_error("ALGLIB: error in 'nleqsolve' (some derivatives were not provided?)"); - } - alglib_impl::ae_state_clear(&_alglib_env_state); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - - -/************************************************************************* -NLEQ solver results - -INPUT PARAMETERS: - State - algorithm state. - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -4 ERROR: algorithm has converged to the - stationary point Xf which is local minimum of - f=F[0]^2+...+F[m-1]^2, but is not solution of - nonlinear system. - * 1 sqrt(f)<=EpsF. - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - * ActiveConstraints contains number of active constraints - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqresults(const nleqstate &state, real_1d_array &x, nleqreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqresults(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -NLEQ solver results - -Buffered implementation of NLEQResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqresultsbuf(const nleqstate &state, real_1d_array &x, nleqreport &rep) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqresultsbuf(const_cast(state.c_ptr()), const_cast(x.c_ptr()), const_cast(rep.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -This subroutine restarts CG algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used for reverse communication previously - allocated with MinCGCreate call. - X - new starting point. - BndL - new lower bounds - BndU - new upper bounds - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqrestartfrom(const nleqstate &state, const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::nleqrestartfrom(const_cast(state.c_ptr()), const_cast(x.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static void densesolver_rmatrixlusolveinternal(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_bool havea, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -static void densesolver_spdmatrixcholeskysolveinternal(/* Real */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_matrix* a, - ae_bool havea, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -static void densesolver_cmatrixlusolveinternal(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_bool havea, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -static void densesolver_hpdmatrixcholeskysolveinternal(/* Complex */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_matrix* a, - ae_bool havea, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -static ae_int_t densesolver_densesolverrfsmax(ae_int_t n, - double r1, - double rinf, - ae_state *_state); -static ae_int_t densesolver_densesolverrfsmaxv2(ae_int_t n, - double r2, - ae_state *_state); -static void densesolver_rbasiclusolve(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Real */ ae_vector* xb, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void densesolver_spdbasiccholeskysolve(/* Real */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* xb, - /* Real */ ae_vector* tmp, - ae_state *_state); -static void densesolver_cbasiclusolve(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Complex */ ae_vector* xb, - /* Complex */ ae_vector* tmp, - ae_state *_state); -static void densesolver_hpdbasiccholeskysolve(/* Complex */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* xb, - /* Complex */ ae_vector* tmp, - ae_state *_state); - - -static double linlsqr_atol = 1.0E-6; -static double linlsqr_btol = 1.0E-6; -static void linlsqr_clearrfields(linlsqrstate* state, ae_state *_state); - - -static double lincg_defaultprecision = 1.0E-6; -static void lincg_clearrfields(lincgstate* state, ae_state *_state); -static void lincg_updateitersdata(lincgstate* state, ae_state *_state); - - -static void nleq_clearrequestfields(nleqstate* state, ae_state *_state); -static ae_bool nleq_increaselambda(double* lambdav, - double* nu, - double lambdaup, - ae_state *_state); -static void nleq_decreaselambda(double* lambdav, - double* nu, - double lambdadown, - ae_state *_state); - - - - - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*x=b, where A is NxN non-denegerate -real matrix, x and b are vectors. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - return code: - * -3 A is singular, or VERY close to singular. - X is filled by zeros in such cases. - * -1 N<=0 was passed - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - X - array[0..N-1], it contains: - * solution of A*x=b if A is non-singular (well-conditioned - or ill-conditioned, but not very close to singular) - * zeros, if A is singular or VERY close to singular - (in this case Info=-3). - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolve(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_move(&bm.ptr.pp_double[0][0], bm.stride, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - rmatrixsolvem(a, n, &bm, 1, ae_true, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_move(&x->ptr.p_double[0], 1, &xm.ptr.pp_double[0][0], xm.stride, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. - -Similar to RMatrixSolve() but solves task with multiple right parts (where -b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* optional iterative refinement -* O(N^3+M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - RFS - iterative refinement switch: - * True - refinement is used. - Less performance, more precision. - * False - refinement is not used. - More performance, less precision. - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolvem(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_bool rfs, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix da; - ae_matrix emptya; - ae_vector p; - double scalea; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&da, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&emptya, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&da, n, n, _state); - - /* - * 1. scale matrix, max(|A[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - scalea = 0; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - scalea = ae_maxreal(scalea, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_eq(scalea,0) ) - { - scalea = 1; - } - scalea = 1/scalea; - for(i=0; i<=n-1; i++) - { - ae_v_move(&da.ptr.pp_double[i][0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,n-1)); - } - rmatrixlu(&da, n, n, &p, _state); - if( rfs ) - { - densesolver_rmatrixlusolveinternal(&da, &p, scalea, n, a, ae_true, b, m, info, rep, x, _state); - } - else - { - densesolver_rmatrixlusolveinternal(&da, &p, scalea, n, &emptya, ae_false, b, m, info, rep, x, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*X=B, where A is NxN non-denegerate -real matrix given by its LU decomposition, X and B are NxM real matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixlusolve(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_move(&bm.ptr.pp_double[0][0], bm.stride, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - rmatrixlusolvem(lua, p, n, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_move(&x->ptr.p_double[0], 1, &xm.ptr.pp_double[0][0], xm.stride, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. - -Similar to RMatrixLUSolve() but solves task with multiple right parts -(where b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixlusolvem(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix emptya; - ae_int_t i; - ae_int_t j; - double scalea; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&emptya, 0, 0, DT_REAL, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - - /* - * 1. scale matrix, max(|U[i,j]|) - * we assume that LU is in its normal form, i.e. |L[i,j]|<=1 - * 2. solve - */ - scalea = 0; - for(i=0; i<=n-1; i++) - { - for(j=i; j<=n-1; j++) - { - scalea = ae_maxreal(scalea, ae_fabs(lua->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_eq(scalea,0) ) - { - scalea = 1; - } - scalea = 1/scalea; - densesolver_rmatrixlusolveinternal(lua, p, scalea, n, &emptya, ae_false, b, m, info, rep, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*x=b, where BOTH ORIGINAL A AND ITS -LU DECOMPOSITION ARE KNOWN. You can use it if for some reasons you have -both A and its LU decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixmixedsolve(/* Real */ ae_matrix* a, - /* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_move(&bm.ptr.pp_double[0][0], bm.stride, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - rmatrixmixedsolvem(a, lua, p, n, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_move(&x->ptr.p_double[0], 1, &xm.ptr.pp_double[0][0], xm.stride, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. - -Similar to RMatrixMixedSolve() but solves task with multiple right parts -(where b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixmixedsolvem(/* Real */ ae_matrix* a, - /* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - double scalea; - ae_int_t i; - ae_int_t j; - - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - return; - } - - /* - * 1. scale matrix, max(|A[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - scalea = 0; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - scalea = ae_maxreal(scalea, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_eq(scalea,0) ) - { - scalea = 1; - } - scalea = 1/scalea; - densesolver_rmatrixlusolveinternal(lua, p, scalea, n, a, ae_true, b, m, info, rep, x, _state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3+M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - RFS - iterative refinement switch: - * True - refinement is used. - Less performance, more precision. - * False - refinement is not used. - More performance, less precision. - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixsolvem(/* Complex */ ae_matrix* a, - ae_int_t n, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_bool rfs, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix da; - ae_matrix emptya; - ae_vector p; - double scalea; - ae_int_t i; - ae_int_t j; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&da, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&emptya, 0, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&p, 0, DT_INT, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&da, n, n, _state); - - /* - * 1. scale matrix, max(|A[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - scalea = 0; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - scalea = ae_maxreal(scalea, ae_c_abs(a->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_eq(scalea,0) ) - { - scalea = 1; - } - scalea = 1/scalea; - for(i=0; i<=n-1; i++) - { - ae_v_cmove(&da.ptr.pp_complex[i][0], 1, &a->ptr.pp_complex[i][0], 1, "N", ae_v_len(0,n-1)); - } - cmatrixlu(&da, n, n, &p, _state); - if( rfs ) - { - densesolver_cmatrixlusolveinternal(&da, &p, scalea, n, a, ae_true, b, m, info, rep, x, _state); - } - else - { - densesolver_cmatrixlusolveinternal(&da, &p, scalea, n, &emptya, ae_false, b, m, info, rep, x, _state); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixsolve(/* Complex */ ae_matrix* a, - ae_int_t n, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_COMPLEX, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_cmove(&bm.ptr.pp_complex[0][0], bm.stride, &b->ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - cmatrixsolvem(a, n, &bm, 1, ae_true, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_cmove(&x->ptr.p_complex[0], 1, &xm.ptr.pp_complex[0][0], xm.stride, "N", ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use CMatrixSolve or CMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixlusolvem(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix emptya; - ae_int_t i; - ae_int_t j; - double scalea; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&emptya, 0, 0, DT_COMPLEX, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - - /* - * 1. scale matrix, max(|U[i,j]|) - * we assume that LU is in its normal form, i.e. |L[i,j]|<=1 - * 2. solve - */ - scalea = 0; - for(i=0; i<=n-1; i++) - { - for(j=i; j<=n-1; j++) - { - scalea = ae_maxreal(scalea, ae_c_abs(lua->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_eq(scalea,0) ) - { - scalea = 1; - } - scalea = 1/scalea; - densesolver_cmatrixlusolveinternal(lua, p, scalea, n, &emptya, ae_false, b, m, info, rep, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use CMatrixSolve or CMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixlusolve(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_COMPLEX, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_cmove(&bm.ptr.pp_complex[0][0], bm.stride, &b->ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - cmatrixlusolvem(lua, p, n, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_cmove(&x->ptr.p_complex[0], 1, &xm.ptr.pp_complex[0][0], xm.stride, "N", ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixMixedSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixmixedsolvem(/* Complex */ ae_matrix* a, - /* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - double scalea; - ae_int_t i; - ae_int_t j; - - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - return; - } - - /* - * 1. scale matrix, max(|A[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - scalea = 0; - for(i=0; i<=n-1; i++) - { - for(j=0; j<=n-1; j++) - { - scalea = ae_maxreal(scalea, ae_c_abs(a->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_eq(scalea,0) ) - { - scalea = 1; - } - scalea = 1/scalea; - densesolver_cmatrixlusolveinternal(lua, p, scalea, n, a, ae_true, b, m, info, rep, x, _state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixMixedSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixmixedsolve(/* Complex */ ae_matrix* a, - /* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_COMPLEX, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_cmove(&bm.ptr.pp_complex[0][0], bm.stride, &b->ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - cmatrixmixedsolvem(a, lua, p, n, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_cmove(&x->ptr.p_complex[0], 1, &xm.ptr.pp_complex[0][0], xm.stride, "N", ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for symmetric positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3+M*N^2) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve. - Returns -3 for non-SPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixsolvem(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix da; - double sqrtscalea; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&da, 0, 0, DT_REAL, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&da, n, n, _state); - - /* - * 1. scale matrix, max(|A[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - sqrtscalea = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - sqrtscalea = ae_maxreal(sqrtscalea, ae_fabs(a->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_eq(sqrtscalea,0) ) - { - sqrtscalea = 1; - } - sqrtscalea = 1/sqrtscalea; - sqrtscalea = ae_sqrt(sqrtscalea, _state); - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - ae_v_move(&da.ptr.pp_double[i][j1], 1, &a->ptr.pp_double[i][j1], 1, ae_v_len(j1,j2)); - } - if( !spdmatrixcholesky(&da, n, isupper, _state) ) - { - ae_matrix_set_length(x, n, m, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - x->ptr.pp_double[i][j] = 0; - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - *info = 1; - densesolver_spdmatrixcholeskysolveinternal(&da, sqrtscalea, n, isupper, a, ae_true, b, m, info, rep, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for SPD matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Returns -3 for non-SPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixsolve(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_move(&bm.ptr.pp_double[0][0], bm.stride, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - spdmatrixsolvem(a, n, isupper, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_move(&x->ptr.p_double[0], 1, &xm.ptr.pp_double[0][0], xm.stride, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for SPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of CHA - IsUpper - what half of CHA is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskysolvem(/* Real */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix emptya; - double sqrtscalea; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&emptya, 0, 0, DT_REAL, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - - /* - * 1. scale matrix, max(|U[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - sqrtscalea = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - sqrtscalea = ae_maxreal(sqrtscalea, ae_fabs(cha->ptr.pp_double[i][j], _state), _state); - } - } - if( ae_fp_eq(sqrtscalea,0) ) - { - sqrtscalea = 1; - } - sqrtscalea = 1/sqrtscalea; - densesolver_spdmatrixcholeskysolveinternal(cha, sqrtscalea, n, isupper, &emptya, ae_false, b, m, info, rep, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for SPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of A - IsUpper - what half of CHA is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskysolve(/* Real */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_REAL, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_move(&bm.ptr.pp_double[0][0], bm.stride, &b->ptr.p_double[0], 1, ae_v_len(0,n-1)); - spdmatrixcholeskysolvem(cha, n, isupper, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_move(&x->ptr.p_double[0], 1, &xm.ptr.pp_double[0][0], xm.stride, ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for Hermitian positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3+M*N^2) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve. - Returns -3 for non-HPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixsolvem(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix da; - double sqrtscalea; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&da, 0, 0, DT_COMPLEX, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&da, n, n, _state); - - /* - * 1. scale matrix, max(|A[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - sqrtscalea = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - sqrtscalea = ae_maxreal(sqrtscalea, ae_c_abs(a->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_eq(sqrtscalea,0) ) - { - sqrtscalea = 1; - } - sqrtscalea = 1/sqrtscalea; - sqrtscalea = ae_sqrt(sqrtscalea, _state); - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - ae_v_cmove(&da.ptr.pp_complex[i][j1], 1, &a->ptr.pp_complex[i][j1], 1, "N", ae_v_len(j1,j2)); - } - if( !hpdmatrixcholesky(&da, n, isupper, _state) ) - { - ae_matrix_set_length(x, n, m, _state); - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - x->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - *info = 1; - densesolver_hpdmatrixcholeskysolveinternal(&da, sqrtscalea, n, isupper, a, ae_true, b, m, info, rep, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for Hermitian positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Returns -3 for non-HPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixsolve(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_COMPLEX, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_cmove(&bm.ptr.pp_complex[0][0], bm.stride, &b->ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - hpdmatrixsolvem(a, n, isupper, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_cmove(&x->ptr.p_complex[0], 1, &xm.ptr.pp_complex[0][0], xm.stride, "N", ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for HPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - HPDMatrixCholesky result - N - size of CHA - IsUpper - what half of CHA is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskysolvem(/* Complex */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix emptya; - double sqrtscalea; - ae_int_t i; - ae_int_t j; - ae_int_t j1; - ae_int_t j2; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_matrix_init(&emptya, 0, 0, DT_COMPLEX, _state, ae_true); - - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - - /* - * 1. scale matrix, max(|U[i,j]|) - * 2. factorize scaled matrix - * 3. solve - */ - sqrtscalea = 0; - for(i=0; i<=n-1; i++) - { - if( isupper ) - { - j1 = i; - j2 = n-1; - } - else - { - j1 = 0; - j2 = i; - } - for(j=j1; j<=j2; j++) - { - sqrtscalea = ae_maxreal(sqrtscalea, ae_c_abs(cha->ptr.pp_complex[i][j], _state), _state); - } - } - if( ae_fp_eq(sqrtscalea,0) ) - { - sqrtscalea = 1; - } - sqrtscalea = 1/sqrtscalea; - densesolver_hpdmatrixcholeskysolveinternal(cha, sqrtscalea, n, isupper, &emptya, ae_false, b, m, info, rep, x, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for HPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of A - IsUpper - what half of CHA is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskysolve(/* Complex */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix bm; - ae_matrix xm; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_vector_clear(x); - ae_matrix_init(&bm, 0, 0, DT_COMPLEX, _state, ae_true); - ae_matrix_init(&xm, 0, 0, DT_COMPLEX, _state, ae_true); - - if( n<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(&bm, n, 1, _state); - ae_v_cmove(&bm.ptr.pp_complex[0][0], bm.stride, &b->ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - hpdmatrixcholeskysolvem(cha, n, isupper, &bm, 1, info, rep, &xm, _state); - ae_vector_set_length(x, n, _state); - ae_v_cmove(&x->ptr.p_complex[0], 1, &xm.ptr.pp_complex[0][0], xm.stride, "N", ae_v_len(0,n-1)); - ae_frame_leave(_state); -} - - -/************************************************************************* -Dense solver. - -This subroutine finds solution of the linear system A*X=B with non-square, -possibly degenerate A. System is solved in the least squares sense, and -general least squares solution X = X0 + CX*y which minimizes |A*X-B| is -returned. If A is non-degenerate, solution in the usual sense is returned. - -Algorithm features: -* automatic detection (and correct handling!) of degenerate cases -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..NRows-1,0..NCols-1], system matrix - NRows - vertical size of A - NCols - horizontal size of A - B - array[0..NCols-1], right part - Threshold- a number in [0,1]. Singular values beyond Threshold are - considered zero. Set it to 0.0, if you don't understand - what it means, so the solver will choose good value on its - own. - -OUTPUT PARAMETERS - Info - return code: - * -4 SVD subroutine failed - * -1 if NRows<=0 or NCols<=0 or Threshold<0 was passed - * 1 if task is solved - Rep - solver report, see below for more info - X - array[0..N-1,0..M-1], it contains: - * solution of A*X=B (even for singular A) - * zeros, if SVD subroutine failed - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R2 reciprocal of condition number: 1/cond(A), 2-norm. -* N = NCols -* K dim(Null(A)) -* CX array[0..N-1,0..K-1], kernel of A. - Columns of CX store such vectors that A*CX[i]=0. - - -- ALGLIB -- - Copyright 24.08.2009 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolvels(/* Real */ ae_matrix* a, - ae_int_t nrows, - ae_int_t ncols, - /* Real */ ae_vector* b, - double threshold, - ae_int_t* info, - densesolverlsreport* rep, - /* Real */ ae_vector* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector sv; - ae_matrix u; - ae_matrix vt; - ae_vector rp; - ae_vector utb; - ae_vector sutb; - ae_vector tmp; - ae_vector ta; - ae_vector tx; - ae_vector buf; - ae_vector w; - ae_int_t i; - ae_int_t j; - ae_int_t nsv; - ae_int_t kernelidx; - double v; - double verr; - ae_bool svdfailed; - ae_bool zeroa; - ae_int_t rfs; - ae_int_t nrfs; - ae_bool terminatenexttime; - ae_bool smallerr; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverlsreport_clear(rep); - ae_vector_clear(x); - ae_vector_init(&sv, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&u, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&vt, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&rp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&utb, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sutb, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tmp, 0, DT_REAL, _state, ae_true); - ae_vector_init(&ta, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&buf, 0, DT_REAL, _state, ae_true); - ae_vector_init(&w, 0, DT_REAL, _state, ae_true); - - if( (nrows<=0||ncols<=0)||ae_fp_less(threshold,0) ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - if( ae_fp_eq(threshold,0) ) - { - threshold = 1000*ae_machineepsilon; - } - - /* - * Factorize A first - */ - svdfailed = !rmatrixsvd(a, nrows, ncols, 1, 2, 2, &sv, &u, &vt, _state); - zeroa = ae_fp_eq(sv.ptr.p_double[0],0); - if( svdfailed||zeroa ) - { - if( svdfailed ) - { - *info = -4; - } - else - { - *info = 1; - } - ae_vector_set_length(x, ncols, _state); - for(i=0; i<=ncols-1; i++) - { - x->ptr.p_double[i] = 0; - } - rep->n = ncols; - rep->k = ncols; - ae_matrix_set_length(&rep->cx, ncols, ncols, _state); - for(i=0; i<=ncols-1; i++) - { - for(j=0; j<=ncols-1; j++) - { - if( i==j ) - { - rep->cx.ptr.pp_double[i][j] = 1; - } - else - { - rep->cx.ptr.pp_double[i][j] = 0; - } - } - } - rep->r2 = 0; - ae_frame_leave(_state); - return; - } - nsv = ae_minint(ncols, nrows, _state); - if( nsv==ncols ) - { - rep->r2 = sv.ptr.p_double[nsv-1]/sv.ptr.p_double[0]; - } - else - { - rep->r2 = 0; - } - rep->n = ncols; - *info = 1; - - /* - * Iterative refinement of xc combined with solution: - * 1. xc = 0 - * 2. calculate r = bc-A*xc using extra-precise dot product - * 3. solve A*y = r - * 4. update x:=x+r - * 5. goto 2 - * - * This cycle is executed until one of two things happens: - * 1. maximum number of iterations reached - * 2. last iteration decreased error to the lower limit - */ - ae_vector_set_length(&utb, nsv, _state); - ae_vector_set_length(&sutb, nsv, _state); - ae_vector_set_length(x, ncols, _state); - ae_vector_set_length(&tmp, ncols, _state); - ae_vector_set_length(&ta, ncols+1, _state); - ae_vector_set_length(&tx, ncols+1, _state); - ae_vector_set_length(&buf, ncols+1, _state); - for(i=0; i<=ncols-1; i++) - { - x->ptr.p_double[i] = 0; - } - kernelidx = nsv; - for(i=0; i<=nsv-1; i++) - { - if( ae_fp_less_eq(sv.ptr.p_double[i],threshold*sv.ptr.p_double[0]) ) - { - kernelidx = i; - break; - } - } - rep->k = ncols-kernelidx; - nrfs = densesolver_densesolverrfsmaxv2(ncols, rep->r2, _state); - terminatenexttime = ae_false; - ae_vector_set_length(&rp, nrows, _state); - for(rfs=0; rfs<=nrfs; rfs++) - { - if( terminatenexttime ) - { - break; - } - - /* - * calculate right part - */ - if( rfs==0 ) - { - ae_v_move(&rp.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,nrows-1)); - } - else - { - smallerr = ae_true; - for(i=0; i<=nrows-1; i++) - { - ae_v_move(&ta.ptr.p_double[0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,ncols-1)); - ta.ptr.p_double[ncols] = -1; - ae_v_move(&tx.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,ncols-1)); - tx.ptr.p_double[ncols] = b->ptr.p_double[i]; - xdot(&ta, &tx, ncols+1, &buf, &v, &verr, _state); - rp.ptr.p_double[i] = -v; - smallerr = smallerr&&ae_fp_less(ae_fabs(v, _state),4*verr); - } - if( smallerr ) - { - terminatenexttime = ae_true; - } - } - - /* - * solve A*dx = rp - */ - for(i=0; i<=ncols-1; i++) - { - tmp.ptr.p_double[i] = 0; - } - for(i=0; i<=nsv-1; i++) - { - utb.ptr.p_double[i] = 0; - } - for(i=0; i<=nrows-1; i++) - { - v = rp.ptr.p_double[i]; - ae_v_addd(&utb.ptr.p_double[0], 1, &u.ptr.pp_double[i][0], 1, ae_v_len(0,nsv-1), v); - } - for(i=0; i<=nsv-1; i++) - { - if( iptr.p_double[0], 1, &tmp.ptr.p_double[0], 1, ae_v_len(0,ncols-1)); - } - - /* - * fill CX - */ - if( rep->k>0 ) - { - ae_matrix_set_length(&rep->cx, ncols, rep->k, _state); - for(i=0; i<=rep->k-1; i++) - { - ae_v_move(&rep->cx.ptr.pp_double[0][i], rep->cx.stride, &vt.ptr.pp_double[kernelidx+i][0], 1, ae_v_len(0,ncols-1)); - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal LU solver - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_rmatrixlusolveinternal(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Real */ ae_matrix* a, - ae_bool havea, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t rfs; - ae_int_t nrfs; - ae_vector xc; - ae_vector y; - ae_vector bc; - ae_vector xa; - ae_vector xb; - ae_vector tx; - double v; - double verr; - double mxb; - double scaleright; - ae_bool smallerr; - ae_bool terminatenexttime; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_vector_init(&xc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xa, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xb, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tx, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_fp_greater(scalea,0), "Assertion failed", _state); - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - if( p->ptr.p_int[i]>n-1||p->ptr.p_int[i]r1 = rmatrixlurcond1(lua, n, _state); - rep->rinf = rmatrixlurcondinf(lua, n, _state); - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - x->ptr.pp_double[i][j] = 0; - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * solve - */ - for(k=0; k<=m-1; k++) - { - - /* - * copy B to contiguous storage - */ - ae_v_move(&bc.ptr.p_double[0], 1, &b->ptr.pp_double[0][k], b->stride, ae_v_len(0,n-1)); - - /* - * Scale right part: - * * MX stores max(|Bi|) - * * ScaleRight stores actual scaling applied to B when solving systems - * it is chosen to make |scaleRight*b| close to 1. - */ - mxb = 0; - for(i=0; i<=n-1; i++) - { - mxb = ae_maxreal(mxb, ae_fabs(bc.ptr.p_double[i], _state), _state); - } - if( ae_fp_eq(mxb,0) ) - { - mxb = 1; - } - scaleright = 1/mxb; - - /* - * First, non-iterative part of solution process. - * We use separate code for this task because - * XDot is quite slow and we want to save time. - */ - ae_v_moved(&xc.ptr.p_double[0], 1, &bc.ptr.p_double[0], 1, ae_v_len(0,n-1), scaleright); - densesolver_rbasiclusolve(lua, p, scalea, n, &xc, &tx, _state); - - /* - * Iterative refinement of xc: - * * calculate r = bc-A*xc using extra-precise dot product - * * solve A*y = r - * * update x:=x+r - * - * This cycle is executed until one of two things happens: - * 1. maximum number of iterations reached - * 2. last iteration decreased error to the lower limit - */ - if( havea ) - { - nrfs = densesolver_densesolverrfsmax(n, rep->r1, rep->rinf, _state); - terminatenexttime = ae_false; - for(rfs=0; rfs<=nrfs-1; rfs++) - { - if( terminatenexttime ) - { - break; - } - - /* - * generate right part - */ - smallerr = ae_true; - ae_v_move(&xb.ptr.p_double[0], 1, &xc.ptr.p_double[0], 1, ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - ae_v_moved(&xa.ptr.p_double[0], 1, &a->ptr.pp_double[i][0], 1, ae_v_len(0,n-1), scalea); - xa.ptr.p_double[n] = -1; - xb.ptr.p_double[n] = scaleright*bc.ptr.p_double[i]; - xdot(&xa, &xb, n+1, &tx, &v, &verr, _state); - y.ptr.p_double[i] = -v; - smallerr = smallerr&&ae_fp_less(ae_fabs(v, _state),4*verr); - } - if( smallerr ) - { - terminatenexttime = ae_true; - } - - /* - * solve and update - */ - densesolver_rbasiclusolve(lua, p, scalea, n, &y, &tx, _state); - ae_v_add(&xc.ptr.p_double[0], 1, &y.ptr.p_double[0], 1, ae_v_len(0,n-1)); - } - } - - /* - * Store xc. - * Post-scale result. - */ - v = scalea*mxb; - ae_v_moved(&x->ptr.pp_double[0][k], x->stride, &xc.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal Cholesky solver - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_spdmatrixcholeskysolveinternal(/* Real */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_matrix* a, - ae_bool havea, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_vector xc; - ae_vector y; - ae_vector bc; - ae_vector xa; - ae_vector xb; - ae_vector tx; - double v; - double mxb; - double scaleright; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_vector_init(&xc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y, 0, DT_REAL, _state, ae_true); - ae_vector_init(&bc, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xa, 0, DT_REAL, _state, ae_true); - ae_vector_init(&xb, 0, DT_REAL, _state, ae_true); - ae_vector_init(&tx, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_fp_greater(sqrtscalea,0), "Assertion failed", _state); - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(x, n, m, _state); - ae_vector_set_length(&y, n, _state); - ae_vector_set_length(&xc, n, _state); - ae_vector_set_length(&bc, n, _state); - ae_vector_set_length(&tx, n+1, _state); - ae_vector_set_length(&xa, n+1, _state); - ae_vector_set_length(&xb, n+1, _state); - - /* - * estimate condition number, test for near singularity - */ - rep->r1 = spdmatrixcholeskyrcond(cha, n, isupper, _state); - rep->rinf = rep->r1; - if( ae_fp_less(rep->r1,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - x->ptr.pp_double[i][j] = 0; - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * solve - */ - for(k=0; k<=m-1; k++) - { - - /* - * copy B to contiguous storage - */ - ae_v_move(&bc.ptr.p_double[0], 1, &b->ptr.pp_double[0][k], b->stride, ae_v_len(0,n-1)); - - /* - * Scale right part: - * * MX stores max(|Bi|) - * * ScaleRight stores actual scaling applied to B when solving systems - * it is chosen to make |scaleRight*b| close to 1. - */ - mxb = 0; - for(i=0; i<=n-1; i++) - { - mxb = ae_maxreal(mxb, ae_fabs(bc.ptr.p_double[i], _state), _state); - } - if( ae_fp_eq(mxb,0) ) - { - mxb = 1; - } - scaleright = 1/mxb; - - /* - * First, non-iterative part of solution process. - * We use separate code for this task because - * XDot is quite slow and we want to save time. - */ - ae_v_moved(&xc.ptr.p_double[0], 1, &bc.ptr.p_double[0], 1, ae_v_len(0,n-1), scaleright); - densesolver_spdbasiccholeskysolve(cha, sqrtscalea, n, isupper, &xc, &tx, _state); - - /* - * Store xc. - * Post-scale result. - */ - v = ae_sqr(sqrtscalea, _state)*mxb; - ae_v_moved(&x->ptr.pp_double[0][k], x->stride, &xc.ptr.p_double[0], 1, ae_v_len(0,n-1), v); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal LU solver - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_cmatrixlusolveinternal(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Complex */ ae_matrix* a, - ae_bool havea, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t rfs; - ae_int_t nrfs; - ae_vector xc; - ae_vector y; - ae_vector bc; - ae_vector xa; - ae_vector xb; - ae_vector tx; - ae_vector tmpbuf; - ae_complex v; - double verr; - double mxb; - double scaleright; - ae_bool smallerr; - ae_bool terminatenexttime; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_vector_init(&xc, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&y, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&bc, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&xa, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&xb, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&tx, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&tmpbuf, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_fp_greater(scalea,0), "Assertion failed", _state); - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - for(i=0; i<=n-1; i++) - { - if( p->ptr.p_int[i]>n-1||p->ptr.p_int[i]r1 = cmatrixlurcond1(lua, n, _state); - rep->rinf = cmatrixlurcondinf(lua, n, _state); - if( ae_fp_less(rep->r1,rcondthreshold(_state))||ae_fp_less(rep->rinf,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - x->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * solve - */ - for(k=0; k<=m-1; k++) - { - - /* - * copy B to contiguous storage - */ - ae_v_cmove(&bc.ptr.p_complex[0], 1, &b->ptr.pp_complex[0][k], b->stride, "N", ae_v_len(0,n-1)); - - /* - * Scale right part: - * * MX stores max(|Bi|) - * * ScaleRight stores actual scaling applied to B when solving systems - * it is chosen to make |scaleRight*b| close to 1. - */ - mxb = 0; - for(i=0; i<=n-1; i++) - { - mxb = ae_maxreal(mxb, ae_c_abs(bc.ptr.p_complex[i], _state), _state); - } - if( ae_fp_eq(mxb,0) ) - { - mxb = 1; - } - scaleright = 1/mxb; - - /* - * First, non-iterative part of solution process. - * We use separate code for this task because - * XDot is quite slow and we want to save time. - */ - ae_v_cmoved(&xc.ptr.p_complex[0], 1, &bc.ptr.p_complex[0], 1, "N", ae_v_len(0,n-1), scaleright); - densesolver_cbasiclusolve(lua, p, scalea, n, &xc, &tx, _state); - - /* - * Iterative refinement of xc: - * * calculate r = bc-A*xc using extra-precise dot product - * * solve A*y = r - * * update x:=x+r - * - * This cycle is executed until one of two things happens: - * 1. maximum number of iterations reached - * 2. last iteration decreased error to the lower limit - */ - if( havea ) - { - nrfs = densesolver_densesolverrfsmax(n, rep->r1, rep->rinf, _state); - terminatenexttime = ae_false; - for(rfs=0; rfs<=nrfs-1; rfs++) - { - if( terminatenexttime ) - { - break; - } - - /* - * generate right part - */ - smallerr = ae_true; - ae_v_cmove(&xb.ptr.p_complex[0], 1, &xc.ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - for(i=0; i<=n-1; i++) - { - ae_v_cmoved(&xa.ptr.p_complex[0], 1, &a->ptr.pp_complex[i][0], 1, "N", ae_v_len(0,n-1), scalea); - xa.ptr.p_complex[n] = ae_complex_from_d(-1); - xb.ptr.p_complex[n] = ae_c_mul_d(bc.ptr.p_complex[i],scaleright); - xcdot(&xa, &xb, n+1, &tmpbuf, &v, &verr, _state); - y.ptr.p_complex[i] = ae_c_neg(v); - smallerr = smallerr&&ae_fp_less(ae_c_abs(v, _state),4*verr); - } - if( smallerr ) - { - terminatenexttime = ae_true; - } - - /* - * solve and update - */ - densesolver_cbasiclusolve(lua, p, scalea, n, &y, &tx, _state); - ae_v_cadd(&xc.ptr.p_complex[0], 1, &y.ptr.p_complex[0], 1, "N", ae_v_len(0,n-1)); - } - } - - /* - * Store xc. - * Post-scale result. - */ - v = ae_complex_from_d(scalea*mxb); - ae_v_cmovec(&x->ptr.pp_complex[0][k], x->stride, &xc.ptr.p_complex[0], 1, "N", ae_v_len(0,n-1), v); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal Cholesky solver - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_hpdmatrixcholeskysolveinternal(/* Complex */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_matrix* a, - ae_bool havea, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_vector xc; - ae_vector y; - ae_vector bc; - ae_vector xa; - ae_vector xb; - ae_vector tx; - double v; - double mxb; - double scaleright; - - ae_frame_make(_state, &_frame_block); - *info = 0; - _densesolverreport_clear(rep); - ae_matrix_clear(x); - ae_vector_init(&xc, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&y, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&bc, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&xa, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&xb, 0, DT_COMPLEX, _state, ae_true); - ae_vector_init(&tx, 0, DT_COMPLEX, _state, ae_true); - - ae_assert(ae_fp_greater(sqrtscalea,0), "Assertion failed", _state); - - /* - * prepare: check inputs, allocate space... - */ - if( n<=0||m<=0 ) - { - *info = -1; - ae_frame_leave(_state); - return; - } - ae_matrix_set_length(x, n, m, _state); - ae_vector_set_length(&y, n, _state); - ae_vector_set_length(&xc, n, _state); - ae_vector_set_length(&bc, n, _state); - ae_vector_set_length(&tx, n+1, _state); - ae_vector_set_length(&xa, n+1, _state); - ae_vector_set_length(&xb, n+1, _state); - - /* - * estimate condition number, test for near singularity - */ - rep->r1 = hpdmatrixcholeskyrcond(cha, n, isupper, _state); - rep->rinf = rep->r1; - if( ae_fp_less(rep->r1,rcondthreshold(_state)) ) - { - for(i=0; i<=n-1; i++) - { - for(j=0; j<=m-1; j++) - { - x->ptr.pp_complex[i][j] = ae_complex_from_d(0); - } - } - rep->r1 = 0; - rep->rinf = 0; - *info = -3; - ae_frame_leave(_state); - return; - } - *info = 1; - - /* - * solve - */ - for(k=0; k<=m-1; k++) - { - - /* - * copy B to contiguous storage - */ - ae_v_cmove(&bc.ptr.p_complex[0], 1, &b->ptr.pp_complex[0][k], b->stride, "N", ae_v_len(0,n-1)); - - /* - * Scale right part: - * * MX stores max(|Bi|) - * * ScaleRight stores actual scaling applied to B when solving systems - * it is chosen to make |scaleRight*b| close to 1. - */ - mxb = 0; - for(i=0; i<=n-1; i++) - { - mxb = ae_maxreal(mxb, ae_c_abs(bc.ptr.p_complex[i], _state), _state); - } - if( ae_fp_eq(mxb,0) ) - { - mxb = 1; - } - scaleright = 1/mxb; - - /* - * First, non-iterative part of solution process. - * We use separate code for this task because - * XDot is quite slow and we want to save time. - */ - ae_v_cmoved(&xc.ptr.p_complex[0], 1, &bc.ptr.p_complex[0], 1, "N", ae_v_len(0,n-1), scaleright); - densesolver_hpdbasiccholeskysolve(cha, sqrtscalea, n, isupper, &xc, &tx, _state); - - /* - * Store xc. - * Post-scale result. - */ - v = ae_sqr(sqrtscalea, _state)*mxb; - ae_v_cmoved(&x->ptr.pp_complex[0][k], x->stride, &xc.ptr.p_complex[0], 1, "N", ae_v_len(0,n-1), v); - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Internal subroutine. -Returns maximum count of RFS iterations as function of: -1. machine epsilon -2. task size. -3. condition number - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static ae_int_t densesolver_densesolverrfsmax(ae_int_t n, - double r1, - double rinf, - ae_state *_state) -{ - ae_int_t result; - - - result = 5; - return result; -} - - -/************************************************************************* -Internal subroutine. -Returns maximum count of RFS iterations as function of: -1. machine epsilon -2. task size. -3. norm-2 condition number - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static ae_int_t densesolver_densesolverrfsmaxv2(ae_int_t n, - double r2, - ae_state *_state) -{ - ae_int_t result; - - - result = densesolver_densesolverrfsmax(n, 0, 0, _state); - return result; -} - - -/************************************************************************* -Basic LU solver for ScaleA*PLU*x = y. - -This subroutine assumes that: -* L is well-scaled, and it is U which needs scaling by ScaleA. -* A=PLU is well-conditioned, so no zero divisions or overflow may occur - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_rbasiclusolve(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Real */ ae_vector* xb, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - double v; - - - for(i=0; i<=n-1; i++) - { - if( p->ptr.p_int[i]!=i ) - { - v = xb->ptr.p_double[i]; - xb->ptr.p_double[i] = xb->ptr.p_double[p->ptr.p_int[i]]; - xb->ptr.p_double[p->ptr.p_int[i]] = v; - } - } - for(i=1; i<=n-1; i++) - { - v = ae_v_dotproduct(&lua->ptr.pp_double[i][0], 1, &xb->ptr.p_double[0], 1, ae_v_len(0,i-1)); - xb->ptr.p_double[i] = xb->ptr.p_double[i]-v; - } - xb->ptr.p_double[n-1] = xb->ptr.p_double[n-1]/(scalea*lua->ptr.pp_double[n-1][n-1]); - for(i=n-2; i>=0; i--) - { - ae_v_moved(&tmp->ptr.p_double[i+1], 1, &lua->ptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), scalea); - v = ae_v_dotproduct(&tmp->ptr.p_double[i+1], 1, &xb->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1)); - xb->ptr.p_double[i] = (xb->ptr.p_double[i]-v)/(scalea*lua->ptr.pp_double[i][i]); - } -} - - -/************************************************************************* -Basic Cholesky solver for ScaleA*Cholesky(A)'*x = y. - -This subroutine assumes that: -* A*ScaleA is well scaled -* A is well-conditioned, so no zero divisions or overflow may occur - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_spdbasiccholeskysolve(/* Real */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* xb, - /* Real */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - double v; - - - - /* - * A = L*L' or A=U'*U - */ - if( isupper ) - { - - /* - * Solve U'*y=b first. - */ - for(i=0; i<=n-1; i++) - { - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - if( iptr.p_double[i]; - ae_v_moved(&tmp->ptr.p_double[i+1], 1, &cha->ptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), sqrtscalea); - ae_v_subd(&xb->ptr.p_double[i+1], 1, &tmp->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1), v); - } - } - - /* - * Solve U*x=y then. - */ - for(i=n-1; i>=0; i--) - { - if( iptr.p_double[i+1], 1, &cha->ptr.pp_double[i][i+1], 1, ae_v_len(i+1,n-1), sqrtscalea); - v = ae_v_dotproduct(&tmp->ptr.p_double[i+1], 1, &xb->ptr.p_double[i+1], 1, ae_v_len(i+1,n-1)); - xb->ptr.p_double[i] = xb->ptr.p_double[i]-v; - } - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - } - } - else - { - - /* - * Solve L*y=b first - */ - for(i=0; i<=n-1; i++) - { - if( i>0 ) - { - ae_v_moved(&tmp->ptr.p_double[0], 1, &cha->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), sqrtscalea); - v = ae_v_dotproduct(&tmp->ptr.p_double[0], 1, &xb->ptr.p_double[0], 1, ae_v_len(0,i-1)); - xb->ptr.p_double[i] = xb->ptr.p_double[i]-v; - } - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - } - - /* - * Solve L'*x=y then. - */ - for(i=n-1; i>=0; i--) - { - xb->ptr.p_double[i] = xb->ptr.p_double[i]/(sqrtscalea*cha->ptr.pp_double[i][i]); - if( i>0 ) - { - v = xb->ptr.p_double[i]; - ae_v_moved(&tmp->ptr.p_double[0], 1, &cha->ptr.pp_double[i][0], 1, ae_v_len(0,i-1), sqrtscalea); - ae_v_subd(&xb->ptr.p_double[0], 1, &tmp->ptr.p_double[0], 1, ae_v_len(0,i-1), v); - } - } - } -} - - -/************************************************************************* -Basic LU solver for ScaleA*PLU*x = y. - -This subroutine assumes that: -* L is well-scaled, and it is U which needs scaling by ScaleA. -* A=PLU is well-conditioned, so no zero divisions or overflow may occur - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_cbasiclusolve(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - double scalea, - ae_int_t n, - /* Complex */ ae_vector* xb, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_complex v; - - - for(i=0; i<=n-1; i++) - { - if( p->ptr.p_int[i]!=i ) - { - v = xb->ptr.p_complex[i]; - xb->ptr.p_complex[i] = xb->ptr.p_complex[p->ptr.p_int[i]]; - xb->ptr.p_complex[p->ptr.p_int[i]] = v; - } - } - for(i=1; i<=n-1; i++) - { - v = ae_v_cdotproduct(&lua->ptr.pp_complex[i][0], 1, "N", &xb->ptr.p_complex[0], 1, "N", ae_v_len(0,i-1)); - xb->ptr.p_complex[i] = ae_c_sub(xb->ptr.p_complex[i],v); - } - xb->ptr.p_complex[n-1] = ae_c_div(xb->ptr.p_complex[n-1],ae_c_mul_d(lua->ptr.pp_complex[n-1][n-1],scalea)); - for(i=n-2; i>=0; i--) - { - ae_v_cmoved(&tmp->ptr.p_complex[i+1], 1, &lua->ptr.pp_complex[i][i+1], 1, "N", ae_v_len(i+1,n-1), scalea); - v = ae_v_cdotproduct(&tmp->ptr.p_complex[i+1], 1, "N", &xb->ptr.p_complex[i+1], 1, "N", ae_v_len(i+1,n-1)); - xb->ptr.p_complex[i] = ae_c_div(ae_c_sub(xb->ptr.p_complex[i],v),ae_c_mul_d(lua->ptr.pp_complex[i][i],scalea)); - } -} - - -/************************************************************************* -Basic Cholesky solver for ScaleA*Cholesky(A)'*x = y. - -This subroutine assumes that: -* A*ScaleA is well scaled -* A is well-conditioned, so no zero divisions or overflow may occur - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -static void densesolver_hpdbasiccholeskysolve(/* Complex */ ae_matrix* cha, - double sqrtscalea, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* xb, - /* Complex */ ae_vector* tmp, - ae_state *_state) -{ - ae_int_t i; - ae_complex v; - - - - /* - * A = L*L' or A=U'*U - */ - if( isupper ) - { - - /* - * Solve U'*y=b first. - */ - for(i=0; i<=n-1; i++) - { - xb->ptr.p_complex[i] = ae_c_div(xb->ptr.p_complex[i],ae_c_mul_d(ae_c_conj(cha->ptr.pp_complex[i][i], _state),sqrtscalea)); - if( iptr.p_complex[i]; - ae_v_cmoved(&tmp->ptr.p_complex[i+1], 1, &cha->ptr.pp_complex[i][i+1], 1, "Conj", ae_v_len(i+1,n-1), sqrtscalea); - ae_v_csubc(&xb->ptr.p_complex[i+1], 1, &tmp->ptr.p_complex[i+1], 1, "N", ae_v_len(i+1,n-1), v); - } - } - - /* - * Solve U*x=y then. - */ - for(i=n-1; i>=0; i--) - { - if( iptr.p_complex[i+1], 1, &cha->ptr.pp_complex[i][i+1], 1, "N", ae_v_len(i+1,n-1), sqrtscalea); - v = ae_v_cdotproduct(&tmp->ptr.p_complex[i+1], 1, "N", &xb->ptr.p_complex[i+1], 1, "N", ae_v_len(i+1,n-1)); - xb->ptr.p_complex[i] = ae_c_sub(xb->ptr.p_complex[i],v); - } - xb->ptr.p_complex[i] = ae_c_div(xb->ptr.p_complex[i],ae_c_mul_d(cha->ptr.pp_complex[i][i],sqrtscalea)); - } - } - else - { - - /* - * Solve L*y=b first - */ - for(i=0; i<=n-1; i++) - { - if( i>0 ) - { - ae_v_cmoved(&tmp->ptr.p_complex[0], 1, &cha->ptr.pp_complex[i][0], 1, "N", ae_v_len(0,i-1), sqrtscalea); - v = ae_v_cdotproduct(&tmp->ptr.p_complex[0], 1, "N", &xb->ptr.p_complex[0], 1, "N", ae_v_len(0,i-1)); - xb->ptr.p_complex[i] = ae_c_sub(xb->ptr.p_complex[i],v); - } - xb->ptr.p_complex[i] = ae_c_div(xb->ptr.p_complex[i],ae_c_mul_d(cha->ptr.pp_complex[i][i],sqrtscalea)); - } - - /* - * Solve L'*x=y then. - */ - for(i=n-1; i>=0; i--) - { - xb->ptr.p_complex[i] = ae_c_div(xb->ptr.p_complex[i],ae_c_mul_d(ae_c_conj(cha->ptr.pp_complex[i][i], _state),sqrtscalea)); - if( i>0 ) - { - v = xb->ptr.p_complex[i]; - ae_v_cmoved(&tmp->ptr.p_complex[0], 1, &cha->ptr.pp_complex[i][0], 1, "Conj", ae_v_len(0,i-1), sqrtscalea); - ae_v_csubc(&xb->ptr.p_complex[0], 1, &tmp->ptr.p_complex[0], 1, "N", ae_v_len(0,i-1), v); - } - } - } -} - - -ae_bool _densesolverreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - densesolverreport *p = (densesolverreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _densesolverreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - densesolverreport *dst = (densesolverreport*)_dst; - densesolverreport *src = (densesolverreport*)_src; - dst->r1 = src->r1; - dst->rinf = src->rinf; - return ae_true; -} - - -void _densesolverreport_clear(void* _p) -{ - densesolverreport *p = (densesolverreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _densesolverreport_destroy(void* _p) -{ - densesolverreport *p = (densesolverreport*)_p; - ae_touch_ptr((void*)p); -} - - -ae_bool _densesolverlsreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - densesolverlsreport *p = (densesolverlsreport*)_p; - ae_touch_ptr((void*)p); - if( !ae_matrix_init(&p->cx, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _densesolverlsreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - densesolverlsreport *dst = (densesolverlsreport*)_dst; - densesolverlsreport *src = (densesolverlsreport*)_src; - dst->r2 = src->r2; - if( !ae_matrix_init_copy(&dst->cx, &src->cx, _state, make_automatic) ) - return ae_false; - dst->n = src->n; - dst->k = src->k; - return ae_true; -} - - -void _densesolverlsreport_clear(void* _p) -{ - densesolverlsreport *p = (densesolverlsreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_clear(&p->cx); -} - - -void _densesolverlsreport_destroy(void* _p) -{ - densesolverlsreport *p = (densesolverlsreport*)_p; - ae_touch_ptr((void*)p); - ae_matrix_destroy(&p->cx); -} - - - - -/************************************************************************* -This function initializes linear LSQR Solver. This solver is used to solve -non-symmetric (and, possibly, non-square) problems. Least squares solution -is returned for non-compatible systems. - -USAGE: -1. User initializes algorithm state with LinLSQRCreate() call -2. User tunes solver parameters with LinLSQRSetCond() and other functions -3. User calls LinLSQRSolveSparse() function which takes algorithm state - and SparseMatrix object. -4. User calls LinLSQRResults() to get solution -5. Optionally, user may call LinLSQRSolveSparse() again to solve another - problem with different matrix and/or right part without reinitializing - LinLSQRState structure. - -INPUT PARAMETERS: - M - number of rows in A - N - number of variables, N>0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrcreate(ae_int_t m, - ae_int_t n, - linlsqrstate* state, - ae_state *_state) -{ - ae_int_t i; - - _linlsqrstate_clear(state); - - ae_assert(m>0, "LinLSQRCreate: M<=0", _state); - ae_assert(n>0, "LinLSQRCreate: N<=0", _state); - state->m = m; - state->n = n; - state->prectype = 0; - state->epsa = linlsqr_atol; - state->epsb = linlsqr_btol; - state->epsc = 1/ae_sqrt(ae_machineepsilon, _state); - state->maxits = 0; - state->lambdai = 0; - state->xrep = ae_false; - state->running = ae_false; - - /* - * * allocate arrays - * * set RX to NAN (just for the case user calls Results() without - * calling SolveSparse() - * * set B to zero - */ - normestimatorcreate(m, n, 2, 2, &state->nes, _state); - ae_vector_set_length(&state->rx, state->n, _state); - ae_vector_set_length(&state->ui, state->m+state->n, _state); - ae_vector_set_length(&state->uip1, state->m+state->n, _state); - ae_vector_set_length(&state->vip1, state->n, _state); - ae_vector_set_length(&state->vi, state->n, _state); - ae_vector_set_length(&state->omegai, state->n, _state); - ae_vector_set_length(&state->omegaip1, state->n, _state); - ae_vector_set_length(&state->d, state->n, _state); - ae_vector_set_length(&state->x, state->m+state->n, _state); - ae_vector_set_length(&state->mv, state->m+state->n, _state); - ae_vector_set_length(&state->mtv, state->n, _state); - ae_vector_set_length(&state->b, state->m, _state); - for(i=0; i<=n-1; i++) - { - state->rx.ptr.p_double[i] = _state->v_nan; - } - for(i=0; i<=m-1; i++) - { - state->b.ptr.p_double[i] = 0; - } - ae_vector_set_length(&state->rstate.ia, 1+1, _state); - ae_vector_set_length(&state->rstate.ra, 0+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -This function sets right part. By default, right part is zero. - -INPUT PARAMETERS: - B - right part, array[N]. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetb(linlsqrstate* state, - /* Real */ ae_vector* b, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(!state->running, "LinLSQRSetB: you can not change B when LinLSQRIteration is running", _state); - ae_assert(state->m<=b->cnt, "LinLSQRSetB: Length(B)m, _state), "LinLSQRSetB: B contains infinite or NaN values", _state); - state->bnorm2 = 0; - for(i=0; i<=state->m-1; i++) - { - state->b.ptr.p_double[i] = b->ptr.p_double[i]; - state->bnorm2 = state->bnorm2+b->ptr.p_double[i]*b->ptr.p_double[i]; - } -} - - -/************************************************************************* -This function changes preconditioning settings of LinLSQQSolveSparse() -function. By default, SolveSparse() uses diagonal preconditioner, but if -you want to use solver without preconditioning, you can call this function -which forces solver to use unit matrix for preconditioning. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetprecunit(linlsqrstate* state, ae_state *_state) -{ - - - ae_assert(!state->running, "LinLSQRSetPrecUnit: you can not change preconditioner, because function LinLSQRIteration is running!", _state); - state->prectype = -1; -} - - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. LinCGSolveSparse() will use diagonal of the system matrix as -preconditioner. This preconditioning mode is active by default. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetprecdiag(linlsqrstate* state, ae_state *_state) -{ - - - ae_assert(!state->running, "LinLSQRSetPrecDiag: you can not change preconditioner, because function LinCGIteration is running!", _state); - state->prectype = 0; -} - - -/************************************************************************* -This function sets optional Tikhonov regularization coefficient. -It is zero by default. - -INPUT PARAMETERS: - LambdaI - regularization factor, LambdaI>=0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetlambdai(linlsqrstate* state, - double lambdai, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinLSQRSetLambdaI: you can not set LambdaI, because function LinLSQRIteration is running", _state); - ae_assert(ae_isfinite(lambdai, _state)&&ae_fp_greater_eq(lambdai,0), "LinLSQRSetLambdaI: LambdaI is infinite or NaN", _state); - state->lambdai = lambdai; -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -ae_bool linlsqriteration(linlsqrstate* state, ae_state *_state) -{ - ae_int_t summn; - double bnorm; - ae_int_t i; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - summn = state->rstate.ia.ptr.p_int[0]; - i = state->rstate.ia.ptr.p_int[1]; - bnorm = state->rstate.ra.ptr.p_double[0]; - } - else - { - summn = -983; - i = -989; - bnorm = -834; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - - /* - * Routine body - */ - ae_assert(state->b.cnt>0, "LinLSQRIteration: using non-allocated array B", _state); - bnorm = ae_sqrt(state->bnorm2, _state); - state->running = ae_true; - state->repnmv = 0; - linlsqr_clearrfields(state, _state); - state->repiterationscount = 0; - summn = state->m+state->n; - state->r2 = state->bnorm2; - - /* - *estimate for ANorm - */ - normestimatorrestart(&state->nes, _state); -lbl_7: - if( !normestimatoriteration(&state->nes, _state) ) - { - goto lbl_8; - } - if( !state->nes.needmv ) - { - goto lbl_9; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->nes.x.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->repnmv = state->repnmv+1; - linlsqr_clearrfields(state, _state); - state->needmv = ae_true; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->needmv = ae_false; - ae_v_move(&state->nes.mv.ptr.p_double[0], 1, &state->mv.ptr.p_double[0], 1, ae_v_len(0,state->m-1)); - goto lbl_7; -lbl_9: - if( !state->nes.needmtv ) - { - goto lbl_11; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->nes.x.ptr.p_double[0], 1, ae_v_len(0,state->m-1)); - - /* - *matrix-vector multiplication - */ - state->repnmv = state->repnmv+1; - linlsqr_clearrfields(state, _state); - state->needmtv = ae_true; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->needmtv = ae_false; - ae_v_move(&state->nes.mtv.ptr.p_double[0], 1, &state->mtv.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - goto lbl_7; -lbl_11: - goto lbl_7; -lbl_8: - normestimatorresults(&state->nes, &state->anorm, _state); - - /* - *initialize .RX by zeros - */ - for(i=0; i<=state->n-1; i++) - { - state->rx.ptr.p_double[i] = 0; - } - - /* - *output first report - */ - if( !state->xrep ) - { - goto lbl_13; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - linlsqr_clearrfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->xupdated = ae_false; -lbl_13: - - /* - * LSQR, Step 0. - * - * Algorithm outline corresponds to one which was described at p.50 of - * "LSQR - an algorithm for sparse linear equations and sparse least - * squares" by C.Paige and M.Saunders with one small addition - we - * explicitly extend system matrix by additional N lines in order - * to handle non-zero lambda, i.e. original A is replaced by - * [ A ] - * A_mod = [ ] - * [ lambda*I ]. - * - * Step 0: - * x[0] = 0 - * beta[1]*u[1] = b - * alpha[1]*v[1] = A_mod'*u[1] - * w[1] = v[1] - * phiBar[1] = beta[1] - * rhoBar[1] = alpha[1] - * d[0] = 0 - * - * NOTE: - * There are three criteria for stopping: - * (S0) maximum number of iterations - * (S1) ||Rk||<=EpsB*||B||; - * (S2) ||A^T*Rk||/(||A||*||Rk||)<=EpsA. - * It is very important that S2 always checked AFTER S1. It is necessary - * to avoid division by zero when Rk=0. - */ - state->betai = bnorm; - if( ae_fp_eq(state->betai,0) ) - { - - /* - * Zero right part - */ - state->running = ae_false; - state->repterminationtype = 1; - result = ae_false; - return result; - } - for(i=0; i<=summn-1; i++) - { - if( im ) - { - state->ui.ptr.p_double[i] = state->b.ptr.p_double[i]/state->betai; - } - else - { - state->ui.ptr.p_double[i] = 0; - } - state->x.ptr.p_double[i] = state->ui.ptr.p_double[i]; - } - state->repnmv = state->repnmv+1; - linlsqr_clearrfields(state, _state); - state->needmtv = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needmtv = ae_false; - for(i=0; i<=state->n-1; i++) - { - state->mtv.ptr.p_double[i] = state->mtv.ptr.p_double[i]+state->lambdai*state->ui.ptr.p_double[state->m+i]; - } - state->alphai = 0; - for(i=0; i<=state->n-1; i++) - { - state->alphai = state->alphai+state->mtv.ptr.p_double[i]*state->mtv.ptr.p_double[i]; - } - state->alphai = ae_sqrt(state->alphai, _state); - if( ae_fp_eq(state->alphai,0) ) - { - - /* - * Orthogonality stopping criterion is met - */ - state->running = ae_false; - state->repterminationtype = 4; - result = ae_false; - return result; - } - for(i=0; i<=state->n-1; i++) - { - state->vi.ptr.p_double[i] = state->mtv.ptr.p_double[i]/state->alphai; - state->omegai.ptr.p_double[i] = state->vi.ptr.p_double[i]; - } - state->phibari = state->betai; - state->rhobari = state->alphai; - for(i=0; i<=state->n-1; i++) - { - state->d.ptr.p_double[i] = 0; - } - state->dnorm = 0; - - /* - * Steps I=1, 2, ... - */ -lbl_15: - if( ae_false ) - { - goto lbl_16; - } - - /* - * At I-th step State.RepIterationsCount=I. - */ - state->repiterationscount = state->repiterationscount+1; - - /* - * Bidiagonalization part: - * beta[i+1]*u[i+1] = A_mod*v[i]-alpha[i]*u[i] - * alpha[i+1]*v[i+1] = A_mod'*u[i+1] - beta[i+1]*v[i] - * - * NOTE: beta[i+1]=0 or alpha[i+1]=0 will lead to successful termination - * in the end of the current iteration. In this case u/v are zero. - * NOTE2: algorithm won't fail on zero alpha or beta (there will be no - * division by zero because it will be stopped BEFORE division - * occurs). However, near-zero alpha and beta won't stop algorithm - * and, although no division by zero will happen, orthogonality - * in U and V will be lost. - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->vi.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->repnmv = state->repnmv+1; - linlsqr_clearrfields(state, _state); - state->needmv = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->needmv = ae_false; - for(i=0; i<=state->n-1; i++) - { - state->mv.ptr.p_double[state->m+i] = state->lambdai*state->vi.ptr.p_double[i]; - } - state->betaip1 = 0; - for(i=0; i<=summn-1; i++) - { - state->uip1.ptr.p_double[i] = state->mv.ptr.p_double[i]-state->alphai*state->ui.ptr.p_double[i]; - state->betaip1 = state->betaip1+state->uip1.ptr.p_double[i]*state->uip1.ptr.p_double[i]; - } - if( ae_fp_neq(state->betaip1,0) ) - { - state->betaip1 = ae_sqrt(state->betaip1, _state); - for(i=0; i<=summn-1; i++) - { - state->uip1.ptr.p_double[i] = state->uip1.ptr.p_double[i]/state->betaip1; - } - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->uip1.ptr.p_double[0], 1, ae_v_len(0,state->m-1)); - state->repnmv = state->repnmv+1; - linlsqr_clearrfields(state, _state); - state->needmtv = ae_true; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->needmtv = ae_false; - for(i=0; i<=state->n-1; i++) - { - state->mtv.ptr.p_double[i] = state->mtv.ptr.p_double[i]+state->lambdai*state->uip1.ptr.p_double[state->m+i]; - } - state->alphaip1 = 0; - for(i=0; i<=state->n-1; i++) - { - state->vip1.ptr.p_double[i] = state->mtv.ptr.p_double[i]-state->betaip1*state->vi.ptr.p_double[i]; - state->alphaip1 = state->alphaip1+state->vip1.ptr.p_double[i]*state->vip1.ptr.p_double[i]; - } - if( ae_fp_neq(state->alphaip1,0) ) - { - state->alphaip1 = ae_sqrt(state->alphaip1, _state); - for(i=0; i<=state->n-1; i++) - { - state->vip1.ptr.p_double[i] = state->vip1.ptr.p_double[i]/state->alphaip1; - } - } - - /* - * Build next orthogonal transformation - */ - state->rhoi = safepythag2(state->rhobari, state->betaip1, _state); - state->ci = state->rhobari/state->rhoi; - state->si = state->betaip1/state->rhoi; - state->theta = state->si*state->alphaip1; - state->rhobarip1 = -state->ci*state->alphaip1; - state->phii = state->ci*state->phibari; - state->phibarip1 = state->si*state->phibari; - - /* - * Update .RNorm - * - * This tricky formula is necessary because simply writing - * State.R2:=State.PhiBarIP1*State.PhiBarIP1 does NOT guarantees - * monotonic decrease of R2. Roundoff error combined with 80-bit - * precision used internally by Intel chips allows R2 to increase - * slightly in some rare, but possible cases. This property is - * undesirable, so we prefer to guard against R increase. - */ - state->r2 = ae_minreal(state->r2, state->phibarip1*state->phibarip1, _state); - - /* - * Update d and DNorm, check condition-related stopping criteria - */ - for(i=0; i<=state->n-1; i++) - { - state->d.ptr.p_double[i] = 1/state->rhoi*(state->vi.ptr.p_double[i]-state->theta*state->d.ptr.p_double[i]); - state->dnorm = state->dnorm+state->d.ptr.p_double[i]*state->d.ptr.p_double[i]; - } - if( ae_fp_greater_eq(ae_sqrt(state->dnorm, _state)*state->anorm,state->epsc) ) - { - state->running = ae_false; - state->repterminationtype = 7; - result = ae_false; - return result; - } - - /* - * Update x, output report - */ - for(i=0; i<=state->n-1; i++) - { - state->rx.ptr.p_double[i] = state->rx.ptr.p_double[i]+state->phii/state->rhoi*state->omegai.ptr.p_double[i]; - } - if( !state->xrep ) - { - goto lbl_17; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - linlsqr_clearrfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->xupdated = ae_false; -lbl_17: - - /* - * Check stopping criteria - * 1. achieved required number of iterations; - * 2. ||Rk||<=EpsB*||B||; - * 3. ||A^T*Rk||/(||A||*||Rk||)<=EpsA; - */ - if( state->maxits>0&&state->repiterationscount>=state->maxits ) - { - - /* - * Achieved required number of iterations - */ - state->running = ae_false; - state->repterminationtype = 5; - result = ae_false; - return result; - } - if( ae_fp_less_eq(state->phibarip1,state->epsb*bnorm) ) - { - - /* - * ||Rk||<=EpsB*||B||, here ||Rk||=PhiBar - */ - state->running = ae_false; - state->repterminationtype = 1; - result = ae_false; - return result; - } - if( ae_fp_less_eq(state->alphaip1*ae_fabs(state->ci, _state)/state->anorm,state->epsa) ) - { - - /* - * ||A^T*Rk||/(||A||*||Rk||)<=EpsA, here ||A^T*Rk||=PhiBar*Alpha[i+1]*|.C| - */ - state->running = ae_false; - state->repterminationtype = 4; - result = ae_false; - return result; - } - - /* - * Update omega - */ - for(i=0; i<=state->n-1; i++) - { - state->omegaip1.ptr.p_double[i] = state->vip1.ptr.p_double[i]-state->theta/state->rhoi*state->omegai.ptr.p_double[i]; - } - - /* - * Prepare for the next iteration - rename variables: - * u[i] := u[i+1] - * v[i] := v[i+1] - * rho[i] := rho[i+1] - * ... - */ - ae_v_move(&state->ui.ptr.p_double[0], 1, &state->uip1.ptr.p_double[0], 1, ae_v_len(0,summn-1)); - ae_v_move(&state->vi.ptr.p_double[0], 1, &state->vip1.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_v_move(&state->omegai.ptr.p_double[0], 1, &state->omegaip1.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->alphai = state->alphaip1; - state->betai = state->betaip1; - state->phibari = state->phibarip1; - state->rhobari = state->rhobarip1; - goto lbl_15; -lbl_16: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = summn; - state->rstate.ia.ptr.p_int[1] = i; - state->rstate.ra.ptr.p_double[0] = bnorm; - return result; -} - - -/************************************************************************* -Procedure for solution of A*x=b with sparse A. - -INPUT PARAMETERS: - State - algorithm state - A - sparse M*N matrix in the CRS format (you MUST contvert it - to CRS format by calling SparseConvertToCRS() function - BEFORE you pass it to this function). - B - right part, array[M] - -RESULT: - This function returns no result. - You can get solution by calling LinCGResults() - -NOTE: this function uses lightweight preconditioning - multiplication by - inverse of diag(A). If you want, you can turn preconditioning off by - calling LinLSQRSetPrecUnit(). However, preconditioning cost is low - and preconditioner is very important for solution of badly scaled - problems. - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsolvesparse(linlsqrstate* state, - sparsematrix* a, - /* Real */ ae_vector* b, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - ae_int_t j; - ae_int_t t0; - ae_int_t t1; - double v; - - - n = state->n; - ae_assert(!state->running, "LinLSQRSolveSparse: you can not call this function when LinLSQRIteration is running", _state); - ae_assert(b->cnt>=state->m, "LinLSQRSolveSparse: Length(B)m, _state), "LinLSQRSolveSparse: B contains infinite or NaN values", _state); - - /* - * Allocate temporaries - */ - rvectorsetlengthatleast(&state->tmpd, n, _state); - rvectorsetlengthatleast(&state->tmpx, n, _state); - - /* - * Compute diagonal scaling matrix D - */ - if( state->prectype==0 ) - { - - /* - * Default preconditioner - inverse of column norms - */ - for(i=0; i<=n-1; i++) - { - state->tmpd.ptr.p_double[i] = 0; - } - t0 = 0; - t1 = 0; - while(sparseenumerate(a, &t0, &t1, &i, &j, &v, _state)) - { - state->tmpd.ptr.p_double[j] = state->tmpd.ptr.p_double[j]+ae_sqr(v, _state); - } - for(i=0; i<=n-1; i++) - { - if( ae_fp_greater(state->tmpd.ptr.p_double[i],0) ) - { - state->tmpd.ptr.p_double[i] = 1/ae_sqrt(state->tmpd.ptr.p_double[i], _state); - } - else - { - state->tmpd.ptr.p_double[i] = 1; - } - } - } - else - { - - /* - * No diagonal scaling - */ - for(i=0; i<=n-1; i++) - { - state->tmpd.ptr.p_double[i] = 1; - } - } - - /* - * Solve. - * - * Instead of solving A*x=b we solve preconditioned system (A*D)*(inv(D)*x)=b. - * Transformed A is not calculated explicitly, we just modify multiplication - * by A or A'. After solution we modify State.RX so it will store untransformed - * variables - */ - linlsqrsetb(state, b, _state); - linlsqrrestart(state, _state); - while(linlsqriteration(state, _state)) - { - if( state->needmv ) - { - for(i=0; i<=n-1; i++) - { - state->tmpx.ptr.p_double[i] = state->tmpd.ptr.p_double[i]*state->x.ptr.p_double[i]; - } - sparsemv(a, &state->tmpx, &state->mv, _state); - } - if( state->needmtv ) - { - sparsemtv(a, &state->x, &state->mtv, _state); - for(i=0; i<=n-1; i++) - { - state->mtv.ptr.p_double[i] = state->tmpd.ptr.p_double[i]*state->mtv.ptr.p_double[i]; - } - } - } - for(i=0; i<=n-1; i++) - { - state->rx.ptr.p_double[i] = state->tmpd.ptr.p_double[i]*state->rx.ptr.p_double[i]; - } -} - - -/************************************************************************* -This function sets stopping criteria. - -INPUT PARAMETERS: - EpsA - algorithm will be stopped if ||A^T*Rk||/(||A||*||Rk||)<=EpsA. - EpsB - algorithm will be stopped if ||Rk||<=EpsB*||B|| - MaxIts - algorithm will be stopped if number of iterations - more than MaxIts. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: if EpsA,EpsB,EpsC and MaxIts are zero then these variables will -be setted as default values. - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetcond(linlsqrstate* state, - double epsa, - double epsb, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinLSQRSetCond: you can not call this function when LinLSQRIteration is running", _state); - ae_assert(ae_isfinite(epsa, _state)&&ae_fp_greater_eq(epsa,0), "LinLSQRSetCond: EpsA is negative, INF or NAN", _state); - ae_assert(ae_isfinite(epsb, _state)&&ae_fp_greater_eq(epsb,0), "LinLSQRSetCond: EpsB is negative, INF or NAN", _state); - ae_assert(maxits>=0, "LinLSQRSetCond: MaxIts is negative", _state); - if( (ae_fp_eq(epsa,0)&&ae_fp_eq(epsb,0))&&maxits==0 ) - { - state->epsa = linlsqr_atol; - state->epsb = linlsqr_btol; - state->maxits = state->n; - } - else - { - state->epsa = epsa; - state->epsb = epsb; - state->maxits = maxits; - } -} - - -/************************************************************************* -LSQR solver: results. - -This function must be called after LinLSQRSolve - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[N], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * 1 ||Rk||<=EpsB*||B|| - * 4 ||A^T*Rk||/(||A||*||Rk||)<=EpsA - * 5 MaxIts steps was taken - * 7 rounding errors prevent further progress, - X contains best point found so far. - (sometimes returned on singular systems) - * Rep.IterationsCount contains iterations count - * NMV countains number of matrix-vector calculations - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrresults(linlsqrstate* state, - /* Real */ ae_vector* x, - linlsqrreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _linlsqrreport_clear(rep); - - ae_assert(!state->running, "LinLSQRResult: you can not call this function when LinLSQRIteration is running", _state); - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->nmv = state->repnmv; - rep->terminationtype = state->repterminationtype; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetxrep(linlsqrstate* state, - ae_bool needxrep, - ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function restarts LinLSQRIteration - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrrestart(linlsqrstate* state, ae_state *_state) -{ - - - ae_vector_set_length(&state->rstate.ia, 1+1, _state); - ae_vector_set_length(&state->rstate.ra, 0+1, _state); - state->rstate.stage = -1; - linlsqr_clearrfields(state, _state); -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void linlsqr_clearrfields(linlsqrstate* state, ae_state *_state) -{ - - - state->xupdated = ae_false; - state->needmv = ae_false; - state->needmtv = ae_false; - state->needmv2 = ae_false; - state->needvmv = ae_false; - state->needprec = ae_false; -} - - -ae_bool _linlsqrstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - linlsqrstate *p = (linlsqrstate*)_p; - ae_touch_ptr((void*)p); - if( !_normestimatorstate_init(&p->nes, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->b, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->ui, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->uip1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->vi, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->vip1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->omegai, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->omegaip1, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->d, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mv, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mtv, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpd, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _linlsqrstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - linlsqrstate *dst = (linlsqrstate*)_dst; - linlsqrstate *src = (linlsqrstate*)_src; - if( !_normestimatorstate_init_copy(&dst->nes, &src->nes, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rx, &src->rx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->b, &src->b, _state, make_automatic) ) - return ae_false; - dst->n = src->n; - dst->m = src->m; - dst->prectype = src->prectype; - if( !ae_vector_init_copy(&dst->ui, &src->ui, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->uip1, &src->uip1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->vi, &src->vi, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->vip1, &src->vip1, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->omegai, &src->omegai, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->omegaip1, &src->omegaip1, _state, make_automatic) ) - return ae_false; - dst->alphai = src->alphai; - dst->alphaip1 = src->alphaip1; - dst->betai = src->betai; - dst->betaip1 = src->betaip1; - dst->phibari = src->phibari; - dst->phibarip1 = src->phibarip1; - dst->phii = src->phii; - dst->rhobari = src->rhobari; - dst->rhobarip1 = src->rhobarip1; - dst->rhoi = src->rhoi; - dst->ci = src->ci; - dst->si = src->si; - dst->theta = src->theta; - dst->lambdai = src->lambdai; - if( !ae_vector_init_copy(&dst->d, &src->d, _state, make_automatic) ) - return ae_false; - dst->anorm = src->anorm; - dst->bnorm2 = src->bnorm2; - dst->dnorm = src->dnorm; - dst->r2 = src->r2; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mv, &src->mv, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mtv, &src->mtv, _state, make_automatic) ) - return ae_false; - dst->epsa = src->epsa; - dst->epsb = src->epsb; - dst->epsc = src->epsc; - dst->maxits = src->maxits; - dst->xrep = src->xrep; - dst->xupdated = src->xupdated; - dst->needmv = src->needmv; - dst->needmtv = src->needmtv; - dst->needmv2 = src->needmv2; - dst->needvmv = src->needvmv; - dst->needprec = src->needprec; - dst->repiterationscount = src->repiterationscount; - dst->repnmv = src->repnmv; - dst->repterminationtype = src->repterminationtype; - dst->running = src->running; - if( !ae_vector_init_copy(&dst->tmpd, &src->tmpd, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->tmpx, &src->tmpx, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _linlsqrstate_clear(void* _p) -{ - linlsqrstate *p = (linlsqrstate*)_p; - ae_touch_ptr((void*)p); - _normestimatorstate_clear(&p->nes); - ae_vector_clear(&p->rx); - ae_vector_clear(&p->b); - ae_vector_clear(&p->ui); - ae_vector_clear(&p->uip1); - ae_vector_clear(&p->vi); - ae_vector_clear(&p->vip1); - ae_vector_clear(&p->omegai); - ae_vector_clear(&p->omegaip1); - ae_vector_clear(&p->d); - ae_vector_clear(&p->x); - ae_vector_clear(&p->mv); - ae_vector_clear(&p->mtv); - ae_vector_clear(&p->tmpd); - ae_vector_clear(&p->tmpx); - _rcommstate_clear(&p->rstate); -} - - -void _linlsqrstate_destroy(void* _p) -{ - linlsqrstate *p = (linlsqrstate*)_p; - ae_touch_ptr((void*)p); - _normestimatorstate_destroy(&p->nes); - ae_vector_destroy(&p->rx); - ae_vector_destroy(&p->b); - ae_vector_destroy(&p->ui); - ae_vector_destroy(&p->uip1); - ae_vector_destroy(&p->vi); - ae_vector_destroy(&p->vip1); - ae_vector_destroy(&p->omegai); - ae_vector_destroy(&p->omegaip1); - ae_vector_destroy(&p->d); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->mv); - ae_vector_destroy(&p->mtv); - ae_vector_destroy(&p->tmpd); - ae_vector_destroy(&p->tmpx); - _rcommstate_destroy(&p->rstate); -} - - -ae_bool _linlsqrreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - linlsqrreport *p = (linlsqrreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _linlsqrreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - linlsqrreport *dst = (linlsqrreport*)_dst; - linlsqrreport *src = (linlsqrreport*)_src; - dst->iterationscount = src->iterationscount; - dst->nmv = src->nmv; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _linlsqrreport_clear(void* _p) -{ - linlsqrreport *p = (linlsqrreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _linlsqrreport_destroy(void* _p) -{ - linlsqrreport *p = (linlsqrreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* -This function initializes linear CG Solver. This solver is used to solve -symmetric positive definite problems. If you want to solve nonsymmetric -(or non-positive definite) problem you may use LinLSQR solver provided by -ALGLIB. - -USAGE: -1. User initializes algorithm state with LinCGCreate() call -2. User tunes solver parameters with LinCGSetCond() and other functions -3. Optionally, user sets starting point with LinCGSetStartingPoint() -4. User calls LinCGSolveSparse() function which takes algorithm state and - SparseMatrix object. -5. User calls LinCGResults() to get solution -6. Optionally, user may call LinCGSolveSparse() again to solve another - problem with different matrix and/or right part without reinitializing - LinCGState structure. - -INPUT PARAMETERS: - N - problem dimension, N>0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgcreate(ae_int_t n, lincgstate* state, ae_state *_state) -{ - ae_int_t i; - - _lincgstate_clear(state); - - ae_assert(n>0, "LinCGCreate: N<=0", _state); - state->n = n; - state->prectype = 0; - state->itsbeforerestart = n; - state->itsbeforerupdate = 10; - state->epsf = lincg_defaultprecision; - state->maxits = 0; - state->xrep = ae_false; - state->running = ae_false; - - /* - * * allocate arrays - * * set RX to NAN (just for the case user calls Results() without - * calling SolveSparse() - * * set starting point to zero - * * we do NOT initialize B here because we assume that user should - * initializate it using LinCGSetB() function. In case he forgets - * to do so, exception will be thrown in the LinCGIteration(). - */ - ae_vector_set_length(&state->rx, state->n, _state); - ae_vector_set_length(&state->startx, state->n, _state); - ae_vector_set_length(&state->b, state->n, _state); - for(i=0; i<=state->n-1; i++) - { - state->rx.ptr.p_double[i] = _state->v_nan; - state->startx.ptr.p_double[i] = 0.0; - state->b.ptr.p_double[i] = 0; - } - ae_vector_set_length(&state->cx, state->n, _state); - ae_vector_set_length(&state->p, state->n, _state); - ae_vector_set_length(&state->r, state->n, _state); - ae_vector_set_length(&state->cr, state->n, _state); - ae_vector_set_length(&state->z, state->n, _state); - ae_vector_set_length(&state->cz, state->n, _state); - ae_vector_set_length(&state->x, state->n, _state); - ae_vector_set_length(&state->mv, state->n, _state); - ae_vector_set_length(&state->pv, state->n, _state); - lincg_updateitersdata(state, _state); - ae_vector_set_length(&state->rstate.ia, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; -} - - -/************************************************************************* -This function sets starting point. -By default, zero starting point is used. - -INPUT PARAMETERS: - X - starting point, array[N] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetstartingpoint(lincgstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetStartingPoint: you can not change starting point because LinCGIteration() function is running", _state); - ae_assert(state->n<=x->cnt, "LinCGSetStartingPoint: Length(X)n, _state), "LinCGSetStartingPoint: X contains infinite or NaN values!", _state); - ae_v_move(&state->startx.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); -} - - -/************************************************************************* -This function sets right part. By default, right part is zero. - -INPUT PARAMETERS: - B - right part, array[N]. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetb(lincgstate* state, - /* Real */ ae_vector* b, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetB: you can not set B, because function LinCGIteration is running!", _state); - ae_assert(b->cnt>=state->n, "LinCGSetB: Length(B)n, _state), "LinCGSetB: B contains infinite or NaN values!", _state); - ae_v_move(&state->b.ptr.p_double[0], 1, &b->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); -} - - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. By default, SolveSparse() uses diagonal preconditioner, but if -you want to use solver without preconditioning, you can call this function -which forces solver to use unit matrix for preconditioning. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void lincgsetprecunit(lincgstate* state, ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetPrecUnit: you can not change preconditioner, because function LinCGIteration is running!", _state); - state->prectype = -1; -} - - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. LinCGSolveSparse() will use diagonal of the system matrix as -preconditioner. This preconditioning mode is active by default. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void lincgsetprecdiag(lincgstate* state, ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetPrecDiag: you can not change preconditioner, because function LinCGIteration is running!", _state); - state->prectype = 0; -} - - -/************************************************************************* -This function sets stopping criteria. - -INPUT PARAMETERS: - EpsF - algorithm will be stopped if norm of residual is less than - EpsF*||b||. - MaxIts - algorithm will be stopped if number of iterations is more - than MaxIts. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -If both EpsF and MaxIts are zero then small EpsF will be set to small -value. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetcond(lincgstate* state, - double epsf, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetCond: you can not change stopping criteria when LinCGIteration() is running", _state); - ae_assert(ae_isfinite(epsf, _state)&&ae_fp_greater_eq(epsf,0), "LinCGSetCond: EpsF is negative or contains infinite or NaN values", _state); - ae_assert(maxits>=0, "LinCGSetCond: MaxIts is negative", _state); - if( ae_fp_eq(epsf,0)&&maxits==0 ) - { - state->epsf = lincg_defaultprecision; - state->maxits = maxits; - } - else - { - state->epsf = epsf; - state->maxits = maxits; - } -} - - -/************************************************************************* -Reverse communication version of linear CG. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -ae_bool lincgiteration(lincgstate* state, ae_state *_state) -{ - ae_int_t i; - double uvar; - double bnorm; - double v; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - i = state->rstate.ia.ptr.p_int[0]; - uvar = state->rstate.ra.ptr.p_double[0]; - bnorm = state->rstate.ra.ptr.p_double[1]; - v = state->rstate.ra.ptr.p_double[2]; - } - else - { - i = -983; - uvar = -989; - bnorm = -834; - v = 900; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - if( state->rstate.stage==5 ) - { - goto lbl_5; - } - if( state->rstate.stage==6 ) - { - goto lbl_6; - } - if( state->rstate.stage==7 ) - { - goto lbl_7; - } - - /* - * Routine body - */ - ae_assert(state->b.cnt>0, "LinCGIteration: B is not initialized (you must initialize B by LinCGSetB() call", _state); - state->running = ae_true; - state->repnmv = 0; - lincg_clearrfields(state, _state); - lincg_updateitersdata(state, _state); - - /* - * Start 0-th iteration - */ - ae_v_move(&state->rx.ptr.p_double[0], 1, &state->startx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_v_move(&state->x.ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->repnmv = state->repnmv+1; - lincg_clearrfields(state, _state); - state->needvmv = ae_true; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->needvmv = ae_false; - bnorm = 0; - state->r2 = 0; - state->meritfunction = 0; - for(i=0; i<=state->n-1; i++) - { - state->r.ptr.p_double[i] = state->b.ptr.p_double[i]-state->mv.ptr.p_double[i]; - state->r2 = state->r2+state->r.ptr.p_double[i]*state->r.ptr.p_double[i]; - state->meritfunction = state->meritfunction+state->mv.ptr.p_double[i]*state->rx.ptr.p_double[i]-2*state->b.ptr.p_double[i]*state->rx.ptr.p_double[i]; - bnorm = bnorm+state->b.ptr.p_double[i]*state->b.ptr.p_double[i]; - } - bnorm = ae_sqrt(bnorm, _state); - - /* - * Output first report - */ - if( !state->xrep ) - { - goto lbl_8; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - lincg_clearrfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->xupdated = ae_false; -lbl_8: - - /* - * Is x0 a solution? - */ - if( !ae_isfinite(state->r2, _state)||ae_fp_less_eq(ae_sqrt(state->r2, _state),state->epsf*bnorm) ) - { - state->running = ae_false; - if( ae_isfinite(state->r2, _state) ) - { - state->repterminationtype = 1; - } - else - { - state->repterminationtype = -4; - } - result = ae_false; - return result; - } - - /* - * Calculate Z and P - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->r.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->repnmv = state->repnmv+1; - lincg_clearrfields(state, _state); - state->needprec = ae_true; - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->needprec = ae_false; - for(i=0; i<=state->n-1; i++) - { - state->z.ptr.p_double[i] = state->pv.ptr.p_double[i]; - state->p.ptr.p_double[i] = state->z.ptr.p_double[i]; - } - - /* - * Other iterations(1..N) - */ - state->repiterationscount = 0; -lbl_10: - if( ae_false ) - { - goto lbl_11; - } - state->repiterationscount = state->repiterationscount+1; - - /* - * Calculate Alpha - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->p.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->repnmv = state->repnmv+1; - lincg_clearrfields(state, _state); - state->needvmv = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needvmv = ae_false; - if( !ae_isfinite(state->vmv, _state)||ae_fp_less_eq(state->vmv,0) ) - { - - /* - * a) Overflow when calculating VMV - * b) non-positive VMV (non-SPD matrix) - */ - state->running = ae_false; - if( ae_isfinite(state->vmv, _state) ) - { - state->repterminationtype = -5; - } - else - { - state->repterminationtype = -4; - } - result = ae_false; - return result; - } - state->alpha = 0; - for(i=0; i<=state->n-1; i++) - { - state->alpha = state->alpha+state->r.ptr.p_double[i]*state->z.ptr.p_double[i]; - } - state->alpha = state->alpha/state->vmv; - if( !ae_isfinite(state->alpha, _state) ) - { - - /* - * Overflow when calculating Alpha - */ - state->running = ae_false; - state->repterminationtype = -4; - result = ae_false; - return result; - } - - /* - * Next step toward solution - */ - for(i=0; i<=state->n-1; i++) - { - state->cx.ptr.p_double[i] = state->rx.ptr.p_double[i]+state->alpha*state->p.ptr.p_double[i]; - } - - /* - * Calculate R: - * * use recurrent relation to update R - * * at every ItsBeforeRUpdate-th iteration recalculate it from scratch, using matrix-vector product - * in case R grows instead of decreasing, algorithm is terminated with positive completion code - */ - if( !(state->itsbeforerupdate==0||state->repiterationscount%state->itsbeforerupdate!=0) ) - { - goto lbl_12; - } - - /* - * Calculate R using recurrent formula - */ - for(i=0; i<=state->n-1; i++) - { - state->cr.ptr.p_double[i] = state->r.ptr.p_double[i]-state->alpha*state->mv.ptr.p_double[i]; - state->x.ptr.p_double[i] = state->cr.ptr.p_double[i]; - } - goto lbl_13; -lbl_12: - - /* - * Calculate R using matrix-vector multiplication - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->cx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->repnmv = state->repnmv+1; - lincg_clearrfields(state, _state); - state->needmv = ae_true; - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->needmv = ae_false; - for(i=0; i<=state->n-1; i++) - { - state->cr.ptr.p_double[i] = state->b.ptr.p_double[i]-state->mv.ptr.p_double[i]; - state->x.ptr.p_double[i] = state->cr.ptr.p_double[i]; - } - - /* - * Calculating merit function - * Check emergency stopping criterion - */ - v = 0; - for(i=0; i<=state->n-1; i++) - { - v = v+state->mv.ptr.p_double[i]*state->cx.ptr.p_double[i]-2*state->b.ptr.p_double[i]*state->cx.ptr.p_double[i]; - } - if( ae_fp_less(v,state->meritfunction) ) - { - goto lbl_14; - } - for(i=0; i<=state->n-1; i++) - { - if( !ae_isfinite(state->rx.ptr.p_double[i], _state) ) - { - state->running = ae_false; - state->repterminationtype = -4; - result = ae_false; - return result; - } - } - - /* - *output last report - */ - if( !state->xrep ) - { - goto lbl_16; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - lincg_clearrfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 5; - goto lbl_rcomm; -lbl_5: - state->xupdated = ae_false; -lbl_16: - state->running = ae_false; - state->repterminationtype = 7; - result = ae_false; - return result; -lbl_14: - state->meritfunction = v; -lbl_13: - ae_v_move(&state->rx.ptr.p_double[0], 1, &state->cx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - - /* - * calculating RNorm - * - * NOTE: monotonic decrease of R2 is not guaranteed by algorithm. - */ - state->r2 = 0; - for(i=0; i<=state->n-1; i++) - { - state->r2 = state->r2+state->cr.ptr.p_double[i]*state->cr.ptr.p_double[i]; - } - - /* - *output report - */ - if( !state->xrep ) - { - goto lbl_18; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - lincg_clearrfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 6; - goto lbl_rcomm; -lbl_6: - state->xupdated = ae_false; -lbl_18: - - /* - *stopping criterion - *achieved the required precision - */ - if( !ae_isfinite(state->r2, _state)||ae_fp_less_eq(ae_sqrt(state->r2, _state),state->epsf*bnorm) ) - { - state->running = ae_false; - if( ae_isfinite(state->r2, _state) ) - { - state->repterminationtype = 1; - } - else - { - state->repterminationtype = -4; - } - result = ae_false; - return result; - } - if( state->repiterationscount>=state->maxits&&state->maxits>0 ) - { - for(i=0; i<=state->n-1; i++) - { - if( !ae_isfinite(state->rx.ptr.p_double[i], _state) ) - { - state->running = ae_false; - state->repterminationtype = -4; - result = ae_false; - return result; - } - } - - /* - *if X is finite number - */ - state->running = ae_false; - state->repterminationtype = 5; - result = ae_false; - return result; - } - ae_v_move(&state->x.ptr.p_double[0], 1, &state->cr.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - - /* - *prepere of parameters for next iteration - */ - state->repnmv = state->repnmv+1; - lincg_clearrfields(state, _state); - state->needprec = ae_true; - state->rstate.stage = 7; - goto lbl_rcomm; -lbl_7: - state->needprec = ae_false; - ae_v_move(&state->cz.ptr.p_double[0], 1, &state->pv.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - if( state->repiterationscount%state->itsbeforerestart!=0 ) - { - state->beta = 0; - uvar = 0; - for(i=0; i<=state->n-1; i++) - { - state->beta = state->beta+state->cz.ptr.p_double[i]*state->cr.ptr.p_double[i]; - uvar = uvar+state->z.ptr.p_double[i]*state->r.ptr.p_double[i]; - } - - /* - *check that UVar is't INF or is't zero - */ - if( !ae_isfinite(uvar, _state)||ae_fp_eq(uvar,0) ) - { - state->running = ae_false; - state->repterminationtype = -4; - result = ae_false; - return result; - } - - /* - *calculate .BETA - */ - state->beta = state->beta/uvar; - - /* - *check that .BETA neither INF nor NaN - */ - if( !ae_isfinite(state->beta, _state) ) - { - state->running = ae_false; - state->repterminationtype = -1; - result = ae_false; - return result; - } - for(i=0; i<=state->n-1; i++) - { - state->p.ptr.p_double[i] = state->cz.ptr.p_double[i]+state->beta*state->p.ptr.p_double[i]; - } - } - else - { - ae_v_move(&state->p.ptr.p_double[0], 1, &state->cz.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - } - - /* - *prepere data for next iteration - */ - for(i=0; i<=state->n-1; i++) - { - - /* - *write (k+1)th iteration to (k )th iteration - */ - state->r.ptr.p_double[i] = state->cr.ptr.p_double[i]; - state->z.ptr.p_double[i] = state->cz.ptr.p_double[i]; - } - goto lbl_10; -lbl_11: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = i; - state->rstate.ra.ptr.p_double[0] = uvar; - state->rstate.ra.ptr.p_double[1] = bnorm; - state->rstate.ra.ptr.p_double[2] = v; - return result; -} - - -/************************************************************************* -Procedure for solution of A*x=b with sparse A. - -INPUT PARAMETERS: - State - algorithm state - A - sparse matrix in the CRS format (you MUST contvert it to - CRS format by calling SparseConvertToCRS() function). - IsUpper - whether upper or lower triangle of A is used: - * IsUpper=True => only upper triangle is used and lower - triangle is not referenced at all - * IsUpper=False => only lower triangle is used and upper - triangle is not referenced at all - B - right part, array[N] - -RESULT: - This function returns no result. - You can get solution by calling LinCGResults() - -NOTE: this function uses lightweight preconditioning - multiplication by - inverse of diag(A). If you want, you can turn preconditioning off by - calling LinCGSetPrecUnit(). However, preconditioning cost is low and - preconditioner is very important for solution of badly scaled - problems. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsolvesparse(lincgstate* state, - sparsematrix* a, - ae_bool isupper, - /* Real */ ae_vector* b, - ae_state *_state) -{ - ae_int_t n; - ae_int_t i; - double v; - double vmv; - - - n = state->n; - ae_assert(b->cnt>=state->n, "LinCGSetB: Length(B)n, _state), "LinCGSetB: B contains infinite or NaN values!", _state); - - /* - * Allocate temporaries - */ - rvectorsetlengthatleast(&state->tmpd, n, _state); - - /* - * Compute diagonal scaling matrix D - */ - if( state->prectype==0 ) - { - - /* - * Default preconditioner - inverse of matrix diagonal - */ - for(i=0; i<=n-1; i++) - { - v = sparsegetdiagonal(a, i, _state); - if( ae_fp_greater(v,0) ) - { - state->tmpd.ptr.p_double[i] = 1/ae_sqrt(v, _state); - } - else - { - state->tmpd.ptr.p_double[i] = 1; - } - } - } - else - { - - /* - * No diagonal scaling - */ - for(i=0; i<=n-1; i++) - { - state->tmpd.ptr.p_double[i] = 1; - } - } - - /* - * Solve - */ - lincgrestart(state, _state); - lincgsetb(state, b, _state); - while(lincgiteration(state, _state)) - { - - /* - * Process different requests from optimizer - */ - if( state->needmv ) - { - sparsesmv(a, isupper, &state->x, &state->mv, _state); - } - if( state->needvmv ) - { - sparsesmv(a, isupper, &state->x, &state->mv, _state); - vmv = ae_v_dotproduct(&state->x.ptr.p_double[0], 1, &state->mv.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - state->vmv = vmv; - } - if( state->needprec ) - { - for(i=0; i<=n-1; i++) - { - state->pv.ptr.p_double[i] = state->x.ptr.p_double[i]*ae_sqr(state->tmpd.ptr.p_double[i], _state); - } - } - } -} - - -/************************************************************************* -CG-solver: results. - -This function must be called after LinCGSolve - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[N], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -5 input matrix is either not positive definite, - too large or too small - * -4 overflow/underflow during solution - (ill conditioned problem) - * 1 ||residual||<=EpsF*||b|| - * 5 MaxIts steps was taken - * 7 rounding errors prevent further progress, - best point found is returned - * Rep.IterationsCount contains iterations count - * NMV countains number of matrix-vector calculations - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgresults(lincgstate* state, - /* Real */ ae_vector* x, - lincgreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _lincgreport_clear(rep); - - ae_assert(!state->running, "LinCGResult: you can not get result, because function LinCGIteration has been launched!", _state); - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->rx.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->nmv = state->repnmv; - rep->terminationtype = state->repterminationtype; - rep->r2 = state->r2; -} - - -/************************************************************************* -This function sets restart frequency. By default, algorithm is restarted -after N subsequent iterations. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetrestartfreq(lincgstate* state, - ae_int_t srf, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetRestartFreq: you can not change restart frequency when LinCGIteration() is running", _state); - ae_assert(srf>0, "LinCGSetRestartFreq: non-positive SRF", _state); - state->itsbeforerestart = srf; -} - - -/************************************************************************* -This function sets frequency of residual recalculations. - -Algorithm updates residual r_k using iterative formula, but recalculates -it from scratch after each 10 iterations. It is done to avoid accumulation -of numerical errors and to stop algorithm when r_k starts to grow. - -Such low update frequence (1/10) gives very little overhead, but makes -algorithm a bit more robust against numerical errors. However, you may -change it - -INPUT PARAMETERS: - Freq - desired update frequency, Freq>=0. - Zero value means that no updates will be done. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetrupdatefreq(lincgstate* state, - ae_int_t freq, - ae_state *_state) -{ - - - ae_assert(!state->running, "LinCGSetRUpdateFreq: you can not change update frequency when LinCGIteration() is running", _state); - ae_assert(freq>=0, "LinCGSetRUpdateFreq: non-positive Freq", _state); - state->itsbeforerupdate = freq; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetxrep(lincgstate* state, ae_bool needxrep, ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -Procedure for restart function LinCGIteration - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgrestart(lincgstate* state, ae_state *_state) -{ - - - ae_vector_set_length(&state->rstate.ia, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 2+1, _state); - state->rstate.stage = -1; - lincg_clearrfields(state, _state); -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void lincg_clearrfields(lincgstate* state, ae_state *_state) -{ - - - state->xupdated = ae_false; - state->needmv = ae_false; - state->needmtv = ae_false; - state->needmv2 = ae_false; - state->needvmv = ae_false; - state->needprec = ae_false; -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void lincg_updateitersdata(lincgstate* state, ae_state *_state) -{ - - - state->repiterationscount = 0; - state->repnmv = 0; - state->repterminationtype = 0; -} - - -ae_bool _lincgstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - lincgstate *p = (lincgstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->rx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->b, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cr, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cz, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->p, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->r, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->z, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->mv, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->pv, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->startx, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->tmpd, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _lincgstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - lincgstate *dst = (lincgstate*)_dst; - lincgstate *src = (lincgstate*)_src; - if( !ae_vector_init_copy(&dst->rx, &src->rx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->b, &src->b, _state, make_automatic) ) - return ae_false; - dst->n = src->n; - dst->prectype = src->prectype; - if( !ae_vector_init_copy(&dst->cx, &src->cx, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cr, &src->cr, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cz, &src->cz, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->p, &src->p, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->r, &src->r, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->z, &src->z, _state, make_automatic) ) - return ae_false; - dst->alpha = src->alpha; - dst->beta = src->beta; - dst->r2 = src->r2; - dst->meritfunction = src->meritfunction; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->mv, &src->mv, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->pv, &src->pv, _state, make_automatic) ) - return ae_false; - dst->vmv = src->vmv; - if( !ae_vector_init_copy(&dst->startx, &src->startx, _state, make_automatic) ) - return ae_false; - dst->epsf = src->epsf; - dst->maxits = src->maxits; - dst->itsbeforerestart = src->itsbeforerestart; - dst->itsbeforerupdate = src->itsbeforerupdate; - dst->xrep = src->xrep; - dst->xupdated = src->xupdated; - dst->needmv = src->needmv; - dst->needmtv = src->needmtv; - dst->needmv2 = src->needmv2; - dst->needvmv = src->needvmv; - dst->needprec = src->needprec; - dst->repiterationscount = src->repiterationscount; - dst->repnmv = src->repnmv; - dst->repterminationtype = src->repterminationtype; - dst->running = src->running; - if( !ae_vector_init_copy(&dst->tmpd, &src->tmpd, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _lincgstate_clear(void* _p) -{ - lincgstate *p = (lincgstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->rx); - ae_vector_clear(&p->b); - ae_vector_clear(&p->cx); - ae_vector_clear(&p->cr); - ae_vector_clear(&p->cz); - ae_vector_clear(&p->p); - ae_vector_clear(&p->r); - ae_vector_clear(&p->z); - ae_vector_clear(&p->x); - ae_vector_clear(&p->mv); - ae_vector_clear(&p->pv); - ae_vector_clear(&p->startx); - ae_vector_clear(&p->tmpd); - _rcommstate_clear(&p->rstate); -} - - -void _lincgstate_destroy(void* _p) -{ - lincgstate *p = (lincgstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->rx); - ae_vector_destroy(&p->b); - ae_vector_destroy(&p->cx); - ae_vector_destroy(&p->cr); - ae_vector_destroy(&p->cz); - ae_vector_destroy(&p->p); - ae_vector_destroy(&p->r); - ae_vector_destroy(&p->z); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->mv); - ae_vector_destroy(&p->pv); - ae_vector_destroy(&p->startx); - ae_vector_destroy(&p->tmpd); - _rcommstate_destroy(&p->rstate); -} - - -ae_bool _lincgreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - lincgreport *p = (lincgreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _lincgreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - lincgreport *dst = (lincgreport*)_dst; - lincgreport *src = (lincgreport*)_src; - dst->iterationscount = src->iterationscount; - dst->nmv = src->nmv; - dst->terminationtype = src->terminationtype; - dst->r2 = src->r2; - return ae_true; -} - - -void _lincgreport_clear(void* _p) -{ - lincgreport *p = (lincgreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _lincgreport_destroy(void* _p) -{ - lincgreport *p = (lincgreport*)_p; - ae_touch_ptr((void*)p); -} - - - - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE NONLINEAR SOLVER - -DESCRIPTION: -This algorithm solves system of nonlinear equations - F[0](x[0], ..., x[n-1]) = 0 - F[1](x[0], ..., x[n-1]) = 0 - ... - F[M-1](x[0], ..., x[n-1]) = 0 -with M/N do not necessarily coincide. Algorithm converges quadratically -under following conditions: - * the solution set XS is nonempty - * for some xs in XS there exist such neighbourhood N(xs) that: - * vector function F(x) and its Jacobian J(x) are continuously - differentiable on N - * ||F(x)|| provides local error bound on N, i.e. there exists such - c1, that ||F(x)||>c1*distance(x,XS) -Note that these conditions are much more weaker than usual non-singularity -conditions. For example, algorithm will converge for any affine function -F (whether its Jacobian singular or not). - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function vector F[] and Jacobian matrix at given point X -* value of merit function f(x)=F[0]^2(x)+...+F[M-1]^2(x) at given point X - - -USAGE: -1. User initializes algorithm state with NLEQCreateLM() call -2. User tunes solver parameters with NLEQSetCond(), NLEQSetStpMax() and - other functions -3. User calls NLEQSolve() function which takes algorithm state and - pointers (delegates, etc.) to callback functions which calculate merit - function value and Jacobian. -4. User calls NLEQResults() to get solution -5. Optionally, user may call NLEQRestartFrom() to solve another problem - with same parameters (N/M) but another starting point and/or another - function vector. NLEQRestartFrom() allows to reuse already initialized - structure. - - -INPUT PARAMETERS: - N - space dimension, N>1: - * if provided, only leading N elements of X are used - * if not provided, determined automatically from size of X - M - system size - X - starting point - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with NLEQSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use NLEQSetStpMax() function to bound algorithm's steps. -3. this algorithm is a slightly modified implementation of the method - described in 'Levenberg-Marquardt method for constrained nonlinear - equations with strong local convergence properties' by Christian Kanzow - Nobuo Yamashita and Masao Fukushima and further developed in 'On the - convergence of a New Levenberg-Marquardt Method' by Jin-yan Fan and - Ya-Xiang Yuan. - - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqcreatelm(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - nleqstate* state, - ae_state *_state) -{ - - _nleqstate_clear(state); - - ae_assert(n>=1, "NLEQCreateLM: N<1!", _state); - ae_assert(m>=1, "NLEQCreateLM: M<1!", _state); - ae_assert(x->cnt>=n, "NLEQCreateLM: Length(X)n = n; - state->m = m; - nleqsetcond(state, 0, 0, _state); - nleqsetxrep(state, ae_false, _state); - nleqsetstpmax(state, 0, _state); - ae_vector_set_length(&state->x, n, _state); - ae_vector_set_length(&state->xbase, n, _state); - ae_matrix_set_length(&state->j, m, n, _state); - ae_vector_set_length(&state->fi, m, _state); - ae_vector_set_length(&state->rightpart, n, _state); - ae_vector_set_length(&state->candstep, n, _state); - nleqrestartfrom(state, x, _state); -} - - -/************************************************************************* -This function sets stopping conditions for the nonlinear solver - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsF - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition ||F||<=EpsF is satisfied - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsF=0 and MaxIts=0 simultaneously will lead to automatic -stopping criterion selection (small EpsF). - -NOTES: - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetcond(nleqstate* state, - double epsf, - ae_int_t maxits, - ae_state *_state) -{ - - - ae_assert(ae_isfinite(epsf, _state), "NLEQSetCond: EpsF is not finite number!", _state); - ae_assert(ae_fp_greater_eq(epsf,0), "NLEQSetCond: negative EpsF!", _state); - ae_assert(maxits>=0, "NLEQSetCond: negative MaxIts!", _state); - if( ae_fp_eq(epsf,0)&&maxits==0 ) - { - epsf = 1.0E-6; - } - state->epsf = epsf; - state->maxits = maxits; -} - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to NLEQSolve(). - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetxrep(nleqstate* state, ae_bool needxrep, ae_state *_state) -{ - - - state->xrep = needxrep; -} - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when target function contains exp() or other fast -growing functions, and algorithm makes too large steps which lead to -overflow. This function allows us to reject steps that are too large (and -therefore expose us to the possible overflow) without actually calculating -function value at the x+stp*d. - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetstpmax(nleqstate* state, double stpmax, ae_state *_state) -{ - - - ae_assert(ae_isfinite(stpmax, _state), "NLEQSetStpMax: StpMax is not finite!", _state); - ae_assert(ae_fp_greater_eq(stpmax,0), "NLEQSetStpMax: StpMax<0!", _state); - state->stpmax = stpmax; -} - - -/************************************************************************* - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey -*************************************************************************/ -ae_bool nleqiteration(nleqstate* state, ae_state *_state) -{ - ae_int_t n; - ae_int_t m; - ae_int_t i; - double lambdaup; - double lambdadown; - double lambdav; - double rho; - double mu; - double stepnorm; - ae_bool b; - ae_bool result; - - - - /* - * Reverse communication preparations - * I know it looks ugly, but it works the same way - * anywhere from C++ to Python. - * - * This code initializes locals by: - * * random values determined during code - * generation - on first subroutine call - * * values from previous call - on subsequent calls - */ - if( state->rstate.stage>=0 ) - { - n = state->rstate.ia.ptr.p_int[0]; - m = state->rstate.ia.ptr.p_int[1]; - i = state->rstate.ia.ptr.p_int[2]; - b = state->rstate.ba.ptr.p_bool[0]; - lambdaup = state->rstate.ra.ptr.p_double[0]; - lambdadown = state->rstate.ra.ptr.p_double[1]; - lambdav = state->rstate.ra.ptr.p_double[2]; - rho = state->rstate.ra.ptr.p_double[3]; - mu = state->rstate.ra.ptr.p_double[4]; - stepnorm = state->rstate.ra.ptr.p_double[5]; - } - else - { - n = -983; - m = -989; - i = -834; - b = ae_false; - lambdaup = -287; - lambdadown = 364; - lambdav = 214; - rho = -338; - mu = -686; - stepnorm = 912; - } - if( state->rstate.stage==0 ) - { - goto lbl_0; - } - if( state->rstate.stage==1 ) - { - goto lbl_1; - } - if( state->rstate.stage==2 ) - { - goto lbl_2; - } - if( state->rstate.stage==3 ) - { - goto lbl_3; - } - if( state->rstate.stage==4 ) - { - goto lbl_4; - } - - /* - * Routine body - */ - - /* - * Prepare - */ - n = state->n; - m = state->m; - state->repterminationtype = 0; - state->repiterationscount = 0; - state->repnfunc = 0; - state->repnjac = 0; - - /* - * Calculate F/G, initialize algorithm - */ - nleq_clearrequestfields(state, _state); - state->needf = ae_true; - state->rstate.stage = 0; - goto lbl_rcomm; -lbl_0: - state->needf = ae_false; - state->repnfunc = state->repnfunc+1; - ae_v_move(&state->xbase.ptr.p_double[0], 1, &state->x.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->fbase = state->f; - state->fprev = ae_maxrealnumber; - if( !state->xrep ) - { - goto lbl_5; - } - - /* - * progress report - */ - nleq_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->rstate.stage = 1; - goto lbl_rcomm; -lbl_1: - state->xupdated = ae_false; -lbl_5: - if( ae_fp_less_eq(state->f,ae_sqr(state->epsf, _state)) ) - { - state->repterminationtype = 1; - result = ae_false; - return result; - } - - /* - * Main cycle - */ - lambdaup = 10; - lambdadown = 0.3; - lambdav = 0.001; - rho = 1; -lbl_7: - if( ae_false ) - { - goto lbl_8; - } - - /* - * Get Jacobian; - * before we get to this point we already have State.XBase filled - * with current point and State.FBase filled with function value - * at XBase - */ - nleq_clearrequestfields(state, _state); - state->needfij = ae_true; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->rstate.stage = 2; - goto lbl_rcomm; -lbl_2: - state->needfij = ae_false; - state->repnfunc = state->repnfunc+1; - state->repnjac = state->repnjac+1; - rmatrixmv(n, m, &state->j, 0, 0, 1, &state->fi, 0, &state->rightpart, 0, _state); - ae_v_muld(&state->rightpart.ptr.p_double[0], 1, ae_v_len(0,n-1), -1); - - /* - * Inner cycle: find good lambda - */ -lbl_9: - if( ae_false ) - { - goto lbl_10; - } - - /* - * Solve (J^T*J + (Lambda+Mu)*I)*y = J^T*F - * to get step d=-y where: - * * Mu=||F|| - is damping parameter for nonlinear system - * * Lambda - is additional Levenberg-Marquardt parameter - * for better convergence when far away from minimum - */ - for(i=0; i<=n-1; i++) - { - state->candstep.ptr.p_double[i] = 0; - } - fblssolvecgx(&state->j, m, n, lambdav, &state->rightpart, &state->candstep, &state->cgbuf, _state); - - /* - * Normalize step (it must be no more than StpMax) - */ - stepnorm = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(state->candstep.ptr.p_double[i],0) ) - { - stepnorm = 1; - break; - } - } - linminnormalized(&state->candstep, &stepnorm, n, _state); - if( ae_fp_neq(state->stpmax,0) ) - { - stepnorm = ae_minreal(stepnorm, state->stpmax, _state); - } - - /* - * Test new step - is it good enough? - * * if not, Lambda is increased and we try again. - * * if step is good, we decrease Lambda and move on. - * - * We can break this cycle on two occasions: - * * step is so small that x+step==x (in floating point arithmetics) - * * lambda is so large - */ - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - ae_v_addd(&state->x.ptr.p_double[0], 1, &state->candstep.ptr.p_double[0], 1, ae_v_len(0,n-1), stepnorm); - b = ae_true; - for(i=0; i<=n-1; i++) - { - if( ae_fp_neq(state->x.ptr.p_double[i],state->xbase.ptr.p_double[i]) ) - { - b = ae_false; - break; - } - } - if( b ) - { - - /* - * Step is too small, force zero step and break - */ - stepnorm = 0; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fbase; - goto lbl_10; - } - nleq_clearrequestfields(state, _state); - state->needf = ae_true; - state->rstate.stage = 3; - goto lbl_rcomm; -lbl_3: - state->needf = ae_false; - state->repnfunc = state->repnfunc+1; - if( ae_fp_less(state->f,state->fbase) ) - { - - /* - * function value decreased, move on - */ - nleq_decreaselambda(&lambdav, &rho, lambdadown, _state); - goto lbl_10; - } - if( !nleq_increaselambda(&lambdav, &rho, lambdaup, _state) ) - { - - /* - * Lambda is too large (near overflow), force zero step and break - */ - stepnorm = 0; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->f = state->fbase; - goto lbl_10; - } - goto lbl_9; -lbl_10: - - /* - * Accept step: - * * new position - * * new function value - */ - state->fbase = state->f; - ae_v_addd(&state->xbase.ptr.p_double[0], 1, &state->candstep.ptr.p_double[0], 1, ae_v_len(0,n-1), stepnorm); - state->repiterationscount = state->repiterationscount+1; - - /* - * Report new iteration - */ - if( !state->xrep ) - { - goto lbl_11; - } - nleq_clearrequestfields(state, _state); - state->xupdated = ae_true; - state->f = state->fbase; - ae_v_move(&state->x.ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,n-1)); - state->rstate.stage = 4; - goto lbl_rcomm; -lbl_4: - state->xupdated = ae_false; -lbl_11: - - /* - * Test stopping conditions on F, step (zero/non-zero) and MaxIts; - * If one of the conditions is met, RepTerminationType is changed. - */ - if( ae_fp_less_eq(ae_sqrt(state->f, _state),state->epsf) ) - { - state->repterminationtype = 1; - } - if( ae_fp_eq(stepnorm,0)&&state->repterminationtype==0 ) - { - state->repterminationtype = -4; - } - if( state->repiterationscount>=state->maxits&&state->maxits>0 ) - { - state->repterminationtype = 5; - } - if( state->repterminationtype!=0 ) - { - goto lbl_8; - } - - /* - * Now, iteration is finally over - */ - goto lbl_7; -lbl_8: - result = ae_false; - return result; - - /* - * Saving state - */ -lbl_rcomm: - result = ae_true; - state->rstate.ia.ptr.p_int[0] = n; - state->rstate.ia.ptr.p_int[1] = m; - state->rstate.ia.ptr.p_int[2] = i; - state->rstate.ba.ptr.p_bool[0] = b; - state->rstate.ra.ptr.p_double[0] = lambdaup; - state->rstate.ra.ptr.p_double[1] = lambdadown; - state->rstate.ra.ptr.p_double[2] = lambdav; - state->rstate.ra.ptr.p_double[3] = rho; - state->rstate.ra.ptr.p_double[4] = mu; - state->rstate.ra.ptr.p_double[5] = stepnorm; - return result; -} - - -/************************************************************************* -NLEQ solver results - -INPUT PARAMETERS: - State - algorithm state. - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -4 ERROR: algorithm has converged to the - stationary point Xf which is local minimum of - f=F[0]^2+...+F[m-1]^2, but is not solution of - nonlinear system. - * 1 sqrt(f)<=EpsF. - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - * ActiveConstraints contains number of active constraints - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqresults(nleqstate* state, - /* Real */ ae_vector* x, - nleqreport* rep, - ae_state *_state) -{ - - ae_vector_clear(x); - _nleqreport_clear(rep); - - nleqresultsbuf(state, x, rep, _state); -} - - -/************************************************************************* -NLEQ solver results - -Buffered implementation of NLEQResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqresultsbuf(nleqstate* state, - /* Real */ ae_vector* x, - nleqreport* rep, - ae_state *_state) -{ - - - if( x->cntn ) - { - ae_vector_set_length(x, state->n, _state); - } - ae_v_move(&x->ptr.p_double[0], 1, &state->xbase.ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - rep->iterationscount = state->repiterationscount; - rep->nfunc = state->repnfunc; - rep->njac = state->repnjac; - rep->terminationtype = state->repterminationtype; -} - - -/************************************************************************* -This subroutine restarts CG algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used for reverse communication previously - allocated with MinCGCreate call. - X - new starting point. - BndL - new lower bounds - BndU - new upper bounds - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqrestartfrom(nleqstate* state, - /* Real */ ae_vector* x, - ae_state *_state) -{ - - - ae_assert(x->cnt>=state->n, "NLEQRestartFrom: Length(X)n, _state), "NLEQRestartFrom: X contains infinite or NaN values!", _state); - ae_v_move(&state->x.ptr.p_double[0], 1, &x->ptr.p_double[0], 1, ae_v_len(0,state->n-1)); - ae_vector_set_length(&state->rstate.ia, 2+1, _state); - ae_vector_set_length(&state->rstate.ba, 0+1, _state); - ae_vector_set_length(&state->rstate.ra, 5+1, _state); - state->rstate.stage = -1; - nleq_clearrequestfields(state, _state); -} - - -/************************************************************************* -Clears request fileds (to be sure that we don't forgot to clear something) -*************************************************************************/ -static void nleq_clearrequestfields(nleqstate* state, ae_state *_state) -{ - - - state->needf = ae_false; - state->needfij = ae_false; - state->xupdated = ae_false; -} - - -/************************************************************************* -Increases lambda, returns False when there is a danger of overflow -*************************************************************************/ -static ae_bool nleq_increaselambda(double* lambdav, - double* nu, - double lambdaup, - ae_state *_state) -{ - double lnlambda; - double lnnu; - double lnlambdaup; - double lnmax; - ae_bool result; - - - result = ae_false; - lnlambda = ae_log(*lambdav, _state); - lnlambdaup = ae_log(lambdaup, _state); - lnnu = ae_log(*nu, _state); - lnmax = 0.5*ae_log(ae_maxrealnumber, _state); - if( ae_fp_greater(lnlambda+lnlambdaup+lnnu,lnmax) ) - { - return result; - } - if( ae_fp_greater(lnnu+ae_log(2, _state),lnmax) ) - { - return result; - } - *lambdav = *lambdav*lambdaup*(*nu); - *nu = *nu*2; - result = ae_true; - return result; -} - - -/************************************************************************* -Decreases lambda, but leaves it unchanged when there is danger of underflow. -*************************************************************************/ -static void nleq_decreaselambda(double* lambdav, - double* nu, - double lambdadown, - ae_state *_state) -{ - - - *nu = 1; - if( ae_fp_less(ae_log(*lambdav, _state)+ae_log(lambdadown, _state),ae_log(ae_minrealnumber, _state)) ) - { - *lambdav = ae_minrealnumber; - } - else - { - *lambdav = *lambdav*lambdadown; - } -} - - -ae_bool _nleqstate_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - nleqstate *p = (nleqstate*)_p; - ae_touch_ptr((void*)p); - if( !ae_vector_init(&p->x, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->fi, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init(&p->j, 0, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !_rcommstate_init(&p->rstate, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->xbase, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->candstep, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->rightpart, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init(&p->cgbuf, 0, DT_REAL, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -ae_bool _nleqstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - nleqstate *dst = (nleqstate*)_dst; - nleqstate *src = (nleqstate*)_src; - dst->n = src->n; - dst->m = src->m; - dst->epsf = src->epsf; - dst->maxits = src->maxits; - dst->xrep = src->xrep; - dst->stpmax = src->stpmax; - if( !ae_vector_init_copy(&dst->x, &src->x, _state, make_automatic) ) - return ae_false; - dst->f = src->f; - if( !ae_vector_init_copy(&dst->fi, &src->fi, _state, make_automatic) ) - return ae_false; - if( !ae_matrix_init_copy(&dst->j, &src->j, _state, make_automatic) ) - return ae_false; - dst->needf = src->needf; - dst->needfij = src->needfij; - dst->xupdated = src->xupdated; - if( !_rcommstate_init_copy(&dst->rstate, &src->rstate, _state, make_automatic) ) - return ae_false; - dst->repiterationscount = src->repiterationscount; - dst->repnfunc = src->repnfunc; - dst->repnjac = src->repnjac; - dst->repterminationtype = src->repterminationtype; - if( !ae_vector_init_copy(&dst->xbase, &src->xbase, _state, make_automatic) ) - return ae_false; - dst->fbase = src->fbase; - dst->fprev = src->fprev; - if( !ae_vector_init_copy(&dst->candstep, &src->candstep, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->rightpart, &src->rightpart, _state, make_automatic) ) - return ae_false; - if( !ae_vector_init_copy(&dst->cgbuf, &src->cgbuf, _state, make_automatic) ) - return ae_false; - return ae_true; -} - - -void _nleqstate_clear(void* _p) -{ - nleqstate *p = (nleqstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_clear(&p->x); - ae_vector_clear(&p->fi); - ae_matrix_clear(&p->j); - _rcommstate_clear(&p->rstate); - ae_vector_clear(&p->xbase); - ae_vector_clear(&p->candstep); - ae_vector_clear(&p->rightpart); - ae_vector_clear(&p->cgbuf); -} - - -void _nleqstate_destroy(void* _p) -{ - nleqstate *p = (nleqstate*)_p; - ae_touch_ptr((void*)p); - ae_vector_destroy(&p->x); - ae_vector_destroy(&p->fi); - ae_matrix_destroy(&p->j); - _rcommstate_destroy(&p->rstate); - ae_vector_destroy(&p->xbase); - ae_vector_destroy(&p->candstep); - ae_vector_destroy(&p->rightpart); - ae_vector_destroy(&p->cgbuf); -} - - -ae_bool _nleqreport_init(void* _p, ae_state *_state, ae_bool make_automatic) -{ - nleqreport *p = (nleqreport*)_p; - ae_touch_ptr((void*)p); - return ae_true; -} - - -ae_bool _nleqreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic) -{ - nleqreport *dst = (nleqreport*)_dst; - nleqreport *src = (nleqreport*)_src; - dst->iterationscount = src->iterationscount; - dst->nfunc = src->nfunc; - dst->njac = src->njac; - dst->terminationtype = src->terminationtype; - return ae_true; -} - - -void _nleqreport_clear(void* _p) -{ - nleqreport *p = (nleqreport*)_p; - ae_touch_ptr((void*)p); -} - - -void _nleqreport_destroy(void* _p) -{ - nleqreport *p = (nleqreport*)_p; - ae_touch_ptr((void*)p); -} - - - -} - diff --git a/src/inc/alglib/solvers.h b/src/inc/alglib/solvers.h deleted file mode 100644 index 3c94873..0000000 --- a/src/inc/alglib/solvers.h +++ /dev/null @@ -1,2016 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _solvers_pkg_h -#define _solvers_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "linalg.h" -#include "alglibmisc.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -typedef struct -{ - double r1; - double rinf; -} densesolverreport; -typedef struct -{ - double r2; - ae_matrix cx; - ae_int_t n; - ae_int_t k; -} densesolverlsreport; -typedef struct -{ - normestimatorstate nes; - ae_vector rx; - ae_vector b; - ae_int_t n; - ae_int_t m; - ae_int_t prectype; - ae_vector ui; - ae_vector uip1; - ae_vector vi; - ae_vector vip1; - ae_vector omegai; - ae_vector omegaip1; - double alphai; - double alphaip1; - double betai; - double betaip1; - double phibari; - double phibarip1; - double phii; - double rhobari; - double rhobarip1; - double rhoi; - double ci; - double si; - double theta; - double lambdai; - ae_vector d; - double anorm; - double bnorm2; - double dnorm; - double r2; - ae_vector x; - ae_vector mv; - ae_vector mtv; - double epsa; - double epsb; - double epsc; - ae_int_t maxits; - ae_bool xrep; - ae_bool xupdated; - ae_bool needmv; - ae_bool needmtv; - ae_bool needmv2; - ae_bool needvmv; - ae_bool needprec; - ae_int_t repiterationscount; - ae_int_t repnmv; - ae_int_t repterminationtype; - ae_bool running; - ae_vector tmpd; - ae_vector tmpx; - rcommstate rstate; -} linlsqrstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nmv; - ae_int_t terminationtype; -} linlsqrreport; -typedef struct -{ - ae_vector rx; - ae_vector b; - ae_int_t n; - ae_int_t prectype; - ae_vector cx; - ae_vector cr; - ae_vector cz; - ae_vector p; - ae_vector r; - ae_vector z; - double alpha; - double beta; - double r2; - double meritfunction; - ae_vector x; - ae_vector mv; - ae_vector pv; - double vmv; - ae_vector startx; - double epsf; - ae_int_t maxits; - ae_int_t itsbeforerestart; - ae_int_t itsbeforerupdate; - ae_bool xrep; - ae_bool xupdated; - ae_bool needmv; - ae_bool needmtv; - ae_bool needmv2; - ae_bool needvmv; - ae_bool needprec; - ae_int_t repiterationscount; - ae_int_t repnmv; - ae_int_t repterminationtype; - ae_bool running; - ae_vector tmpd; - rcommstate rstate; -} lincgstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nmv; - ae_int_t terminationtype; - double r2; -} lincgreport; -typedef struct -{ - ae_int_t n; - ae_int_t m; - double epsf; - ae_int_t maxits; - ae_bool xrep; - double stpmax; - ae_vector x; - double f; - ae_vector fi; - ae_matrix j; - ae_bool needf; - ae_bool needfij; - ae_bool xupdated; - rcommstate rstate; - ae_int_t repiterationscount; - ae_int_t repnfunc; - ae_int_t repnjac; - ae_int_t repterminationtype; - ae_vector xbase; - double fbase; - double fprev; - ae_vector candstep; - ae_vector rightpart; - ae_vector cgbuf; -} nleqstate; -typedef struct -{ - ae_int_t iterationscount; - ae_int_t nfunc; - ae_int_t njac; - ae_int_t terminationtype; -} nleqreport; - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - -/************************************************************************* - -*************************************************************************/ -class _densesolverreport_owner -{ -public: - _densesolverreport_owner(); - _densesolverreport_owner(const _densesolverreport_owner &rhs); - _densesolverreport_owner& operator=(const _densesolverreport_owner &rhs); - virtual ~_densesolverreport_owner(); - alglib_impl::densesolverreport* c_ptr(); - alglib_impl::densesolverreport* c_ptr() const; -protected: - alglib_impl::densesolverreport *p_struct; -}; -class densesolverreport : public _densesolverreport_owner -{ -public: - densesolverreport(); - densesolverreport(const densesolverreport &rhs); - densesolverreport& operator=(const densesolverreport &rhs); - virtual ~densesolverreport(); - double &r1; - double &rinf; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _densesolverlsreport_owner -{ -public: - _densesolverlsreport_owner(); - _densesolverlsreport_owner(const _densesolverlsreport_owner &rhs); - _densesolverlsreport_owner& operator=(const _densesolverlsreport_owner &rhs); - virtual ~_densesolverlsreport_owner(); - alglib_impl::densesolverlsreport* c_ptr(); - alglib_impl::densesolverlsreport* c_ptr() const; -protected: - alglib_impl::densesolverlsreport *p_struct; -}; -class densesolverlsreport : public _densesolverlsreport_owner -{ -public: - densesolverlsreport(); - densesolverlsreport(const densesolverlsreport &rhs); - densesolverlsreport& operator=(const densesolverlsreport &rhs); - virtual ~densesolverlsreport(); - double &r2; - real_2d_array cx; - ae_int_t &n; - ae_int_t &k; - -}; - -/************************************************************************* -This object stores state of the LinLSQR method. - -You should use ALGLIB functions to work with this object. -*************************************************************************/ -class _linlsqrstate_owner -{ -public: - _linlsqrstate_owner(); - _linlsqrstate_owner(const _linlsqrstate_owner &rhs); - _linlsqrstate_owner& operator=(const _linlsqrstate_owner &rhs); - virtual ~_linlsqrstate_owner(); - alglib_impl::linlsqrstate* c_ptr(); - alglib_impl::linlsqrstate* c_ptr() const; -protected: - alglib_impl::linlsqrstate *p_struct; -}; -class linlsqrstate : public _linlsqrstate_owner -{ -public: - linlsqrstate(); - linlsqrstate(const linlsqrstate &rhs); - linlsqrstate& operator=(const linlsqrstate &rhs); - virtual ~linlsqrstate(); - -}; - - -/************************************************************************* - -*************************************************************************/ -class _linlsqrreport_owner -{ -public: - _linlsqrreport_owner(); - _linlsqrreport_owner(const _linlsqrreport_owner &rhs); - _linlsqrreport_owner& operator=(const _linlsqrreport_owner &rhs); - virtual ~_linlsqrreport_owner(); - alglib_impl::linlsqrreport* c_ptr(); - alglib_impl::linlsqrreport* c_ptr() const; -protected: - alglib_impl::linlsqrreport *p_struct; -}; -class linlsqrreport : public _linlsqrreport_owner -{ -public: - linlsqrreport(); - linlsqrreport(const linlsqrreport &rhs); - linlsqrreport& operator=(const linlsqrreport &rhs); - virtual ~linlsqrreport(); - ae_int_t &iterationscount; - ae_int_t &nmv; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -This object stores state of the linear CG method. - -You should use ALGLIB functions to work with this object. -Never try to access its fields directly! -*************************************************************************/ -class _lincgstate_owner -{ -public: - _lincgstate_owner(); - _lincgstate_owner(const _lincgstate_owner &rhs); - _lincgstate_owner& operator=(const _lincgstate_owner &rhs); - virtual ~_lincgstate_owner(); - alglib_impl::lincgstate* c_ptr(); - alglib_impl::lincgstate* c_ptr() const; -protected: - alglib_impl::lincgstate *p_struct; -}; -class lincgstate : public _lincgstate_owner -{ -public: - lincgstate(); - lincgstate(const lincgstate &rhs); - lincgstate& operator=(const lincgstate &rhs); - virtual ~lincgstate(); - -}; - - -/************************************************************************* - -*************************************************************************/ -class _lincgreport_owner -{ -public: - _lincgreport_owner(); - _lincgreport_owner(const _lincgreport_owner &rhs); - _lincgreport_owner& operator=(const _lincgreport_owner &rhs); - virtual ~_lincgreport_owner(); - alglib_impl::lincgreport* c_ptr(); - alglib_impl::lincgreport* c_ptr() const; -protected: - alglib_impl::lincgreport *p_struct; -}; -class lincgreport : public _lincgreport_owner -{ -public: - lincgreport(); - lincgreport(const lincgreport &rhs); - lincgreport& operator=(const lincgreport &rhs); - virtual ~lincgreport(); - ae_int_t &iterationscount; - ae_int_t &nmv; - ae_int_t &terminationtype; - double &r2; - -}; - -/************************************************************************* - -*************************************************************************/ -class _nleqstate_owner -{ -public: - _nleqstate_owner(); - _nleqstate_owner(const _nleqstate_owner &rhs); - _nleqstate_owner& operator=(const _nleqstate_owner &rhs); - virtual ~_nleqstate_owner(); - alglib_impl::nleqstate* c_ptr(); - alglib_impl::nleqstate* c_ptr() const; -protected: - alglib_impl::nleqstate *p_struct; -}; -class nleqstate : public _nleqstate_owner -{ -public: - nleqstate(); - nleqstate(const nleqstate &rhs); - nleqstate& operator=(const nleqstate &rhs); - virtual ~nleqstate(); - ae_bool &needf; - ae_bool &needfij; - ae_bool &xupdated; - double &f; - real_1d_array fi; - real_2d_array j; - real_1d_array x; - -}; - - -/************************************************************************* - -*************************************************************************/ -class _nleqreport_owner -{ -public: - _nleqreport_owner(); - _nleqreport_owner(const _nleqreport_owner &rhs); - _nleqreport_owner& operator=(const _nleqreport_owner &rhs); - virtual ~_nleqreport_owner(); - alglib_impl::nleqreport* c_ptr(); - alglib_impl::nleqreport* c_ptr() const; -protected: - alglib_impl::nleqreport *p_struct; -}; -class nleqreport : public _nleqreport_owner -{ -public: - nleqreport(); - nleqreport(const nleqreport &rhs); - nleqreport& operator=(const nleqreport &rhs); - virtual ~nleqreport(); - ae_int_t &iterationscount; - ae_int_t &nfunc; - ae_int_t &njac; - ae_int_t &terminationtype; - -}; - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*x=b, where A is NxN non-denegerate -real matrix, x and b are vectors. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - return code: - * -3 A is singular, or VERY close to singular. - X is filled by zeros in such cases. - * -1 N<=0 was passed - * 1 task is solved (but matrix A may be ill-conditioned, - check R1/RInf parameters for condition numbers). - Rep - solver report, see below for more info - X - array[0..N-1], it contains: - * solution of A*x=b if A is non-singular (well-conditioned - or ill-conditioned, but not very close to singular) - * zeros, if A is singular or VERY close to singular - (in this case Info=-3). - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R1 reciprocal of condition number: 1/cond(A), 1-norm. -* RInf reciprocal of condition number: 1/cond(A), inf-norm. - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolve(const real_2d_array &a, const ae_int_t n, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x); - - -/************************************************************************* -Dense solver. - -Similar to RMatrixSolve() but solves task with multiple right parts (where -b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* optional iterative refinement -* O(N^3+M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - RFS - iterative refinement switch: - * True - refinement is used. - Less performance, more precision. - * False - refinement is not used. - More performance, less precision. - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolvem(const real_2d_array &a, const ae_int_t n, const real_2d_array &b, const ae_int_t m, const bool rfs, ae_int_t &info, densesolverreport &rep, real_2d_array &x); - - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*X=B, where A is NxN non-denegerate -real matrix given by its LU decomposition, X and B are NxM real matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixlusolve(const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x); - - -/************************************************************************* -Dense solver. - -Similar to RMatrixLUSolve() but solves task with multiple right parts -(where b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixlusolvem(const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x); - - -/************************************************************************* -Dense solver. - -This subroutine solves a system A*x=b, where BOTH ORIGINAL A AND ITS -LU DECOMPOSITION ARE KNOWN. You can use it if for some reasons you have -both A and its LU decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixmixedsolve(const real_2d_array &a, const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x); - - -/************************************************************************* -Dense solver. - -Similar to RMatrixMixedSolve() but solves task with multiple right parts -(where b and x are NxM matrices). - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void rmatrixmixedsolvem(const real_2d_array &a, const real_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3+M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - RFS - iterative refinement switch: - * True - refinement is used. - Less performance, more precision. - * False - refinement is not used. - More performance, less precision. - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixsolvem(const complex_2d_array &a, const ae_int_t n, const complex_2d_array &b, const ae_int_t m, const bool rfs, ae_int_t &info, densesolverreport &rep, complex_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixsolve(const complex_2d_array &a, const ae_int_t n, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use CMatrixSolve or CMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, RMatrixLU result - P - array[0..N-1], pivots array, RMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixlusolvem(const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation - -No iterative refinement is provided because exact form of original matrix -is not known to subroutine. Use CMatrixSolve or CMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixlusolve(const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixMixedSolveM(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(M*N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixmixedsolvem(const complex_2d_array &a, const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixMixedSolve(), but for complex matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* iterative refinement -* O(N^2) complexity - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - LUA - array[0..N-1,0..N-1], LU decomposition, CMatrixLU result - P - array[0..N-1], pivots array, CMatrixLU result - N - size of A - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolveM - Rep - same as in RMatrixSolveM - X - same as in RMatrixSolveM - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void cmatrixmixedsolve(const complex_2d_array &a, const complex_2d_array &lua, const integer_1d_array &p, const ae_int_t n, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for symmetric positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3+M*N^2) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve. - Returns -3 for non-SPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixsolvem(const real_2d_array &a, const ae_int_t n, const bool isupper, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for SPD matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Returns -3 for non-SPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixsolve(const real_2d_array &a, const ae_int_t n, const bool isupper, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for SPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of CHA - IsUpper - what half of CHA is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskysolvem(const real_2d_array &cha, const ae_int_t n, const bool isupper, const real_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, real_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for SPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of A - IsUpper - what half of CHA is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void spdmatrixcholeskysolve(const real_2d_array &cha, const ae_int_t n, const bool isupper, const real_1d_array &b, ae_int_t &info, densesolverreport &rep, real_1d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixSolveM(), but for Hermitian positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3+M*N^2) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve. - Returns -3 for non-HPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixsolvem(const complex_2d_array &a, const ae_int_t n, const bool isupper, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixSolve(), but for Hermitian positive definite -matrices. - -Algorithm features: -* automatic detection of degenerate cases -* condition number estimation -* O(N^3) complexity -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - A - array[0..N-1,0..N-1], system matrix - N - size of A - IsUpper - what half of A is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Returns -3 for non-HPD matrices. - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixsolve(const complex_2d_array &a, const ae_int_t n, const bool isupper, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolveM(), but for HPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(M*N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - HPDMatrixCholesky result - N - size of CHA - IsUpper - what half of CHA is provided - B - array[0..N-1,0..M-1], right part - M - right part size - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskysolvem(const complex_2d_array &cha, const ae_int_t n, const bool isupper, const complex_2d_array &b, const ae_int_t m, ae_int_t &info, densesolverreport &rep, complex_2d_array &x); - - -/************************************************************************* -Dense solver. Same as RMatrixLUSolve(), but for HPD matrices represented -by their Cholesky decomposition. - -Algorithm features: -* automatic detection of degenerate cases -* O(N^2) complexity -* condition number estimation -* matrix is represented by its upper or lower triangle - -No iterative refinement is provided because such partial representation of -matrix does not allow efficient calculation of extra-precise matrix-vector -products for large matrices. Use RMatrixSolve or RMatrixMixedSolve if you -need iterative refinement. - -INPUT PARAMETERS - CHA - array[0..N-1,0..N-1], Cholesky decomposition, - SPDMatrixCholesky result - N - size of A - IsUpper - what half of CHA is provided - B - array[0..N-1], right part - -OUTPUT PARAMETERS - Info - same as in RMatrixSolve - Rep - same as in RMatrixSolve - X - same as in RMatrixSolve - - -- ALGLIB -- - Copyright 27.01.2010 by Bochkanov Sergey -*************************************************************************/ -void hpdmatrixcholeskysolve(const complex_2d_array &cha, const ae_int_t n, const bool isupper, const complex_1d_array &b, ae_int_t &info, densesolverreport &rep, complex_1d_array &x); - - -/************************************************************************* -Dense solver. - -This subroutine finds solution of the linear system A*X=B with non-square, -possibly degenerate A. System is solved in the least squares sense, and -general least squares solution X = X0 + CX*y which minimizes |A*X-B| is -returned. If A is non-degenerate, solution in the usual sense is returned. - -Algorithm features: -* automatic detection (and correct handling!) of degenerate cases -* iterative refinement -* O(N^3) complexity - -INPUT PARAMETERS - A - array[0..NRows-1,0..NCols-1], system matrix - NRows - vertical size of A - NCols - horizontal size of A - B - array[0..NCols-1], right part - Threshold- a number in [0,1]. Singular values beyond Threshold are - considered zero. Set it to 0.0, if you don't understand - what it means, so the solver will choose good value on its - own. - -OUTPUT PARAMETERS - Info - return code: - * -4 SVD subroutine failed - * -1 if NRows<=0 or NCols<=0 or Threshold<0 was passed - * 1 if task is solved - Rep - solver report, see below for more info - X - array[0..N-1,0..M-1], it contains: - * solution of A*X=B (even for singular A) - * zeros, if SVD subroutine failed - -SOLVER REPORT - -Subroutine sets following fields of the Rep structure: -* R2 reciprocal of condition number: 1/cond(A), 2-norm. -* N = NCols -* K dim(Null(A)) -* CX array[0..N-1,0..K-1], kernel of A. - Columns of CX store such vectors that A*CX[i]=0. - - -- ALGLIB -- - Copyright 24.08.2009 by Bochkanov Sergey -*************************************************************************/ -void rmatrixsolvels(const real_2d_array &a, const ae_int_t nrows, const ae_int_t ncols, const real_1d_array &b, const double threshold, ae_int_t &info, densesolverlsreport &rep, real_1d_array &x); - -/************************************************************************* -This function initializes linear LSQR Solver. This solver is used to solve -non-symmetric (and, possibly, non-square) problems. Least squares solution -is returned for non-compatible systems. - -USAGE: -1. User initializes algorithm state with LinLSQRCreate() call -2. User tunes solver parameters with LinLSQRSetCond() and other functions -3. User calls LinLSQRSolveSparse() function which takes algorithm state - and SparseMatrix object. -4. User calls LinLSQRResults() to get solution -5. Optionally, user may call LinLSQRSolveSparse() again to solve another - problem with different matrix and/or right part without reinitializing - LinLSQRState structure. - -INPUT PARAMETERS: - M - number of rows in A - N - number of variables, N>0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrcreate(const ae_int_t m, const ae_int_t n, linlsqrstate &state); - - -/************************************************************************* -This function changes preconditioning settings of LinLSQQSolveSparse() -function. By default, SolveSparse() uses diagonal preconditioner, but if -you want to use solver without preconditioning, you can call this function -which forces solver to use unit matrix for preconditioning. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetprecunit(const linlsqrstate &state); - - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. LinCGSolveSparse() will use diagonal of the system matrix as -preconditioner. This preconditioning mode is active by default. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetprecdiag(const linlsqrstate &state); - - -/************************************************************************* -This function sets optional Tikhonov regularization coefficient. -It is zero by default. - -INPUT PARAMETERS: - LambdaI - regularization factor, LambdaI>=0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetlambdai(const linlsqrstate &state, const double lambdai); - - -/************************************************************************* -Procedure for solution of A*x=b with sparse A. - -INPUT PARAMETERS: - State - algorithm state - A - sparse M*N matrix in the CRS format (you MUST contvert it - to CRS format by calling SparseConvertToCRS() function - BEFORE you pass it to this function). - B - right part, array[M] - -RESULT: - This function returns no result. - You can get solution by calling LinCGResults() - -NOTE: this function uses lightweight preconditioning - multiplication by - inverse of diag(A). If you want, you can turn preconditioning off by - calling LinLSQRSetPrecUnit(). However, preconditioning cost is low - and preconditioner is very important for solution of badly scaled - problems. - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsolvesparse(const linlsqrstate &state, const sparsematrix &a, const real_1d_array &b); - - -/************************************************************************* -This function sets stopping criteria. - -INPUT PARAMETERS: - EpsA - algorithm will be stopped if ||A^T*Rk||/(||A||*||Rk||)<=EpsA. - EpsB - algorithm will be stopped if ||Rk||<=EpsB*||B|| - MaxIts - algorithm will be stopped if number of iterations - more than MaxIts. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTE: if EpsA,EpsB,EpsC and MaxIts are zero then these variables will -be setted as default values. - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetcond(const linlsqrstate &state, const double epsa, const double epsb, const ae_int_t maxits); - - -/************************************************************************* -LSQR solver: results. - -This function must be called after LinLSQRSolve - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[N], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * 1 ||Rk||<=EpsB*||B|| - * 4 ||A^T*Rk||/(||A||*||Rk||)<=EpsA - * 5 MaxIts steps was taken - * 7 rounding errors prevent further progress, - X contains best point found so far. - (sometimes returned on singular systems) - * Rep.IterationsCount contains iterations count - * NMV countains number of matrix-vector calculations - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrresults(const linlsqrstate &state, real_1d_array &x, linlsqrreport &rep); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 30.11.2011 by Bochkanov Sergey -*************************************************************************/ -void linlsqrsetxrep(const linlsqrstate &state, const bool needxrep); - -/************************************************************************* -This function initializes linear CG Solver. This solver is used to solve -symmetric positive definite problems. If you want to solve nonsymmetric -(or non-positive definite) problem you may use LinLSQR solver provided by -ALGLIB. - -USAGE: -1. User initializes algorithm state with LinCGCreate() call -2. User tunes solver parameters with LinCGSetCond() and other functions -3. Optionally, user sets starting point with LinCGSetStartingPoint() -4. User calls LinCGSolveSparse() function which takes algorithm state and - SparseMatrix object. -5. User calls LinCGResults() to get solution -6. Optionally, user may call LinCGSolveSparse() again to solve another - problem with different matrix and/or right part without reinitializing - LinCGState structure. - -INPUT PARAMETERS: - N - problem dimension, N>0 - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgcreate(const ae_int_t n, lincgstate &state); - - -/************************************************************************* -This function sets starting point. -By default, zero starting point is used. - -INPUT PARAMETERS: - X - starting point, array[N] - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetstartingpoint(const lincgstate &state, const real_1d_array &x); - - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. By default, SolveSparse() uses diagonal preconditioner, but if -you want to use solver without preconditioning, you can call this function -which forces solver to use unit matrix for preconditioning. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void lincgsetprecunit(const lincgstate &state); - - -/************************************************************************* -This function changes preconditioning settings of LinCGSolveSparse() -function. LinCGSolveSparse() will use diagonal of the system matrix as -preconditioner. This preconditioning mode is active by default. - -INPUT PARAMETERS: - State - structure which stores algorithm state - - -- ALGLIB -- - Copyright 19.11.2012 by Bochkanov Sergey -*************************************************************************/ -void lincgsetprecdiag(const lincgstate &state); - - -/************************************************************************* -This function sets stopping criteria. - -INPUT PARAMETERS: - EpsF - algorithm will be stopped if norm of residual is less than - EpsF*||b||. - MaxIts - algorithm will be stopped if number of iterations is more - than MaxIts. - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - -NOTES: -If both EpsF and MaxIts are zero then small EpsF will be set to small -value. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetcond(const lincgstate &state, const double epsf, const ae_int_t maxits); - - -/************************************************************************* -Procedure for solution of A*x=b with sparse A. - -INPUT PARAMETERS: - State - algorithm state - A - sparse matrix in the CRS format (you MUST contvert it to - CRS format by calling SparseConvertToCRS() function). - IsUpper - whether upper or lower triangle of A is used: - * IsUpper=True => only upper triangle is used and lower - triangle is not referenced at all - * IsUpper=False => only lower triangle is used and upper - triangle is not referenced at all - B - right part, array[N] - -RESULT: - This function returns no result. - You can get solution by calling LinCGResults() - -NOTE: this function uses lightweight preconditioning - multiplication by - inverse of diag(A). If you want, you can turn preconditioning off by - calling LinCGSetPrecUnit(). However, preconditioning cost is low and - preconditioner is very important for solution of badly scaled - problems. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsolvesparse(const lincgstate &state, const sparsematrix &a, const bool isupper, const real_1d_array &b); - - -/************************************************************************* -CG-solver: results. - -This function must be called after LinCGSolve - -INPUT PARAMETERS: - State - algorithm state - -OUTPUT PARAMETERS: - X - array[N], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -5 input matrix is either not positive definite, - too large or too small - * -4 overflow/underflow during solution - (ill conditioned problem) - * 1 ||residual||<=EpsF*||b|| - * 5 MaxIts steps was taken - * 7 rounding errors prevent further progress, - best point found is returned - * Rep.IterationsCount contains iterations count - * NMV countains number of matrix-vector calculations - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgresults(const lincgstate &state, real_1d_array &x, lincgreport &rep); - - -/************************************************************************* -This function sets restart frequency. By default, algorithm is restarted -after N subsequent iterations. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetrestartfreq(const lincgstate &state, const ae_int_t srf); - - -/************************************************************************* -This function sets frequency of residual recalculations. - -Algorithm updates residual r_k using iterative formula, but recalculates -it from scratch after each 10 iterations. It is done to avoid accumulation -of numerical errors and to stop algorithm when r_k starts to grow. - -Such low update frequence (1/10) gives very little overhead, but makes -algorithm a bit more robust against numerical errors. However, you may -change it - -INPUT PARAMETERS: - Freq - desired update frequency, Freq>=0. - Zero value means that no updates will be done. - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetrupdatefreq(const lincgstate &state, const ae_int_t freq); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to MinCGOptimize(). - - -- ALGLIB -- - Copyright 14.11.2011 by Bochkanov Sergey -*************************************************************************/ -void lincgsetxrep(const lincgstate &state, const bool needxrep); - -/************************************************************************* - LEVENBERG-MARQUARDT-LIKE NONLINEAR SOLVER - -DESCRIPTION: -This algorithm solves system of nonlinear equations - F[0](x[0], ..., x[n-1]) = 0 - F[1](x[0], ..., x[n-1]) = 0 - ... - F[M-1](x[0], ..., x[n-1]) = 0 -with M/N do not necessarily coincide. Algorithm converges quadratically -under following conditions: - * the solution set XS is nonempty - * for some xs in XS there exist such neighbourhood N(xs) that: - * vector function F(x) and its Jacobian J(x) are continuously - differentiable on N - * ||F(x)|| provides local error bound on N, i.e. there exists such - c1, that ||F(x)||>c1*distance(x,XS) -Note that these conditions are much more weaker than usual non-singularity -conditions. For example, algorithm will converge for any affine function -F (whether its Jacobian singular or not). - - -REQUIREMENTS: -Algorithm will request following information during its operation: -* function vector F[] and Jacobian matrix at given point X -* value of merit function f(x)=F[0]^2(x)+...+F[M-1]^2(x) at given point X - - -USAGE: -1. User initializes algorithm state with NLEQCreateLM() call -2. User tunes solver parameters with NLEQSetCond(), NLEQSetStpMax() and - other functions -3. User calls NLEQSolve() function which takes algorithm state and - pointers (delegates, etc.) to callback functions which calculate merit - function value and Jacobian. -4. User calls NLEQResults() to get solution -5. Optionally, user may call NLEQRestartFrom() to solve another problem - with same parameters (N/M) but another starting point and/or another - function vector. NLEQRestartFrom() allows to reuse already initialized - structure. - - -INPUT PARAMETERS: - N - space dimension, N>1: - * if provided, only leading N elements of X are used - * if not provided, determined automatically from size of X - M - system size - X - starting point - - -OUTPUT PARAMETERS: - State - structure which stores algorithm state - - -NOTES: -1. you may tune stopping conditions with NLEQSetCond() function -2. if target function contains exp() or other fast growing functions, and - optimization algorithm makes too large steps which leads to overflow, - use NLEQSetStpMax() function to bound algorithm's steps. -3. this algorithm is a slightly modified implementation of the method - described in 'Levenberg-Marquardt method for constrained nonlinear - equations with strong local convergence properties' by Christian Kanzow - Nobuo Yamashita and Masao Fukushima and further developed in 'On the - convergence of a New Levenberg-Marquardt Method' by Jin-yan Fan and - Ya-Xiang Yuan. - - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqcreatelm(const ae_int_t n, const ae_int_t m, const real_1d_array &x, nleqstate &state); -void nleqcreatelm(const ae_int_t m, const real_1d_array &x, nleqstate &state); - - -/************************************************************************* -This function sets stopping conditions for the nonlinear solver - -INPUT PARAMETERS: - State - structure which stores algorithm state - EpsF - >=0 - The subroutine finishes its work if on k+1-th iteration - the condition ||F||<=EpsF is satisfied - MaxIts - maximum number of iterations. If MaxIts=0, the number of - iterations is unlimited. - -Passing EpsF=0 and MaxIts=0 simultaneously will lead to automatic -stopping criterion selection (small EpsF). - -NOTES: - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetcond(const nleqstate &state, const double epsf, const ae_int_t maxits); - - -/************************************************************************* -This function turns on/off reporting. - -INPUT PARAMETERS: - State - structure which stores algorithm state - NeedXRep- whether iteration reports are needed or not - -If NeedXRep is True, algorithm will call rep() callback function if it is -provided to NLEQSolve(). - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetxrep(const nleqstate &state, const bool needxrep); - - -/************************************************************************* -This function sets maximum step length - -INPUT PARAMETERS: - State - structure which stores algorithm state - StpMax - maximum step length, >=0. Set StpMax to 0.0, if you don't - want to limit step length. - -Use this subroutine when target function contains exp() or other fast -growing functions, and algorithm makes too large steps which lead to -overflow. This function allows us to reject steps that are too large (and -therefore expose us to the possible overflow) without actually calculating -function value at the x+stp*d. - - -- ALGLIB -- - Copyright 20.08.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqsetstpmax(const nleqstate &state, const double stpmax); - - -/************************************************************************* -This function provides reverse communication interface -Reverse communication interface is not documented or recommended to use. -See below for functions which provide better documented API -*************************************************************************/ -bool nleqiteration(const nleqstate &state); - - -/************************************************************************* -This family of functions is used to launcn iterations of nonlinear solver - -These functions accept following parameters: - state - algorithm state - func - callback which calculates function (or merit function) - value func at given point x - jac - callback which calculates function vector fi[] - and Jacobian jac at given point x - rep - optional callback which is called after each iteration - can be NULL - ptr - optional pointer which is passed to func/grad/hess/jac/rep - can be NULL - - - -- ALGLIB -- - Copyright 20.03.2009 by Bochkanov Sergey - -*************************************************************************/ -void nleqsolve(nleqstate &state, - void (*func)(const real_1d_array &x, double &func, void *ptr), - void (*jac)(const real_1d_array &x, real_1d_array &fi, real_2d_array &jac, void *ptr), - void (*rep)(const real_1d_array &x, double func, void *ptr) = NULL, - void *ptr = NULL); - - -/************************************************************************* -NLEQ solver results - -INPUT PARAMETERS: - State - algorithm state. - -OUTPUT PARAMETERS: - X - array[0..N-1], solution - Rep - optimization report: - * Rep.TerminationType completetion code: - * -4 ERROR: algorithm has converged to the - stationary point Xf which is local minimum of - f=F[0]^2+...+F[m-1]^2, but is not solution of - nonlinear system. - * 1 sqrt(f)<=EpsF. - * 5 MaxIts steps was taken - * 7 stopping conditions are too stringent, - further improvement is impossible - * Rep.IterationsCount contains iterations count - * NFEV countains number of function calculations - * ActiveConstraints contains number of active constraints - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqresults(const nleqstate &state, real_1d_array &x, nleqreport &rep); - - -/************************************************************************* -NLEQ solver results - -Buffered implementation of NLEQResults(), which uses pre-allocated buffer -to store X[]. If buffer size is too small, it resizes buffer. It is -intended to be used in the inner cycles of performance critical algorithms -where array reallocation penalty is too large to be ignored. - - -- ALGLIB -- - Copyright 20.08.2009 by Bochkanov Sergey -*************************************************************************/ -void nleqresultsbuf(const nleqstate &state, real_1d_array &x, nleqreport &rep); - - -/************************************************************************* -This subroutine restarts CG algorithm from new point. All optimization -parameters are left unchanged. - -This function allows to solve multiple optimization problems (which -must have same number of dimensions) without object reallocation penalty. - -INPUT PARAMETERS: - State - structure used for reverse communication previously - allocated with MinCGCreate call. - X - new starting point. - BndL - new lower bounds - BndU - new upper bounds - - -- ALGLIB -- - Copyright 30.07.2010 by Bochkanov Sergey -*************************************************************************/ -void nleqrestartfrom(const nleqstate &state, const real_1d_array &x); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void rmatrixsolve(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state); -void rmatrixsolvem(/* Real */ ae_matrix* a, - ae_int_t n, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_bool rfs, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -void rmatrixlusolve(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state); -void rmatrixlusolvem(/* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -void rmatrixmixedsolve(/* Real */ ae_matrix* a, - /* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state); -void rmatrixmixedsolvem(/* Real */ ae_matrix* a, - /* Real */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -void cmatrixsolvem(/* Complex */ ae_matrix* a, - ae_int_t n, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_bool rfs, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -void cmatrixsolve(/* Complex */ ae_matrix* a, - ae_int_t n, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state); -void cmatrixlusolvem(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -void cmatrixlusolve(/* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state); -void cmatrixmixedsolvem(/* Complex */ ae_matrix* a, - /* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -void cmatrixmixedsolve(/* Complex */ ae_matrix* a, - /* Complex */ ae_matrix* lua, - /* Integer */ ae_vector* p, - ae_int_t n, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state); -void spdmatrixsolvem(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -void spdmatrixsolve(/* Real */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state); -void spdmatrixcholeskysolvem(/* Real */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_matrix* x, - ae_state *_state); -void spdmatrixcholeskysolve(/* Real */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Real */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Real */ ae_vector* x, - ae_state *_state); -void hpdmatrixsolvem(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -void hpdmatrixsolve(/* Complex */ ae_matrix* a, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state); -void hpdmatrixcholeskysolvem(/* Complex */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_matrix* b, - ae_int_t m, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_matrix* x, - ae_state *_state); -void hpdmatrixcholeskysolve(/* Complex */ ae_matrix* cha, - ae_int_t n, - ae_bool isupper, - /* Complex */ ae_vector* b, - ae_int_t* info, - densesolverreport* rep, - /* Complex */ ae_vector* x, - ae_state *_state); -void rmatrixsolvels(/* Real */ ae_matrix* a, - ae_int_t nrows, - ae_int_t ncols, - /* Real */ ae_vector* b, - double threshold, - ae_int_t* info, - densesolverlsreport* rep, - /* Real */ ae_vector* x, - ae_state *_state); -ae_bool _densesolverreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _densesolverreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _densesolverreport_clear(void* _p); -void _densesolverreport_destroy(void* _p); -ae_bool _densesolverlsreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _densesolverlsreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _densesolverlsreport_clear(void* _p); -void _densesolverlsreport_destroy(void* _p); -void linlsqrcreate(ae_int_t m, - ae_int_t n, - linlsqrstate* state, - ae_state *_state); -void linlsqrsetb(linlsqrstate* state, - /* Real */ ae_vector* b, - ae_state *_state); -void linlsqrsetprecunit(linlsqrstate* state, ae_state *_state); -void linlsqrsetprecdiag(linlsqrstate* state, ae_state *_state); -void linlsqrsetlambdai(linlsqrstate* state, - double lambdai, - ae_state *_state); -ae_bool linlsqriteration(linlsqrstate* state, ae_state *_state); -void linlsqrsolvesparse(linlsqrstate* state, - sparsematrix* a, - /* Real */ ae_vector* b, - ae_state *_state); -void linlsqrsetcond(linlsqrstate* state, - double epsa, - double epsb, - ae_int_t maxits, - ae_state *_state); -void linlsqrresults(linlsqrstate* state, - /* Real */ ae_vector* x, - linlsqrreport* rep, - ae_state *_state); -void linlsqrsetxrep(linlsqrstate* state, - ae_bool needxrep, - ae_state *_state); -void linlsqrrestart(linlsqrstate* state, ae_state *_state); -ae_bool _linlsqrstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _linlsqrstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _linlsqrstate_clear(void* _p); -void _linlsqrstate_destroy(void* _p); -ae_bool _linlsqrreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _linlsqrreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _linlsqrreport_clear(void* _p); -void _linlsqrreport_destroy(void* _p); -void lincgcreate(ae_int_t n, lincgstate* state, ae_state *_state); -void lincgsetstartingpoint(lincgstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -void lincgsetb(lincgstate* state, - /* Real */ ae_vector* b, - ae_state *_state); -void lincgsetprecunit(lincgstate* state, ae_state *_state); -void lincgsetprecdiag(lincgstate* state, ae_state *_state); -void lincgsetcond(lincgstate* state, - double epsf, - ae_int_t maxits, - ae_state *_state); -ae_bool lincgiteration(lincgstate* state, ae_state *_state); -void lincgsolvesparse(lincgstate* state, - sparsematrix* a, - ae_bool isupper, - /* Real */ ae_vector* b, - ae_state *_state); -void lincgresults(lincgstate* state, - /* Real */ ae_vector* x, - lincgreport* rep, - ae_state *_state); -void lincgsetrestartfreq(lincgstate* state, - ae_int_t srf, - ae_state *_state); -void lincgsetrupdatefreq(lincgstate* state, - ae_int_t freq, - ae_state *_state); -void lincgsetxrep(lincgstate* state, ae_bool needxrep, ae_state *_state); -void lincgrestart(lincgstate* state, ae_state *_state); -ae_bool _lincgstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _lincgstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _lincgstate_clear(void* _p); -void _lincgstate_destroy(void* _p); -ae_bool _lincgreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _lincgreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _lincgreport_clear(void* _p); -void _lincgreport_destroy(void* _p); -void nleqcreatelm(ae_int_t n, - ae_int_t m, - /* Real */ ae_vector* x, - nleqstate* state, - ae_state *_state); -void nleqsetcond(nleqstate* state, - double epsf, - ae_int_t maxits, - ae_state *_state); -void nleqsetxrep(nleqstate* state, ae_bool needxrep, ae_state *_state); -void nleqsetstpmax(nleqstate* state, double stpmax, ae_state *_state); -ae_bool nleqiteration(nleqstate* state, ae_state *_state); -void nleqresults(nleqstate* state, - /* Real */ ae_vector* x, - nleqreport* rep, - ae_state *_state); -void nleqresultsbuf(nleqstate* state, - /* Real */ ae_vector* x, - nleqreport* rep, - ae_state *_state); -void nleqrestartfrom(nleqstate* state, - /* Real */ ae_vector* x, - ae_state *_state); -ae_bool _nleqstate_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _nleqstate_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _nleqstate_clear(void* _p); -void _nleqstate_destroy(void* _p); -ae_bool _nleqreport_init(void* _p, ae_state *_state, ae_bool make_automatic); -ae_bool _nleqreport_init_copy(void* _dst, void* _src, ae_state *_state, ae_bool make_automatic); -void _nleqreport_clear(void* _p); -void _nleqreport_destroy(void* _p); - -} -#endif - diff --git a/src/inc/alglib/specialfunctions.cpp b/src/inc/alglib/specialfunctions.cpp deleted file mode 100644 index bd786b6..0000000 --- a/src/inc/alglib/specialfunctions.cpp +++ /dev/null @@ -1,9637 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "specialfunctions.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Gamma function - -Input parameters: - X - argument - -Domain: - 0 < X < 171.6 - -170 < X < 0, X is not an integer. - -Relative error: - arithmetic domain # trials peak rms - IEEE -170,-33 20000 2.3e-15 3.3e-16 - IEEE -33, 33 20000 9.4e-16 2.2e-16 - IEEE 33, 171.6 20000 2.3e-15 3.2e-16 - -Cephes Math Library Release 2.8: June, 2000 -Original copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier -Translated to AlgoPascal by Bochkanov Sergey (2005, 2006, 2007). -*************************************************************************/ -double gammafunction(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::gammafunction(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Natural logarithm of gamma function - -Input parameters: - X - argument - -Result: - logarithm of the absolute value of the Gamma(X). - -Output parameters: - SgnGam - sign(Gamma(X)) - -Domain: - 0 < X < 2.55e305 - -2.55e305 < X < 0, X is not an integer. - -ACCURACY: -arithmetic domain # trials peak rms - IEEE 0, 3 28000 5.4e-16 1.1e-16 - IEEE 2.718, 2.556e305 40000 3.5e-16 8.3e-17 -The error criterion was relative when the function magnitude -was greater than one but absolute when it was less than one. - -The following test used the relative error criterion, though -at certain points the relative error could be much higher than -indicated. - IEEE -200, -4 10000 4.8e-16 1.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier -Translated to AlgoPascal by Bochkanov Sergey (2005, 2006, 2007). -*************************************************************************/ -double lngamma(const double x, double &sgngam) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::lngamma(x, &sgngam, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Error function - -The integral is - - x - - - 2 | | 2 - erf(x) = -------- | exp( - t ) dt. - sqrt(pi) | | - - - 0 - -For 0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2); otherwise -erf(x) = 1 - erfc(x). - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 3.7e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double errorfunction(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::errorfunction(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complementary error function - - 1 - erf(x) = - - inf. - - - 2 | | 2 - erfc(x) = -------- | exp( - t ) dt - sqrt(pi) | | - - - x - - -For small x, erfc(x) = 1 - erf(x); otherwise rational -approximations are computed. - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,26.6417 30000 5.7e-14 1.5e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double errorfunctionc(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::errorfunctionc(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Normal distribution function - -Returns the area under the Gaussian probability density -function, integrated from minus infinity to x: - - x - - - 1 | | 2 - ndtr(x) = --------- | exp( - t /2 ) dt - sqrt(2pi) | | - - - -inf. - - = ( 1 + erf(z) ) / 2 - = erfc(z) / 2 - -where z = x/sqrt(2). Computation is via the functions -erf and erfc. - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE -13,0 30000 3.4e-14 6.7e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double normaldistribution(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::normaldistribution(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse of the error function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double inverf(const double e) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::inverf(e, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse of Normal distribution function - -Returns the argument, x, for which the area under the -Gaussian probability density function (integrated from -minus infinity to x) is equal to y. - - -For small arguments 0 < y < exp(-2), the program computes -z = sqrt( -2.0 * log(y) ); then the approximation is -x = z - log(z)/z - (1/z) P(1/z) / Q(1/z). -There are two rational functions P/Q, one for 0 < y < exp(-32) -and the other for y up to exp(-2). For larger arguments, -w = y - 0.5, and x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0.125, 1 20000 7.2e-16 1.3e-16 - IEEE 3e-308, 0.135 50000 4.6e-16 9.8e-17 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double invnormaldistribution(const double y0) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invnormaldistribution(y0, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Incomplete gamma integral - -The function is defined by - - x - - - 1 | | -t a-1 - igam(a,x) = ----- | e t dt. - - | | - | (a) - - 0 - - -In this implementation both arguments must be positive. -The integral is evaluated by either a power series or -continued fraction expansion, depending on the relative -values of a and x. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 200000 3.6e-14 2.9e-15 - IEEE 0,100 300000 9.9e-14 1.5e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletegamma(const double a, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::incompletegamma(a, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complemented incomplete gamma integral - -The function is defined by - - - igamc(a,x) = 1 - igam(a,x) - - inf. - - - 1 | | -t a-1 - = ----- | e t dt. - - | | - | (a) - - x - - -In this implementation both arguments must be positive. -The integral is evaluated by either a power series or -continued fraction expansion, depending on the relative -values of a and x. - -ACCURACY: - -Tested at random a, x. - a x Relative error: -arithmetic domain domain # trials peak rms - IEEE 0.5,100 0,100 200000 1.9e-14 1.7e-15 - IEEE 0.01,0.5 0,100 200000 1.4e-13 1.6e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletegammac(const double a, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::incompletegammac(a, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse of complemented imcomplete gamma integral - -Given p, the function finds x such that - - igamc( a, x ) = p. - -Starting with the approximate value - - 3 - x = a t - - where - - t = 1 - d - ndtri(p) sqrt(d) - -and - - d = 1/9a, - -the routine performs up to 10 Newton iterations to find the -root of igamc(a,x) - p = 0. - -ACCURACY: - -Tested at random a, p in the intervals indicated. - - a p Relative error: -arithmetic domain domain # trials peak rms - IEEE 0.5,100 0,0.5 100000 1.0e-14 1.7e-15 - IEEE 0.01,0.5 0,0.5 100000 9.0e-14 3.4e-15 - IEEE 0.5,10000 0,0.5 20000 2.3e-13 3.8e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invincompletegammac(const double a, const double y0) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invincompletegammac(a, y0, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Airy function - -Solution of the differential equation - -y"(x) = xy. - -The function returns the two independent solutions Ai, Bi -and their first derivatives Ai'(x), Bi'(x). - -Evaluation is by power series summation for small x, -by rational minimax approximations for large x. - - - -ACCURACY: -Error criterion is absolute when function <= 1, relative -when function > 1, except * denotes relative error criterion. -For large negative x, the absolute error increases as x^1.5. -For large positive x, the relative error increases as x^1.5. - -Arithmetic domain function # trials peak rms -IEEE -10, 0 Ai 10000 1.6e-15 2.7e-16 -IEEE 0, 10 Ai 10000 2.3e-14* 1.8e-15* -IEEE -10, 0 Ai' 10000 4.6e-15 7.6e-16 -IEEE 0, 10 Ai' 10000 1.8e-14* 1.5e-15* -IEEE -10, 10 Bi 30000 4.2e-15 5.3e-16 -IEEE -10, 10 Bi' 30000 4.9e-15 7.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -void airy(const double x, double &ai, double &aip, double &bi, double &bip) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::airy(x, &ai, &aip, &bi, &bip, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bessel function of order zero - -Returns Bessel function of order zero of the argument. - -The domain is divided into the intervals [0, 5] and -(5, infinity). In the first interval the following rational -approximation is used: - - - 2 2 -(w - r ) (w - r ) P (w) / Q (w) - 1 2 3 8 - - 2 -where w = x and the two r's are zeros of the function. - -In the second interval, the Hankel asymptotic expansion -is employed with two rational functions of degree 6/6 -and 7/7. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 60000 4.2e-16 1.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselj0(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besselj0(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bessel function of order one - -Returns Bessel function of order one of the argument. - -The domain is divided into the intervals [0, 8] and -(8, infinity). In the first interval a 24 term Chebyshev -expansion is used. In the second, the asymptotic -trigonometric representation is employed using two -rational functions of degree 5/5. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 2.6e-16 1.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselj1(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besselj1(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bessel function of integer order - -Returns Bessel function of order n, where n is a -(possibly negative) integer. - -The ratio of jn(x) to j0(x) is computed by backward -recurrence. First the ratio jn/jn-1 is found by a -continued fraction expansion. Then the recurrence -relating successive orders is applied until j0 or j1 is -reached. - -If n = 0 or 1 the routine for j0 or j1 is called -directly. - -ACCURACY: - - Absolute error: -arithmetic range # trials peak rms - IEEE 0, 30 5000 4.4e-16 7.9e-17 - - -Not suitable for large n or x. Use jv() (fractional order) instead. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseljn(const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besseljn(n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bessel function of the second kind, order zero - -Returns Bessel function of the second kind, of order -zero, of the argument. - -The domain is divided into the intervals [0, 5] and -(5, infinity). In the first interval a rational approximation -R(x) is employed to compute - y0(x) = R(x) + 2 * log(x) * j0(x) / PI. -Thus a call to j0() is required. - -In the second interval, the Hankel asymptotic expansion -is employed with two rational functions of degree 6/6 -and 7/7. - - - -ACCURACY: - - Absolute error, when y0(x) < 1; else relative error: - -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.3e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double bessely0(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::bessely0(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bessel function of second kind of order one - -Returns Bessel function of the second kind of order one -of the argument. - -The domain is divided into the intervals [0, 8] and -(8, infinity). In the first interval a 25 term Chebyshev -expansion is used, and a call to j1() is required. -In the second, the asymptotic trigonometric representation -is employed using two rational functions of degree 5/5. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.0e-15 1.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double bessely1(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::bessely1(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Bessel function of second kind of integer order - -Returns Bessel function of order n, where n is a -(possibly negative) integer. - -The function is evaluated by forward recurrence on -n, starting with values computed by the routines -y0() and y1(). - -If n = 0 or 1 the routine for y0 or y1 is called -directly. - -ACCURACY: - Absolute error, except relative - when y > 1: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 3.4e-15 4.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselyn(const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besselyn(n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modified Bessel function of order zero - -Returns modified Bessel function of order zero of the -argument. - -The function is defined as i0(x) = j0( ix ). - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 30000 5.8e-16 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseli0(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besseli0(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modified Bessel function of order one - -Returns modified Bessel function of order one of the -argument. - -The function is defined as i1(x) = -i j1( ix ). - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.9e-15 2.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseli1(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besseli1(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modified Bessel function, second kind, order zero - -Returns modified Bessel function of the second kind -of order zero of the argument. - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - -Tested at 2000 random points between 0 and 8. Peak absolute -error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15. - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.2e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselk0(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besselk0(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modified Bessel function, second kind, order one - -Computes the modified Bessel function of the second kind -of order one of the argument. - -The range is partitioned into the two intervals [0,2] and -(2, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.2e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselk1(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besselk1(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Modified Bessel function, second kind, integer order - -Returns modified Bessel function of the second kind -of order n of the argument. - -The range is partitioned into the two intervals [0,9.55] and -(9.55, infinity). An ascending power series is used in the -low range, and an asymptotic expansion in the high range. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 90000 1.8e-8 3.0e-10 - -Error is high only near the crossover point x = 9.55 -between the two expansions used. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselkn(const ae_int_t nn, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::besselkn(nn, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Beta function - - - - - - | (a) | (b) -beta( a, b ) = -----------. - - - | (a+b) - -For large arguments the logarithm of the function is -evaluated using lgam(), then exponentiated. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 30000 8.1e-14 1.1e-14 - -Cephes Math Library Release 2.0: April, 1987 -Copyright 1984, 1987 by Stephen L. Moshier -*************************************************************************/ -double beta(const double a, const double b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::beta(a, b, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Incomplete beta integral - -Returns incomplete beta integral of the arguments, evaluated -from zero to x. The function is defined as - - x - - - - | (a+b) | | a-1 b-1 - ----------- | t (1-t) dt. - - - | | - | (a) | (b) - - 0 - -The domain of definition is 0 <= x <= 1. In this -implementation a and b are restricted to positive values. -The integral from x to 1 may be obtained by the symmetry -relation - - 1 - incbet( a, b, x ) = incbet( b, a, 1-x ). - -The integral is evaluated by a continued fraction expansion -or, when b*x is small, by a power series. - -ACCURACY: - -Tested at uniformly distributed random points (a,b,x) with a and b -in "domain" and x between 0 and 1. - Relative error -arithmetic domain # trials peak rms - IEEE 0,5 10000 6.9e-15 4.5e-16 - IEEE 0,85 250000 2.2e-13 1.7e-14 - IEEE 0,1000 30000 5.3e-12 6.3e-13 - IEEE 0,10000 250000 9.3e-11 7.1e-12 - IEEE 0,100000 10000 8.7e-10 4.8e-11 -Outputs smaller than the IEEE gradual underflow threshold -were excluded from these statistics. - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletebeta(const double a, const double b, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::incompletebeta(a, b, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse of imcomplete beta integral - -Given y, the function finds x such that - - incbet( a, b, x ) = y . - -The routine performs interval halving or Newton iterations to find the -root of incbet(a,b,x) - y = 0. - - -ACCURACY: - - Relative error: - x a,b -arithmetic domain domain # trials peak rms - IEEE 0,1 .5,10000 50000 5.8e-12 1.3e-13 - IEEE 0,1 .25,100 100000 1.8e-13 3.9e-15 - IEEE 0,1 0,5 50000 1.1e-12 5.5e-15 -With a and b constrained to half-integer or integer values: - IEEE 0,1 .5,10000 50000 5.8e-12 1.1e-13 - IEEE 0,1 .5,100 100000 1.7e-14 7.9e-16 -With a = .5, b constrained to half-integer or integer values: - IEEE 0,1 .5,10000 10000 8.3e-11 1.0e-11 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1996, 2000 by Stephen L. Moshier -*************************************************************************/ -double invincompletebeta(const double a, const double b, const double y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invincompletebeta(a, b, y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Binomial distribution - -Returns the sum of the terms 0 through k of the Binomial -probability density: - - k - -- ( n ) j n-j - > ( ) p (1-p) - -- ( j ) - j=0 - -The terms are not summed directly; instead the incomplete -beta integral is employed, according to the formula - -y = bdtr( k, n, p ) = incbet( n-k, k+1, 1-p ). - -The arguments must be positive, with p ranging from 0 to 1. - -ACCURACY: - -Tested at random points (a,b,p), with p between 0 and 1. - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 4.3e-15 2.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double binomialdistribution(const ae_int_t k, const ae_int_t n, const double p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::binomialdistribution(k, n, p, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complemented binomial distribution - -Returns the sum of the terms k+1 through n of the Binomial -probability density: - - n - -- ( n ) j n-j - > ( ) p (1-p) - -- ( j ) - j=k+1 - -The terms are not summed directly; instead the incomplete -beta integral is employed, according to the formula - -y = bdtrc( k, n, p ) = incbet( k+1, n-k, p ). - -The arguments must be positive, with p ranging from 0 to 1. - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 6.7e-15 8.2e-16 - For p between 0 and .001: - IEEE 0,100 100000 1.5e-13 2.7e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double binomialcdistribution(const ae_int_t k, const ae_int_t n, const double p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::binomialcdistribution(k, n, p, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse binomial distribution - -Finds the event probability p such that the sum of the -terms 0 through k of the Binomial probability density -is equal to the given cumulative probability y. - -This is accomplished using the inverse beta integral -function and the relation - -1 - p = incbi( n-k, k+1, y ). - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 2.3e-14 6.4e-16 - IEEE 0,10000 100000 6.6e-12 1.2e-13 - For p between 10^-6 and 0.001: - IEEE 0,100 100000 2.0e-12 1.3e-14 - IEEE 0,10000 100000 1.5e-12 3.2e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invbinomialdistribution(const ae_int_t k, const ae_int_t n, const double y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invbinomialdistribution(k, n, y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the value of the Chebyshev polynomials of the -first and second kinds. - -Parameters: - r - polynomial kind, either 1 or 2. - n - degree, n>=0 - x - argument, -1 <= x <= 1 - -Result: - the value of the Chebyshev polynomial at x -*************************************************************************/ -double chebyshevcalculate(const ae_int_t r, const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::chebyshevcalculate(r, n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Summation of Chebyshev polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*T0(x) + c[1]*T1(x) + ... + c[N]*TN(x) -or - c[0]*U0(x) + c[1]*U1(x) + ... + c[N]*UN(x) -depending on the R. - -Parameters: - r - polynomial kind, either 1 or 2. - n - degree, n>=0 - x - argument - -Result: - the value of the Chebyshev polynomial at x -*************************************************************************/ -double chebyshevsum(const real_1d_array &c, const ae_int_t r, const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::chebyshevsum(const_cast(c.c_ptr()), r, n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Representation of Tn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void chebyshevcoefficients(const ae_int_t n, real_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::chebyshevcoefficients(n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Conversion of a series of Chebyshev polynomials to a power series. - -Represents A[0]*T0(x) + A[1]*T1(x) + ... + A[N]*Tn(x) as -B[0] + B[1]*X + ... + B[N]*X^N. - -Input parameters: - A - Chebyshev series coefficients - N - degree, N>=0 - -Output parameters - B - power series coefficients -*************************************************************************/ -void fromchebyshev(const real_1d_array &a, const ae_int_t n, real_1d_array &b) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fromchebyshev(const_cast(a.c_ptr()), n, const_cast(b.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Chi-square distribution - -Returns the area under the left hand tail (from 0 to x) -of the Chi square probability density function with -v degrees of freedom. - - - x - - - 1 | | v/2-1 -t/2 - P( x | v ) = ----------- | t e dt - v/2 - | | - 2 | (v/2) - - 0 - -where x is the Chi-square variable. - -The incomplete gamma integral is used, according to the -formula - -y = chdtr( v, x ) = igam( v/2.0, x/2.0 ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double chisquaredistribution(const double v, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::chisquaredistribution(v, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complemented Chi-square distribution - -Returns the area under the right hand tail (from x to -infinity) of the Chi square probability density function -with v degrees of freedom: - - inf. - - - 1 | | v/2-1 -t/2 - P( x | v ) = ----------- | t e dt - v/2 - | | - 2 | (v/2) - - x - -where x is the Chi-square variable. - -The incomplete gamma integral is used, according to the -formula - -y = chdtr( v, x ) = igamc( v/2.0, x/2.0 ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double chisquarecdistribution(const double v, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::chisquarecdistribution(v, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse of complemented Chi-square distribution - -Finds the Chi-square argument x such that the integral -from x to infinity of the Chi-square density is equal -to the given cumulative probability y. - -This is accomplished using the inverse gamma integral -function and the relation - - x/2 = igami( df/2, y ); - -ACCURACY: - -See inverse incomplete gamma function - - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double invchisquaredistribution(const double v, const double y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invchisquaredistribution(v, y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Dawson's Integral - -Approximates the integral - - x - - - 2 | | 2 - dawsn(x) = exp( -x ) | exp( t ) dt - | | - - - 0 - -Three different rational approximations are employed, for -the intervals 0 to 3.25; 3.25 to 6.25; and 6.25 up. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,10 10000 6.9e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double dawsonintegral(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::dawsonintegral(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complete elliptic integral of the first kind - -Approximates the integral - - - - pi/2 - - - | | - | dt -K(m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -using the approximation - - P(x) - log x Q(x). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 2.5e-16 6.8e-17 - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double ellipticintegralk(const double m) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::ellipticintegralk(m, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complete elliptic integral of the first kind - -Approximates the integral - - - - pi/2 - - - | | - | dt -K(m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -where m = 1 - m1, using the approximation - - P(x) - log x Q(x). - -The argument m1 is used rather than m so that the logarithmic -singularity at m = 1 will be shifted to the origin; this -preserves maximum accuracy. - -K(0) = pi/2. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 2.5e-16 6.8e-17 - -Àëãîðèòì âçÿò èç áèáëèîòåêè Cephes -*************************************************************************/ -double ellipticintegralkhighprecision(const double m1) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::ellipticintegralkhighprecision(m1, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Incomplete elliptic integral of the first kind F(phi|m) - -Approximates the integral - - - - phi - - - | | - | dt -F(phi_\m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -of amplitude phi and modulus m, using the arithmetic - -geometric mean algorithm. - - - - -ACCURACY: - -Tested at random points with m in [0, 1] and phi as indicated. - - Relative error: -arithmetic domain # trials peak rms - IEEE -10,10 200000 7.4e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompleteellipticintegralk(const double phi, const double m) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::incompleteellipticintegralk(phi, m, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complete elliptic integral of the second kind - -Approximates the integral - - - pi/2 - - - | | 2 -E(m) = | sqrt( 1 - m sin t ) dt - | | - - - 0 - -using the approximation - - P(x) - x log x Q(x). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 1 10000 2.1e-16 7.3e-17 - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double ellipticintegrale(const double m) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::ellipticintegrale(m, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Incomplete elliptic integral of the second kind - -Approximates the integral - - - phi - - - | | - | 2 -E(phi_\m) = | sqrt( 1 - m sin t ) dt - | - | | - - - 0 - -of amplitude phi and modulus m, using the arithmetic - -geometric mean algorithm. - -ACCURACY: - -Tested at random arguments with phi in [-10, 10] and m in -[0, 1]. - Relative error: -arithmetic domain # trials peak rms - IEEE -10,10 150000 3.3e-15 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1993, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompleteellipticintegrale(const double phi, const double m) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::incompleteellipticintegrale(phi, m, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Exponential integral Ei(x) - - x - - t - | | e - Ei(x) = -|- --- dt . - | | t - - - -inf - -Not defined for x <= 0. -See also expn.c. - - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,100 50000 8.6e-16 1.3e-16 - -Cephes Math Library Release 2.8: May, 1999 -Copyright 1999 by Stephen L. Moshier -*************************************************************************/ -double exponentialintegralei(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::exponentialintegralei(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Exponential integral En(x) - -Evaluates the exponential integral - - inf. - - - | | -xt - | e - E (x) = | ---- dt. - n | n - | | t - - - 1 - - -Both n and x must be nonnegative. - -The routine employs either a power series, a continued -fraction, or an asymptotic formula depending on the -relative values of n and x. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 10000 1.7e-15 3.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 2000 by Stephen L. Moshier -*************************************************************************/ -double exponentialintegralen(const double x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::exponentialintegralen(x, n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -F distribution - -Returns the area from zero to x under the F density -function (also known as Snedcor's density or the -variance ratio density). This is the density -of x = (u1/df1)/(u2/df2), where u1 and u2 are random -variables having Chi square distributions with df1 -and df2 degrees of freedom, respectively. -The incomplete beta integral is used, according to the -formula - -P(x) = incbet( df1/2, df2/2, (df1*x/(df2 + df1*x) ). - - -The arguments a and b are greater than zero, and x is -nonnegative. - -ACCURACY: - -Tested at random points (a,b,x). - - x a,b Relative error: -arithmetic domain domain # trials peak rms - IEEE 0,1 0,100 100000 9.8e-15 1.7e-15 - IEEE 1,5 0,100 100000 6.5e-15 3.5e-16 - IEEE 0,1 1,10000 100000 2.2e-11 3.3e-12 - IEEE 1,5 1,10000 100000 1.1e-11 1.7e-13 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double fdistribution(const ae_int_t a, const ae_int_t b, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::fdistribution(a, b, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complemented F distribution - -Returns the area from x to infinity under the F density -function (also known as Snedcor's density or the -variance ratio density). - - - inf. - - - 1 | | a-1 b-1 -1-P(x) = ------ | t (1-t) dt - B(a,b) | | - - - x - - -The incomplete beta integral is used, according to the -formula - -P(x) = incbet( df2/2, df1/2, (df2/(df2 + df1*x) ). - - -ACCURACY: - -Tested at random points (a,b,x) in the indicated intervals. - x a,b Relative error: -arithmetic domain domain # trials peak rms - IEEE 0,1 1,100 100000 3.7e-14 5.9e-16 - IEEE 1,5 1,100 100000 8.0e-15 1.6e-15 - IEEE 0,1 1,10000 100000 1.8e-11 3.5e-13 - IEEE 1,5 1,10000 100000 2.0e-11 3.0e-12 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double fcdistribution(const ae_int_t a, const ae_int_t b, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::fcdistribution(a, b, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse of complemented F distribution - -Finds the F density argument x such that the integral -from x to infinity of the F density is equal to the -given probability p. - -This is accomplished using the inverse beta integral -function and the relations - - z = incbi( df2/2, df1/2, p ) - x = df2 (1-z) / (df1 z). - -Note: the following relations hold for the inverse of -the uncomplemented F distribution: - - z = incbi( df1/2, df2/2, p ) - x = df2 z / (df1 (1-z)). - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between .001 and 1: - IEEE 1,100 100000 8.3e-15 4.7e-16 - IEEE 1,10000 100000 2.1e-11 1.4e-13 - For p between 10^-6 and 10^-3: - IEEE 1,100 50000 1.3e-12 8.4e-15 - IEEE 1,10000 50000 3.0e-12 4.8e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invfdistribution(const ae_int_t a, const ae_int_t b, const double y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invfdistribution(a, b, y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Fresnel integral - -Evaluates the Fresnel integrals - - x - - - | | -C(x) = | cos(pi/2 t**2) dt, - | | - - - 0 - - x - - - | | -S(x) = | sin(pi/2 t**2) dt. - | | - - - 0 - - -The integrals are evaluated by a power series for x < 1. -For x >= 1 auxiliary functions f(x) and g(x) are employed -such that - -C(x) = 0.5 + f(x) sin( pi/2 x**2 ) - g(x) cos( pi/2 x**2 ) -S(x) = 0.5 - f(x) cos( pi/2 x**2 ) - g(x) sin( pi/2 x**2 ) - - - -ACCURACY: - - Relative error. - -Arithmetic function domain # trials peak rms - IEEE S(x) 0, 10 10000 2.0e-15 3.2e-16 - IEEE C(x) 0, 10 10000 1.8e-15 3.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -void fresnelintegral(const double x, double &c, double &s) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::fresnelintegral(x, &c, &s, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the value of the Hermite polynomial. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Hermite polynomial Hn at x -*************************************************************************/ -double hermitecalculate(const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hermitecalculate(n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Summation of Hermite polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*H0(x) + c[1]*H1(x) + ... + c[N]*HN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Hermite polynomial at x -*************************************************************************/ -double hermitesum(const real_1d_array &c, const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::hermitesum(const_cast(c.c_ptr()), n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Representation of Hn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void hermitecoefficients(const ae_int_t n, real_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hermitecoefficients(n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Jacobian Elliptic Functions - -Evaluates the Jacobian elliptic functions sn(u|m), cn(u|m), -and dn(u|m) of parameter m between 0 and 1, and real -argument u. - -These functions are periodic, with quarter-period on the -real axis equal to the complete elliptic integral -ellpk(1.0-m). - -Relation to incomplete elliptic integral: -If u = ellik(phi,m), then sn(u|m) = sin(phi), -and cn(u|m) = cos(phi). Phi is called the amplitude of u. - -Computation is by means of the arithmetic-geometric mean -algorithm, except when m is within 1e-9 of 0 or 1. In the -latter case with m close to 1, the approximation applies -only for phi < pi/2. - -ACCURACY: - -Tested at random points with u between 0 and 10, m between -0 and 1. - - Absolute error (* = relative error): -arithmetic function # trials peak rms - IEEE phi 10000 9.2e-16* 1.4e-16* - IEEE sn 50000 4.1e-15 4.6e-16 - IEEE cn 40000 3.6e-15 4.4e-16 - IEEE dn 10000 1.3e-12 1.8e-14 - - Peak error observed in consistency check using addition -theorem for sn(u+v) was 4e-16 (absolute). Also tested by -the above relation to the incomplete elliptic integral. -Accuracy deteriorates when u is large. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -void jacobianellipticfunctions(const double u, const double m, double &sn, double &cn, double &dn, double &ph) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::jacobianellipticfunctions(u, m, &sn, &cn, &dn, &ph, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the value of the Laguerre polynomial. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Laguerre polynomial Ln at x -*************************************************************************/ -double laguerrecalculate(const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::laguerrecalculate(n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Summation of Laguerre polynomials using Clenshaw’s recurrence formula. - -This routine calculates c[0]*L0(x) + c[1]*L1(x) + ... + c[N]*LN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Laguerre polynomial at x -*************************************************************************/ -double laguerresum(const real_1d_array &c, const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::laguerresum(const_cast(c.c_ptr()), n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Representation of Ln as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void laguerrecoefficients(const ae_int_t n, real_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::laguerrecoefficients(n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the value of the Legendre polynomial Pn. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Legendre polynomial Pn at x -*************************************************************************/ -double legendrecalculate(const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::legendrecalculate(n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Summation of Legendre polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*P0(x) + c[1]*P1(x) + ... + c[N]*PN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Legendre polynomial at x -*************************************************************************/ -double legendresum(const real_1d_array &c, const ae_int_t n, const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::legendresum(const_cast(c.c_ptr()), n, x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Representation of Pn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void legendrecoefficients(const ae_int_t n, real_1d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::legendrecoefficients(n, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Poisson distribution - -Returns the sum of the first k+1 terms of the Poisson -distribution: - - k j - -- -m m - > e -- - -- j! - j=0 - -The terms are not summed directly; instead the incomplete -gamma integral is employed, according to the relation - -y = pdtr( k, m ) = igamc( k+1, m ). - -The arguments must both be positive. -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double poissondistribution(const ae_int_t k, const double m) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::poissondistribution(k, m, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Complemented Poisson distribution - -Returns the sum of the terms k+1 to infinity of the Poisson -distribution: - - inf. j - -- -m m - > e -- - -- j! - j=k+1 - -The terms are not summed directly; instead the incomplete -gamma integral is employed, according to the formula - -y = pdtrc( k, m ) = igam( k+1, m ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double poissoncdistribution(const ae_int_t k, const double m) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::poissoncdistribution(k, m, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Inverse Poisson distribution - -Finds the Poisson variable x such that the integral -from 0 to x of the Poisson density is equal to the -given probability y. - -This is accomplished using the inverse gamma integral -function and the relation - - m = igami( k+1, y ). - -ACCURACY: - -See inverse incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invpoissondistribution(const ae_int_t k, const double y) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invpoissondistribution(k, y, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Psi (digamma) function - - d - - psi(x) = -- ln | (x) - dx - -is the logarithmic derivative of the gamma function. -For integer x, - n-1 - - -psi(n) = -EUL + > 1/k. - - - k=1 - -This formula is used for 0 < n <= 10. If x is negative, it -is transformed to a positive argument by the reflection -formula psi(1-x) = psi(x) + pi cot(pi x). -For general positive x, the argument is made greater than 10 -using the recurrence psi(x+1) = psi(x) + 1/x. -Then the following asymptotic expansion is applied: - - inf. B - - 2k -psi(x) = log(x) - 1/2x - > ------- - - 2k - k=1 2k x - -where the B2k are Bernoulli numbers. - -ACCURACY: - Relative error (except absolute when |psi| < 1): -arithmetic domain # trials peak rms - IEEE 0,30 30000 1.3e-15 1.4e-16 - IEEE -30,0 40000 1.5e-15 2.2e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double psi(const double x) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::psi(x, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Student's t distribution - -Computes the integral from minus infinity to t of the Student -t distribution with integer k > 0 degrees of freedom: - - t - - - | | - - | 2 -(k+1)/2 - | ( (k+1)/2 ) | ( x ) - ---------------------- | ( 1 + --- ) dx - - | ( k ) - sqrt( k pi ) | ( k/2 ) | - | | - - - -inf. - -Relation to incomplete beta integral: - - 1 - stdtr(k,t) = 0.5 * incbet( k/2, 1/2, z ) -where - z = k/(k + t**2). - -For t < -2, this is the method of computation. For higher t, -a direct method is derived from integration by parts. -Since the function is symmetric about t=0, the area under the -right tail of the density is found by calling the function -with -t instead of t. - -ACCURACY: - -Tested at random 1 <= k <= 25. The "domain" refers to t. - Relative error: -arithmetic domain # trials peak rms - IEEE -100,-2 50000 5.9e-15 1.4e-15 - IEEE -2,100 500000 2.7e-15 4.9e-17 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double studenttdistribution(const ae_int_t k, const double t) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::studenttdistribution(k, t, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Functional inverse of Student's t distribution - -Given probability p, finds the argument t such that stdtr(k,t) -is equal to p. - -ACCURACY: - -Tested at random 1 <= k <= 100. The "domain" refers to p: - Relative error: -arithmetic domain # trials peak rms - IEEE .001,.999 25000 5.7e-15 8.0e-16 - IEEE 10^-6,.001 25000 2.0e-12 2.9e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invstudenttdistribution(const ae_int_t k, const double p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::invstudenttdistribution(k, p, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Sine and cosine integrals - -Evaluates the integrals - - x - - - | cos t - 1 - Ci(x) = eul + ln x + | --------- dt, - | t - - - 0 - x - - - | sin t - Si(x) = | ----- dt - | t - - - 0 - -where eul = 0.57721566490153286061 is Euler's constant. -The integrals are approximated by rational functions. -For x > 8 auxiliary functions f(x) and g(x) are employed -such that - -Ci(x) = f(x) sin(x) - g(x) cos(x) -Si(x) = pi/2 - f(x) cos(x) - g(x) sin(x) - - -ACCURACY: - Test interval = [0,50]. -Absolute error, except relative when > 1: -arithmetic function # trials peak rms - IEEE Si 30000 4.4e-16 7.3e-17 - IEEE Ci 30000 6.9e-16 5.1e-17 - -Cephes Math Library Release 2.1: January, 1989 -Copyright 1984, 1987, 1989 by Stephen L. Moshier -*************************************************************************/ -void sinecosineintegrals(const double x, double &si, double &ci) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sinecosineintegrals(x, &si, &ci, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Hyperbolic sine and cosine integrals - -Approximates the integrals - - x - - - | | cosh t - 1 - Chi(x) = eul + ln x + | ----------- dt, - | | t - - - 0 - - x - - - | | sinh t - Shi(x) = | ------ dt - | | t - - - 0 - -where eul = 0.57721566490153286061 is Euler's constant. -The integrals are evaluated by power series for x < 8 -and by Chebyshev expansions for x between 8 and 88. -For large x, both functions approach exp(x)/2x. -Arguments greater than 88 in magnitude return MAXNUM. - - -ACCURACY: - -Test interval 0 to 88. - Relative error: -arithmetic function # trials peak rms - IEEE Shi 30000 6.9e-16 1.6e-16 - Absolute error, except relative when |Chi| > 1: - IEEE Chi 30000 8.4e-16 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -void hyperbolicsinecosineintegrals(const double x, double &shi, double &chi) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::hyperbolicsinecosineintegrals(x, &shi, &chi, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static double gammafunc_gammastirf(double x, ae_state *_state); - - - - - - - - -static void bessel_besselmfirstcheb(double c, - double* b0, - double* b1, - double* b2, - ae_state *_state); -static void bessel_besselmnextcheb(double x, - double c, - double* b0, - double* b1, - double* b2, - ae_state *_state); -static void bessel_besselm1firstcheb(double c, - double* b0, - double* b1, - double* b2, - ae_state *_state); -static void bessel_besselm1nextcheb(double x, - double c, - double* b0, - double* b1, - double* b2, - ae_state *_state); -static void bessel_besselasympt0(double x, - double* pzero, - double* qzero, - ae_state *_state); -static void bessel_besselasympt1(double x, - double* pzero, - double* qzero, - ae_state *_state); - - - - -static double ibetaf_incompletebetafe(double a, - double b, - double x, - double big, - double biginv, - ae_state *_state); -static double ibetaf_incompletebetafe2(double a, - double b, - double x, - double big, - double biginv, - ae_state *_state); -static double ibetaf_incompletebetaps(double a, - double b, - double x, - double maxgam, - ae_state *_state); - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -static void trigintegrals_chebiterationshichi(double x, - double c, - double* b0, - double* b1, - double* b2, - ae_state *_state); - - - - - -/************************************************************************* -Gamma function - -Input parameters: - X - argument - -Domain: - 0 < X < 171.6 - -170 < X < 0, X is not an integer. - -Relative error: - arithmetic domain # trials peak rms - IEEE -170,-33 20000 2.3e-15 3.3e-16 - IEEE -33, 33 20000 9.4e-16 2.2e-16 - IEEE 33, 171.6 20000 2.3e-15 3.2e-16 - -Cephes Math Library Release 2.8: June, 2000 -Original copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier -Translated to AlgoPascal by Bochkanov Sergey (2005, 2006, 2007). -*************************************************************************/ -double gammafunction(double x, ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_SPECFUNCS - double p; - double pp; - double q; - double qq; - double z; - ae_int_t i; - double sgngam; - double result; - - - sgngam = 1; - q = ae_fabs(x, _state); - if( ae_fp_greater(q,33.0) ) - { - if( ae_fp_less(x,0.0) ) - { - p = ae_ifloor(q, _state); - i = ae_round(p, _state); - if( i%2==0 ) - { - sgngam = -1; - } - z = q-p; - if( ae_fp_greater(z,0.5) ) - { - p = p+1; - z = q-p; - } - z = q*ae_sin(ae_pi*z, _state); - z = ae_fabs(z, _state); - z = ae_pi/(z*gammafunc_gammastirf(q, _state)); - } - else - { - z = gammafunc_gammastirf(x, _state); - } - result = sgngam*z; - return result; - } - z = 1; - while(ae_fp_greater_eq(x,3)) - { - x = x-1; - z = z*x; - } - while(ae_fp_less(x,0)) - { - if( ae_fp_greater(x,-0.000000001) ) - { - result = z/((1+0.5772156649015329*x)*x); - return result; - } - z = z/x; - x = x+1; - } - while(ae_fp_less(x,2)) - { - if( ae_fp_less(x,0.000000001) ) - { - result = z/((1+0.5772156649015329*x)*x); - return result; - } - z = z/x; - x = x+1.0; - } - if( ae_fp_eq(x,2) ) - { - result = z; - return result; - } - x = x-2.0; - pp = 1.60119522476751861407E-4; - pp = 1.19135147006586384913E-3+x*pp; - pp = 1.04213797561761569935E-2+x*pp; - pp = 4.76367800457137231464E-2+x*pp; - pp = 2.07448227648435975150E-1+x*pp; - pp = 4.94214826801497100753E-1+x*pp; - pp = 9.99999999999999996796E-1+x*pp; - qq = -2.31581873324120129819E-5; - qq = 5.39605580493303397842E-4+x*qq; - qq = -4.45641913851797240494E-3+x*qq; - qq = 1.18139785222060435552E-2+x*qq; - qq = 3.58236398605498653373E-2+x*qq; - qq = -2.34591795718243348568E-1+x*qq; - qq = 7.14304917030273074085E-2+x*qq; - qq = 1.00000000000000000320+x*qq; - result = z*pp/qq; - return result; -#else - return _ialglib_i_gammafunction(x); -#endif -} - - -/************************************************************************* -Natural logarithm of gamma function - -Input parameters: - X - argument - -Result: - logarithm of the absolute value of the Gamma(X). - -Output parameters: - SgnGam - sign(Gamma(X)) - -Domain: - 0 < X < 2.55e305 - -2.55e305 < X < 0, X is not an integer. - -ACCURACY: -arithmetic domain # trials peak rms - IEEE 0, 3 28000 5.4e-16 1.1e-16 - IEEE 2.718, 2.556e305 40000 3.5e-16 8.3e-17 -The error criterion was relative when the function magnitude -was greater than one but absolute when it was less than one. - -The following test used the relative error criterion, though -at certain points the relative error could be much higher than -indicated. - IEEE -200, -4 10000 4.8e-16 1.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier -Translated to AlgoPascal by Bochkanov Sergey (2005, 2006, 2007). -*************************************************************************/ -double lngamma(double x, double* sgngam, ae_state *_state) -{ -#ifndef ALGLIB_INTERCEPTS_SPECFUNCS - double a; - double b; - double c; - double p; - double q; - double u; - double w; - double z; - ae_int_t i; - double logpi; - double ls2pi; - double tmp; - double result; - - *sgngam = 0; - - *sgngam = 1; - logpi = 1.14472988584940017414; - ls2pi = 0.91893853320467274178; - if( ae_fp_less(x,-34.0) ) - { - q = -x; - w = lngamma(q, &tmp, _state); - p = ae_ifloor(q, _state); - i = ae_round(p, _state); - if( i%2==0 ) - { - *sgngam = -1; - } - else - { - *sgngam = 1; - } - z = q-p; - if( ae_fp_greater(z,0.5) ) - { - p = p+1; - z = p-q; - } - z = q*ae_sin(ae_pi*z, _state); - result = logpi-ae_log(z, _state)-w; - return result; - } - if( ae_fp_less(x,13) ) - { - z = 1; - p = 0; - u = x; - while(ae_fp_greater_eq(u,3)) - { - p = p-1; - u = x+p; - z = z*u; - } - while(ae_fp_less(u,2)) - { - z = z/u; - p = p+1; - u = x+p; - } - if( ae_fp_less(z,0) ) - { - *sgngam = -1; - z = -z; - } - else - { - *sgngam = 1; - } - if( ae_fp_eq(u,2) ) - { - result = ae_log(z, _state); - return result; - } - p = p-2; - x = x+p; - b = -1378.25152569120859100; - b = -38801.6315134637840924+x*b; - b = -331612.992738871184744+x*b; - b = -1162370.97492762307383+x*b; - b = -1721737.00820839662146+x*b; - b = -853555.664245765465627+x*b; - c = 1; - c = -351.815701436523470549+x*c; - c = -17064.2106651881159223+x*c; - c = -220528.590553854454839+x*c; - c = -1139334.44367982507207+x*c; - c = -2532523.07177582951285+x*c; - c = -2018891.41433532773231+x*c; - p = x*b/c; - result = ae_log(z, _state)+p; - return result; - } - q = (x-0.5)*ae_log(x, _state)-x+ls2pi; - if( ae_fp_greater(x,100000000) ) - { - result = q; - return result; - } - p = 1/(x*x); - if( ae_fp_greater_eq(x,1000.0) ) - { - q = q+((7.9365079365079365079365*0.0001*p-2.7777777777777777777778*0.001)*p+0.0833333333333333333333)/x; - } - else - { - a = 8.11614167470508450300*0.0001; - a = -5.95061904284301438324*0.0001+p*a; - a = 7.93650340457716943945*0.0001+p*a; - a = -2.77777777730099687205*0.001+p*a; - a = 8.33333333333331927722*0.01+p*a; - q = q+a/x; - } - result = q; - return result; -#else - return _ialglib_i_lngamma(x, sgngam); -#endif -} - - -static double gammafunc_gammastirf(double x, ae_state *_state) -{ - double y; - double w; - double v; - double stir; - double result; - - - w = 1/x; - stir = 7.87311395793093628397E-4; - stir = -2.29549961613378126380E-4+w*stir; - stir = -2.68132617805781232825E-3+w*stir; - stir = 3.47222221605458667310E-3+w*stir; - stir = 8.33333333333482257126E-2+w*stir; - w = 1+w*stir; - y = ae_exp(x, _state); - if( ae_fp_greater(x,143.01608) ) - { - v = ae_pow(x, 0.5*x-0.25, _state); - y = v*(v/y); - } - else - { - y = ae_pow(x, x-0.5, _state)/y; - } - result = 2.50662827463100050242*y*w; - return result; -} - - - - -/************************************************************************* -Error function - -The integral is - - x - - - 2 | | 2 - erf(x) = -------- | exp( - t ) dt. - sqrt(pi) | | - - - 0 - -For 0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2); otherwise -erf(x) = 1 - erfc(x). - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 3.7e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double errorfunction(double x, ae_state *_state) -{ - double xsq; - double s; - double p; - double q; - double result; - - - s = ae_sign(x, _state); - x = ae_fabs(x, _state); - if( ae_fp_less(x,0.5) ) - { - xsq = x*x; - p = 0.007547728033418631287834; - p = -0.288805137207594084924010+xsq*p; - p = 14.3383842191748205576712+xsq*p; - p = 38.0140318123903008244444+xsq*p; - p = 3017.82788536507577809226+xsq*p; - p = 7404.07142710151470082064+xsq*p; - p = 80437.3630960840172832162+xsq*p; - q = 0.0; - q = 1.00000000000000000000000+xsq*q; - q = 38.0190713951939403753468+xsq*q; - q = 658.070155459240506326937+xsq*q; - q = 6379.60017324428279487120+xsq*q; - q = 34216.5257924628539769006+xsq*q; - q = 80437.3630960840172826266+xsq*q; - result = s*1.1283791670955125738961589031*x*p/q; - return result; - } - if( ae_fp_greater_eq(x,10) ) - { - result = s; - return result; - } - result = s*(1-errorfunctionc(x, _state)); - return result; -} - - -/************************************************************************* -Complementary error function - - 1 - erf(x) = - - inf. - - - 2 | | 2 - erfc(x) = -------- | exp( - t ) dt - sqrt(pi) | | - - - x - - -For small x, erfc(x) = 1 - erf(x); otherwise rational -approximations are computed. - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,26.6417 30000 5.7e-14 1.5e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double errorfunctionc(double x, ae_state *_state) -{ - double p; - double q; - double result; - - - if( ae_fp_less(x,0) ) - { - result = 2-errorfunctionc(-x, _state); - return result; - } - if( ae_fp_less(x,0.5) ) - { - result = 1.0-errorfunction(x, _state); - return result; - } - if( ae_fp_greater_eq(x,10) ) - { - result = 0; - return result; - } - p = 0.0; - p = 0.5641877825507397413087057563+x*p; - p = 9.675807882987265400604202961+x*p; - p = 77.08161730368428609781633646+x*p; - p = 368.5196154710010637133875746+x*p; - p = 1143.262070703886173606073338+x*p; - p = 2320.439590251635247384768711+x*p; - p = 2898.0293292167655611275846+x*p; - p = 1826.3348842295112592168999+x*p; - q = 1.0; - q = 17.14980943627607849376131193+x*q; - q = 137.1255960500622202878443578+x*q; - q = 661.7361207107653469211984771+x*q; - q = 2094.384367789539593790281779+x*q; - q = 4429.612803883682726711528526+x*q; - q = 6089.5424232724435504633068+x*q; - q = 4958.82756472114071495438422+x*q; - q = 1826.3348842295112595576438+x*q; - result = ae_exp(-ae_sqr(x, _state), _state)*p/q; - return result; -} - - -/************************************************************************* -Normal distribution function - -Returns the area under the Gaussian probability density -function, integrated from minus infinity to x: - - x - - - 1 | | 2 - ndtr(x) = --------- | exp( - t /2 ) dt - sqrt(2pi) | | - - - -inf. - - = ( 1 + erf(z) ) / 2 - = erfc(z) / 2 - -where z = x/sqrt(2). Computation is via the functions -erf and erfc. - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE -13,0 30000 3.4e-14 6.7e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double normaldistribution(double x, ae_state *_state) -{ - double result; - - - result = 0.5*(errorfunction(x/1.41421356237309504880, _state)+1); - return result; -} - - -/************************************************************************* -Inverse of the error function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double inverf(double e, ae_state *_state) -{ - double result; - - - result = invnormaldistribution(0.5*(e+1), _state)/ae_sqrt(2, _state); - return result; -} - - -/************************************************************************* -Inverse of Normal distribution function - -Returns the argument, x, for which the area under the -Gaussian probability density function (integrated from -minus infinity to x) is equal to y. - - -For small arguments 0 < y < exp(-2), the program computes -z = sqrt( -2.0 * log(y) ); then the approximation is -x = z - log(z)/z - (1/z) P(1/z) / Q(1/z). -There are two rational functions P/Q, one for 0 < y < exp(-32) -and the other for y up to exp(-2). For larger arguments, -w = y - 0.5, and x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0.125, 1 20000 7.2e-16 1.3e-16 - IEEE 3e-308, 0.135 50000 4.6e-16 9.8e-17 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double invnormaldistribution(double y0, ae_state *_state) -{ - double expm2; - double s2pi; - double x; - double y; - double z; - double y2; - double x0; - double x1; - ae_int_t code; - double p0; - double q0; - double p1; - double q1; - double p2; - double q2; - double result; - - - expm2 = 0.13533528323661269189; - s2pi = 2.50662827463100050242; - if( ae_fp_less_eq(y0,0) ) - { - result = -ae_maxrealnumber; - return result; - } - if( ae_fp_greater_eq(y0,1) ) - { - result = ae_maxrealnumber; - return result; - } - code = 1; - y = y0; - if( ae_fp_greater(y,1.0-expm2) ) - { - y = 1.0-y; - code = 0; - } - if( ae_fp_greater(y,expm2) ) - { - y = y-0.5; - y2 = y*y; - p0 = -59.9633501014107895267; - p0 = 98.0010754185999661536+y2*p0; - p0 = -56.6762857469070293439+y2*p0; - p0 = 13.9312609387279679503+y2*p0; - p0 = -1.23916583867381258016+y2*p0; - q0 = 1; - q0 = 1.95448858338141759834+y2*q0; - q0 = 4.67627912898881538453+y2*q0; - q0 = 86.3602421390890590575+y2*q0; - q0 = -225.462687854119370527+y2*q0; - q0 = 200.260212380060660359+y2*q0; - q0 = -82.0372256168333339912+y2*q0; - q0 = 15.9056225126211695515+y2*q0; - q0 = -1.18331621121330003142+y2*q0; - x = y+y*y2*p0/q0; - x = x*s2pi; - result = x; - return result; - } - x = ae_sqrt(-2.0*ae_log(y, _state), _state); - x0 = x-ae_log(x, _state)/x; - z = 1.0/x; - if( ae_fp_less(x,8.0) ) - { - p1 = 4.05544892305962419923; - p1 = 31.5251094599893866154+z*p1; - p1 = 57.1628192246421288162+z*p1; - p1 = 44.0805073893200834700+z*p1; - p1 = 14.6849561928858024014+z*p1; - p1 = 2.18663306850790267539+z*p1; - p1 = -1.40256079171354495875*0.1+z*p1; - p1 = -3.50424626827848203418*0.01+z*p1; - p1 = -8.57456785154685413611*0.0001+z*p1; - q1 = 1; - q1 = 15.7799883256466749731+z*q1; - q1 = 45.3907635128879210584+z*q1; - q1 = 41.3172038254672030440+z*q1; - q1 = 15.0425385692907503408+z*q1; - q1 = 2.50464946208309415979+z*q1; - q1 = -1.42182922854787788574*0.1+z*q1; - q1 = -3.80806407691578277194*0.01+z*q1; - q1 = -9.33259480895457427372*0.0001+z*q1; - x1 = z*p1/q1; - } - else - { - p2 = 3.23774891776946035970; - p2 = 6.91522889068984211695+z*p2; - p2 = 3.93881025292474443415+z*p2; - p2 = 1.33303460815807542389+z*p2; - p2 = 2.01485389549179081538*0.1+z*p2; - p2 = 1.23716634817820021358*0.01+z*p2; - p2 = 3.01581553508235416007*0.0001+z*p2; - p2 = 2.65806974686737550832*0.000001+z*p2; - p2 = 6.23974539184983293730*0.000000001+z*p2; - q2 = 1; - q2 = 6.02427039364742014255+z*q2; - q2 = 3.67983563856160859403+z*q2; - q2 = 1.37702099489081330271+z*q2; - q2 = 2.16236993594496635890*0.1+z*q2; - q2 = 1.34204006088543189037*0.01+z*q2; - q2 = 3.28014464682127739104*0.0001+z*q2; - q2 = 2.89247864745380683936*0.000001+z*q2; - q2 = 6.79019408009981274425*0.000000001+z*q2; - x1 = z*p2/q2; - } - x = x0-x1; - if( code!=0 ) - { - x = -x; - } - result = x; - return result; -} - - - - -/************************************************************************* -Incomplete gamma integral - -The function is defined by - - x - - - 1 | | -t a-1 - igam(a,x) = ----- | e t dt. - - | | - | (a) - - 0 - - -In this implementation both arguments must be positive. -The integral is evaluated by either a power series or -continued fraction expansion, depending on the relative -values of a and x. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 200000 3.6e-14 2.9e-15 - IEEE 0,100 300000 9.9e-14 1.5e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletegamma(double a, double x, ae_state *_state) -{ - double igammaepsilon; - double ans; - double ax; - double c; - double r; - double tmp; - double result; - - - igammaepsilon = 0.000000000000001; - if( ae_fp_less_eq(x,0)||ae_fp_less_eq(a,0) ) - { - result = 0; - return result; - } - if( ae_fp_greater(x,1)&&ae_fp_greater(x,a) ) - { - result = 1-incompletegammac(a, x, _state); - return result; - } - ax = a*ae_log(x, _state)-x-lngamma(a, &tmp, _state); - if( ae_fp_less(ax,-709.78271289338399) ) - { - result = 0; - return result; - } - ax = ae_exp(ax, _state); - r = a; - c = 1; - ans = 1; - do - { - r = r+1; - c = c*x/r; - ans = ans+c; - } - while(ae_fp_greater(c/ans,igammaepsilon)); - result = ans*ax/a; - return result; -} - - -/************************************************************************* -Complemented incomplete gamma integral - -The function is defined by - - - igamc(a,x) = 1 - igam(a,x) - - inf. - - - 1 | | -t a-1 - = ----- | e t dt. - - | | - | (a) - - x - - -In this implementation both arguments must be positive. -The integral is evaluated by either a power series or -continued fraction expansion, depending on the relative -values of a and x. - -ACCURACY: - -Tested at random a, x. - a x Relative error: -arithmetic domain domain # trials peak rms - IEEE 0.5,100 0,100 200000 1.9e-14 1.7e-15 - IEEE 0.01,0.5 0,100 200000 1.4e-13 1.6e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletegammac(double a, double x, ae_state *_state) -{ - double igammaepsilon; - double igammabignumber; - double igammabignumberinv; - double ans; - double ax; - double c; - double yc; - double r; - double t; - double y; - double z; - double pk; - double pkm1; - double pkm2; - double qk; - double qkm1; - double qkm2; - double tmp; - double result; - - - igammaepsilon = 0.000000000000001; - igammabignumber = 4503599627370496.0; - igammabignumberinv = 2.22044604925031308085*0.0000000000000001; - if( ae_fp_less_eq(x,0)||ae_fp_less_eq(a,0) ) - { - result = 1; - return result; - } - if( ae_fp_less(x,1)||ae_fp_less(x,a) ) - { - result = 1-incompletegamma(a, x, _state); - return result; - } - ax = a*ae_log(x, _state)-x-lngamma(a, &tmp, _state); - if( ae_fp_less(ax,-709.78271289338399) ) - { - result = 0; - return result; - } - ax = ae_exp(ax, _state); - y = 1-a; - z = x+y+1; - c = 0; - pkm2 = 1; - qkm2 = x; - pkm1 = x+1; - qkm1 = z*x; - ans = pkm1/qkm1; - do - { - c = c+1; - y = y+1; - z = z+2; - yc = y*c; - pk = pkm1*z-pkm2*yc; - qk = qkm1*z-qkm2*yc; - if( ae_fp_neq(qk,0) ) - { - r = pk/qk; - t = ae_fabs((ans-r)/r, _state); - ans = r; - } - else - { - t = 1; - } - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - if( ae_fp_greater(ae_fabs(pk, _state),igammabignumber) ) - { - pkm2 = pkm2*igammabignumberinv; - pkm1 = pkm1*igammabignumberinv; - qkm2 = qkm2*igammabignumberinv; - qkm1 = qkm1*igammabignumberinv; - } - } - while(ae_fp_greater(t,igammaepsilon)); - result = ans*ax; - return result; -} - - -/************************************************************************* -Inverse of complemented imcomplete gamma integral - -Given p, the function finds x such that - - igamc( a, x ) = p. - -Starting with the approximate value - - 3 - x = a t - - where - - t = 1 - d - ndtri(p) sqrt(d) - -and - - d = 1/9a, - -the routine performs up to 10 Newton iterations to find the -root of igamc(a,x) - p = 0. - -ACCURACY: - -Tested at random a, p in the intervals indicated. - - a p Relative error: -arithmetic domain domain # trials peak rms - IEEE 0.5,100 0,0.5 100000 1.0e-14 1.7e-15 - IEEE 0.01,0.5 0,0.5 100000 9.0e-14 3.4e-15 - IEEE 0.5,10000 0,0.5 20000 2.3e-13 3.8e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invincompletegammac(double a, double y0, ae_state *_state) -{ - double igammaepsilon; - double iinvgammabignumber; - double x0; - double x1; - double x; - double yl; - double yh; - double y; - double d; - double lgm; - double dithresh; - ae_int_t i; - ae_int_t dir; - double tmp; - double result; - - - igammaepsilon = 0.000000000000001; - iinvgammabignumber = 4503599627370496.0; - x0 = iinvgammabignumber; - yl = 0; - x1 = 0; - yh = 1; - dithresh = 5*igammaepsilon; - d = 1/(9*a); - y = 1-d-invnormaldistribution(y0, _state)*ae_sqrt(d, _state); - x = a*y*y*y; - lgm = lngamma(a, &tmp, _state); - i = 0; - while(i<10) - { - if( ae_fp_greater(x,x0)||ae_fp_less(x,x1) ) - { - d = 0.0625; - break; - } - y = incompletegammac(a, x, _state); - if( ae_fp_less(y,yl)||ae_fp_greater(y,yh) ) - { - d = 0.0625; - break; - } - if( ae_fp_less(y,y0) ) - { - x0 = x; - yl = y; - } - else - { - x1 = x; - yh = y; - } - d = (a-1)*ae_log(x, _state)-x-lgm; - if( ae_fp_less(d,-709.78271289338399) ) - { - d = 0.0625; - break; - } - d = -ae_exp(d, _state); - d = (y-y0)/d; - if( ae_fp_less(ae_fabs(d/x, _state),igammaepsilon) ) - { - result = x; - return result; - } - x = x-d; - i = i+1; - } - if( ae_fp_eq(x0,iinvgammabignumber) ) - { - if( ae_fp_less_eq(x,0) ) - { - x = 1; - } - while(ae_fp_eq(x0,iinvgammabignumber)) - { - x = (1+d)*x; - y = incompletegammac(a, x, _state); - if( ae_fp_less(y,y0) ) - { - x0 = x; - yl = y; - break; - } - d = d+d; - } - } - d = 0.5; - dir = 0; - i = 0; - while(i<400) - { - x = x1+d*(x0-x1); - y = incompletegammac(a, x, _state); - lgm = (x0-x1)/(x1+x0); - if( ae_fp_less(ae_fabs(lgm, _state),dithresh) ) - { - break; - } - lgm = (y-y0)/y0; - if( ae_fp_less(ae_fabs(lgm, _state),dithresh) ) - { - break; - } - if( ae_fp_less_eq(x,0.0) ) - { - break; - } - if( ae_fp_greater_eq(y,y0) ) - { - x1 = x; - yh = y; - if( dir<0 ) - { - dir = 0; - d = 0.5; - } - else - { - if( dir>1 ) - { - d = 0.5*d+0.5; - } - else - { - d = (y0-yl)/(yh-yl); - } - } - dir = dir+1; - } - else - { - x0 = x; - yl = y; - if( dir>0 ) - { - dir = 0; - d = 0.5; - } - else - { - if( dir<-1 ) - { - d = 0.5*d; - } - else - { - d = (y0-yl)/(yh-yl); - } - } - dir = dir-1; - } - i = i+1; - } - result = x; - return result; -} - - - - -/************************************************************************* -Airy function - -Solution of the differential equation - -y"(x) = xy. - -The function returns the two independent solutions Ai, Bi -and their first derivatives Ai'(x), Bi'(x). - -Evaluation is by power series summation for small x, -by rational minimax approximations for large x. - - - -ACCURACY: -Error criterion is absolute when function <= 1, relative -when function > 1, except * denotes relative error criterion. -For large negative x, the absolute error increases as x^1.5. -For large positive x, the relative error increases as x^1.5. - -Arithmetic domain function # trials peak rms -IEEE -10, 0 Ai 10000 1.6e-15 2.7e-16 -IEEE 0, 10 Ai 10000 2.3e-14* 1.8e-15* -IEEE -10, 0 Ai' 10000 4.6e-15 7.6e-16 -IEEE 0, 10 Ai' 10000 1.8e-14* 1.5e-15* -IEEE -10, 10 Bi 30000 4.2e-15 5.3e-16 -IEEE -10, 10 Bi' 30000 4.9e-15 7.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -void airy(double x, - double* ai, - double* aip, - double* bi, - double* bip, - ae_state *_state) -{ - double z; - double zz; - double t; - double f; - double g; - double uf; - double ug; - double k; - double zeta; - double theta; - ae_int_t domflg; - double c1; - double c2; - double sqrt3; - double sqpii; - double afn; - double afd; - double agn; - double agd; - double apfn; - double apfd; - double apgn; - double apgd; - double an; - double ad; - double apn; - double apd; - double bn16; - double bd16; - double bppn; - double bppd; - - *ai = 0; - *aip = 0; - *bi = 0; - *bip = 0; - - sqpii = 5.64189583547756286948E-1; - c1 = 0.35502805388781723926; - c2 = 0.258819403792806798405; - sqrt3 = 1.732050807568877293527; - domflg = 0; - if( ae_fp_greater(x,25.77) ) - { - *ai = 0; - *aip = 0; - *bi = ae_maxrealnumber; - *bip = ae_maxrealnumber; - return; - } - if( ae_fp_less(x,-2.09) ) - { - domflg = 15; - t = ae_sqrt(-x, _state); - zeta = -2.0*x*t/3.0; - t = ae_sqrt(t, _state); - k = sqpii/t; - z = 1.0/zeta; - zz = z*z; - afn = -1.31696323418331795333E-1; - afn = afn*zz-6.26456544431912369773E-1; - afn = afn*zz-6.93158036036933542233E-1; - afn = afn*zz-2.79779981545119124951E-1; - afn = afn*zz-4.91900132609500318020E-2; - afn = afn*zz-4.06265923594885404393E-3; - afn = afn*zz-1.59276496239262096340E-4; - afn = afn*zz-2.77649108155232920844E-6; - afn = afn*zz-1.67787698489114633780E-8; - afd = 1.00000000000000000000E0; - afd = afd*zz+1.33560420706553243746E1; - afd = afd*zz+3.26825032795224613948E1; - afd = afd*zz+2.67367040941499554804E1; - afd = afd*zz+9.18707402907259625840E0; - afd = afd*zz+1.47529146771666414581E0; - afd = afd*zz+1.15687173795188044134E-1; - afd = afd*zz+4.40291641615211203805E-3; - afd = afd*zz+7.54720348287414296618E-5; - afd = afd*zz+4.51850092970580378464E-7; - uf = 1.0+zz*afn/afd; - agn = 1.97339932091685679179E-2; - agn = agn*zz+3.91103029615688277255E-1; - agn = agn*zz+1.06579897599595591108E0; - agn = agn*zz+9.39169229816650230044E-1; - agn = agn*zz+3.51465656105547619242E-1; - agn = agn*zz+6.33888919628925490927E-2; - agn = agn*zz+5.85804113048388458567E-3; - agn = agn*zz+2.82851600836737019778E-4; - agn = agn*zz+6.98793669997260967291E-6; - agn = agn*zz+8.11789239554389293311E-8; - agn = agn*zz+3.41551784765923618484E-10; - agd = 1.00000000000000000000E0; - agd = agd*zz+9.30892908077441974853E0; - agd = agd*zz+1.98352928718312140417E1; - agd = agd*zz+1.55646628932864612953E1; - agd = agd*zz+5.47686069422975497931E0; - agd = agd*zz+9.54293611618961883998E-1; - agd = agd*zz+8.64580826352392193095E-2; - agd = agd*zz+4.12656523824222607191E-3; - agd = agd*zz+1.01259085116509135510E-4; - agd = agd*zz+1.17166733214413521882E-6; - agd = agd*zz+4.91834570062930015649E-9; - ug = z*agn/agd; - theta = zeta+0.25*ae_pi; - f = ae_sin(theta, _state); - g = ae_cos(theta, _state); - *ai = k*(f*uf-g*ug); - *bi = k*(g*uf+f*ug); - apfn = 1.85365624022535566142E-1; - apfn = apfn*zz+8.86712188052584095637E-1; - apfn = apfn*zz+9.87391981747398547272E-1; - apfn = apfn*zz+4.01241082318003734092E-1; - apfn = apfn*zz+7.10304926289631174579E-2; - apfn = apfn*zz+5.90618657995661810071E-3; - apfn = apfn*zz+2.33051409401776799569E-4; - apfn = apfn*zz+4.08718778289035454598E-6; - apfn = apfn*zz+2.48379932900442457853E-8; - apfd = 1.00000000000000000000E0; - apfd = apfd*zz+1.47345854687502542552E1; - apfd = apfd*zz+3.75423933435489594466E1; - apfd = apfd*zz+3.14657751203046424330E1; - apfd = apfd*zz+1.09969125207298778536E1; - apfd = apfd*zz+1.78885054766999417817E0; - apfd = apfd*zz+1.41733275753662636873E-1; - apfd = apfd*zz+5.44066067017226003627E-3; - apfd = apfd*zz+9.39421290654511171663E-5; - apfd = apfd*zz+5.65978713036027009243E-7; - uf = 1.0+zz*apfn/apfd; - apgn = -3.55615429033082288335E-2; - apgn = apgn*zz-6.37311518129435504426E-1; - apgn = apgn*zz-1.70856738884312371053E0; - apgn = apgn*zz-1.50221872117316635393E0; - apgn = apgn*zz-5.63606665822102676611E-1; - apgn = apgn*zz-1.02101031120216891789E-1; - apgn = apgn*zz-9.48396695961445269093E-3; - apgn = apgn*zz-4.60325307486780994357E-4; - apgn = apgn*zz-1.14300836484517375919E-5; - apgn = apgn*zz-1.33415518685547420648E-7; - apgn = apgn*zz-5.63803833958893494476E-10; - apgd = 1.00000000000000000000E0; - apgd = apgd*zz+9.85865801696130355144E0; - apgd = apgd*zz+2.16401867356585941885E1; - apgd = apgd*zz+1.73130776389749389525E1; - apgd = apgd*zz+6.17872175280828766327E0; - apgd = apgd*zz+1.08848694396321495475E0; - apgd = apgd*zz+9.95005543440888479402E-2; - apgd = apgd*zz+4.78468199683886610842E-3; - apgd = apgd*zz+1.18159633322838625562E-4; - apgd = apgd*zz+1.37480673554219441465E-6; - apgd = apgd*zz+5.79912514929147598821E-9; - ug = z*apgn/apgd; - k = sqpii*t; - *aip = -k*(g*uf+f*ug); - *bip = k*(f*uf-g*ug); - return; - } - if( ae_fp_greater_eq(x,2.09) ) - { - domflg = 5; - t = ae_sqrt(x, _state); - zeta = 2.0*x*t/3.0; - g = ae_exp(zeta, _state); - t = ae_sqrt(t, _state); - k = 2.0*t*g; - z = 1.0/zeta; - an = 3.46538101525629032477E-1; - an = an*z+1.20075952739645805542E1; - an = an*z+7.62796053615234516538E1; - an = an*z+1.68089224934630576269E2; - an = an*z+1.59756391350164413639E2; - an = an*z+7.05360906840444183113E1; - an = an*z+1.40264691163389668864E1; - an = an*z+9.99999999999999995305E-1; - ad = 5.67594532638770212846E-1; - ad = ad*z+1.47562562584847203173E1; - ad = ad*z+8.45138970141474626562E1; - ad = ad*z+1.77318088145400459522E2; - ad = ad*z+1.64234692871529701831E2; - ad = ad*z+7.14778400825575695274E1; - ad = ad*z+1.40959135607834029598E1; - ad = ad*z+1.00000000000000000470E0; - f = an/ad; - *ai = sqpii*f/k; - k = -0.5*sqpii*t/g; - apn = 6.13759184814035759225E-1; - apn = apn*z+1.47454670787755323881E1; - apn = apn*z+8.20584123476060982430E1; - apn = apn*z+1.71184781360976385540E2; - apn = apn*z+1.59317847137141783523E2; - apn = apn*z+6.99778599330103016170E1; - apn = apn*z+1.39470856980481566958E1; - apn = apn*z+1.00000000000000000550E0; - apd = 3.34203677749736953049E-1; - apd = apd*z+1.11810297306158156705E1; - apd = apd*z+7.11727352147859965283E1; - apd = apd*z+1.58778084372838313640E2; - apd = apd*z+1.53206427475809220834E2; - apd = apd*z+6.86752304592780337944E1; - apd = apd*z+1.38498634758259442477E1; - apd = apd*z+9.99999999999999994502E-1; - f = apn/apd; - *aip = f*k; - if( ae_fp_greater(x,8.3203353) ) - { - bn16 = -2.53240795869364152689E-1; - bn16 = bn16*z+5.75285167332467384228E-1; - bn16 = bn16*z-3.29907036873225371650E-1; - bn16 = bn16*z+6.44404068948199951727E-2; - bn16 = bn16*z-3.82519546641336734394E-3; - bd16 = 1.00000000000000000000E0; - bd16 = bd16*z-7.15685095054035237902E0; - bd16 = bd16*z+1.06039580715664694291E1; - bd16 = bd16*z-5.23246636471251500874E0; - bd16 = bd16*z+9.57395864378383833152E-1; - bd16 = bd16*z-5.50828147163549611107E-2; - f = z*bn16/bd16; - k = sqpii*g; - *bi = k*(1.0+f)/t; - bppn = 4.65461162774651610328E-1; - bppn = bppn*z-1.08992173800493920734E0; - bppn = bppn*z+6.38800117371827987759E-1; - bppn = bppn*z-1.26844349553102907034E-1; - bppn = bppn*z+7.62487844342109852105E-3; - bppd = 1.00000000000000000000E0; - bppd = bppd*z-8.70622787633159124240E0; - bppd = bppd*z+1.38993162704553213172E1; - bppd = bppd*z-7.14116144616431159572E0; - bppd = bppd*z+1.34008595960680518666E0; - bppd = bppd*z-7.84273211323341930448E-2; - f = z*bppn/bppd; - *bip = k*t*(1.0+f); - return; - } - } - f = 1.0; - g = x; - t = 1.0; - uf = 1.0; - ug = x; - k = 1.0; - z = x*x*x; - while(ae_fp_greater(t,ae_machineepsilon)) - { - uf = uf*z; - k = k+1.0; - uf = uf/k; - ug = ug*z; - k = k+1.0; - ug = ug/k; - uf = uf/k; - f = f+uf; - k = k+1.0; - ug = ug/k; - g = g+ug; - t = ae_fabs(uf/f, _state); - } - uf = c1*f; - ug = c2*g; - if( domflg%2==0 ) - { - *ai = uf-ug; - } - if( domflg/2%2==0 ) - { - *bi = sqrt3*(uf+ug); - } - k = 4.0; - uf = x*x/2.0; - ug = z/3.0; - f = uf; - g = 1.0+ug; - uf = uf/3.0; - t = 1.0; - while(ae_fp_greater(t,ae_machineepsilon)) - { - uf = uf*z; - ug = ug/k; - k = k+1.0; - ug = ug*z; - uf = uf/k; - f = f+uf; - k = k+1.0; - ug = ug/k; - uf = uf/k; - g = g+ug; - k = k+1.0; - t = ae_fabs(ug/g, _state); - } - uf = c1*f; - ug = c2*g; - if( domflg/4%2==0 ) - { - *aip = uf-ug; - } - if( domflg/8%2==0 ) - { - *bip = sqrt3*(uf+ug); - } -} - - - - -/************************************************************************* -Bessel function of order zero - -Returns Bessel function of order zero of the argument. - -The domain is divided into the intervals [0, 5] and -(5, infinity). In the first interval the following rational -approximation is used: - - - 2 2 -(w - r ) (w - r ) P (w) / Q (w) - 1 2 3 8 - - 2 -where w = x and the two r's are zeros of the function. - -In the second interval, the Hankel asymptotic expansion -is employed with two rational functions of degree 6/6 -and 7/7. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 60000 4.2e-16 1.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselj0(double x, ae_state *_state) -{ - double xsq; - double nn; - double pzero; - double qzero; - double p1; - double q1; - double result; - - - if( ae_fp_less(x,0) ) - { - x = -x; - } - if( ae_fp_greater(x,8.0) ) - { - bessel_besselasympt0(x, &pzero, &qzero, _state); - nn = x-ae_pi/4; - result = ae_sqrt(2/ae_pi/x, _state)*(pzero*ae_cos(nn, _state)-qzero*ae_sin(nn, _state)); - return result; - } - xsq = ae_sqr(x, _state); - p1 = 26857.86856980014981415848441; - p1 = -40504123.71833132706360663322+xsq*p1; - p1 = 25071582855.36881945555156435+xsq*p1; - p1 = -8085222034853.793871199468171+xsq*p1; - p1 = 1434354939140344.111664316553+xsq*p1; - p1 = -136762035308817138.6865416609+xsq*p1; - p1 = 6382059341072356562.289432465+xsq*p1; - p1 = -117915762910761053603.8440800+xsq*p1; - p1 = 493378725179413356181.6813446+xsq*p1; - q1 = 1.0; - q1 = 1363.063652328970604442810507+xsq*q1; - q1 = 1114636.098462985378182402543+xsq*q1; - q1 = 669998767.2982239671814028660+xsq*q1; - q1 = 312304311494.1213172572469442+xsq*q1; - q1 = 112775673967979.8507056031594+xsq*q1; - q1 = 30246356167094626.98627330784+xsq*q1; - q1 = 5428918384092285160.200195092+xsq*q1; - q1 = 493378725179413356211.3278438+xsq*q1; - result = p1/q1; - return result; -} - - -/************************************************************************* -Bessel function of order one - -Returns Bessel function of order one of the argument. - -The domain is divided into the intervals [0, 8] and -(8, infinity). In the first interval a 24 term Chebyshev -expansion is used. In the second, the asymptotic -trigonometric representation is employed using two -rational functions of degree 5/5. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 2.6e-16 1.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselj1(double x, ae_state *_state) -{ - double s; - double xsq; - double nn; - double pzero; - double qzero; - double p1; - double q1; - double result; - - - s = ae_sign(x, _state); - if( ae_fp_less(x,0) ) - { - x = -x; - } - if( ae_fp_greater(x,8.0) ) - { - bessel_besselasympt1(x, &pzero, &qzero, _state); - nn = x-3*ae_pi/4; - result = ae_sqrt(2/ae_pi/x, _state)*(pzero*ae_cos(nn, _state)-qzero*ae_sin(nn, _state)); - if( ae_fp_less(s,0) ) - { - result = -result; - } - return result; - } - xsq = ae_sqr(x, _state); - p1 = 2701.122710892323414856790990; - p1 = -4695753.530642995859767162166+xsq*p1; - p1 = 3413234182.301700539091292655+xsq*p1; - p1 = -1322983480332.126453125473247+xsq*p1; - p1 = 290879526383477.5409737601689+xsq*p1; - p1 = -35888175699101060.50743641413+xsq*p1; - p1 = 2316433580634002297.931815435+xsq*p1; - p1 = -66721065689249162980.20941484+xsq*p1; - p1 = 581199354001606143928.050809+xsq*p1; - q1 = 1.0; - q1 = 1606.931573481487801970916749+xsq*q1; - q1 = 1501793.594998585505921097578+xsq*q1; - q1 = 1013863514.358673989967045588+xsq*q1; - q1 = 524371026216.7649715406728642+xsq*q1; - q1 = 208166122130760.7351240184229+xsq*q1; - q1 = 60920613989175217.46105196863+xsq*q1; - q1 = 11857707121903209998.37113348+xsq*q1; - q1 = 1162398708003212287858.529400+xsq*q1; - result = s*x*p1/q1; - return result; -} - - -/************************************************************************* -Bessel function of integer order - -Returns Bessel function of order n, where n is a -(possibly negative) integer. - -The ratio of jn(x) to j0(x) is computed by backward -recurrence. First the ratio jn/jn-1 is found by a -continued fraction expansion. Then the recurrence -relating successive orders is applied until j0 or j1 is -reached. - -If n = 0 or 1 the routine for j0 or j1 is called -directly. - -ACCURACY: - - Absolute error: -arithmetic range # trials peak rms - IEEE 0, 30 5000 4.4e-16 7.9e-17 - - -Not suitable for large n or x. Use jv() (fractional order) instead. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseljn(ae_int_t n, double x, ae_state *_state) -{ - double pkm2; - double pkm1; - double pk; - double xk; - double r; - double ans; - ae_int_t k; - ae_int_t sg; - double result; - - - if( n<0 ) - { - n = -n; - if( n%2==0 ) - { - sg = 1; - } - else - { - sg = -1; - } - } - else - { - sg = 1; - } - if( ae_fp_less(x,0) ) - { - if( n%2!=0 ) - { - sg = -sg; - } - x = -x; - } - if( n==0 ) - { - result = sg*besselj0(x, _state); - return result; - } - if( n==1 ) - { - result = sg*besselj1(x, _state); - return result; - } - if( n==2 ) - { - if( ae_fp_eq(x,0) ) - { - result = 0; - } - else - { - result = sg*(2.0*besselj1(x, _state)/x-besselj0(x, _state)); - } - return result; - } - if( ae_fp_less(x,ae_machineepsilon) ) - { - result = 0; - return result; - } - k = 53; - pk = 2*(n+k); - ans = pk; - xk = x*x; - do - { - pk = pk-2.0; - ans = pk-xk/ans; - k = k-1; - } - while(k!=0); - ans = x/ans; - pk = 1.0; - pkm1 = 1.0/ans; - k = n-1; - r = 2*k; - do - { - pkm2 = (pkm1*r-pk*x)/x; - pk = pkm1; - pkm1 = pkm2; - r = r-2.0; - k = k-1; - } - while(k!=0); - if( ae_fp_greater(ae_fabs(pk, _state),ae_fabs(pkm1, _state)) ) - { - ans = besselj1(x, _state)/pk; - } - else - { - ans = besselj0(x, _state)/pkm1; - } - result = sg*ans; - return result; -} - - -/************************************************************************* -Bessel function of the second kind, order zero - -Returns Bessel function of the second kind, of order -zero, of the argument. - -The domain is divided into the intervals [0, 5] and -(5, infinity). In the first interval a rational approximation -R(x) is employed to compute - y0(x) = R(x) + 2 * log(x) * j0(x) / PI. -Thus a call to j0() is required. - -In the second interval, the Hankel asymptotic expansion -is employed with two rational functions of degree 6/6 -and 7/7. - - - -ACCURACY: - - Absolute error, when y0(x) < 1; else relative error: - -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.3e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double bessely0(double x, ae_state *_state) -{ - double nn; - double xsq; - double pzero; - double qzero; - double p4; - double q4; - double result; - - - if( ae_fp_greater(x,8.0) ) - { - bessel_besselasympt0(x, &pzero, &qzero, _state); - nn = x-ae_pi/4; - result = ae_sqrt(2/ae_pi/x, _state)*(pzero*ae_sin(nn, _state)+qzero*ae_cos(nn, _state)); - return result; - } - xsq = ae_sqr(x, _state); - p4 = -41370.35497933148554125235152; - p4 = 59152134.65686889654273830069+xsq*p4; - p4 = -34363712229.79040378171030138+xsq*p4; - p4 = 10255208596863.94284509167421+xsq*p4; - p4 = -1648605817185729.473122082537+xsq*p4; - p4 = 137562431639934407.8571335453+xsq*p4; - p4 = -5247065581112764941.297350814+xsq*p4; - p4 = 65874732757195549259.99402049+xsq*p4; - p4 = -27502866786291095837.01933175+xsq*p4; - q4 = 1.0; - q4 = 1282.452772478993804176329391+xsq*q4; - q4 = 1001702.641288906265666651753+xsq*q4; - q4 = 579512264.0700729537480087915+xsq*q4; - q4 = 261306575504.1081249568482092+xsq*q4; - q4 = 91620380340751.85262489147968+xsq*q4; - q4 = 23928830434997818.57439356652+xsq*q4; - q4 = 4192417043410839973.904769661+xsq*q4; - q4 = 372645883898616588198.9980+xsq*q4; - result = p4/q4+2/ae_pi*besselj0(x, _state)*ae_log(x, _state); - return result; -} - - -/************************************************************************* -Bessel function of second kind of order one - -Returns Bessel function of the second kind of order one -of the argument. - -The domain is divided into the intervals [0, 8] and -(8, infinity). In the first interval a 25 term Chebyshev -expansion is used, and a call to j1() is required. -In the second, the asymptotic trigonometric representation -is employed using two rational functions of degree 5/5. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.0e-15 1.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double bessely1(double x, ae_state *_state) -{ - double nn; - double xsq; - double pzero; - double qzero; - double p4; - double q4; - double result; - - - if( ae_fp_greater(x,8.0) ) - { - bessel_besselasympt1(x, &pzero, &qzero, _state); - nn = x-3*ae_pi/4; - result = ae_sqrt(2/ae_pi/x, _state)*(pzero*ae_sin(nn, _state)+qzero*ae_cos(nn, _state)); - return result; - } - xsq = ae_sqr(x, _state); - p4 = -2108847.540133123652824139923; - p4 = 3639488548.124002058278999428+xsq*p4; - p4 = -2580681702194.450950541426399+xsq*p4; - p4 = 956993023992168.3481121552788+xsq*p4; - p4 = -196588746272214065.8820322248+xsq*p4; - p4 = 21931073399177975921.11427556+xsq*p4; - p4 = -1212297555414509577913.561535+xsq*p4; - p4 = 26554738314348543268942.48968+xsq*p4; - p4 = -99637534243069222259967.44354+xsq*p4; - q4 = 1.0; - q4 = 1612.361029677000859332072312+xsq*q4; - q4 = 1563282.754899580604737366452+xsq*q4; - q4 = 1128686837.169442121732366891+xsq*q4; - q4 = 646534088126.5275571961681500+xsq*q4; - q4 = 297663212564727.6729292742282+xsq*q4; - q4 = 108225825940881955.2553850180+xsq*q4; - q4 = 29549879358971486742.90758119+xsq*q4; - q4 = 5435310377188854170800.653097+xsq*q4; - q4 = 508206736694124324531442.4152+xsq*q4; - result = x*p4/q4+2/ae_pi*(besselj1(x, _state)*ae_log(x, _state)-1/x); - return result; -} - - -/************************************************************************* -Bessel function of second kind of integer order - -Returns Bessel function of order n, where n is a -(possibly negative) integer. - -The function is evaluated by forward recurrence on -n, starting with values computed by the routines -y0() and y1(). - -If n = 0 or 1 the routine for y0 or y1 is called -directly. - -ACCURACY: - Absolute error, except relative - when y > 1: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 3.4e-15 4.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselyn(ae_int_t n, double x, ae_state *_state) -{ - ae_int_t i; - double a; - double b; - double tmp; - double s; - double result; - - - s = 1; - if( n<0 ) - { - n = -n; - if( n%2!=0 ) - { - s = -1; - } - } - if( n==0 ) - { - result = bessely0(x, _state); - return result; - } - if( n==1 ) - { - result = s*bessely1(x, _state); - return result; - } - a = bessely0(x, _state); - b = bessely1(x, _state); - for(i=1; i<=n-1; i++) - { - tmp = b; - b = 2*i/x*b-a; - a = tmp; - } - result = s*b; - return result; -} - - -/************************************************************************* -Modified Bessel function of order zero - -Returns modified Bessel function of order zero of the -argument. - -The function is defined as i0(x) = j0( ix ). - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 30000 5.8e-16 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseli0(double x, ae_state *_state) -{ - double y; - double v; - double z; - double b0; - double b1; - double b2; - double result; - - - if( ae_fp_less(x,0) ) - { - x = -x; - } - if( ae_fp_less_eq(x,8.0) ) - { - y = x/2.0-2.0; - bessel_besselmfirstcheb(-4.41534164647933937950E-18, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 3.33079451882223809783E-17, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -2.43127984654795469359E-16, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.71539128555513303061E-15, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -1.16853328779934516808E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 7.67618549860493561688E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -4.85644678311192946090E-13, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 2.95505266312963983461E-12, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -1.72682629144155570723E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 9.67580903537323691224E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -5.18979560163526290666E-10, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 2.65982372468238665035E-9, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -1.30002500998624804212E-8, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 6.04699502254191894932E-8, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -2.67079385394061173391E-7, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.11738753912010371815E-6, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -4.41673835845875056359E-6, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.64484480707288970893E-5, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -5.75419501008210370398E-5, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.88502885095841655729E-4, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -5.76375574538582365885E-4, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.63947561694133579842E-3, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -4.32430999505057594430E-3, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.05464603945949983183E-2, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -2.37374148058994688156E-2, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 4.93052842396707084878E-2, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -9.49010970480476444210E-2, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.71620901522208775349E-1, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -3.04682672343198398683E-1, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 6.76795274409476084995E-1, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - result = ae_exp(x, _state)*v; - return result; - } - z = 32.0/x-2.0; - bessel_besselmfirstcheb(-7.23318048787475395456E-18, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -4.83050448594418207126E-18, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 4.46562142029675999901E-17, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 3.46122286769746109310E-17, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -2.82762398051658348494E-16, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -3.42548561967721913462E-16, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.77256013305652638360E-15, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 3.81168066935262242075E-15, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -9.55484669882830764870E-15, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -4.15056934728722208663E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.54008621752140982691E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 3.85277838274214270114E-13, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 7.18012445138366623367E-13, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.79417853150680611778E-12, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.32158118404477131188E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -3.14991652796324136454E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.18891471078464383424E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 4.94060238822496958910E-10, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 3.39623202570838634515E-9, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 2.26666899049817806459E-8, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 2.04891858946906374183E-7, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 2.89137052083475648297E-6, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 6.88975834691682398426E-5, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 3.36911647825569408990E-3, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 8.04490411014108831608E-1, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - result = ae_exp(x, _state)*v/ae_sqrt(x, _state); - return result; -} - - -/************************************************************************* -Modified Bessel function of order one - -Returns modified Bessel function of order one of the -argument. - -The function is defined as i1(x) = -i j1( ix ). - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.9e-15 2.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseli1(double x, ae_state *_state) -{ - double y; - double z; - double v; - double b0; - double b1; - double b2; - double result; - - - z = ae_fabs(x, _state); - if( ae_fp_less_eq(z,8.0) ) - { - y = z/2.0-2.0; - bessel_besselm1firstcheb(2.77791411276104639959E-18, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.11142121435816608115E-17, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.55363195773620046921E-16, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.10559694773538630805E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 7.60068429473540693410E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -5.04218550472791168711E-14, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 3.22379336594557470981E-13, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.98397439776494371520E-12, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.17361862988909016308E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -6.66348972350202774223E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 3.62559028155211703701E-10, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.88724975172282928790E-9, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 9.38153738649577178388E-9, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -4.44505912879632808065E-8, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.00329475355213526229E-7, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -8.56872026469545474066E-7, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 3.47025130813767847674E-6, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.32731636560394358279E-5, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 4.78156510755005422638E-5, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.61760815825896745588E-4, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 5.12285956168575772895E-4, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.51357245063125314899E-3, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 4.15642294431288815669E-3, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.05640848946261981558E-2, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.47264490306265168283E-2, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -5.29459812080949914269E-2, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.02643658689847095384E-1, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.76416518357834055153E-1, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.52587186443633654823E-1, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - z = v*z*ae_exp(z, _state); - } - else - { - y = 32.0/z-2.0; - bessel_besselm1firstcheb(7.51729631084210481353E-18, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 4.41434832307170791151E-18, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -4.65030536848935832153E-17, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -3.20952592199342395980E-17, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.96262899764595013876E-16, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 3.30820231092092828324E-16, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.88035477551078244854E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -3.81440307243700780478E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.04202769841288027642E-14, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 4.27244001671195135429E-14, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.10154184277266431302E-14, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -4.08355111109219731823E-13, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -7.19855177624590851209E-13, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.03562854414708950722E-12, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.41258074366137813316E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 3.25260358301548823856E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.89749581235054123450E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -5.58974346219658380687E-10, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -3.83538038596423702205E-9, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.63146884688951950684E-8, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.51223623787020892529E-7, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -3.88256480887769039346E-6, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.10588938762623716291E-4, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -9.76109749136146840777E-3, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 7.78576235018280120474E-1, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - z = v*ae_exp(z, _state)/ae_sqrt(z, _state); - } - if( ae_fp_less(x,0) ) - { - z = -z; - } - result = z; - return result; -} - - -/************************************************************************* -Modified Bessel function, second kind, order zero - -Returns modified Bessel function of the second kind -of order zero of the argument. - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - -Tested at 2000 random points between 0 and 8. Peak absolute -error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15. - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.2e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselk0(double x, ae_state *_state) -{ - double y; - double z; - double v; - double b0; - double b1; - double b2; - double result; - - - ae_assert(ae_fp_greater(x,0), "Domain error in BesselK0: x<=0", _state); - if( ae_fp_less_eq(x,2) ) - { - y = x*x-2.0; - bessel_besselmfirstcheb(1.37446543561352307156E-16, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 4.25981614279661018399E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.03496952576338420167E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.90451637722020886025E-9, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 2.53479107902614945675E-7, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 2.28621210311945178607E-5, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 1.26461541144692592338E-3, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 3.59799365153615016266E-2, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, 3.44289899924628486886E-1, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(y, -5.35327393233902768720E-1, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - v = v-ae_log(0.5*x, _state)*besseli0(x, _state); - } - else - { - z = 8.0/x-2.0; - bessel_besselmfirstcheb(5.30043377268626276149E-18, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.64758043015242134646E-17, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 5.21039150503902756861E-17, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.67823109680541210385E-16, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 5.51205597852431940784E-16, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.84859337734377901440E-15, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 6.34007647740507060557E-15, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -2.22751332699166985548E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 8.03289077536357521100E-14, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -2.98009692317273043925E-13, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.14034058820847496303E-12, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -4.51459788337394416547E-12, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.85594911495471785253E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -7.95748924447710747776E-11, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 3.57739728140030116597E-10, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.69753450938905987466E-9, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 8.57403401741422608519E-9, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -4.66048989768794782956E-8, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 2.76681363944501510342E-7, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.83175552271911948767E-6, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.39498137188764993662E-5, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -1.28495495816278026384E-4, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 1.56988388573005337491E-3, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, -3.14481013119645005427E-2, &b0, &b1, &b2, _state); - bessel_besselmnextcheb(z, 2.44030308206595545468E0, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - v = v*ae_exp(-x, _state)/ae_sqrt(x, _state); - } - result = v; - return result; -} - - -/************************************************************************* -Modified Bessel function, second kind, order one - -Computes the modified Bessel function of the second kind -of order one of the argument. - -The range is partitioned into the two intervals [0,2] and -(2, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.2e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselk1(double x, ae_state *_state) -{ - double y; - double z; - double v; - double b0; - double b1; - double b2; - double result; - - - z = 0.5*x; - ae_assert(ae_fp_greater(z,0), "Domain error in K1", _state); - if( ae_fp_less_eq(x,2) ) - { - y = x*x-2.0; - bessel_besselm1firstcheb(-7.02386347938628759343E-18, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.42744985051936593393E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -6.66690169419932900609E-13, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.41148839263352776110E-10, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.21338763073472585583E-8, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.43340614156596823496E-6, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.73028895751305206302E-4, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -6.97572385963986435018E-3, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.22611180822657148235E-1, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -3.53155960776544875667E-1, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.52530022733894777053E0, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - result = ae_log(z, _state)*besseli1(x, _state)+v/x; - } - else - { - y = 8.0/x-2.0; - bessel_besselm1firstcheb(-5.75674448366501715755E-18, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.79405087314755922667E-17, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -5.68946255844285935196E-17, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.83809354436663880070E-16, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -6.05704724837331885336E-16, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.03870316562433424052E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -7.01983709041831346144E-15, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.47715442448130437068E-14, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -8.97670518232499435011E-14, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 3.34841966607842919884E-13, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.28917396095102890680E-12, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 5.13963967348173025100E-12, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.12996783842756842877E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 9.21831518760500529508E-11, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -4.19035475934189648750E-10, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.01504975519703286596E-9, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.03457624656780970260E-8, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 5.74108412545004946722E-8, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -3.50196060308781257119E-7, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.40648494783721712015E-6, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -1.93619797416608296024E-5, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.95215518471351631108E-4, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, -2.85781685962277938680E-3, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 1.03923736576817238437E-1, &b0, &b1, &b2, _state); - bessel_besselm1nextcheb(y, 2.72062619048444266945E0, &b0, &b1, &b2, _state); - v = 0.5*(b0-b2); - result = ae_exp(-x, _state)*v/ae_sqrt(x, _state); - } - return result; -} - - -/************************************************************************* -Modified Bessel function, second kind, integer order - -Returns modified Bessel function of the second kind -of order n of the argument. - -The range is partitioned into the two intervals [0,9.55] and -(9.55, infinity). An ascending power series is used in the -low range, and an asymptotic expansion in the high range. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 90000 1.8e-8 3.0e-10 - -Error is high only near the crossover point x = 9.55 -between the two expansions used. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselkn(ae_int_t nn, double x, ae_state *_state) -{ - double k; - double kf; - double nk1f; - double nkf; - double zn; - double t; - double s; - double z0; - double z; - double ans; - double fn; - double pn; - double pk; - double zmn; - double tlg; - double tox; - ae_int_t i; - ae_int_t n; - double eul; - double result; - - - eul = 5.772156649015328606065e-1; - if( nn<0 ) - { - n = -nn; - } - else - { - n = nn; - } - ae_assert(n<=31, "Overflow in BesselKN", _state); - ae_assert(ae_fp_greater(x,0), "Domain error in BesselKN", _state); - if( ae_fp_less_eq(x,9.55) ) - { - ans = 0.0; - z0 = 0.25*x*x; - fn = 1.0; - pn = 0.0; - zmn = 1.0; - tox = 2.0/x; - if( n>0 ) - { - pn = -eul; - k = 1.0; - for(i=1; i<=n-1; i++) - { - pn = pn+1.0/k; - k = k+1.0; - fn = fn*k; - } - zmn = tox; - if( n==1 ) - { - ans = 1.0/x; - } - else - { - nk1f = fn/n; - kf = 1.0; - s = nk1f; - z = -z0; - zn = 1.0; - for(i=1; i<=n-1; i++) - { - nk1f = nk1f/(n-i); - kf = kf*i; - zn = zn*z; - t = nk1f*zn/kf; - s = s+t; - ae_assert(ae_fp_greater(ae_maxrealnumber-ae_fabs(t, _state),ae_fabs(s, _state)), "Overflow in BesselKN", _state); - ae_assert(!(ae_fp_greater(tox,1.0)&&ae_fp_less(ae_maxrealnumber/tox,zmn)), "Overflow in BesselKN", _state); - zmn = zmn*tox; - } - s = s*0.5; - t = ae_fabs(s, _state); - ae_assert(!(ae_fp_greater(zmn,1.0)&&ae_fp_less(ae_maxrealnumber/zmn,t)), "Overflow in BesselKN", _state); - ae_assert(!(ae_fp_greater(t,1.0)&&ae_fp_less(ae_maxrealnumber/t,zmn)), "Overflow in BesselKN", _state); - ans = s*zmn; - } - } - tlg = 2.0*ae_log(0.5*x, _state); - pk = -eul; - if( n==0 ) - { - pn = pk; - t = 1.0; - } - else - { - pn = pn+1.0/n; - t = 1.0/fn; - } - s = (pk+pn-tlg)*t; - k = 1.0; - do - { - t = t*(z0/(k*(k+n))); - pk = pk+1.0/k; - pn = pn+1.0/(k+n); - s = s+(pk+pn-tlg)*t; - k = k+1.0; - } - while(ae_fp_greater(ae_fabs(t/s, _state),ae_machineepsilon)); - s = 0.5*s/zmn; - if( n%2!=0 ) - { - s = -s; - } - ans = ans+s; - result = ans; - return result; - } - if( ae_fp_greater(x,ae_log(ae_maxrealnumber, _state)) ) - { - result = 0; - return result; - } - k = n; - pn = 4.0*k*k; - pk = 1.0; - z0 = 8.0*x; - fn = 1.0; - t = 1.0; - s = t; - nkf = ae_maxrealnumber; - i = 0; - do - { - z = pn-pk*pk; - t = t*z/(fn*z0); - nk1f = ae_fabs(t, _state); - if( i>=n&&ae_fp_greater(nk1f,nkf) ) - { - break; - } - nkf = nk1f; - s = s+t; - fn = fn+1.0; - pk = pk+2.0; - i = i+1; - } - while(ae_fp_greater(ae_fabs(t/s, _state),ae_machineepsilon)); - result = ae_exp(-x, _state)*ae_sqrt(ae_pi/(2.0*x), _state)*s; - return result; -} - - -/************************************************************************* -Internal subroutine - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -static void bessel_besselmfirstcheb(double c, - double* b0, - double* b1, - double* b2, - ae_state *_state) -{ - - - *b0 = c; - *b1 = 0.0; - *b2 = 0.0; -} - - -/************************************************************************* -Internal subroutine - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -static void bessel_besselmnextcheb(double x, - double c, - double* b0, - double* b1, - double* b2, - ae_state *_state) -{ - - - *b2 = *b1; - *b1 = *b0; - *b0 = x*(*b1)-(*b2)+c; -} - - -/************************************************************************* -Internal subroutine - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -static void bessel_besselm1firstcheb(double c, - double* b0, - double* b1, - double* b2, - ae_state *_state) -{ - - - *b0 = c; - *b1 = 0.0; - *b2 = 0.0; -} - - -/************************************************************************* -Internal subroutine - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -static void bessel_besselm1nextcheb(double x, - double c, - double* b0, - double* b1, - double* b2, - ae_state *_state) -{ - - - *b2 = *b1; - *b1 = *b0; - *b0 = x*(*b1)-(*b2)+c; -} - - -static void bessel_besselasympt0(double x, - double* pzero, - double* qzero, - ae_state *_state) -{ - double xsq; - double p2; - double q2; - double p3; - double q3; - - *pzero = 0; - *qzero = 0; - - xsq = 64.0/(x*x); - p2 = 0.0; - p2 = 2485.271928957404011288128951+xsq*p2; - p2 = 153982.6532623911470917825993+xsq*p2; - p2 = 2016135.283049983642487182349+xsq*p2; - p2 = 8413041.456550439208464315611+xsq*p2; - p2 = 12332384.76817638145232406055+xsq*p2; - p2 = 5393485.083869438325262122897+xsq*p2; - q2 = 1.0; - q2 = 2615.700736920839685159081813+xsq*q2; - q2 = 156001.7276940030940592769933+xsq*q2; - q2 = 2025066.801570134013891035236+xsq*q2; - q2 = 8426449.050629797331554404810+xsq*q2; - q2 = 12338310.22786324960844856182+xsq*q2; - q2 = 5393485.083869438325560444960+xsq*q2; - p3 = -0.0; - p3 = -4.887199395841261531199129300+xsq*p3; - p3 = -226.2630641933704113967255053+xsq*p3; - p3 = -2365.956170779108192723612816+xsq*p3; - p3 = -8239.066313485606568803548860+xsq*p3; - p3 = -10381.41698748464093880530341+xsq*p3; - p3 = -3984.617357595222463506790588+xsq*p3; - q3 = 1.0; - q3 = 408.7714673983499223402830260+xsq*q3; - q3 = 15704.89191515395519392882766+xsq*q3; - q3 = 156021.3206679291652539287109+xsq*q3; - q3 = 533291.3634216897168722255057+xsq*q3; - q3 = 666745.4239319826986004038103+xsq*q3; - q3 = 255015.5108860942382983170882+xsq*q3; - *pzero = p2/q2; - *qzero = 8*p3/q3/x; -} - - -static void bessel_besselasympt1(double x, - double* pzero, - double* qzero, - ae_state *_state) -{ - double xsq; - double p2; - double q2; - double p3; - double q3; - - *pzero = 0; - *qzero = 0; - - xsq = 64.0/(x*x); - p2 = -1611.616644324610116477412898; - p2 = -109824.0554345934672737413139+xsq*p2; - p2 = -1523529.351181137383255105722+xsq*p2; - p2 = -6603373.248364939109255245434+xsq*p2; - p2 = -9942246.505077641195658377899+xsq*p2; - p2 = -4435757.816794127857114720794+xsq*p2; - q2 = 1.0; - q2 = -1455.009440190496182453565068+xsq*q2; - q2 = -107263.8599110382011903063867+xsq*q2; - q2 = -1511809.506634160881644546358+xsq*q2; - q2 = -6585339.479723087072826915069+xsq*q2; - q2 = -9934124.389934585658967556309+xsq*q2; - q2 = -4435757.816794127856828016962+xsq*q2; - p3 = 35.26513384663603218592175580; - p3 = 1706.375429020768002061283546+xsq*p3; - p3 = 18494.26287322386679652009819+xsq*p3; - p3 = 66178.83658127083517939992166+xsq*p3; - p3 = 85145.16067533570196555001171+xsq*p3; - p3 = 33220.91340985722351859704442+xsq*p3; - q3 = 1.0; - q3 = 863.8367769604990967475517183+xsq*q3; - q3 = 37890.22974577220264142952256+xsq*q3; - q3 = 400294.4358226697511708610813+xsq*q3; - q3 = 1419460.669603720892855755253+xsq*q3; - q3 = 1819458.042243997298924553839+xsq*q3; - q3 = 708712.8194102874357377502472+xsq*q3; - *pzero = p2/q2; - *qzero = 8*p3/q3/x; -} - - - - -/************************************************************************* -Beta function - - - - - - | (a) | (b) -beta( a, b ) = -----------. - - - | (a+b) - -For large arguments the logarithm of the function is -evaluated using lgam(), then exponentiated. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 30000 8.1e-14 1.1e-14 - -Cephes Math Library Release 2.0: April, 1987 -Copyright 1984, 1987 by Stephen L. Moshier -*************************************************************************/ -double beta(double a, double b, ae_state *_state) -{ - double y; - double sg; - double s; - double result; - - - sg = 1; - ae_assert(ae_fp_greater(a,0)||ae_fp_neq(a,ae_ifloor(a, _state)), "Overflow in Beta", _state); - ae_assert(ae_fp_greater(b,0)||ae_fp_neq(b,ae_ifloor(b, _state)), "Overflow in Beta", _state); - y = a+b; - if( ae_fp_greater(ae_fabs(y, _state),171.624376956302725) ) - { - y = lngamma(y, &s, _state); - sg = sg*s; - y = lngamma(b, &s, _state)-y; - sg = sg*s; - y = lngamma(a, &s, _state)+y; - sg = sg*s; - ae_assert(ae_fp_less_eq(y,ae_log(ae_maxrealnumber, _state)), "Overflow in Beta", _state); - result = sg*ae_exp(y, _state); - return result; - } - y = gammafunction(y, _state); - ae_assert(ae_fp_neq(y,0), "Overflow in Beta", _state); - if( ae_fp_greater(a,b) ) - { - y = gammafunction(a, _state)/y; - y = y*gammafunction(b, _state); - } - else - { - y = gammafunction(b, _state)/y; - y = y*gammafunction(a, _state); - } - result = y; - return result; -} - - - - -/************************************************************************* -Incomplete beta integral - -Returns incomplete beta integral of the arguments, evaluated -from zero to x. The function is defined as - - x - - - - | (a+b) | | a-1 b-1 - ----------- | t (1-t) dt. - - - | | - | (a) | (b) - - 0 - -The domain of definition is 0 <= x <= 1. In this -implementation a and b are restricted to positive values. -The integral from x to 1 may be obtained by the symmetry -relation - - 1 - incbet( a, b, x ) = incbet( b, a, 1-x ). - -The integral is evaluated by a continued fraction expansion -or, when b*x is small, by a power series. - -ACCURACY: - -Tested at uniformly distributed random points (a,b,x) with a and b -in "domain" and x between 0 and 1. - Relative error -arithmetic domain # trials peak rms - IEEE 0,5 10000 6.9e-15 4.5e-16 - IEEE 0,85 250000 2.2e-13 1.7e-14 - IEEE 0,1000 30000 5.3e-12 6.3e-13 - IEEE 0,10000 250000 9.3e-11 7.1e-12 - IEEE 0,100000 10000 8.7e-10 4.8e-11 -Outputs smaller than the IEEE gradual underflow threshold -were excluded from these statistics. - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletebeta(double a, double b, double x, ae_state *_state) -{ - double t; - double xc; - double w; - double y; - ae_int_t flag; - double sg; - double big; - double biginv; - double maxgam; - double minlog; - double maxlog; - double result; - - - big = 4.503599627370496e15; - biginv = 2.22044604925031308085e-16; - maxgam = 171.624376956302725; - minlog = ae_log(ae_minrealnumber, _state); - maxlog = ae_log(ae_maxrealnumber, _state); - ae_assert(ae_fp_greater(a,0)&&ae_fp_greater(b,0), "Domain error in IncompleteBeta", _state); - ae_assert(ae_fp_greater_eq(x,0)&&ae_fp_less_eq(x,1), "Domain error in IncompleteBeta", _state); - if( ae_fp_eq(x,0) ) - { - result = 0; - return result; - } - if( ae_fp_eq(x,1) ) - { - result = 1; - return result; - } - flag = 0; - if( ae_fp_less_eq(b*x,1.0)&&ae_fp_less_eq(x,0.95) ) - { - result = ibetaf_incompletebetaps(a, b, x, maxgam, _state); - return result; - } - w = 1.0-x; - if( ae_fp_greater(x,a/(a+b)) ) - { - flag = 1; - t = a; - a = b; - b = t; - xc = x; - x = w; - } - else - { - xc = w; - } - if( (flag==1&&ae_fp_less_eq(b*x,1.0))&&ae_fp_less_eq(x,0.95) ) - { - t = ibetaf_incompletebetaps(a, b, x, maxgam, _state); - if( ae_fp_less_eq(t,ae_machineepsilon) ) - { - result = 1.0-ae_machineepsilon; - } - else - { - result = 1.0-t; - } - return result; - } - y = x*(a+b-2.0)-(a-1.0); - if( ae_fp_less(y,0.0) ) - { - w = ibetaf_incompletebetafe(a, b, x, big, biginv, _state); - } - else - { - w = ibetaf_incompletebetafe2(a, b, x, big, biginv, _state)/xc; - } - y = a*ae_log(x, _state); - t = b*ae_log(xc, _state); - if( (ae_fp_less(a+b,maxgam)&&ae_fp_less(ae_fabs(y, _state),maxlog))&&ae_fp_less(ae_fabs(t, _state),maxlog) ) - { - t = ae_pow(xc, b, _state); - t = t*ae_pow(x, a, _state); - t = t/a; - t = t*w; - t = t*(gammafunction(a+b, _state)/(gammafunction(a, _state)*gammafunction(b, _state))); - if( flag==1 ) - { - if( ae_fp_less_eq(t,ae_machineepsilon) ) - { - result = 1.0-ae_machineepsilon; - } - else - { - result = 1.0-t; - } - } - else - { - result = t; - } - return result; - } - y = y+t+lngamma(a+b, &sg, _state)-lngamma(a, &sg, _state)-lngamma(b, &sg, _state); - y = y+ae_log(w/a, _state); - if( ae_fp_less(y,minlog) ) - { - t = 0.0; - } - else - { - t = ae_exp(y, _state); - } - if( flag==1 ) - { - if( ae_fp_less_eq(t,ae_machineepsilon) ) - { - t = 1.0-ae_machineepsilon; - } - else - { - t = 1.0-t; - } - } - result = t; - return result; -} - - -/************************************************************************* -Inverse of imcomplete beta integral - -Given y, the function finds x such that - - incbet( a, b, x ) = y . - -The routine performs interval halving or Newton iterations to find the -root of incbet(a,b,x) - y = 0. - - -ACCURACY: - - Relative error: - x a,b -arithmetic domain domain # trials peak rms - IEEE 0,1 .5,10000 50000 5.8e-12 1.3e-13 - IEEE 0,1 .25,100 100000 1.8e-13 3.9e-15 - IEEE 0,1 0,5 50000 1.1e-12 5.5e-15 -With a and b constrained to half-integer or integer values: - IEEE 0,1 .5,10000 50000 5.8e-12 1.1e-13 - IEEE 0,1 .5,100 100000 1.7e-14 7.9e-16 -With a = .5, b constrained to half-integer or integer values: - IEEE 0,1 .5,10000 10000 8.3e-11 1.0e-11 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1996, 2000 by Stephen L. Moshier -*************************************************************************/ -double invincompletebeta(double a, double b, double y, ae_state *_state) -{ - double aaa; - double bbb; - double y0; - double d; - double yyy; - double x; - double x0; - double x1; - double lgm; - double yp; - double di; - double dithresh; - double yl; - double yh; - double xt; - ae_int_t i; - ae_int_t rflg; - ae_int_t dir; - ae_int_t nflg; - double s; - ae_int_t mainlooppos; - ae_int_t ihalve; - ae_int_t ihalvecycle; - ae_int_t newt; - ae_int_t newtcycle; - ae_int_t breaknewtcycle; - ae_int_t breakihalvecycle; - double result; - - - i = 0; - ae_assert(ae_fp_greater_eq(y,0)&&ae_fp_less_eq(y,1), "Domain error in InvIncompleteBeta", _state); - - /* - * special cases - */ - if( ae_fp_eq(y,0) ) - { - result = 0; - return result; - } - if( ae_fp_eq(y,1.0) ) - { - result = 1; - return result; - } - - /* - * these initializations are not really necessary, - * but without them compiler complains about 'possibly uninitialized variables'. - */ - dithresh = 0; - rflg = 0; - aaa = 0; - bbb = 0; - y0 = 0; - x = 0; - yyy = 0; - lgm = 0; - dir = 0; - di = 0; - - /* - * normal initializations - */ - x0 = 0.0; - yl = 0.0; - x1 = 1.0; - yh = 1.0; - nflg = 0; - mainlooppos = 0; - ihalve = 1; - ihalvecycle = 2; - newt = 3; - newtcycle = 4; - breaknewtcycle = 5; - breakihalvecycle = 6; - - /* - * main loop - */ - for(;;) - { - - /* - * start - */ - if( mainlooppos==0 ) - { - if( ae_fp_less_eq(a,1.0)||ae_fp_less_eq(b,1.0) ) - { - dithresh = 1.0e-6; - rflg = 0; - aaa = a; - bbb = b; - y0 = y; - x = aaa/(aaa+bbb); - yyy = incompletebeta(aaa, bbb, x, _state); - mainlooppos = ihalve; - continue; - } - else - { - dithresh = 1.0e-4; - } - yp = -invnormaldistribution(y, _state); - if( ae_fp_greater(y,0.5) ) - { - rflg = 1; - aaa = b; - bbb = a; - y0 = 1.0-y; - yp = -yp; - } - else - { - rflg = 0; - aaa = a; - bbb = b; - y0 = y; - } - lgm = (yp*yp-3.0)/6.0; - x = 2.0/(1.0/(2.0*aaa-1.0)+1.0/(2.0*bbb-1.0)); - d = yp*ae_sqrt(x+lgm, _state)/x-(1.0/(2.0*bbb-1.0)-1.0/(2.0*aaa-1.0))*(lgm+5.0/6.0-2.0/(3.0*x)); - d = 2.0*d; - if( ae_fp_less(d,ae_log(ae_minrealnumber, _state)) ) - { - x = 0; - break; - } - x = aaa/(aaa+bbb*ae_exp(d, _state)); - yyy = incompletebeta(aaa, bbb, x, _state); - yp = (yyy-y0)/y0; - if( ae_fp_less(ae_fabs(yp, _state),0.2) ) - { - mainlooppos = newt; - continue; - } - mainlooppos = ihalve; - continue; - } - - /* - * ihalve - */ - if( mainlooppos==ihalve ) - { - dir = 0; - di = 0.5; - i = 0; - mainlooppos = ihalvecycle; - continue; - } - - /* - * ihalvecycle - */ - if( mainlooppos==ihalvecycle ) - { - if( i<=99 ) - { - if( i!=0 ) - { - x = x0+di*(x1-x0); - if( ae_fp_eq(x,1.0) ) - { - x = 1.0-ae_machineepsilon; - } - if( ae_fp_eq(x,0.0) ) - { - di = 0.5; - x = x0+di*(x1-x0); - if( ae_fp_eq(x,0.0) ) - { - break; - } - } - yyy = incompletebeta(aaa, bbb, x, _state); - yp = (x1-x0)/(x1+x0); - if( ae_fp_less(ae_fabs(yp, _state),dithresh) ) - { - mainlooppos = newt; - continue; - } - yp = (yyy-y0)/y0; - if( ae_fp_less(ae_fabs(yp, _state),dithresh) ) - { - mainlooppos = newt; - continue; - } - } - if( ae_fp_less(yyy,y0) ) - { - x0 = x; - yl = yyy; - if( dir<0 ) - { - dir = 0; - di = 0.5; - } - else - { - if( dir>3 ) - { - di = 1.0-(1.0-di)*(1.0-di); - } - else - { - if( dir>1 ) - { - di = 0.5*di+0.5; - } - else - { - di = (y0-yyy)/(yh-yl); - } - } - } - dir = dir+1; - if( ae_fp_greater(x0,0.75) ) - { - if( rflg==1 ) - { - rflg = 0; - aaa = a; - bbb = b; - y0 = y; - } - else - { - rflg = 1; - aaa = b; - bbb = a; - y0 = 1.0-y; - } - x = 1.0-x; - yyy = incompletebeta(aaa, bbb, x, _state); - x0 = 0.0; - yl = 0.0; - x1 = 1.0; - yh = 1.0; - mainlooppos = ihalve; - continue; - } - } - else - { - x1 = x; - if( rflg==1&&ae_fp_less(x1,ae_machineepsilon) ) - { - x = 0.0; - break; - } - yh = yyy; - if( dir>0 ) - { - dir = 0; - di = 0.5; - } - else - { - if( dir<-3 ) - { - di = di*di; - } - else - { - if( dir<-1 ) - { - di = 0.5*di; - } - else - { - di = (yyy-y0)/(yh-yl); - } - } - } - dir = dir-1; - } - i = i+1; - mainlooppos = ihalvecycle; - continue; - } - else - { - mainlooppos = breakihalvecycle; - continue; - } - } - - /* - * breakihalvecycle - */ - if( mainlooppos==breakihalvecycle ) - { - if( ae_fp_greater_eq(x0,1.0) ) - { - x = 1.0-ae_machineepsilon; - break; - } - if( ae_fp_less_eq(x,0.0) ) - { - x = 0.0; - break; - } - mainlooppos = newt; - continue; - } - - /* - * newt - */ - if( mainlooppos==newt ) - { - if( nflg!=0 ) - { - break; - } - nflg = 1; - lgm = lngamma(aaa+bbb, &s, _state)-lngamma(aaa, &s, _state)-lngamma(bbb, &s, _state); - i = 0; - mainlooppos = newtcycle; - continue; - } - - /* - * newtcycle - */ - if( mainlooppos==newtcycle ) - { - if( i<=7 ) - { - if( i!=0 ) - { - yyy = incompletebeta(aaa, bbb, x, _state); - } - if( ae_fp_less(yyy,yl) ) - { - x = x0; - yyy = yl; - } - else - { - if( ae_fp_greater(yyy,yh) ) - { - x = x1; - yyy = yh; - } - else - { - if( ae_fp_less(yyy,y0) ) - { - x0 = x; - yl = yyy; - } - else - { - x1 = x; - yh = yyy; - } - } - } - if( ae_fp_eq(x,1.0)||ae_fp_eq(x,0.0) ) - { - mainlooppos = breaknewtcycle; - continue; - } - d = (aaa-1.0)*ae_log(x, _state)+(bbb-1.0)*ae_log(1.0-x, _state)+lgm; - if( ae_fp_less(d,ae_log(ae_minrealnumber, _state)) ) - { - break; - } - if( ae_fp_greater(d,ae_log(ae_maxrealnumber, _state)) ) - { - mainlooppos = breaknewtcycle; - continue; - } - d = ae_exp(d, _state); - d = (yyy-y0)/d; - xt = x-d; - if( ae_fp_less_eq(xt,x0) ) - { - yyy = (x-x0)/(x1-x0); - xt = x0+0.5*yyy*(x-x0); - if( ae_fp_less_eq(xt,0.0) ) - { - mainlooppos = breaknewtcycle; - continue; - } - } - if( ae_fp_greater_eq(xt,x1) ) - { - yyy = (x1-x)/(x1-x0); - xt = x1-0.5*yyy*(x1-x); - if( ae_fp_greater_eq(xt,1.0) ) - { - mainlooppos = breaknewtcycle; - continue; - } - } - x = xt; - if( ae_fp_less(ae_fabs(d/x, _state),128.0*ae_machineepsilon) ) - { - break; - } - i = i+1; - mainlooppos = newtcycle; - continue; - } - else - { - mainlooppos = breaknewtcycle; - continue; - } - } - - /* - * breaknewtcycle - */ - if( mainlooppos==breaknewtcycle ) - { - dithresh = 256.0*ae_machineepsilon; - mainlooppos = ihalve; - continue; - } - } - - /* - * done - */ - if( rflg!=0 ) - { - if( ae_fp_less_eq(x,ae_machineepsilon) ) - { - x = 1.0-ae_machineepsilon; - } - else - { - x = 1.0-x; - } - } - result = x; - return result; -} - - -/************************************************************************* -Continued fraction expansion #1 for incomplete beta integral - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -static double ibetaf_incompletebetafe(double a, - double b, - double x, - double big, - double biginv, - ae_state *_state) -{ - double xk; - double pk; - double pkm1; - double pkm2; - double qk; - double qkm1; - double qkm2; - double k1; - double k2; - double k3; - double k4; - double k5; - double k6; - double k7; - double k8; - double r; - double t; - double ans; - double thresh; - ae_int_t n; - double result; - - - k1 = a; - k2 = a+b; - k3 = a; - k4 = a+1.0; - k5 = 1.0; - k6 = b-1.0; - k7 = k4; - k8 = a+2.0; - pkm2 = 0.0; - qkm2 = 1.0; - pkm1 = 1.0; - qkm1 = 1.0; - ans = 1.0; - r = 1.0; - n = 0; - thresh = 3.0*ae_machineepsilon; - do - { - xk = -x*k1*k2/(k3*k4); - pk = pkm1+pkm2*xk; - qk = qkm1+qkm2*xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - xk = x*k5*k6/(k7*k8); - pk = pkm1+pkm2*xk; - qk = qkm1+qkm2*xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - if( ae_fp_neq(qk,0) ) - { - r = pk/qk; - } - if( ae_fp_neq(r,0) ) - { - t = ae_fabs((ans-r)/r, _state); - ans = r; - } - else - { - t = 1.0; - } - if( ae_fp_less(t,thresh) ) - { - break; - } - k1 = k1+1.0; - k2 = k2+1.0; - k3 = k3+2.0; - k4 = k4+2.0; - k5 = k5+1.0; - k6 = k6-1.0; - k7 = k7+2.0; - k8 = k8+2.0; - if( ae_fp_greater(ae_fabs(qk, _state)+ae_fabs(pk, _state),big) ) - { - pkm2 = pkm2*biginv; - pkm1 = pkm1*biginv; - qkm2 = qkm2*biginv; - qkm1 = qkm1*biginv; - } - if( ae_fp_less(ae_fabs(qk, _state),biginv)||ae_fp_less(ae_fabs(pk, _state),biginv) ) - { - pkm2 = pkm2*big; - pkm1 = pkm1*big; - qkm2 = qkm2*big; - qkm1 = qkm1*big; - } - n = n+1; - } - while(n!=300); - result = ans; - return result; -} - - -/************************************************************************* -Continued fraction expansion #2 -for incomplete beta integral - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -static double ibetaf_incompletebetafe2(double a, - double b, - double x, - double big, - double biginv, - ae_state *_state) -{ - double xk; - double pk; - double pkm1; - double pkm2; - double qk; - double qkm1; - double qkm2; - double k1; - double k2; - double k3; - double k4; - double k5; - double k6; - double k7; - double k8; - double r; - double t; - double ans; - double z; - double thresh; - ae_int_t n; - double result; - - - k1 = a; - k2 = b-1.0; - k3 = a; - k4 = a+1.0; - k5 = 1.0; - k6 = a+b; - k7 = a+1.0; - k8 = a+2.0; - pkm2 = 0.0; - qkm2 = 1.0; - pkm1 = 1.0; - qkm1 = 1.0; - z = x/(1.0-x); - ans = 1.0; - r = 1.0; - n = 0; - thresh = 3.0*ae_machineepsilon; - do - { - xk = -z*k1*k2/(k3*k4); - pk = pkm1+pkm2*xk; - qk = qkm1+qkm2*xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - xk = z*k5*k6/(k7*k8); - pk = pkm1+pkm2*xk; - qk = qkm1+qkm2*xk; - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - if( ae_fp_neq(qk,0) ) - { - r = pk/qk; - } - if( ae_fp_neq(r,0) ) - { - t = ae_fabs((ans-r)/r, _state); - ans = r; - } - else - { - t = 1.0; - } - if( ae_fp_less(t,thresh) ) - { - break; - } - k1 = k1+1.0; - k2 = k2-1.0; - k3 = k3+2.0; - k4 = k4+2.0; - k5 = k5+1.0; - k6 = k6+1.0; - k7 = k7+2.0; - k8 = k8+2.0; - if( ae_fp_greater(ae_fabs(qk, _state)+ae_fabs(pk, _state),big) ) - { - pkm2 = pkm2*biginv; - pkm1 = pkm1*biginv; - qkm2 = qkm2*biginv; - qkm1 = qkm1*biginv; - } - if( ae_fp_less(ae_fabs(qk, _state),biginv)||ae_fp_less(ae_fabs(pk, _state),biginv) ) - { - pkm2 = pkm2*big; - pkm1 = pkm1*big; - qkm2 = qkm2*big; - qkm1 = qkm1*big; - } - n = n+1; - } - while(n!=300); - result = ans; - return result; -} - - -/************************************************************************* -Power series for incomplete beta integral. -Use when b*x is small and x not too close to 1. - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -static double ibetaf_incompletebetaps(double a, - double b, - double x, - double maxgam, - ae_state *_state) -{ - double s; - double t; - double u; - double v; - double n; - double t1; - double z; - double ai; - double sg; - double result; - - - ai = 1.0/a; - u = (1.0-b)*x; - v = u/(a+1.0); - t1 = v; - t = u; - n = 2.0; - s = 0.0; - z = ae_machineepsilon*ai; - while(ae_fp_greater(ae_fabs(v, _state),z)) - { - u = (n-b)*x/n; - t = t*u; - v = t/(a+n); - s = s+v; - n = n+1.0; - } - s = s+t1; - s = s+ai; - u = a*ae_log(x, _state); - if( ae_fp_less(a+b,maxgam)&&ae_fp_less(ae_fabs(u, _state),ae_log(ae_maxrealnumber, _state)) ) - { - t = gammafunction(a+b, _state)/(gammafunction(a, _state)*gammafunction(b, _state)); - s = s*t*ae_pow(x, a, _state); - } - else - { - t = lngamma(a+b, &sg, _state)-lngamma(a, &sg, _state)-lngamma(b, &sg, _state)+u+ae_log(s, _state); - if( ae_fp_less(t,ae_log(ae_minrealnumber, _state)) ) - { - s = 0.0; - } - else - { - s = ae_exp(t, _state); - } - } - result = s; - return result; -} - - - - -/************************************************************************* -Binomial distribution - -Returns the sum of the terms 0 through k of the Binomial -probability density: - - k - -- ( n ) j n-j - > ( ) p (1-p) - -- ( j ) - j=0 - -The terms are not summed directly; instead the incomplete -beta integral is employed, according to the formula - -y = bdtr( k, n, p ) = incbet( n-k, k+1, 1-p ). - -The arguments must be positive, with p ranging from 0 to 1. - -ACCURACY: - -Tested at random points (a,b,p), with p between 0 and 1. - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 4.3e-15 2.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double binomialdistribution(ae_int_t k, - ae_int_t n, - double p, - ae_state *_state) -{ - double dk; - double dn; - double result; - - - ae_assert(ae_fp_greater_eq(p,0)&&ae_fp_less_eq(p,1), "Domain error in BinomialDistribution", _state); - ae_assert(k>=-1&&k<=n, "Domain error in BinomialDistribution", _state); - if( k==-1 ) - { - result = 0; - return result; - } - if( k==n ) - { - result = 1; - return result; - } - dn = n-k; - if( k==0 ) - { - dk = ae_pow(1.0-p, dn, _state); - } - else - { - dk = k+1; - dk = incompletebeta(dn, dk, 1.0-p, _state); - } - result = dk; - return result; -} - - -/************************************************************************* -Complemented binomial distribution - -Returns the sum of the terms k+1 through n of the Binomial -probability density: - - n - -- ( n ) j n-j - > ( ) p (1-p) - -- ( j ) - j=k+1 - -The terms are not summed directly; instead the incomplete -beta integral is employed, according to the formula - -y = bdtrc( k, n, p ) = incbet( k+1, n-k, p ). - -The arguments must be positive, with p ranging from 0 to 1. - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 6.7e-15 8.2e-16 - For p between 0 and .001: - IEEE 0,100 100000 1.5e-13 2.7e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double binomialcdistribution(ae_int_t k, - ae_int_t n, - double p, - ae_state *_state) -{ - double dk; - double dn; - double result; - - - ae_assert(ae_fp_greater_eq(p,0)&&ae_fp_less_eq(p,1), "Domain error in BinomialDistributionC", _state); - ae_assert(k>=-1&&k<=n, "Domain error in BinomialDistributionC", _state); - if( k==-1 ) - { - result = 1; - return result; - } - if( k==n ) - { - result = 0; - return result; - } - dn = n-k; - if( k==0 ) - { - if( ae_fp_less(p,0.01) ) - { - dk = -nuexpm1(dn*nulog1p(-p, _state), _state); - } - else - { - dk = 1.0-ae_pow(1.0-p, dn, _state); - } - } - else - { - dk = k+1; - dk = incompletebeta(dk, dn, p, _state); - } - result = dk; - return result; -} - - -/************************************************************************* -Inverse binomial distribution - -Finds the event probability p such that the sum of the -terms 0 through k of the Binomial probability density -is equal to the given cumulative probability y. - -This is accomplished using the inverse beta integral -function and the relation - -1 - p = incbi( n-k, k+1, y ). - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 2.3e-14 6.4e-16 - IEEE 0,10000 100000 6.6e-12 1.2e-13 - For p between 10^-6 and 0.001: - IEEE 0,100 100000 2.0e-12 1.3e-14 - IEEE 0,10000 100000 1.5e-12 3.2e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invbinomialdistribution(ae_int_t k, - ae_int_t n, - double y, - ae_state *_state) -{ - double dk; - double dn; - double p; - double result; - - - ae_assert(k>=0&&k=0 - x - argument, -1 <= x <= 1 - -Result: - the value of the Chebyshev polynomial at x -*************************************************************************/ -double chebyshevcalculate(ae_int_t r, - ae_int_t n, - double x, - ae_state *_state) -{ - ae_int_t i; - double a; - double b; - double result; - - - result = 0; - - /* - * Prepare A and B - */ - if( r==1 ) - { - a = 1; - b = x; - } - else - { - a = 1; - b = 2*x; - } - - /* - * Special cases: N=0 or N=1 - */ - if( n==0 ) - { - result = a; - return result; - } - if( n==1 ) - { - result = b; - return result; - } - - /* - * General case: N>=2 - */ - for(i=2; i<=n; i++) - { - result = 2*x*b-a; - a = b; - b = result; - } - return result; -} - - -/************************************************************************* -Summation of Chebyshev polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*T0(x) + c[1]*T1(x) + ... + c[N]*TN(x) -or - c[0]*U0(x) + c[1]*U1(x) + ... + c[N]*UN(x) -depending on the R. - -Parameters: - r - polynomial kind, either 1 or 2. - n - degree, n>=0 - x - argument - -Result: - the value of the Chebyshev polynomial at x -*************************************************************************/ -double chebyshevsum(/* Real */ ae_vector* c, - ae_int_t r, - ae_int_t n, - double x, - ae_state *_state) -{ - double b1; - double b2; - ae_int_t i; - double result; - - - b1 = 0; - b2 = 0; - for(i=n; i>=1; i--) - { - result = 2*x*b1-b2+c->ptr.p_double[i]; - b2 = b1; - b1 = result; - } - if( r==1 ) - { - result = -b2+x*b1+c->ptr.p_double[0]; - } - else - { - result = -b2+2*x*b1+c->ptr.p_double[0]; - } - return result; -} - - -/************************************************************************* -Representation of Tn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void chebyshevcoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(c); - - ae_vector_set_length(c, n+1, _state); - for(i=0; i<=n; i++) - { - c->ptr.p_double[i] = 0; - } - if( n==0||n==1 ) - { - c->ptr.p_double[n] = 1; - } - else - { - c->ptr.p_double[n] = ae_exp((n-1)*ae_log(2, _state), _state); - for(i=0; i<=n/2-1; i++) - { - c->ptr.p_double[n-2*(i+1)] = -c->ptr.p_double[n-2*i]*(n-2*i)*(n-2*i-1)/4/(i+1)/(n-i-1); - } - } -} - - -/************************************************************************* -Conversion of a series of Chebyshev polynomials to a power series. - -Represents A[0]*T0(x) + A[1]*T1(x) + ... + A[N]*Tn(x) as -B[0] + B[1]*X + ... + B[N]*X^N. - -Input parameters: - A - Chebyshev series coefficients - N - degree, N>=0 - -Output parameters - B - power series coefficients -*************************************************************************/ -void fromchebyshev(/* Real */ ae_vector* a, - ae_int_t n, - /* Real */ ae_vector* b, - ae_state *_state) -{ - ae_int_t i; - ae_int_t k; - double e; - double d; - - ae_vector_clear(b); - - ae_vector_set_length(b, n+1, _state); - for(i=0; i<=n; i++) - { - b->ptr.p_double[i] = 0; - } - d = 0; - i = 0; - do - { - k = i; - do - { - e = b->ptr.p_double[k]; - b->ptr.p_double[k] = 0; - if( i<=1&&k==i ) - { - b->ptr.p_double[k] = 1; - } - else - { - if( i!=0 ) - { - b->ptr.p_double[k] = 2*d; - } - if( k>i+1 ) - { - b->ptr.p_double[k] = b->ptr.p_double[k]-b->ptr.p_double[k-2]; - } - } - d = e; - k = k+1; - } - while(k<=n); - d = b->ptr.p_double[i]; - e = 0; - k = i; - while(k<=n) - { - e = e+b->ptr.p_double[k]*a->ptr.p_double[k]; - k = k+2; - } - b->ptr.p_double[i] = e; - i = i+1; - } - while(i<=n); -} - - - - -/************************************************************************* -Chi-square distribution - -Returns the area under the left hand tail (from 0 to x) -of the Chi square probability density function with -v degrees of freedom. - - - x - - - 1 | | v/2-1 -t/2 - P( x | v ) = ----------- | t e dt - v/2 - | | - 2 | (v/2) - - 0 - -where x is the Chi-square variable. - -The incomplete gamma integral is used, according to the -formula - -y = chdtr( v, x ) = igam( v/2.0, x/2.0 ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double chisquaredistribution(double v, double x, ae_state *_state) -{ - double result; - - - ae_assert(ae_fp_greater_eq(x,0)&&ae_fp_greater_eq(v,1), "Domain error in ChiSquareDistribution", _state); - result = incompletegamma(v/2.0, x/2.0, _state); - return result; -} - - -/************************************************************************* -Complemented Chi-square distribution - -Returns the area under the right hand tail (from x to -infinity) of the Chi square probability density function -with v degrees of freedom: - - inf. - - - 1 | | v/2-1 -t/2 - P( x | v ) = ----------- | t e dt - v/2 - | | - 2 | (v/2) - - x - -where x is the Chi-square variable. - -The incomplete gamma integral is used, according to the -formula - -y = chdtr( v, x ) = igamc( v/2.0, x/2.0 ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double chisquarecdistribution(double v, double x, ae_state *_state) -{ - double result; - - - ae_assert(ae_fp_greater_eq(x,0)&&ae_fp_greater_eq(v,1), "Domain error in ChiSquareDistributionC", _state); - result = incompletegammac(v/2.0, x/2.0, _state); - return result; -} - - -/************************************************************************* -Inverse of complemented Chi-square distribution - -Finds the Chi-square argument x such that the integral -from x to infinity of the Chi-square density is equal -to the given cumulative probability y. - -This is accomplished using the inverse gamma integral -function and the relation - - x/2 = igami( df/2, y ); - -ACCURACY: - -See inverse incomplete gamma function - - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double invchisquaredistribution(double v, double y, ae_state *_state) -{ - double result; - - - ae_assert((ae_fp_greater_eq(y,0)&&ae_fp_less_eq(y,1))&&ae_fp_greater_eq(v,1), "Domain error in InvChiSquareDistribution", _state); - result = 2*invincompletegammac(0.5*v, y, _state); - return result; -} - - - - -/************************************************************************* -Dawson's Integral - -Approximates the integral - - x - - - 2 | | 2 - dawsn(x) = exp( -x ) | exp( t ) dt - | | - - - 0 - -Three different rational approximations are employed, for -the intervals 0 to 3.25; 3.25 to 6.25; and 6.25 up. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,10 10000 6.9e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double dawsonintegral(double x, ae_state *_state) -{ - double x2; - double y; - ae_int_t sg; - double an; - double ad; - double bn; - double bd; - double cn; - double cd; - double result; - - - sg = 1; - if( ae_fp_less(x,0) ) - { - sg = -1; - x = -x; - } - if( ae_fp_less(x,3.25) ) - { - x2 = x*x; - an = 1.13681498971755972054E-11; - an = an*x2+8.49262267667473811108E-10; - an = an*x2+1.94434204175553054283E-8; - an = an*x2+9.53151741254484363489E-7; - an = an*x2+3.07828309874913200438E-6; - an = an*x2+3.52513368520288738649E-4; - an = an*x2+(-8.50149846724410912031E-4); - an = an*x2+4.22618223005546594270E-2; - an = an*x2+(-9.17480371773452345351E-2); - an = an*x2+9.99999999999999994612E-1; - ad = 2.40372073066762605484E-11; - ad = ad*x2+1.48864681368493396752E-9; - ad = ad*x2+5.21265281010541664570E-8; - ad = ad*x2+1.27258478273186970203E-6; - ad = ad*x2+2.32490249820789513991E-5; - ad = ad*x2+3.25524741826057911661E-4; - ad = ad*x2+3.48805814657162590916E-3; - ad = ad*x2+2.79448531198828973716E-2; - ad = ad*x2+1.58874241960120565368E-1; - ad = ad*x2+5.74918629489320327824E-1; - ad = ad*x2+1.00000000000000000539E0; - y = x*an/ad; - result = sg*y; - return result; - } - x2 = 1.0/(x*x); - if( ae_fp_less(x,6.25) ) - { - bn = 5.08955156417900903354E-1; - bn = bn*x2-2.44754418142697847934E-1; - bn = bn*x2+9.41512335303534411857E-2; - bn = bn*x2-2.18711255142039025206E-2; - bn = bn*x2+3.66207612329569181322E-3; - bn = bn*x2-4.23209114460388756528E-4; - bn = bn*x2+3.59641304793896631888E-5; - bn = bn*x2-2.14640351719968974225E-6; - bn = bn*x2+9.10010780076391431042E-8; - bn = bn*x2-2.40274520828250956942E-9; - bn = bn*x2+3.59233385440928410398E-11; - bd = 1.00000000000000000000E0; - bd = bd*x2-6.31839869873368190192E-1; - bd = bd*x2+2.36706788228248691528E-1; - bd = bd*x2-5.31806367003223277662E-2; - bd = bd*x2+8.48041718586295374409E-3; - bd = bd*x2-9.47996768486665330168E-4; - bd = bd*x2+7.81025592944552338085E-5; - bd = bd*x2-4.55875153252442634831E-6; - bd = bd*x2+1.89100358111421846170E-7; - bd = bd*x2-4.91324691331920606875E-9; - bd = bd*x2+7.18466403235734541950E-11; - y = 1.0/x+x2*bn/(bd*x); - result = sg*0.5*y; - return result; - } - if( ae_fp_greater(x,1.0E9) ) - { - result = sg*0.5/x; - return result; - } - cn = -5.90592860534773254987E-1; - cn = cn*x2+6.29235242724368800674E-1; - cn = cn*x2-1.72858975380388136411E-1; - cn = cn*x2+1.64837047825189632310E-2; - cn = cn*x2-4.86827613020462700845E-4; - cd = 1.00000000000000000000E0; - cd = cd*x2-2.69820057197544900361E0; - cd = cd*x2+1.73270799045947845857E0; - cd = cd*x2-3.93708582281939493482E-1; - cd = cd*x2+3.44278924041233391079E-2; - cd = cd*x2-9.73655226040941223894E-4; - y = 1.0/x+x2*cn/(cd*x); - result = sg*0.5*y; - return result; -} - - - - -/************************************************************************* -Complete elliptic integral of the first kind - -Approximates the integral - - - - pi/2 - - - | | - | dt -K(m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -using the approximation - - P(x) - log x Q(x). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 2.5e-16 6.8e-17 - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double ellipticintegralk(double m, ae_state *_state) -{ - double result; - - - result = ellipticintegralkhighprecision(1.0-m, _state); - return result; -} - - -/************************************************************************* -Complete elliptic integral of the first kind - -Approximates the integral - - - - pi/2 - - - | | - | dt -K(m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -where m = 1 - m1, using the approximation - - P(x) - log x Q(x). - -The argument m1 is used rather than m so that the logarithmic -singularity at m = 1 will be shifted to the origin; this -preserves maximum accuracy. - -K(0) = pi/2. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 2.5e-16 6.8e-17 - -Àëãîðèòì âçÿò èç áèáëèîòåêè Cephes -*************************************************************************/ -double ellipticintegralkhighprecision(double m1, ae_state *_state) -{ - double p; - double q; - double result; - - - if( ae_fp_less_eq(m1,ae_machineepsilon) ) - { - result = 1.3862943611198906188E0-0.5*ae_log(m1, _state); - } - else - { - p = 1.37982864606273237150E-4; - p = p*m1+2.28025724005875567385E-3; - p = p*m1+7.97404013220415179367E-3; - p = p*m1+9.85821379021226008714E-3; - p = p*m1+6.87489687449949877925E-3; - p = p*m1+6.18901033637687613229E-3; - p = p*m1+8.79078273952743772254E-3; - p = p*m1+1.49380448916805252718E-2; - p = p*m1+3.08851465246711995998E-2; - p = p*m1+9.65735902811690126535E-2; - p = p*m1+1.38629436111989062502E0; - q = 2.94078955048598507511E-5; - q = q*m1+9.14184723865917226571E-4; - q = q*m1+5.94058303753167793257E-3; - q = q*m1+1.54850516649762399335E-2; - q = q*m1+2.39089602715924892727E-2; - q = q*m1+3.01204715227604046988E-2; - q = q*m1+3.73774314173823228969E-2; - q = q*m1+4.88280347570998239232E-2; - q = q*m1+7.03124996963957469739E-2; - q = q*m1+1.24999999999870820058E-1; - q = q*m1+4.99999999999999999821E-1; - result = p-q*ae_log(m1, _state); - } - return result; -} - - -/************************************************************************* -Incomplete elliptic integral of the first kind F(phi|m) - -Approximates the integral - - - - phi - - - | | - | dt -F(phi_\m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -of amplitude phi and modulus m, using the arithmetic - -geometric mean algorithm. - - - - -ACCURACY: - -Tested at random points with m in [0, 1] and phi as indicated. - - Relative error: -arithmetic domain # trials peak rms - IEEE -10,10 200000 7.4e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompleteellipticintegralk(double phi, double m, ae_state *_state) -{ - double a; - double b; - double c; - double e; - double temp; - double pio2; - double t; - double k; - ae_int_t d; - ae_int_t md; - ae_int_t s; - ae_int_t npio2; - double result; - - - pio2 = 1.57079632679489661923; - if( ae_fp_eq(m,0) ) - { - result = phi; - return result; - } - a = 1-m; - if( ae_fp_eq(a,0) ) - { - result = ae_log(ae_tan(0.5*(pio2+phi), _state), _state); - return result; - } - npio2 = ae_ifloor(phi/pio2, _state); - if( npio2%2!=0 ) - { - npio2 = npio2+1; - } - if( npio2!=0 ) - { - k = ellipticintegralk(1-a, _state); - phi = phi-npio2*pio2; - } - else - { - k = 0; - } - if( ae_fp_less(phi,0) ) - { - phi = -phi; - s = -1; - } - else - { - s = 0; - } - b = ae_sqrt(a, _state); - t = ae_tan(phi, _state); - if( ae_fp_greater(ae_fabs(t, _state),10) ) - { - e = 1.0/(b*t); - if( ae_fp_less(ae_fabs(e, _state),10) ) - { - e = ae_atan(e, _state); - if( npio2==0 ) - { - k = ellipticintegralk(1-a, _state); - } - temp = k-incompleteellipticintegralk(e, m, _state); - if( s<0 ) - { - temp = -temp; - } - result = temp+npio2*k; - return result; - } - } - a = 1.0; - c = ae_sqrt(m, _state); - d = 1; - md = 0; - while(ae_fp_greater(ae_fabs(c/a, _state),ae_machineepsilon)) - { - temp = b/a; - phi = phi+ae_atan(t*temp, _state)+md*ae_pi; - md = ae_trunc((phi+pio2)/ae_pi, _state); - t = t*(1.0+temp)/(1.0-temp*t*t); - c = 0.5*(a-b); - temp = ae_sqrt(a*b, _state); - a = 0.5*(a+b); - b = temp; - d = d+d; - } - temp = (ae_atan(t, _state)+md*ae_pi)/(d*a); - if( s<0 ) - { - temp = -temp; - } - result = temp+npio2*k; - return result; -} - - -/************************************************************************* -Complete elliptic integral of the second kind - -Approximates the integral - - - pi/2 - - - | | 2 -E(m) = | sqrt( 1 - m sin t ) dt - | | - - - 0 - -using the approximation - - P(x) - x log x Q(x). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 1 10000 2.1e-16 7.3e-17 - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double ellipticintegrale(double m, ae_state *_state) -{ - double p; - double q; - double result; - - - ae_assert(ae_fp_greater_eq(m,0)&&ae_fp_less_eq(m,1), "Domain error in EllipticIntegralE: m<0 or m>1", _state); - m = 1-m; - if( ae_fp_eq(m,0) ) - { - result = 1; - return result; - } - p = 1.53552577301013293365E-4; - p = p*m+2.50888492163602060990E-3; - p = p*m+8.68786816565889628429E-3; - p = p*m+1.07350949056076193403E-2; - p = p*m+7.77395492516787092951E-3; - p = p*m+7.58395289413514708519E-3; - p = p*m+1.15688436810574127319E-2; - p = p*m+2.18317996015557253103E-2; - p = p*m+5.68051945617860553470E-2; - p = p*m+4.43147180560990850618E-1; - p = p*m+1.00000000000000000299E0; - q = 3.27954898576485872656E-5; - q = q*m+1.00962792679356715133E-3; - q = q*m+6.50609489976927491433E-3; - q = q*m+1.68862163993311317300E-2; - q = q*m+2.61769742454493659583E-2; - q = q*m+3.34833904888224918614E-2; - q = q*m+4.27180926518931511717E-2; - q = q*m+5.85936634471101055642E-2; - q = q*m+9.37499997197644278445E-2; - q = q*m+2.49999999999888314361E-1; - result = p-q*m*ae_log(m, _state); - return result; -} - - -/************************************************************************* -Incomplete elliptic integral of the second kind - -Approximates the integral - - - phi - - - | | - | 2 -E(phi_\m) = | sqrt( 1 - m sin t ) dt - | - | | - - - 0 - -of amplitude phi and modulus m, using the arithmetic - -geometric mean algorithm. - -ACCURACY: - -Tested at random arguments with phi in [-10, 10] and m in -[0, 1]. - Relative error: -arithmetic domain # trials peak rms - IEEE -10,10 150000 3.3e-15 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1993, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompleteellipticintegrale(double phi, double m, ae_state *_state) -{ - double pio2; - double a; - double b; - double c; - double e; - double temp; - double lphi; - double t; - double ebig; - ae_int_t d; - ae_int_t md; - ae_int_t npio2; - ae_int_t s; - double result; - - - pio2 = 1.57079632679489661923; - if( ae_fp_eq(m,0) ) - { - result = phi; - return result; - } - lphi = phi; - npio2 = ae_ifloor(lphi/pio2, _state); - if( npio2%2!=0 ) - { - npio2 = npio2+1; - } - lphi = lphi-npio2*pio2; - if( ae_fp_less(lphi,0) ) - { - lphi = -lphi; - s = -1; - } - else - { - s = 1; - } - a = 1.0-m; - ebig = ellipticintegrale(m, _state); - if( ae_fp_eq(a,0) ) - { - temp = ae_sin(lphi, _state); - if( s<0 ) - { - temp = -temp; - } - result = temp+npio2*ebig; - return result; - } - t = ae_tan(lphi, _state); - b = ae_sqrt(a, _state); - - /* - * Thanks to Brian Fitzgerald - * for pointing out an instability near odd multiples of pi/2 - */ - if( ae_fp_greater(ae_fabs(t, _state),10) ) - { - - /* - * Transform the amplitude - */ - e = 1.0/(b*t); - - /* - * ... but avoid multiple recursions. - */ - if( ae_fp_less(ae_fabs(e, _state),10) ) - { - e = ae_atan(e, _state); - temp = ebig+m*ae_sin(lphi, _state)*ae_sin(e, _state)-incompleteellipticintegrale(e, m, _state); - if( s<0 ) - { - temp = -temp; - } - result = temp+npio2*ebig; - return result; - } - } - c = ae_sqrt(m, _state); - a = 1.0; - d = 1; - e = 0.0; - md = 0; - while(ae_fp_greater(ae_fabs(c/a, _state),ae_machineepsilon)) - { - temp = b/a; - lphi = lphi+ae_atan(t*temp, _state)+md*ae_pi; - md = ae_trunc((lphi+pio2)/ae_pi, _state); - t = t*(1.0+temp)/(1.0-temp*t*t); - c = 0.5*(a-b); - temp = ae_sqrt(a*b, _state); - a = 0.5*(a+b); - b = temp; - d = d+d; - e = e+c*ae_sin(lphi, _state); - } - temp = ebig/ellipticintegralk(m, _state); - temp = temp*((ae_atan(t, _state)+md*ae_pi)/(d*a)); - temp = temp+e; - if( s<0 ) - { - temp = -temp; - } - result = temp+npio2*ebig; - return result; -} - - - - -/************************************************************************* -Exponential integral Ei(x) - - x - - t - | | e - Ei(x) = -|- --- dt . - | | t - - - -inf - -Not defined for x <= 0. -See also expn.c. - - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,100 50000 8.6e-16 1.3e-16 - -Cephes Math Library Release 2.8: May, 1999 -Copyright 1999 by Stephen L. Moshier -*************************************************************************/ -double exponentialintegralei(double x, ae_state *_state) -{ - double eul; - double f; - double f1; - double f2; - double w; - double result; - - - eul = 0.5772156649015328606065; - if( ae_fp_less_eq(x,0) ) - { - result = 0; - return result; - } - if( ae_fp_less(x,2) ) - { - f1 = -5.350447357812542947283; - f1 = f1*x+218.5049168816613393830; - f1 = f1*x-4176.572384826693777058; - f1 = f1*x+55411.76756393557601232; - f1 = f1*x-331338.1331178144034309; - f1 = f1*x+1592627.163384945414220; - f2 = 1.000000000000000000000; - f2 = f2*x-52.50547959112862969197; - f2 = f2*x+1259.616186786790571525; - f2 = f2*x-17565.49581973534652631; - f2 = f2*x+149306.2117002725991967; - f2 = f2*x-729494.9239640527645655; - f2 = f2*x+1592627.163384945429726; - f = f1/f2; - result = eul+ae_log(x, _state)+x*f; - return result; - } - if( ae_fp_less(x,4) ) - { - w = 1/x; - f1 = 1.981808503259689673238E-2; - f1 = f1*w-1.271645625984917501326; - f1 = f1*w-2.088160335681228318920; - f1 = f1*w+2.755544509187936721172; - f1 = f1*w-4.409507048701600257171E-1; - f1 = f1*w+4.665623805935891391017E-2; - f1 = f1*w-1.545042679673485262580E-3; - f1 = f1*w+7.059980605299617478514E-5; - f2 = 1.000000000000000000000; - f2 = f2*w+1.476498670914921440652; - f2 = f2*w+5.629177174822436244827E-1; - f2 = f2*w+1.699017897879307263248E-1; - f2 = f2*w+2.291647179034212017463E-2; - f2 = f2*w+4.450150439728752875043E-3; - f2 = f2*w+1.727439612206521482874E-4; - f2 = f2*w+3.953167195549672482304E-5; - f = f1/f2; - result = ae_exp(x, _state)*w*(1+w*f); - return result; - } - if( ae_fp_less(x,8) ) - { - w = 1/x; - f1 = -1.373215375871208729803; - f1 = f1*w-7.084559133740838761406E-1; - f1 = f1*w+1.580806855547941010501; - f1 = f1*w-2.601500427425622944234E-1; - f1 = f1*w+2.994674694113713763365E-2; - f1 = f1*w-1.038086040188744005513E-3; - f1 = f1*w+4.371064420753005429514E-5; - f1 = f1*w+2.141783679522602903795E-6; - f2 = 1.000000000000000000000; - f2 = f2*w+8.585231423622028380768E-1; - f2 = f2*w+4.483285822873995129957E-1; - f2 = f2*w+7.687932158124475434091E-2; - f2 = f2*w+2.449868241021887685904E-2; - f2 = f2*w+8.832165941927796567926E-4; - f2 = f2*w+4.590952299511353531215E-4; - f2 = f2*w+(-4.729848351866523044863E-6); - f2 = f2*w+2.665195537390710170105E-6; - f = f1/f2; - result = ae_exp(x, _state)*w*(1+w*f); - return result; - } - if( ae_fp_less(x,16) ) - { - w = 1/x; - f1 = -2.106934601691916512584; - f1 = f1*w+1.732733869664688041885; - f1 = f1*w-2.423619178935841904839E-1; - f1 = f1*w+2.322724180937565842585E-2; - f1 = f1*w+2.372880440493179832059E-4; - f1 = f1*w-8.343219561192552752335E-5; - f1 = f1*w+1.363408795605250394881E-5; - f1 = f1*w-3.655412321999253963714E-7; - f1 = f1*w+1.464941733975961318456E-8; - f1 = f1*w+6.176407863710360207074E-10; - f2 = 1.000000000000000000000; - f2 = f2*w-2.298062239901678075778E-1; - f2 = f2*w+1.105077041474037862347E-1; - f2 = f2*w-1.566542966630792353556E-2; - f2 = f2*w+2.761106850817352773874E-3; - f2 = f2*w-2.089148012284048449115E-4; - f2 = f2*w+1.708528938807675304186E-5; - f2 = f2*w-4.459311796356686423199E-7; - f2 = f2*w+1.394634930353847498145E-8; - f2 = f2*w+6.150865933977338354138E-10; - f = f1/f2; - result = ae_exp(x, _state)*w*(1+w*f); - return result; - } - if( ae_fp_less(x,32) ) - { - w = 1/x; - f1 = -2.458119367674020323359E-1; - f1 = f1*w-1.483382253322077687183E-1; - f1 = f1*w+7.248291795735551591813E-2; - f1 = f1*w-1.348315687380940523823E-2; - f1 = f1*w+1.342775069788636972294E-3; - f1 = f1*w-7.942465637159712264564E-5; - f1 = f1*w+2.644179518984235952241E-6; - f1 = f1*w-4.239473659313765177195E-8; - f2 = 1.000000000000000000000; - f2 = f2*w-1.044225908443871106315E-1; - f2 = f2*w-2.676453128101402655055E-1; - f2 = f2*w+9.695000254621984627876E-2; - f2 = f2*w-1.601745692712991078208E-2; - f2 = f2*w+1.496414899205908021882E-3; - f2 = f2*w-8.462452563778485013756E-5; - f2 = f2*w+2.728938403476726394024E-6; - f2 = f2*w-4.239462431819542051337E-8; - f = f1/f2; - result = ae_exp(x, _state)*w*(1+w*f); - return result; - } - if( ae_fp_less(x,64) ) - { - w = 1/x; - f1 = 1.212561118105456670844E-1; - f1 = f1*w-5.823133179043894485122E-1; - f1 = f1*w+2.348887314557016779211E-1; - f1 = f1*w-3.040034318113248237280E-2; - f1 = f1*w+1.510082146865190661777E-3; - f1 = f1*w-2.523137095499571377122E-5; - f2 = 1.000000000000000000000; - f2 = f2*w-1.002252150365854016662; - f2 = f2*w+2.928709694872224144953E-1; - f2 = f2*w-3.337004338674007801307E-2; - f2 = f2*w+1.560544881127388842819E-3; - f2 = f2*w-2.523137093603234562648E-5; - f = f1/f2; - result = ae_exp(x, _state)*w*(1+w*f); - return result; - } - w = 1/x; - f1 = -7.657847078286127362028E-1; - f1 = f1*w+6.886192415566705051750E-1; - f1 = f1*w-2.132598113545206124553E-1; - f1 = f1*w+3.346107552384193813594E-2; - f1 = f1*w-3.076541477344756050249E-3; - f1 = f1*w+1.747119316454907477380E-4; - f1 = f1*w-6.103711682274170530369E-6; - f1 = f1*w+1.218032765428652199087E-7; - f1 = f1*w-1.086076102793290233007E-9; - f2 = 1.000000000000000000000; - f2 = f2*w-1.888802868662308731041; - f2 = f2*w+1.066691687211408896850; - f2 = f2*w-2.751915982306380647738E-1; - f2 = f2*w+3.930852688233823569726E-2; - f2 = f2*w-3.414684558602365085394E-3; - f2 = f2*w+1.866844370703555398195E-4; - f2 = f2*w-6.345146083130515357861E-6; - f2 = f2*w+1.239754287483206878024E-7; - f2 = f2*w-1.086076102793126632978E-9; - f = f1/f2; - result = ae_exp(x, _state)*w*(1+w*f); - return result; -} - - -/************************************************************************* -Exponential integral En(x) - -Evaluates the exponential integral - - inf. - - - | | -xt - | e - E (x) = | ---- dt. - n | n - | | t - - - 1 - - -Both n and x must be nonnegative. - -The routine employs either a power series, a continued -fraction, or an asymptotic formula depending on the -relative values of n and x. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 10000 1.7e-15 3.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 2000 by Stephen L. Moshier -*************************************************************************/ -double exponentialintegralen(double x, ae_int_t n, ae_state *_state) -{ - double r; - double t; - double yk; - double xk; - double pk; - double pkm1; - double pkm2; - double qk; - double qkm1; - double qkm2; - double psi; - double z; - ae_int_t i; - ae_int_t k; - double big; - double eul; - double result; - - - eul = 0.57721566490153286060; - big = 1.44115188075855872*ae_pow(10, 17, _state); - if( ((n<0||ae_fp_less(x,0))||ae_fp_greater(x,170))||(ae_fp_eq(x,0)&&n<2) ) - { - result = -1; - return result; - } - if( ae_fp_eq(x,0) ) - { - result = (double)1/(double)(n-1); - return result; - } - if( n==0 ) - { - result = ae_exp(-x, _state)/x; - return result; - } - if( n>5000 ) - { - xk = x+n; - yk = 1/(xk*xk); - t = n; - result = yk*t*(6*x*x-8*t*x+t*t); - result = yk*(result+t*(t-2.0*x)); - result = yk*(result+t); - result = (result+1)*ae_exp(-x, _state)/xk; - return result; - } - if( ae_fp_less_eq(x,1) ) - { - psi = -eul-ae_log(x, _state); - for(i=1; i<=n-1; i++) - { - psi = psi+(double)1/(double)i; - } - z = -x; - xk = 0; - yk = 1; - pk = 1-n; - if( n==1 ) - { - result = 0.0; - } - else - { - result = 1.0/pk; - } - do - { - xk = xk+1; - yk = yk*z/xk; - pk = pk+1; - if( ae_fp_neq(pk,0) ) - { - result = result+yk/pk; - } - if( ae_fp_neq(result,0) ) - { - t = ae_fabs(yk/result, _state); - } - else - { - t = 1; - } - } - while(ae_fp_greater_eq(t,ae_machineepsilon)); - t = 1; - for(i=1; i<=n-1; i++) - { - t = t*z/i; - } - result = psi*t-result; - return result; - } - else - { - k = 1; - pkm2 = 1; - qkm2 = x; - pkm1 = 1.0; - qkm1 = x+n; - result = pkm1/qkm1; - do - { - k = k+1; - if( k%2==1 ) - { - yk = 1; - xk = n+(double)(k-1)/(double)2; - } - else - { - yk = x; - xk = (double)k/(double)2; - } - pk = pkm1*yk+pkm2*xk; - qk = qkm1*yk+qkm2*xk; - if( ae_fp_neq(qk,0) ) - { - r = pk/qk; - t = ae_fabs((result-r)/r, _state); - result = r; - } - else - { - t = 1; - } - pkm2 = pkm1; - pkm1 = pk; - qkm2 = qkm1; - qkm1 = qk; - if( ae_fp_greater(ae_fabs(pk, _state),big) ) - { - pkm2 = pkm2/big; - pkm1 = pkm1/big; - qkm2 = qkm2/big; - qkm1 = qkm1/big; - } - } - while(ae_fp_greater_eq(t,ae_machineepsilon)); - result = result*ae_exp(-x, _state); - } - return result; -} - - - - -/************************************************************************* -F distribution - -Returns the area from zero to x under the F density -function (also known as Snedcor's density or the -variance ratio density). This is the density -of x = (u1/df1)/(u2/df2), where u1 and u2 are random -variables having Chi square distributions with df1 -and df2 degrees of freedom, respectively. -The incomplete beta integral is used, according to the -formula - -P(x) = incbet( df1/2, df2/2, (df1*x/(df2 + df1*x) ). - - -The arguments a and b are greater than zero, and x is -nonnegative. - -ACCURACY: - -Tested at random points (a,b,x). - - x a,b Relative error: -arithmetic domain domain # trials peak rms - IEEE 0,1 0,100 100000 9.8e-15 1.7e-15 - IEEE 1,5 0,100 100000 6.5e-15 3.5e-16 - IEEE 0,1 1,10000 100000 2.2e-11 3.3e-12 - IEEE 1,5 1,10000 100000 1.1e-11 1.7e-13 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double fdistribution(ae_int_t a, ae_int_t b, double x, ae_state *_state) -{ - double w; - double result; - - - ae_assert((a>=1&&b>=1)&&ae_fp_greater_eq(x,0), "Domain error in FDistribution", _state); - w = a*x; - w = w/(b+w); - result = incompletebeta(0.5*a, 0.5*b, w, _state); - return result; -} - - -/************************************************************************* -Complemented F distribution - -Returns the area from x to infinity under the F density -function (also known as Snedcor's density or the -variance ratio density). - - - inf. - - - 1 | | a-1 b-1 -1-P(x) = ------ | t (1-t) dt - B(a,b) | | - - - x - - -The incomplete beta integral is used, according to the -formula - -P(x) = incbet( df2/2, df1/2, (df2/(df2 + df1*x) ). - - -ACCURACY: - -Tested at random points (a,b,x) in the indicated intervals. - x a,b Relative error: -arithmetic domain domain # trials peak rms - IEEE 0,1 1,100 100000 3.7e-14 5.9e-16 - IEEE 1,5 1,100 100000 8.0e-15 1.6e-15 - IEEE 0,1 1,10000 100000 1.8e-11 3.5e-13 - IEEE 1,5 1,10000 100000 2.0e-11 3.0e-12 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double fcdistribution(ae_int_t a, ae_int_t b, double x, ae_state *_state) -{ - double w; - double result; - - - ae_assert((a>=1&&b>=1)&&ae_fp_greater_eq(x,0), "Domain error in FCDistribution", _state); - w = b/(b+a*x); - result = incompletebeta(0.5*b, 0.5*a, w, _state); - return result; -} - - -/************************************************************************* -Inverse of complemented F distribution - -Finds the F density argument x such that the integral -from x to infinity of the F density is equal to the -given probability p. - -This is accomplished using the inverse beta integral -function and the relations - - z = incbi( df2/2, df1/2, p ) - x = df2 (1-z) / (df1 z). - -Note: the following relations hold for the inverse of -the uncomplemented F distribution: - - z = incbi( df1/2, df2/2, p ) - x = df2 z / (df1 (1-z)). - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between .001 and 1: - IEEE 1,100 100000 8.3e-15 4.7e-16 - IEEE 1,10000 100000 2.1e-11 1.4e-13 - For p between 10^-6 and 10^-3: - IEEE 1,100 50000 1.3e-12 8.4e-15 - IEEE 1,10000 50000 3.0e-12 4.8e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invfdistribution(ae_int_t a, - ae_int_t b, - double y, - ae_state *_state) -{ - double w; - double result; - - - ae_assert(((a>=1&&b>=1)&&ae_fp_greater(y,0))&&ae_fp_less_eq(y,1), "Domain error in InvFDistribution", _state); - - /* - * Compute probability for x = 0.5 - */ - w = incompletebeta(0.5*b, 0.5*a, 0.5, _state); - - /* - * If that is greater than y, then the solution w < .5 - * Otherwise, solve at 1-y to remove cancellation in (b - b*w) - */ - if( ae_fp_greater(w,y)||ae_fp_less(y,0.001) ) - { - w = invincompletebeta(0.5*b, 0.5*a, y, _state); - result = (b-b*w)/(a*w); - } - else - { - w = invincompletebeta(0.5*a, 0.5*b, 1.0-y, _state); - result = b*w/(a*(1.0-w)); - } - return result; -} - - - - -/************************************************************************* -Fresnel integral - -Evaluates the Fresnel integrals - - x - - - | | -C(x) = | cos(pi/2 t**2) dt, - | | - - - 0 - - x - - - | | -S(x) = | sin(pi/2 t**2) dt. - | | - - - 0 - - -The integrals are evaluated by a power series for x < 1. -For x >= 1 auxiliary functions f(x) and g(x) are employed -such that - -C(x) = 0.5 + f(x) sin( pi/2 x**2 ) - g(x) cos( pi/2 x**2 ) -S(x) = 0.5 - f(x) cos( pi/2 x**2 ) - g(x) sin( pi/2 x**2 ) - - - -ACCURACY: - - Relative error. - -Arithmetic function domain # trials peak rms - IEEE S(x) 0, 10 10000 2.0e-15 3.2e-16 - IEEE C(x) 0, 10 10000 1.8e-15 3.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -void fresnelintegral(double x, double* c, double* s, ae_state *_state) -{ - double xxa; - double f; - double g; - double cc; - double ss; - double t; - double u; - double x2; - double sn; - double sd; - double cn; - double cd; - double fn; - double fd; - double gn; - double gd; - double mpi; - double mpio2; - - - mpi = 3.14159265358979323846; - mpio2 = 1.57079632679489661923; - xxa = x; - x = ae_fabs(xxa, _state); - x2 = x*x; - if( ae_fp_less(x2,2.5625) ) - { - t = x2*x2; - sn = -2.99181919401019853726E3; - sn = sn*t+7.08840045257738576863E5; - sn = sn*t-6.29741486205862506537E7; - sn = sn*t+2.54890880573376359104E9; - sn = sn*t-4.42979518059697779103E10; - sn = sn*t+3.18016297876567817986E11; - sd = 1.00000000000000000000E0; - sd = sd*t+2.81376268889994315696E2; - sd = sd*t+4.55847810806532581675E4; - sd = sd*t+5.17343888770096400730E6; - sd = sd*t+4.19320245898111231129E8; - sd = sd*t+2.24411795645340920940E10; - sd = sd*t+6.07366389490084639049E11; - cn = -4.98843114573573548651E-8; - cn = cn*t+9.50428062829859605134E-6; - cn = cn*t-6.45191435683965050962E-4; - cn = cn*t+1.88843319396703850064E-2; - cn = cn*t-2.05525900955013891793E-1; - cn = cn*t+9.99999999999999998822E-1; - cd = 3.99982968972495980367E-12; - cd = cd*t+9.15439215774657478799E-10; - cd = cd*t+1.25001862479598821474E-7; - cd = cd*t+1.22262789024179030997E-5; - cd = cd*t+8.68029542941784300606E-4; - cd = cd*t+4.12142090722199792936E-2; - cd = cd*t+1.00000000000000000118E0; - *s = ae_sign(xxa, _state)*x*x2*sn/sd; - *c = ae_sign(xxa, _state)*x*cn/cd; - return; - } - if( ae_fp_greater(x,36974.0) ) - { - *c = ae_sign(xxa, _state)*0.5; - *s = ae_sign(xxa, _state)*0.5; - return; - } - x2 = x*x; - t = mpi*x2; - u = 1/(t*t); - t = 1/t; - fn = 4.21543555043677546506E-1; - fn = fn*u+1.43407919780758885261E-1; - fn = fn*u+1.15220955073585758835E-2; - fn = fn*u+3.45017939782574027900E-4; - fn = fn*u+4.63613749287867322088E-6; - fn = fn*u+3.05568983790257605827E-8; - fn = fn*u+1.02304514164907233465E-10; - fn = fn*u+1.72010743268161828879E-13; - fn = fn*u+1.34283276233062758925E-16; - fn = fn*u+3.76329711269987889006E-20; - fd = 1.00000000000000000000E0; - fd = fd*u+7.51586398353378947175E-1; - fd = fd*u+1.16888925859191382142E-1; - fd = fd*u+6.44051526508858611005E-3; - fd = fd*u+1.55934409164153020873E-4; - fd = fd*u+1.84627567348930545870E-6; - fd = fd*u+1.12699224763999035261E-8; - fd = fd*u+3.60140029589371370404E-11; - fd = fd*u+5.88754533621578410010E-14; - fd = fd*u+4.52001434074129701496E-17; - fd = fd*u+1.25443237090011264384E-20; - gn = 5.04442073643383265887E-1; - gn = gn*u+1.97102833525523411709E-1; - gn = gn*u+1.87648584092575249293E-2; - gn = gn*u+6.84079380915393090172E-4; - gn = gn*u+1.15138826111884280931E-5; - gn = gn*u+9.82852443688422223854E-8; - gn = gn*u+4.45344415861750144738E-10; - gn = gn*u+1.08268041139020870318E-12; - gn = gn*u+1.37555460633261799868E-15; - gn = gn*u+8.36354435630677421531E-19; - gn = gn*u+1.86958710162783235106E-22; - gd = 1.00000000000000000000E0; - gd = gd*u+1.47495759925128324529E0; - gd = gd*u+3.37748989120019970451E-1; - gd = gd*u+2.53603741420338795122E-2; - gd = gd*u+8.14679107184306179049E-4; - gd = gd*u+1.27545075667729118702E-5; - gd = gd*u+1.04314589657571990585E-7; - gd = gd*u+4.60680728146520428211E-10; - gd = gd*u+1.10273215066240270757E-12; - gd = gd*u+1.38796531259578871258E-15; - gd = gd*u+8.39158816283118707363E-19; - gd = gd*u+1.86958710162783236342E-22; - f = 1-u*fn/fd; - g = t*gn/gd; - t = mpio2*x2; - cc = ae_cos(t, _state); - ss = ae_sin(t, _state); - t = mpi*x; - *c = 0.5+(f*ss-g*cc)/t; - *s = 0.5-(f*cc+g*ss)/t; - *c = *c*ae_sign(xxa, _state); - *s = *s*ae_sign(xxa, _state); -} - - - - -/************************************************************************* -Calculation of the value of the Hermite polynomial. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Hermite polynomial Hn at x -*************************************************************************/ -double hermitecalculate(ae_int_t n, double x, ae_state *_state) -{ - ae_int_t i; - double a; - double b; - double result; - - - result = 0; - - /* - * Prepare A and B - */ - a = 1; - b = 2*x; - - /* - * Special cases: N=0 or N=1 - */ - if( n==0 ) - { - result = a; - return result; - } - if( n==1 ) - { - result = b; - return result; - } - - /* - * General case: N>=2 - */ - for(i=2; i<=n; i++) - { - result = 2*x*b-2*(i-1)*a; - a = b; - b = result; - } - return result; -} - - -/************************************************************************* -Summation of Hermite polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*H0(x) + c[1]*H1(x) + ... + c[N]*HN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Hermite polynomial at x -*************************************************************************/ -double hermitesum(/* Real */ ae_vector* c, - ae_int_t n, - double x, - ae_state *_state) -{ - double b1; - double b2; - ae_int_t i; - double result; - - - b1 = 0; - b2 = 0; - result = 0; - for(i=n; i>=0; i--) - { - result = 2*(x*b1-(i+1)*b2)+c->ptr.p_double[i]; - b2 = b1; - b1 = result; - } - return result; -} - - -/************************************************************************* -Representation of Hn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void hermitecoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(c); - - ae_vector_set_length(c, n+1, _state); - for(i=0; i<=n; i++) - { - c->ptr.p_double[i] = 0; - } - c->ptr.p_double[n] = ae_exp(n*ae_log(2, _state), _state); - for(i=0; i<=n/2-1; i++) - { - c->ptr.p_double[n-2*(i+1)] = -c->ptr.p_double[n-2*i]*(n-2*i)*(n-2*i-1)/4/(i+1); - } -} - - - - -/************************************************************************* -Jacobian Elliptic Functions - -Evaluates the Jacobian elliptic functions sn(u|m), cn(u|m), -and dn(u|m) of parameter m between 0 and 1, and real -argument u. - -These functions are periodic, with quarter-period on the -real axis equal to the complete elliptic integral -ellpk(1.0-m). - -Relation to incomplete elliptic integral: -If u = ellik(phi,m), then sn(u|m) = sin(phi), -and cn(u|m) = cos(phi). Phi is called the amplitude of u. - -Computation is by means of the arithmetic-geometric mean -algorithm, except when m is within 1e-9 of 0 or 1. In the -latter case with m close to 1, the approximation applies -only for phi < pi/2. - -ACCURACY: - -Tested at random points with u between 0 and 10, m between -0 and 1. - - Absolute error (* = relative error): -arithmetic function # trials peak rms - IEEE phi 10000 9.2e-16* 1.4e-16* - IEEE sn 50000 4.1e-15 4.6e-16 - IEEE cn 40000 3.6e-15 4.4e-16 - IEEE dn 10000 1.3e-12 1.8e-14 - - Peak error observed in consistency check using addition -theorem for sn(u+v) was 4e-16 (absolute). Also tested by -the above relation to the incomplete elliptic integral. -Accuracy deteriorates when u is large. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -void jacobianellipticfunctions(double u, - double m, - double* sn, - double* cn, - double* dn, - double* ph, - ae_state *_state) -{ - ae_frame _frame_block; - double ai; - double b; - double phi; - double t; - double twon; - ae_vector a; - ae_vector c; - ae_int_t i; - - ae_frame_make(_state, &_frame_block); - *sn = 0; - *cn = 0; - *dn = 0; - *ph = 0; - ae_vector_init(&a, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c, 0, DT_REAL, _state, ae_true); - - ae_assert(ae_fp_greater_eq(m,0)&&ae_fp_less_eq(m,1), "Domain error in JacobianEllipticFunctions: m<0 or m>1", _state); - ae_vector_set_length(&a, 8+1, _state); - ae_vector_set_length(&c, 8+1, _state); - if( ae_fp_less(m,1.0e-9) ) - { - t = ae_sin(u, _state); - b = ae_cos(u, _state); - ai = 0.25*m*(u-t*b); - *sn = t-ai*b; - *cn = b+ai*t; - *ph = u-ai; - *dn = 1.0-0.5*m*t*t; - ae_frame_leave(_state); - return; - } - if( ae_fp_greater_eq(m,0.9999999999) ) - { - ai = 0.25*(1.0-m); - b = ae_cosh(u, _state); - t = ae_tanh(u, _state); - phi = 1.0/b; - twon = b*ae_sinh(u, _state); - *sn = t+ai*(twon-u)/(b*b); - *ph = 2.0*ae_atan(ae_exp(u, _state), _state)-1.57079632679489661923+ai*(twon-u)/b; - ai = ai*t*phi; - *cn = phi-ai*(twon-u); - *dn = phi+ai*(twon+u); - ae_frame_leave(_state); - return; - } - a.ptr.p_double[0] = 1.0; - b = ae_sqrt(1.0-m, _state); - c.ptr.p_double[0] = ae_sqrt(m, _state); - twon = 1.0; - i = 0; - while(ae_fp_greater(ae_fabs(c.ptr.p_double[i]/a.ptr.p_double[i], _state),ae_machineepsilon)) - { - if( i>7 ) - { - ae_assert(ae_false, "Overflow in JacobianEllipticFunctions", _state); - break; - } - ai = a.ptr.p_double[i]; - i = i+1; - c.ptr.p_double[i] = 0.5*(ai-b); - t = ae_sqrt(ai*b, _state); - a.ptr.p_double[i] = 0.5*(ai+b); - b = t; - twon = twon*2.0; - } - phi = twon*a.ptr.p_double[i]*u; - do - { - t = c.ptr.p_double[i]*ae_sin(phi, _state)/a.ptr.p_double[i]; - b = phi; - phi = (ae_asin(t, _state)+phi)/2.0; - i = i-1; - } - while(i!=0); - *sn = ae_sin(phi, _state); - t = ae_cos(phi, _state); - *cn = t; - *dn = t/ae_cos(phi-b, _state); - *ph = phi; - ae_frame_leave(_state); -} - - - - -/************************************************************************* -Calculation of the value of the Laguerre polynomial. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Laguerre polynomial Ln at x -*************************************************************************/ -double laguerrecalculate(ae_int_t n, double x, ae_state *_state) -{ - double a; - double b; - double i; - double result; - - - result = 1; - a = 1; - b = 1-x; - if( n==1 ) - { - result = b; - } - i = 2; - while(ae_fp_less_eq(i,n)) - { - result = ((2*i-1-x)*b-(i-1)*a)/i; - a = b; - b = result; - i = i+1; - } - return result; -} - - -/************************************************************************* -Summation of Laguerre polynomials using Clenshaw’s recurrence formula. - -This routine calculates c[0]*L0(x) + c[1]*L1(x) + ... + c[N]*LN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Laguerre polynomial at x -*************************************************************************/ -double laguerresum(/* Real */ ae_vector* c, - ae_int_t n, - double x, - ae_state *_state) -{ - double b1; - double b2; - ae_int_t i; - double result; - - - b1 = 0; - b2 = 0; - result = 0; - for(i=n; i>=0; i--) - { - result = (2*i+1-x)*b1/(i+1)-(i+1)*b2/(i+2)+c->ptr.p_double[i]; - b2 = b1; - b1 = result; - } - return result; -} - - -/************************************************************************* -Representation of Ln as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void laguerrecoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(c); - - ae_vector_set_length(c, n+1, _state); - c->ptr.p_double[0] = 1; - for(i=0; i<=n-1; i++) - { - c->ptr.p_double[i+1] = -c->ptr.p_double[i]*(n-i)/(i+1)/(i+1); - } -} - - - - -/************************************************************************* -Calculation of the value of the Legendre polynomial Pn. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Legendre polynomial Pn at x -*************************************************************************/ -double legendrecalculate(ae_int_t n, double x, ae_state *_state) -{ - double a; - double b; - ae_int_t i; - double result; - - - result = 1; - a = 1; - b = x; - if( n==0 ) - { - result = a; - return result; - } - if( n==1 ) - { - result = b; - return result; - } - for(i=2; i<=n; i++) - { - result = ((2*i-1)*x*b-(i-1)*a)/i; - a = b; - b = result; - } - return result; -} - - -/************************************************************************* -Summation of Legendre polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*P0(x) + c[1]*P1(x) + ... + c[N]*PN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Legendre polynomial at x -*************************************************************************/ -double legendresum(/* Real */ ae_vector* c, - ae_int_t n, - double x, - ae_state *_state) -{ - double b1; - double b2; - ae_int_t i; - double result; - - - b1 = 0; - b2 = 0; - result = 0; - for(i=n; i>=0; i--) - { - result = (2*i+1)*x*b1/(i+1)-(i+1)*b2/(i+2)+c->ptr.p_double[i]; - b2 = b1; - b1 = result; - } - return result; -} - - -/************************************************************************* -Representation of Pn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void legendrecoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state) -{ - ae_int_t i; - - ae_vector_clear(c); - - ae_vector_set_length(c, n+1, _state); - for(i=0; i<=n; i++) - { - c->ptr.p_double[i] = 0; - } - c->ptr.p_double[n] = 1; - for(i=1; i<=n; i++) - { - c->ptr.p_double[n] = c->ptr.p_double[n]*(n+i)/2/i; - } - for(i=0; i<=n/2-1; i++) - { - c->ptr.p_double[n-2*(i+1)] = -c->ptr.p_double[n-2*i]*(n-2*i)*(n-2*i-1)/2/(i+1)/(2*(n-i)-1); - } -} - - - - -/************************************************************************* -Poisson distribution - -Returns the sum of the first k+1 terms of the Poisson -distribution: - - k j - -- -m m - > e -- - -- j! - j=0 - -The terms are not summed directly; instead the incomplete -gamma integral is employed, according to the relation - -y = pdtr( k, m ) = igamc( k+1, m ). - -The arguments must both be positive. -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double poissondistribution(ae_int_t k, double m, ae_state *_state) -{ - double result; - - - ae_assert(k>=0&&ae_fp_greater(m,0), "Domain error in PoissonDistribution", _state); - result = incompletegammac(k+1, m, _state); - return result; -} - - -/************************************************************************* -Complemented Poisson distribution - -Returns the sum of the terms k+1 to infinity of the Poisson -distribution: - - inf. j - -- -m m - > e -- - -- j! - j=k+1 - -The terms are not summed directly; instead the incomplete -gamma integral is employed, according to the formula - -y = pdtrc( k, m ) = igam( k+1, m ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double poissoncdistribution(ae_int_t k, double m, ae_state *_state) -{ - double result; - - - ae_assert(k>=0&&ae_fp_greater(m,0), "Domain error in PoissonDistributionC", _state); - result = incompletegamma(k+1, m, _state); - return result; -} - - -/************************************************************************* -Inverse Poisson distribution - -Finds the Poisson variable x such that the integral -from 0 to x of the Poisson density is equal to the -given probability y. - -This is accomplished using the inverse gamma integral -function and the relation - - m = igami( k+1, y ). - -ACCURACY: - -See inverse incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invpoissondistribution(ae_int_t k, double y, ae_state *_state) -{ - double result; - - - ae_assert((k>=0&&ae_fp_greater_eq(y,0))&&ae_fp_less(y,1), "Domain error in InvPoissonDistribution", _state); - result = invincompletegammac(k+1, y, _state); - return result; -} - - - - -/************************************************************************* -Psi (digamma) function - - d - - psi(x) = -- ln | (x) - dx - -is the logarithmic derivative of the gamma function. -For integer x, - n-1 - - -psi(n) = -EUL + > 1/k. - - - k=1 - -This formula is used for 0 < n <= 10. If x is negative, it -is transformed to a positive argument by the reflection -formula psi(1-x) = psi(x) + pi cot(pi x). -For general positive x, the argument is made greater than 10 -using the recurrence psi(x+1) = psi(x) + 1/x. -Then the following asymptotic expansion is applied: - - inf. B - - 2k -psi(x) = log(x) - 1/2x - > ------- - - 2k - k=1 2k x - -where the B2k are Bernoulli numbers. - -ACCURACY: - Relative error (except absolute when |psi| < 1): -arithmetic domain # trials peak rms - IEEE 0,30 30000 1.3e-15 1.4e-16 - IEEE -30,0 40000 1.5e-15 2.2e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double psi(double x, ae_state *_state) -{ - double p; - double q; - double nz; - double s; - double w; - double y; - double z; - double polv; - ae_int_t i; - ae_int_t n; - ae_int_t negative; - double result; - - - negative = 0; - nz = 0.0; - if( ae_fp_less_eq(x,0) ) - { - negative = 1; - q = x; - p = ae_ifloor(q, _state); - if( ae_fp_eq(p,q) ) - { - ae_assert(ae_false, "Singularity in Psi(x)", _state); - result = ae_maxrealnumber; - return result; - } - nz = q-p; - if( ae_fp_neq(nz,0.5) ) - { - if( ae_fp_greater(nz,0.5) ) - { - p = p+1.0; - nz = q-p; - } - nz = ae_pi/ae_tan(ae_pi*nz, _state); - } - else - { - nz = 0.0; - } - x = 1.0-x; - } - if( ae_fp_less_eq(x,10.0)&&ae_fp_eq(x,ae_ifloor(x, _state)) ) - { - y = 0.0; - n = ae_ifloor(x, _state); - for(i=1; i<=n-1; i++) - { - w = i; - y = y+1.0/w; - } - y = y-0.57721566490153286061; - } - else - { - s = x; - w = 0.0; - while(ae_fp_less(s,10.0)) - { - w = w+1.0/s; - s = s+1.0; - } - if( ae_fp_less(s,1.0E17) ) - { - z = 1.0/(s*s); - polv = 8.33333333333333333333E-2; - polv = polv*z-2.10927960927960927961E-2; - polv = polv*z+7.57575757575757575758E-3; - polv = polv*z-4.16666666666666666667E-3; - polv = polv*z+3.96825396825396825397E-3; - polv = polv*z-8.33333333333333333333E-3; - polv = polv*z+8.33333333333333333333E-2; - y = z*polv; - } - else - { - y = 0.0; - } - y = ae_log(s, _state)-0.5/s-y-w; - } - if( negative!=0 ) - { - y = y-nz; - } - result = y; - return result; -} - - - - -/************************************************************************* -Student's t distribution - -Computes the integral from minus infinity to t of the Student -t distribution with integer k > 0 degrees of freedom: - - t - - - | | - - | 2 -(k+1)/2 - | ( (k+1)/2 ) | ( x ) - ---------------------- | ( 1 + --- ) dx - - | ( k ) - sqrt( k pi ) | ( k/2 ) | - | | - - - -inf. - -Relation to incomplete beta integral: - - 1 - stdtr(k,t) = 0.5 * incbet( k/2, 1/2, z ) -where - z = k/(k + t**2). - -For t < -2, this is the method of computation. For higher t, -a direct method is derived from integration by parts. -Since the function is symmetric about t=0, the area under the -right tail of the density is found by calling the function -with -t instead of t. - -ACCURACY: - -Tested at random 1 <= k <= 25. The "domain" refers to t. - Relative error: -arithmetic domain # trials peak rms - IEEE -100,-2 50000 5.9e-15 1.4e-15 - IEEE -2,100 500000 2.7e-15 4.9e-17 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double studenttdistribution(ae_int_t k, double t, ae_state *_state) -{ - double x; - double rk; - double z; - double f; - double tz; - double p; - double xsqk; - ae_int_t j; - double result; - - - ae_assert(k>0, "Domain error in StudentTDistribution", _state); - if( ae_fp_eq(t,0) ) - { - result = 0.5; - return result; - } - if( ae_fp_less(t,-2.0) ) - { - rk = k; - z = rk/(rk+t*t); - result = 0.5*incompletebeta(0.5*rk, 0.5, z, _state); - return result; - } - if( ae_fp_less(t,0) ) - { - x = -t; - } - else - { - x = t; - } - rk = k; - z = 1.0+x*x/rk; - if( k%2!=0 ) - { - xsqk = x/ae_sqrt(rk, _state); - p = ae_atan(xsqk, _state); - if( k>1 ) - { - f = 1.0; - tz = 1.0; - j = 3; - while(j<=k-2&&ae_fp_greater(tz/f,ae_machineepsilon)) - { - tz = tz*((j-1)/(z*j)); - f = f+tz; - j = j+2; - } - p = p+f*xsqk/z; - } - p = p*2.0/ae_pi; - } - else - { - f = 1.0; - tz = 1.0; - j = 2; - while(j<=k-2&&ae_fp_greater(tz/f,ae_machineepsilon)) - { - tz = tz*((j-1)/(z*j)); - f = f+tz; - j = j+2; - } - p = f*x/ae_sqrt(z*rk, _state); - } - if( ae_fp_less(t,0) ) - { - p = -p; - } - result = 0.5+0.5*p; - return result; -} - - -/************************************************************************* -Functional inverse of Student's t distribution - -Given probability p, finds the argument t such that stdtr(k,t) -is equal to p. - -ACCURACY: - -Tested at random 1 <= k <= 100. The "domain" refers to p: - Relative error: -arithmetic domain # trials peak rms - IEEE .001,.999 25000 5.7e-15 8.0e-16 - IEEE 10^-6,.001 25000 2.0e-12 2.9e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invstudenttdistribution(ae_int_t k, double p, ae_state *_state) -{ - double t; - double rk; - double z; - ae_int_t rflg; - double result; - - - ae_assert((k>0&&ae_fp_greater(p,0))&&ae_fp_less(p,1), "Domain error in InvStudentTDistribution", _state); - rk = k; - if( ae_fp_greater(p,0.25)&&ae_fp_less(p,0.75) ) - { - if( ae_fp_eq(p,0.5) ) - { - result = 0; - return result; - } - z = 1.0-2.0*p; - z = invincompletebeta(0.5, 0.5*rk, ae_fabs(z, _state), _state); - t = ae_sqrt(rk*z/(1.0-z), _state); - if( ae_fp_less(p,0.5) ) - { - t = -t; - } - result = t; - return result; - } - rflg = -1; - if( ae_fp_greater_eq(p,0.5) ) - { - p = 1.0-p; - rflg = 1; - } - z = invincompletebeta(0.5*rk, 0.5, 2.0*p, _state); - if( ae_fp_less(ae_maxrealnumber*z,rk) ) - { - result = rflg*ae_maxrealnumber; - return result; - } - t = ae_sqrt(rk/z-rk, _state); - result = rflg*t; - return result; -} - - - - -/************************************************************************* -Sine and cosine integrals - -Evaluates the integrals - - x - - - | cos t - 1 - Ci(x) = eul + ln x + | --------- dt, - | t - - - 0 - x - - - | sin t - Si(x) = | ----- dt - | t - - - 0 - -where eul = 0.57721566490153286061 is Euler's constant. -The integrals are approximated by rational functions. -For x > 8 auxiliary functions f(x) and g(x) are employed -such that - -Ci(x) = f(x) sin(x) - g(x) cos(x) -Si(x) = pi/2 - f(x) cos(x) - g(x) sin(x) - - -ACCURACY: - Test interval = [0,50]. -Absolute error, except relative when > 1: -arithmetic function # trials peak rms - IEEE Si 30000 4.4e-16 7.3e-17 - IEEE Ci 30000 6.9e-16 5.1e-17 - -Cephes Math Library Release 2.1: January, 1989 -Copyright 1984, 1987, 1989 by Stephen L. Moshier -*************************************************************************/ -void sinecosineintegrals(double x, - double* si, - double* ci, - ae_state *_state) -{ - double z; - double c; - double s; - double f; - double g; - ae_int_t sg; - double sn; - double sd; - double cn; - double cd; - double fn; - double fd; - double gn; - double gd; - - *si = 0; - *ci = 0; - - if( ae_fp_less(x,0) ) - { - sg = -1; - x = -x; - } - else - { - sg = 0; - } - if( ae_fp_eq(x,0) ) - { - *si = 0; - *ci = -ae_maxrealnumber; - return; - } - if( ae_fp_greater(x,1.0E9) ) - { - *si = 1.570796326794896619-ae_cos(x, _state)/x; - *ci = ae_sin(x, _state)/x; - return; - } - if( ae_fp_less_eq(x,4) ) - { - z = x*x; - sn = -8.39167827910303881427E-11; - sn = sn*z+4.62591714427012837309E-8; - sn = sn*z-9.75759303843632795789E-6; - sn = sn*z+9.76945438170435310816E-4; - sn = sn*z-4.13470316229406538752E-2; - sn = sn*z+1.00000000000000000302E0; - sd = 2.03269266195951942049E-12; - sd = sd*z+1.27997891179943299903E-9; - sd = sd*z+4.41827842801218905784E-7; - sd = sd*z+9.96412122043875552487E-5; - sd = sd*z+1.42085239326149893930E-2; - sd = sd*z+9.99999999999999996984E-1; - s = x*sn/sd; - cn = 2.02524002389102268789E-11; - cn = cn*z-1.35249504915790756375E-8; - cn = cn*z+3.59325051419993077021E-6; - cn = cn*z-4.74007206873407909465E-4; - cn = cn*z+2.89159652607555242092E-2; - cn = cn*z-1.00000000000000000080E0; - cd = 4.07746040061880559506E-12; - cd = cd*z+3.06780997581887812692E-9; - cd = cd*z+1.23210355685883423679E-6; - cd = cd*z+3.17442024775032769882E-4; - cd = cd*z+5.10028056236446052392E-2; - cd = cd*z+4.00000000000000000080E0; - c = z*cn/cd; - if( sg!=0 ) - { - s = -s; - } - *si = s; - *ci = 0.57721566490153286061+ae_log(x, _state)+c; - return; - } - s = ae_sin(x, _state); - c = ae_cos(x, _state); - z = 1.0/(x*x); - if( ae_fp_less(x,8) ) - { - fn = 4.23612862892216586994E0; - fn = fn*z+5.45937717161812843388E0; - fn = fn*z+1.62083287701538329132E0; - fn = fn*z+1.67006611831323023771E-1; - fn = fn*z+6.81020132472518137426E-3; - fn = fn*z+1.08936580650328664411E-4; - fn = fn*z+5.48900223421373614008E-7; - fd = 1.00000000000000000000E0; - fd = fd*z+8.16496634205391016773E0; - fd = fd*z+7.30828822505564552187E0; - fd = fd*z+1.86792257950184183883E0; - fd = fd*z+1.78792052963149907262E-1; - fd = fd*z+7.01710668322789753610E-3; - fd = fd*z+1.10034357153915731354E-4; - fd = fd*z+5.48900252756255700982E-7; - f = fn/(x*fd); - gn = 8.71001698973114191777E-2; - gn = gn*z+6.11379109952219284151E-1; - gn = gn*z+3.97180296392337498885E-1; - gn = gn*z+7.48527737628469092119E-2; - gn = gn*z+5.38868681462177273157E-3; - gn = gn*z+1.61999794598934024525E-4; - gn = gn*z+1.97963874140963632189E-6; - gn = gn*z+7.82579040744090311069E-9; - gd = 1.00000000000000000000E0; - gd = gd*z+1.64402202413355338886E0; - gd = gd*z+6.66296701268987968381E-1; - gd = gd*z+9.88771761277688796203E-2; - gd = gd*z+6.22396345441768420760E-3; - gd = gd*z+1.73221081474177119497E-4; - gd = gd*z+2.02659182086343991969E-6; - gd = gd*z+7.82579218933534490868E-9; - g = z*gn/gd; - } - else - { - fn = 4.55880873470465315206E-1; - fn = fn*z+7.13715274100146711374E-1; - fn = fn*z+1.60300158222319456320E-1; - fn = fn*z+1.16064229408124407915E-2; - fn = fn*z+3.49556442447859055605E-4; - fn = fn*z+4.86215430826454749482E-6; - fn = fn*z+3.20092790091004902806E-8; - fn = fn*z+9.41779576128512936592E-11; - fn = fn*z+9.70507110881952024631E-14; - fd = 1.00000000000000000000E0; - fd = fd*z+9.17463611873684053703E-1; - fd = fd*z+1.78685545332074536321E-1; - fd = fd*z+1.22253594771971293032E-2; - fd = fd*z+3.58696481881851580297E-4; - fd = fd*z+4.92435064317881464393E-6; - fd = fd*z+3.21956939101046018377E-8; - fd = fd*z+9.43720590350276732376E-11; - fd = fd*z+9.70507110881952025725E-14; - f = fn/(x*fd); - gn = 6.97359953443276214934E-1; - gn = gn*z+3.30410979305632063225E-1; - gn = gn*z+3.84878767649974295920E-2; - gn = gn*z+1.71718239052347903558E-3; - gn = gn*z+3.48941165502279436777E-5; - gn = gn*z+3.47131167084116673800E-7; - gn = gn*z+1.70404452782044526189E-9; - gn = gn*z+3.85945925430276600453E-12; - gn = gn*z+3.14040098946363334640E-15; - gd = 1.00000000000000000000E0; - gd = gd*z+1.68548898811011640017E0; - gd = gd*z+4.87852258695304967486E-1; - gd = gd*z+4.67913194259625806320E-2; - gd = gd*z+1.90284426674399523638E-3; - gd = gd*z+3.68475504442561108162E-5; - gd = gd*z+3.57043223443740838771E-7; - gd = gd*z+1.72693748966316146736E-9; - gd = gd*z+3.87830166023954706752E-12; - gd = gd*z+3.14040098946363335242E-15; - g = z*gn/gd; - } - *si = 1.570796326794896619-f*c-g*s; - if( sg!=0 ) - { - *si = -*si; - } - *ci = f*s-g*c; -} - - -/************************************************************************* -Hyperbolic sine and cosine integrals - -Approximates the integrals - - x - - - | | cosh t - 1 - Chi(x) = eul + ln x + | ----------- dt, - | | t - - - 0 - - x - - - | | sinh t - Shi(x) = | ------ dt - | | t - - - 0 - -where eul = 0.57721566490153286061 is Euler's constant. -The integrals are evaluated by power series for x < 8 -and by Chebyshev expansions for x between 8 and 88. -For large x, both functions approach exp(x)/2x. -Arguments greater than 88 in magnitude return MAXNUM. - - -ACCURACY: - -Test interval 0 to 88. - Relative error: -arithmetic function # trials peak rms - IEEE Shi 30000 6.9e-16 1.6e-16 - Absolute error, except relative when |Chi| > 1: - IEEE Chi 30000 8.4e-16 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -void hyperbolicsinecosineintegrals(double x, - double* shi, - double* chi, - ae_state *_state) -{ - double k; - double z; - double c; - double s; - double a; - ae_int_t sg; - double b0; - double b1; - double b2; - - *shi = 0; - *chi = 0; - - if( ae_fp_less(x,0) ) - { - sg = -1; - x = -x; - } - else - { - sg = 0; - } - if( ae_fp_eq(x,0) ) - { - *shi = 0; - *chi = -ae_maxrealnumber; - return; - } - if( ae_fp_less(x,8.0) ) - { - z = x*x; - a = 1.0; - s = 1.0; - c = 0.0; - k = 2.0; - do - { - a = a*z/k; - c = c+a/k; - k = k+1.0; - a = a/k; - s = s+a/k; - k = k+1.0; - } - while(ae_fp_greater_eq(ae_fabs(a/s, _state),ae_machineepsilon)); - s = s*x; - } - else - { - if( ae_fp_less(x,18.0) ) - { - a = (576.0/x-52.0)/10.0; - k = ae_exp(x, _state)/x; - b0 = 1.83889230173399459482E-17; - b1 = 0.0; - trigintegrals_chebiterationshichi(a, -9.55485532279655569575E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.04326105980879882648E-16, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.09896949074905343022E-15, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.31313534344092599234E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 5.93976226264314278932E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.47197010497749154755E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.40059764613117131000E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 9.49044626224223543299E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.61596181145435454033E-11, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.77899784436430310321E-10, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.35455469767246947469E-9, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.03257121792819495123E-9, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.56699611114982536845E-8, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.44818877384267342057E-7, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 7.82018215184051295296E-7, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -5.39919118403805073710E-6, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.12458202168959833422E-5, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 8.90136741950727517826E-5, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.02558474743846862168E-3, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.96064440855633256972E-2, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.11847751047257036625E0, &b0, &b1, &b2, _state); - s = k*0.5*(b0-b2); - b0 = -8.12435385225864036372E-18; - b1 = 0.0; - trigintegrals_chebiterationshichi(a, 2.17586413290339214377E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 5.22624394924072204667E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -9.48812110591690559363E-16, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 5.35546311647465209166E-15, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.21009970113732918701E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -6.00865178553447437951E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 7.16339649156028587775E-13, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -2.93496072607599856104E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.40359438136491256904E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 8.76302288609054966081E-11, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -4.40092476213282340617E-10, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.87992075640569295479E-10, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.31458150989474594064E-8, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -4.75513930924765465590E-8, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -2.21775018801848880741E-7, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.94635531373272490962E-6, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 4.33505889257316408893E-6, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -6.13387001076494349496E-5, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.13085477492997465138E-4, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 4.97164789823116062801E-4, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.64347496031374526641E-2, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.11446150876699213025E0, &b0, &b1, &b2, _state); - c = k*0.5*(b0-b2); - } - else - { - if( ae_fp_less_eq(x,88.0) ) - { - a = (6336.0/x-212.0)/70.0; - k = ae_exp(x, _state)/x; - b0 = -1.05311574154850938805E-17; - b1 = 0.0; - trigintegrals_chebiterationshichi(a, 2.62446095596355225821E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 8.82090135625368160657E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.38459811878103047136E-16, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -8.30608026366935789136E-16, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 3.93397875437050071776E-15, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.01765565969729044505E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -4.21128170307640802703E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.60818204519802480035E-13, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 3.34714954175994481761E-13, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.72600352129153073807E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.66894954752839083608E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.49278141024730899554E-11, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.58580661666482709598E-10, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.79289437183355633342E-10, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.76281629144264523277E-9, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.69050228879421288846E-8, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.25391771228487041649E-7, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.16229947068677338732E-6, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.61038260117376323993E-5, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 3.49810375601053973070E-4, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.28478065259647610779E-2, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.03665722588798326712E0, &b0, &b1, &b2, _state); - s = k*0.5*(b0-b2); - b0 = 8.06913408255155572081E-18; - b1 = 0.0; - trigintegrals_chebiterationshichi(a, -2.08074168180148170312E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -5.98111329658272336816E-17, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.68533951085945765591E-16, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 4.52313941698904694774E-16, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.10734917335299464535E-15, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -4.42823207332531972288E-15, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 3.49639695410806959872E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 6.63406731718911586609E-14, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.71902448093119218395E-13, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.27135418132338309016E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.74851141935315395333E-12, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.33781843985453438400E-11, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 2.71436006377612442764E-11, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -2.56600180000355990529E-10, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -1.61021375163803438552E-9, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -4.72543064876271773512E-9, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, -3.00095178028681682282E-9, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 7.79387474390914922337E-8, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.06942765566401507066E-6, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.59503164802313196374E-5, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 3.49592575153777996871E-4, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.28475387530065247392E-2, &b0, &b1, &b2, _state); - trigintegrals_chebiterationshichi(a, 1.03665693917934275131E0, &b0, &b1, &b2, _state); - c = k*0.5*(b0-b2); - } - else - { - if( sg!=0 ) - { - *shi = -ae_maxrealnumber; - } - else - { - *shi = ae_maxrealnumber; - } - *chi = ae_maxrealnumber; - return; - } - } - } - if( sg!=0 ) - { - s = -s; - } - *shi = s; - *chi = 0.57721566490153286061+ae_log(x, _state)+c; -} - - -static void trigintegrals_chebiterationshichi(double x, - double c, - double* b0, - double* b1, - double* b2, - ae_state *_state) -{ - - - *b2 = *b1; - *b1 = *b0; - *b0 = x*(*b1)-(*b2)+c; -} - - - -} - diff --git a/src/inc/alglib/specialfunctions.h b/src/inc/alglib/specialfunctions.h deleted file mode 100644 index 167aed3..0000000 --- a/src/inc/alglib/specialfunctions.h +++ /dev/null @@ -1,1976 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _specialfunctions_pkg_h -#define _specialfunctions_pkg_h -#include "ap.h" -#include "alglibinternal.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Gamma function - -Input parameters: - X - argument - -Domain: - 0 < X < 171.6 - -170 < X < 0, X is not an integer. - -Relative error: - arithmetic domain # trials peak rms - IEEE -170,-33 20000 2.3e-15 3.3e-16 - IEEE -33, 33 20000 9.4e-16 2.2e-16 - IEEE 33, 171.6 20000 2.3e-15 3.2e-16 - -Cephes Math Library Release 2.8: June, 2000 -Original copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier -Translated to AlgoPascal by Bochkanov Sergey (2005, 2006, 2007). -*************************************************************************/ -double gammafunction(const double x); - - -/************************************************************************* -Natural logarithm of gamma function - -Input parameters: - X - argument - -Result: - logarithm of the absolute value of the Gamma(X). - -Output parameters: - SgnGam - sign(Gamma(X)) - -Domain: - 0 < X < 2.55e305 - -2.55e305 < X < 0, X is not an integer. - -ACCURACY: -arithmetic domain # trials peak rms - IEEE 0, 3 28000 5.4e-16 1.1e-16 - IEEE 2.718, 2.556e305 40000 3.5e-16 8.3e-17 -The error criterion was relative when the function magnitude -was greater than one but absolute when it was less than one. - -The following test used the relative error criterion, though -at certain points the relative error could be much higher than -indicated. - IEEE -200, -4 10000 4.8e-16 1.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 1992, 2000 by Stephen L. Moshier -Translated to AlgoPascal by Bochkanov Sergey (2005, 2006, 2007). -*************************************************************************/ -double lngamma(const double x, double &sgngam); - -/************************************************************************* -Error function - -The integral is - - x - - - 2 | | 2 - erf(x) = -------- | exp( - t ) dt. - sqrt(pi) | | - - - 0 - -For 0 <= |x| < 1, erf(x) = x * P4(x**2)/Q5(x**2); otherwise -erf(x) = 1 - erfc(x). - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 3.7e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double errorfunction(const double x); - - -/************************************************************************* -Complementary error function - - 1 - erf(x) = - - inf. - - - 2 | | 2 - erfc(x) = -------- | exp( - t ) dt - sqrt(pi) | | - - - x - - -For small x, erfc(x) = 1 - erf(x); otherwise rational -approximations are computed. - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,26.6417 30000 5.7e-14 1.5e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double errorfunctionc(const double x); - - -/************************************************************************* -Normal distribution function - -Returns the area under the Gaussian probability density -function, integrated from minus infinity to x: - - x - - - 1 | | 2 - ndtr(x) = --------- | exp( - t /2 ) dt - sqrt(2pi) | | - - - -inf. - - = ( 1 + erf(z) ) / 2 - = erfc(z) / 2 - -where z = x/sqrt(2). Computation is via the functions -erf and erfc. - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE -13,0 30000 3.4e-14 6.7e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double normaldistribution(const double x); - - -/************************************************************************* -Inverse of the error function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double inverf(const double e); - - -/************************************************************************* -Inverse of Normal distribution function - -Returns the argument, x, for which the area under the -Gaussian probability density function (integrated from -minus infinity to x) is equal to y. - - -For small arguments 0 < y < exp(-2), the program computes -z = sqrt( -2.0 * log(y) ); then the approximation is -x = z - log(z)/z - (1/z) P(1/z) / Q(1/z). -There are two rational functions P/Q, one for 0 < y < exp(-32) -and the other for y up to exp(-2). For larger arguments, -w = y - 0.5, and x/sqrt(2pi) = w + w**3 R(w**2)/S(w**2)). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0.125, 1 20000 7.2e-16 1.3e-16 - IEEE 3e-308, 0.135 50000 4.6e-16 9.8e-17 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double invnormaldistribution(const double y0); - -/************************************************************************* -Incomplete gamma integral - -The function is defined by - - x - - - 1 | | -t a-1 - igam(a,x) = ----- | e t dt. - - | | - | (a) - - 0 - - -In this implementation both arguments must be positive. -The integral is evaluated by either a power series or -continued fraction expansion, depending on the relative -values of a and x. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 200000 3.6e-14 2.9e-15 - IEEE 0,100 300000 9.9e-14 1.5e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletegamma(const double a, const double x); - - -/************************************************************************* -Complemented incomplete gamma integral - -The function is defined by - - - igamc(a,x) = 1 - igam(a,x) - - inf. - - - 1 | | -t a-1 - = ----- | e t dt. - - | | - | (a) - - x - - -In this implementation both arguments must be positive. -The integral is evaluated by either a power series or -continued fraction expansion, depending on the relative -values of a and x. - -ACCURACY: - -Tested at random a, x. - a x Relative error: -arithmetic domain domain # trials peak rms - IEEE 0.5,100 0,100 200000 1.9e-14 1.7e-15 - IEEE 0.01,0.5 0,100 200000 1.4e-13 1.6e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletegammac(const double a, const double x); - - -/************************************************************************* -Inverse of complemented imcomplete gamma integral - -Given p, the function finds x such that - - igamc( a, x ) = p. - -Starting with the approximate value - - 3 - x = a t - - where - - t = 1 - d - ndtri(p) sqrt(d) - -and - - d = 1/9a, - -the routine performs up to 10 Newton iterations to find the -root of igamc(a,x) - p = 0. - -ACCURACY: - -Tested at random a, p in the intervals indicated. - - a p Relative error: -arithmetic domain domain # trials peak rms - IEEE 0.5,100 0,0.5 100000 1.0e-14 1.7e-15 - IEEE 0.01,0.5 0,0.5 100000 9.0e-14 3.4e-15 - IEEE 0.5,10000 0,0.5 20000 2.3e-13 3.8e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invincompletegammac(const double a, const double y0); - -/************************************************************************* -Airy function - -Solution of the differential equation - -y"(x) = xy. - -The function returns the two independent solutions Ai, Bi -and their first derivatives Ai'(x), Bi'(x). - -Evaluation is by power series summation for small x, -by rational minimax approximations for large x. - - - -ACCURACY: -Error criterion is absolute when function <= 1, relative -when function > 1, except * denotes relative error criterion. -For large negative x, the absolute error increases as x^1.5. -For large positive x, the relative error increases as x^1.5. - -Arithmetic domain function # trials peak rms -IEEE -10, 0 Ai 10000 1.6e-15 2.7e-16 -IEEE 0, 10 Ai 10000 2.3e-14* 1.8e-15* -IEEE -10, 0 Ai' 10000 4.6e-15 7.6e-16 -IEEE 0, 10 Ai' 10000 1.8e-14* 1.5e-15* -IEEE -10, 10 Bi 30000 4.2e-15 5.3e-16 -IEEE -10, 10 Bi' 30000 4.9e-15 7.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -void airy(const double x, double &ai, double &aip, double &bi, double &bip); - -/************************************************************************* -Bessel function of order zero - -Returns Bessel function of order zero of the argument. - -The domain is divided into the intervals [0, 5] and -(5, infinity). In the first interval the following rational -approximation is used: - - - 2 2 -(w - r ) (w - r ) P (w) / Q (w) - 1 2 3 8 - - 2 -where w = x and the two r's are zeros of the function. - -In the second interval, the Hankel asymptotic expansion -is employed with two rational functions of degree 6/6 -and 7/7. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 60000 4.2e-16 1.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselj0(const double x); - - -/************************************************************************* -Bessel function of order one - -Returns Bessel function of order one of the argument. - -The domain is divided into the intervals [0, 8] and -(8, infinity). In the first interval a 24 term Chebyshev -expansion is used. In the second, the asymptotic -trigonometric representation is employed using two -rational functions of degree 5/5. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 2.6e-16 1.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselj1(const double x); - - -/************************************************************************* -Bessel function of integer order - -Returns Bessel function of order n, where n is a -(possibly negative) integer. - -The ratio of jn(x) to j0(x) is computed by backward -recurrence. First the ratio jn/jn-1 is found by a -continued fraction expansion. Then the recurrence -relating successive orders is applied until j0 or j1 is -reached. - -If n = 0 or 1 the routine for j0 or j1 is called -directly. - -ACCURACY: - - Absolute error: -arithmetic range # trials peak rms - IEEE 0, 30 5000 4.4e-16 7.9e-17 - - -Not suitable for large n or x. Use jv() (fractional order) instead. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseljn(const ae_int_t n, const double x); - - -/************************************************************************* -Bessel function of the second kind, order zero - -Returns Bessel function of the second kind, of order -zero, of the argument. - -The domain is divided into the intervals [0, 5] and -(5, infinity). In the first interval a rational approximation -R(x) is employed to compute - y0(x) = R(x) + 2 * log(x) * j0(x) / PI. -Thus a call to j0() is required. - -In the second interval, the Hankel asymptotic expansion -is employed with two rational functions of degree 6/6 -and 7/7. - - - -ACCURACY: - - Absolute error, when y0(x) < 1; else relative error: - -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.3e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double bessely0(const double x); - - -/************************************************************************* -Bessel function of second kind of order one - -Returns Bessel function of the second kind of order one -of the argument. - -The domain is divided into the intervals [0, 8] and -(8, infinity). In the first interval a 25 term Chebyshev -expansion is used, and a call to j1() is required. -In the second, the asymptotic trigonometric representation -is employed using two rational functions of degree 5/5. - -ACCURACY: - - Absolute error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.0e-15 1.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double bessely1(const double x); - - -/************************************************************************* -Bessel function of second kind of integer order - -Returns Bessel function of order n, where n is a -(possibly negative) integer. - -The function is evaluated by forward recurrence on -n, starting with values computed by the routines -y0() and y1(). - -If n = 0 or 1 the routine for y0 or y1 is called -directly. - -ACCURACY: - Absolute error, except relative - when y > 1: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 3.4e-15 4.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselyn(const ae_int_t n, const double x); - - -/************************************************************************* -Modified Bessel function of order zero - -Returns modified Bessel function of order zero of the -argument. - -The function is defined as i0(x) = j0( ix ). - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 30000 5.8e-16 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseli0(const double x); - - -/************************************************************************* -Modified Bessel function of order one - -Returns modified Bessel function of order one of the -argument. - -The function is defined as i1(x) = -i j1( ix ). - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.9e-15 2.1e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besseli1(const double x); - - -/************************************************************************* -Modified Bessel function, second kind, order zero - -Returns modified Bessel function of the second kind -of order zero of the argument. - -The range is partitioned into the two intervals [0,8] and -(8, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - -Tested at 2000 random points between 0 and 8. Peak absolute -error (relative when K0 > 1) was 1.46e-14; rms, 4.26e-15. - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.2e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselk0(const double x); - - -/************************************************************************* -Modified Bessel function, second kind, order one - -Computes the modified Bessel function of the second kind -of order one of the argument. - -The range is partitioned into the two intervals [0,2] and -(2, infinity). Chebyshev polynomial expansions are employed -in each interval. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 30000 1.2e-15 1.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselk1(const double x); - - -/************************************************************************* -Modified Bessel function, second kind, integer order - -Returns modified Bessel function of the second kind -of order n of the argument. - -The range is partitioned into the two intervals [0,9.55] and -(9.55, infinity). An ascending power series is used in the -low range, and an asymptotic expansion in the high range. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 90000 1.8e-8 3.0e-10 - -Error is high only near the crossover point x = 9.55 -between the two expansions used. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1988, 2000 by Stephen L. Moshier -*************************************************************************/ -double besselkn(const ae_int_t nn, const double x); - -/************************************************************************* -Beta function - - - - - - | (a) | (b) -beta( a, b ) = -----------. - - - | (a+b) - -For large arguments the logarithm of the function is -evaluated using lgam(), then exponentiated. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,30 30000 8.1e-14 1.1e-14 - -Cephes Math Library Release 2.0: April, 1987 -Copyright 1984, 1987 by Stephen L. Moshier -*************************************************************************/ -double beta(const double a, const double b); - -/************************************************************************* -Incomplete beta integral - -Returns incomplete beta integral of the arguments, evaluated -from zero to x. The function is defined as - - x - - - - | (a+b) | | a-1 b-1 - ----------- | t (1-t) dt. - - - | | - | (a) | (b) - - 0 - -The domain of definition is 0 <= x <= 1. In this -implementation a and b are restricted to positive values. -The integral from x to 1 may be obtained by the symmetry -relation - - 1 - incbet( a, b, x ) = incbet( b, a, 1-x ). - -The integral is evaluated by a continued fraction expansion -or, when b*x is small, by a power series. - -ACCURACY: - -Tested at uniformly distributed random points (a,b,x) with a and b -in "domain" and x between 0 and 1. - Relative error -arithmetic domain # trials peak rms - IEEE 0,5 10000 6.9e-15 4.5e-16 - IEEE 0,85 250000 2.2e-13 1.7e-14 - IEEE 0,1000 30000 5.3e-12 6.3e-13 - IEEE 0,10000 250000 9.3e-11 7.1e-12 - IEEE 0,100000 10000 8.7e-10 4.8e-11 -Outputs smaller than the IEEE gradual underflow threshold -were excluded from these statistics. - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompletebeta(const double a, const double b, const double x); - - -/************************************************************************* -Inverse of imcomplete beta integral - -Given y, the function finds x such that - - incbet( a, b, x ) = y . - -The routine performs interval halving or Newton iterations to find the -root of incbet(a,b,x) - y = 0. - - -ACCURACY: - - Relative error: - x a,b -arithmetic domain domain # trials peak rms - IEEE 0,1 .5,10000 50000 5.8e-12 1.3e-13 - IEEE 0,1 .25,100 100000 1.8e-13 3.9e-15 - IEEE 0,1 0,5 50000 1.1e-12 5.5e-15 -With a and b constrained to half-integer or integer values: - IEEE 0,1 .5,10000 50000 5.8e-12 1.1e-13 - IEEE 0,1 .5,100 100000 1.7e-14 7.9e-16 -With a = .5, b constrained to half-integer or integer values: - IEEE 0,1 .5,10000 10000 8.3e-11 1.0e-11 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1996, 2000 by Stephen L. Moshier -*************************************************************************/ -double invincompletebeta(const double a, const double b, const double y); - -/************************************************************************* -Binomial distribution - -Returns the sum of the terms 0 through k of the Binomial -probability density: - - k - -- ( n ) j n-j - > ( ) p (1-p) - -- ( j ) - j=0 - -The terms are not summed directly; instead the incomplete -beta integral is employed, according to the formula - -y = bdtr( k, n, p ) = incbet( n-k, k+1, 1-p ). - -The arguments must be positive, with p ranging from 0 to 1. - -ACCURACY: - -Tested at random points (a,b,p), with p between 0 and 1. - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 4.3e-15 2.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double binomialdistribution(const ae_int_t k, const ae_int_t n, const double p); - - -/************************************************************************* -Complemented binomial distribution - -Returns the sum of the terms k+1 through n of the Binomial -probability density: - - n - -- ( n ) j n-j - > ( ) p (1-p) - -- ( j ) - j=k+1 - -The terms are not summed directly; instead the incomplete -beta integral is employed, according to the formula - -y = bdtrc( k, n, p ) = incbet( k+1, n-k, p ). - -The arguments must be positive, with p ranging from 0 to 1. - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 6.7e-15 8.2e-16 - For p between 0 and .001: - IEEE 0,100 100000 1.5e-13 2.7e-15 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double binomialcdistribution(const ae_int_t k, const ae_int_t n, const double p); - - -/************************************************************************* -Inverse binomial distribution - -Finds the event probability p such that the sum of the -terms 0 through k of the Binomial probability density -is equal to the given cumulative probability y. - -This is accomplished using the inverse beta integral -function and the relation - -1 - p = incbi( n-k, k+1, y ). - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between 0.001 and 1: - IEEE 0,100 100000 2.3e-14 6.4e-16 - IEEE 0,10000 100000 6.6e-12 1.2e-13 - For p between 10^-6 and 0.001: - IEEE 0,100 100000 2.0e-12 1.3e-14 - IEEE 0,10000 100000 1.5e-12 3.2e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invbinomialdistribution(const ae_int_t k, const ae_int_t n, const double y); - -/************************************************************************* -Calculation of the value of the Chebyshev polynomials of the -first and second kinds. - -Parameters: - r - polynomial kind, either 1 or 2. - n - degree, n>=0 - x - argument, -1 <= x <= 1 - -Result: - the value of the Chebyshev polynomial at x -*************************************************************************/ -double chebyshevcalculate(const ae_int_t r, const ae_int_t n, const double x); - - -/************************************************************************* -Summation of Chebyshev polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*T0(x) + c[1]*T1(x) + ... + c[N]*TN(x) -or - c[0]*U0(x) + c[1]*U1(x) + ... + c[N]*UN(x) -depending on the R. - -Parameters: - r - polynomial kind, either 1 or 2. - n - degree, n>=0 - x - argument - -Result: - the value of the Chebyshev polynomial at x -*************************************************************************/ -double chebyshevsum(const real_1d_array &c, const ae_int_t r, const ae_int_t n, const double x); - - -/************************************************************************* -Representation of Tn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void chebyshevcoefficients(const ae_int_t n, real_1d_array &c); - - -/************************************************************************* -Conversion of a series of Chebyshev polynomials to a power series. - -Represents A[0]*T0(x) + A[1]*T1(x) + ... + A[N]*Tn(x) as -B[0] + B[1]*X + ... + B[N]*X^N. - -Input parameters: - A - Chebyshev series coefficients - N - degree, N>=0 - -Output parameters - B - power series coefficients -*************************************************************************/ -void fromchebyshev(const real_1d_array &a, const ae_int_t n, real_1d_array &b); - -/************************************************************************* -Chi-square distribution - -Returns the area under the left hand tail (from 0 to x) -of the Chi square probability density function with -v degrees of freedom. - - - x - - - 1 | | v/2-1 -t/2 - P( x | v ) = ----------- | t e dt - v/2 - | | - 2 | (v/2) - - 0 - -where x is the Chi-square variable. - -The incomplete gamma integral is used, according to the -formula - -y = chdtr( v, x ) = igam( v/2.0, x/2.0 ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double chisquaredistribution(const double v, const double x); - - -/************************************************************************* -Complemented Chi-square distribution - -Returns the area under the right hand tail (from x to -infinity) of the Chi square probability density function -with v degrees of freedom: - - inf. - - - 1 | | v/2-1 -t/2 - P( x | v ) = ----------- | t e dt - v/2 - | | - 2 | (v/2) - - x - -where x is the Chi-square variable. - -The incomplete gamma integral is used, according to the -formula - -y = chdtr( v, x ) = igamc( v/2.0, x/2.0 ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double chisquarecdistribution(const double v, const double x); - - -/************************************************************************* -Inverse of complemented Chi-square distribution - -Finds the Chi-square argument x such that the integral -from x to infinity of the Chi-square density is equal -to the given cumulative probability y. - -This is accomplished using the inverse gamma integral -function and the relation - - x/2 = igami( df/2, y ); - -ACCURACY: - -See inverse incomplete gamma function - - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double invchisquaredistribution(const double v, const double y); - -/************************************************************************* -Dawson's Integral - -Approximates the integral - - x - - - 2 | | 2 - dawsn(x) = exp( -x ) | exp( t ) dt - | | - - - 0 - -Three different rational approximations are employed, for -the intervals 0 to 3.25; 3.25 to 6.25; and 6.25 up. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,10 10000 6.9e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double dawsonintegral(const double x); - -/************************************************************************* -Complete elliptic integral of the first kind - -Approximates the integral - - - - pi/2 - - - | | - | dt -K(m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -using the approximation - - P(x) - log x Q(x). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 2.5e-16 6.8e-17 - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double ellipticintegralk(const double m); - - -/************************************************************************* -Complete elliptic integral of the first kind - -Approximates the integral - - - - pi/2 - - - | | - | dt -K(m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -where m = 1 - m1, using the approximation - - P(x) - log x Q(x). - -The argument m1 is used rather than m so that the logarithmic -singularity at m = 1 will be shifted to the origin; this -preserves maximum accuracy. - -K(0) = pi/2. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,1 30000 2.5e-16 6.8e-17 - -Àëãîðèòì âçÿò èç áèáëèîòåêè Cephes -*************************************************************************/ -double ellipticintegralkhighprecision(const double m1); - - -/************************************************************************* -Incomplete elliptic integral of the first kind F(phi|m) - -Approximates the integral - - - - phi - - - | | - | dt -F(phi_\m) = | ------------------ - | 2 - | | sqrt( 1 - m sin t ) - - - 0 - -of amplitude phi and modulus m, using the arithmetic - -geometric mean algorithm. - - - - -ACCURACY: - -Tested at random points with m in [0, 1] and phi as indicated. - - Relative error: -arithmetic domain # trials peak rms - IEEE -10,10 200000 7.4e-16 1.0e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompleteellipticintegralk(const double phi, const double m); - - -/************************************************************************* -Complete elliptic integral of the second kind - -Approximates the integral - - - pi/2 - - - | | 2 -E(m) = | sqrt( 1 - m sin t ) dt - | | - - - 0 - -using the approximation - - P(x) - x log x Q(x). - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 1 10000 2.1e-16 7.3e-17 - -Cephes Math Library, Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -double ellipticintegrale(const double m); - - -/************************************************************************* -Incomplete elliptic integral of the second kind - -Approximates the integral - - - phi - - - | | - | 2 -E(phi_\m) = | sqrt( 1 - m sin t ) dt - | - | | - - - 0 - -of amplitude phi and modulus m, using the arithmetic - -geometric mean algorithm. - -ACCURACY: - -Tested at random arguments with phi in [-10, 10] and m in -[0, 1]. - Relative error: -arithmetic domain # trials peak rms - IEEE -10,10 150000 3.3e-15 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1993, 2000 by Stephen L. Moshier -*************************************************************************/ -double incompleteellipticintegrale(const double phi, const double m); - -/************************************************************************* -Exponential integral Ei(x) - - x - - t - | | e - Ei(x) = -|- --- dt . - | | t - - - -inf - -Not defined for x <= 0. -See also expn.c. - - - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0,100 50000 8.6e-16 1.3e-16 - -Cephes Math Library Release 2.8: May, 1999 -Copyright 1999 by Stephen L. Moshier -*************************************************************************/ -double exponentialintegralei(const double x); - - -/************************************************************************* -Exponential integral En(x) - -Evaluates the exponential integral - - inf. - - - | | -xt - | e - E (x) = | ---- dt. - n | n - | | t - - - 1 - - -Both n and x must be nonnegative. - -The routine employs either a power series, a continued -fraction, or an asymptotic formula depending on the -relative values of n and x. - -ACCURACY: - - Relative error: -arithmetic domain # trials peak rms - IEEE 0, 30 10000 1.7e-15 3.6e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1985, 2000 by Stephen L. Moshier -*************************************************************************/ -double exponentialintegralen(const double x, const ae_int_t n); - -/************************************************************************* -F distribution - -Returns the area from zero to x under the F density -function (also known as Snedcor's density or the -variance ratio density). This is the density -of x = (u1/df1)/(u2/df2), where u1 and u2 are random -variables having Chi square distributions with df1 -and df2 degrees of freedom, respectively. -The incomplete beta integral is used, according to the -formula - -P(x) = incbet( df1/2, df2/2, (df1*x/(df2 + df1*x) ). - - -The arguments a and b are greater than zero, and x is -nonnegative. - -ACCURACY: - -Tested at random points (a,b,x). - - x a,b Relative error: -arithmetic domain domain # trials peak rms - IEEE 0,1 0,100 100000 9.8e-15 1.7e-15 - IEEE 1,5 0,100 100000 6.5e-15 3.5e-16 - IEEE 0,1 1,10000 100000 2.2e-11 3.3e-12 - IEEE 1,5 1,10000 100000 1.1e-11 1.7e-13 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double fdistribution(const ae_int_t a, const ae_int_t b, const double x); - - -/************************************************************************* -Complemented F distribution - -Returns the area from x to infinity under the F density -function (also known as Snedcor's density or the -variance ratio density). - - - inf. - - - 1 | | a-1 b-1 -1-P(x) = ------ | t (1-t) dt - B(a,b) | | - - - x - - -The incomplete beta integral is used, according to the -formula - -P(x) = incbet( df2/2, df1/2, (df2/(df2 + df1*x) ). - - -ACCURACY: - -Tested at random points (a,b,x) in the indicated intervals. - x a,b Relative error: -arithmetic domain domain # trials peak rms - IEEE 0,1 1,100 100000 3.7e-14 5.9e-16 - IEEE 1,5 1,100 100000 8.0e-15 1.6e-15 - IEEE 0,1 1,10000 100000 1.8e-11 3.5e-13 - IEEE 1,5 1,10000 100000 2.0e-11 3.0e-12 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double fcdistribution(const ae_int_t a, const ae_int_t b, const double x); - - -/************************************************************************* -Inverse of complemented F distribution - -Finds the F density argument x such that the integral -from x to infinity of the F density is equal to the -given probability p. - -This is accomplished using the inverse beta integral -function and the relations - - z = incbi( df2/2, df1/2, p ) - x = df2 (1-z) / (df1 z). - -Note: the following relations hold for the inverse of -the uncomplemented F distribution: - - z = incbi( df1/2, df2/2, p ) - x = df2 z / (df1 (1-z)). - -ACCURACY: - -Tested at random points (a,b,p). - - a,b Relative error: -arithmetic domain # trials peak rms - For p between .001 and 1: - IEEE 1,100 100000 8.3e-15 4.7e-16 - IEEE 1,10000 100000 2.1e-11 1.4e-13 - For p between 10^-6 and 10^-3: - IEEE 1,100 50000 1.3e-12 8.4e-15 - IEEE 1,10000 50000 3.0e-12 4.8e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invfdistribution(const ae_int_t a, const ae_int_t b, const double y); - -/************************************************************************* -Fresnel integral - -Evaluates the Fresnel integrals - - x - - - | | -C(x) = | cos(pi/2 t**2) dt, - | | - - - 0 - - x - - - | | -S(x) = | sin(pi/2 t**2) dt. - | | - - - 0 - - -The integrals are evaluated by a power series for x < 1. -For x >= 1 auxiliary functions f(x) and g(x) are employed -such that - -C(x) = 0.5 + f(x) sin( pi/2 x**2 ) - g(x) cos( pi/2 x**2 ) -S(x) = 0.5 - f(x) cos( pi/2 x**2 ) - g(x) sin( pi/2 x**2 ) - - - -ACCURACY: - - Relative error. - -Arithmetic function domain # trials peak rms - IEEE S(x) 0, 10 10000 2.0e-15 3.2e-16 - IEEE C(x) 0, 10 10000 1.8e-15 3.3e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1989, 2000 by Stephen L. Moshier -*************************************************************************/ -void fresnelintegral(const double x, double &c, double &s); - -/************************************************************************* -Calculation of the value of the Hermite polynomial. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Hermite polynomial Hn at x -*************************************************************************/ -double hermitecalculate(const ae_int_t n, const double x); - - -/************************************************************************* -Summation of Hermite polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*H0(x) + c[1]*H1(x) + ... + c[N]*HN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Hermite polynomial at x -*************************************************************************/ -double hermitesum(const real_1d_array &c, const ae_int_t n, const double x); - - -/************************************************************************* -Representation of Hn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void hermitecoefficients(const ae_int_t n, real_1d_array &c); - -/************************************************************************* -Jacobian Elliptic Functions - -Evaluates the Jacobian elliptic functions sn(u|m), cn(u|m), -and dn(u|m) of parameter m between 0 and 1, and real -argument u. - -These functions are periodic, with quarter-period on the -real axis equal to the complete elliptic integral -ellpk(1.0-m). - -Relation to incomplete elliptic integral: -If u = ellik(phi,m), then sn(u|m) = sin(phi), -and cn(u|m) = cos(phi). Phi is called the amplitude of u. - -Computation is by means of the arithmetic-geometric mean -algorithm, except when m is within 1e-9 of 0 or 1. In the -latter case with m close to 1, the approximation applies -only for phi < pi/2. - -ACCURACY: - -Tested at random points with u between 0 and 10, m between -0 and 1. - - Absolute error (* = relative error): -arithmetic function # trials peak rms - IEEE phi 10000 9.2e-16* 1.4e-16* - IEEE sn 50000 4.1e-15 4.6e-16 - IEEE cn 40000 3.6e-15 4.4e-16 - IEEE dn 10000 1.3e-12 1.8e-14 - - Peak error observed in consistency check using addition -theorem for sn(u+v) was 4e-16 (absolute). Also tested by -the above relation to the incomplete elliptic integral. -Accuracy deteriorates when u is large. - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -void jacobianellipticfunctions(const double u, const double m, double &sn, double &cn, double &dn, double &ph); - -/************************************************************************* -Calculation of the value of the Laguerre polynomial. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Laguerre polynomial Ln at x -*************************************************************************/ -double laguerrecalculate(const ae_int_t n, const double x); - - -/************************************************************************* -Summation of Laguerre polynomials using Clenshaw’s recurrence formula. - -This routine calculates c[0]*L0(x) + c[1]*L1(x) + ... + c[N]*LN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Laguerre polynomial at x -*************************************************************************/ -double laguerresum(const real_1d_array &c, const ae_int_t n, const double x); - - -/************************************************************************* -Representation of Ln as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void laguerrecoefficients(const ae_int_t n, real_1d_array &c); - -/************************************************************************* -Calculation of the value of the Legendre polynomial Pn. - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Legendre polynomial Pn at x -*************************************************************************/ -double legendrecalculate(const ae_int_t n, const double x); - - -/************************************************************************* -Summation of Legendre polynomials using Clenshaw’s recurrence formula. - -This routine calculates - c[0]*P0(x) + c[1]*P1(x) + ... + c[N]*PN(x) - -Parameters: - n - degree, n>=0 - x - argument - -Result: - the value of the Legendre polynomial at x -*************************************************************************/ -double legendresum(const real_1d_array &c, const ae_int_t n, const double x); - - -/************************************************************************* -Representation of Pn as C[0] + C[1]*X + ... + C[N]*X^N - -Input parameters: - N - polynomial degree, n>=0 - -Output parameters: - C - coefficients -*************************************************************************/ -void legendrecoefficients(const ae_int_t n, real_1d_array &c); - -/************************************************************************* -Poisson distribution - -Returns the sum of the first k+1 terms of the Poisson -distribution: - - k j - -- -m m - > e -- - -- j! - j=0 - -The terms are not summed directly; instead the incomplete -gamma integral is employed, according to the relation - -y = pdtr( k, m ) = igamc( k+1, m ). - -The arguments must both be positive. -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double poissondistribution(const ae_int_t k, const double m); - - -/************************************************************************* -Complemented Poisson distribution - -Returns the sum of the terms k+1 to infinity of the Poisson -distribution: - - inf. j - -- -m m - > e -- - -- j! - j=k+1 - -The terms are not summed directly; instead the incomplete -gamma integral is employed, according to the formula - -y = pdtrc( k, m ) = igam( k+1, m ). - -The arguments must both be positive. - -ACCURACY: - -See incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double poissoncdistribution(const ae_int_t k, const double m); - - -/************************************************************************* -Inverse Poisson distribution - -Finds the Poisson variable x such that the integral -from 0 to x of the Poisson density is equal to the -given probability y. - -This is accomplished using the inverse gamma integral -function and the relation - - m = igami( k+1, y ). - -ACCURACY: - -See inverse incomplete gamma function - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invpoissondistribution(const ae_int_t k, const double y); - -/************************************************************************* -Psi (digamma) function - - d - - psi(x) = -- ln | (x) - dx - -is the logarithmic derivative of the gamma function. -For integer x, - n-1 - - -psi(n) = -EUL + > 1/k. - - - k=1 - -This formula is used for 0 < n <= 10. If x is negative, it -is transformed to a positive argument by the reflection -formula psi(1-x) = psi(x) + pi cot(pi x). -For general positive x, the argument is made greater than 10 -using the recurrence psi(x+1) = psi(x) + 1/x. -Then the following asymptotic expansion is applied: - - inf. B - - 2k -psi(x) = log(x) - 1/2x - > ------- - - 2k - k=1 2k x - -where the B2k are Bernoulli numbers. - -ACCURACY: - Relative error (except absolute when |psi| < 1): -arithmetic domain # trials peak rms - IEEE 0,30 30000 1.3e-15 1.4e-16 - IEEE -30,0 40000 1.5e-15 2.2e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1992, 2000 by Stephen L. Moshier -*************************************************************************/ -double psi(const double x); - -/************************************************************************* -Student's t distribution - -Computes the integral from minus infinity to t of the Student -t distribution with integer k > 0 degrees of freedom: - - t - - - | | - - | 2 -(k+1)/2 - | ( (k+1)/2 ) | ( x ) - ---------------------- | ( 1 + --- ) dx - - | ( k ) - sqrt( k pi ) | ( k/2 ) | - | | - - - -inf. - -Relation to incomplete beta integral: - - 1 - stdtr(k,t) = 0.5 * incbet( k/2, 1/2, z ) -where - z = k/(k + t**2). - -For t < -2, this is the method of computation. For higher t, -a direct method is derived from integration by parts. -Since the function is symmetric about t=0, the area under the -right tail of the density is found by calling the function -with -t instead of t. - -ACCURACY: - -Tested at random 1 <= k <= 25. The "domain" refers to t. - Relative error: -arithmetic domain # trials peak rms - IEEE -100,-2 50000 5.9e-15 1.4e-15 - IEEE -2,100 500000 2.7e-15 4.9e-17 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double studenttdistribution(const ae_int_t k, const double t); - - -/************************************************************************* -Functional inverse of Student's t distribution - -Given probability p, finds the argument t such that stdtr(k,t) -is equal to p. - -ACCURACY: - -Tested at random 1 <= k <= 100. The "domain" refers to p: - Relative error: -arithmetic domain # trials peak rms - IEEE .001,.999 25000 5.7e-15 8.0e-16 - IEEE 10^-6,.001 25000 2.0e-12 2.9e-14 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 1995, 2000 by Stephen L. Moshier -*************************************************************************/ -double invstudenttdistribution(const ae_int_t k, const double p); - -/************************************************************************* -Sine and cosine integrals - -Evaluates the integrals - - x - - - | cos t - 1 - Ci(x) = eul + ln x + | --------- dt, - | t - - - 0 - x - - - | sin t - Si(x) = | ----- dt - | t - - - 0 - -where eul = 0.57721566490153286061 is Euler's constant. -The integrals are approximated by rational functions. -For x > 8 auxiliary functions f(x) and g(x) are employed -such that - -Ci(x) = f(x) sin(x) - g(x) cos(x) -Si(x) = pi/2 - f(x) cos(x) - g(x) sin(x) - - -ACCURACY: - Test interval = [0,50]. -Absolute error, except relative when > 1: -arithmetic function # trials peak rms - IEEE Si 30000 4.4e-16 7.3e-17 - IEEE Ci 30000 6.9e-16 5.1e-17 - -Cephes Math Library Release 2.1: January, 1989 -Copyright 1984, 1987, 1989 by Stephen L. Moshier -*************************************************************************/ -void sinecosineintegrals(const double x, double &si, double &ci); - - -/************************************************************************* -Hyperbolic sine and cosine integrals - -Approximates the integrals - - x - - - | | cosh t - 1 - Chi(x) = eul + ln x + | ----------- dt, - | | t - - - 0 - - x - - - | | sinh t - Shi(x) = | ------ dt - | | t - - - 0 - -where eul = 0.57721566490153286061 is Euler's constant. -The integrals are evaluated by power series for x < 8 -and by Chebyshev expansions for x between 8 and 88. -For large x, both functions approach exp(x)/2x. -Arguments greater than 88 in magnitude return MAXNUM. - - -ACCURACY: - -Test interval 0 to 88. - Relative error: -arithmetic function # trials peak rms - IEEE Shi 30000 6.9e-16 1.6e-16 - Absolute error, except relative when |Chi| > 1: - IEEE Chi 30000 8.4e-16 1.4e-16 - -Cephes Math Library Release 2.8: June, 2000 -Copyright 1984, 1987, 2000 by Stephen L. Moshier -*************************************************************************/ -void hyperbolicsinecosineintegrals(const double x, double &shi, double &chi); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -double gammafunction(double x, ae_state *_state); -double lngamma(double x, double* sgngam, ae_state *_state); -double errorfunction(double x, ae_state *_state); -double errorfunctionc(double x, ae_state *_state); -double normaldistribution(double x, ae_state *_state); -double inverf(double e, ae_state *_state); -double invnormaldistribution(double y0, ae_state *_state); -double incompletegamma(double a, double x, ae_state *_state); -double incompletegammac(double a, double x, ae_state *_state); -double invincompletegammac(double a, double y0, ae_state *_state); -void airy(double x, - double* ai, - double* aip, - double* bi, - double* bip, - ae_state *_state); -double besselj0(double x, ae_state *_state); -double besselj1(double x, ae_state *_state); -double besseljn(ae_int_t n, double x, ae_state *_state); -double bessely0(double x, ae_state *_state); -double bessely1(double x, ae_state *_state); -double besselyn(ae_int_t n, double x, ae_state *_state); -double besseli0(double x, ae_state *_state); -double besseli1(double x, ae_state *_state); -double besselk0(double x, ae_state *_state); -double besselk1(double x, ae_state *_state); -double besselkn(ae_int_t nn, double x, ae_state *_state); -double beta(double a, double b, ae_state *_state); -double incompletebeta(double a, double b, double x, ae_state *_state); -double invincompletebeta(double a, double b, double y, ae_state *_state); -double binomialdistribution(ae_int_t k, - ae_int_t n, - double p, - ae_state *_state); -double binomialcdistribution(ae_int_t k, - ae_int_t n, - double p, - ae_state *_state); -double invbinomialdistribution(ae_int_t k, - ae_int_t n, - double y, - ae_state *_state); -double chebyshevcalculate(ae_int_t r, - ae_int_t n, - double x, - ae_state *_state); -double chebyshevsum(/* Real */ ae_vector* c, - ae_int_t r, - ae_int_t n, - double x, - ae_state *_state); -void chebyshevcoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state); -void fromchebyshev(/* Real */ ae_vector* a, - ae_int_t n, - /* Real */ ae_vector* b, - ae_state *_state); -double chisquaredistribution(double v, double x, ae_state *_state); -double chisquarecdistribution(double v, double x, ae_state *_state); -double invchisquaredistribution(double v, double y, ae_state *_state); -double dawsonintegral(double x, ae_state *_state); -double ellipticintegralk(double m, ae_state *_state); -double ellipticintegralkhighprecision(double m1, ae_state *_state); -double incompleteellipticintegralk(double phi, double m, ae_state *_state); -double ellipticintegrale(double m, ae_state *_state); -double incompleteellipticintegrale(double phi, double m, ae_state *_state); -double exponentialintegralei(double x, ae_state *_state); -double exponentialintegralen(double x, ae_int_t n, ae_state *_state); -double fdistribution(ae_int_t a, ae_int_t b, double x, ae_state *_state); -double fcdistribution(ae_int_t a, ae_int_t b, double x, ae_state *_state); -double invfdistribution(ae_int_t a, - ae_int_t b, - double y, - ae_state *_state); -void fresnelintegral(double x, double* c, double* s, ae_state *_state); -double hermitecalculate(ae_int_t n, double x, ae_state *_state); -double hermitesum(/* Real */ ae_vector* c, - ae_int_t n, - double x, - ae_state *_state); -void hermitecoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state); -void jacobianellipticfunctions(double u, - double m, - double* sn, - double* cn, - double* dn, - double* ph, - ae_state *_state); -double laguerrecalculate(ae_int_t n, double x, ae_state *_state); -double laguerresum(/* Real */ ae_vector* c, - ae_int_t n, - double x, - ae_state *_state); -void laguerrecoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state); -double legendrecalculate(ae_int_t n, double x, ae_state *_state); -double legendresum(/* Real */ ae_vector* c, - ae_int_t n, - double x, - ae_state *_state); -void legendrecoefficients(ae_int_t n, - /* Real */ ae_vector* c, - ae_state *_state); -double poissondistribution(ae_int_t k, double m, ae_state *_state); -double poissoncdistribution(ae_int_t k, double m, ae_state *_state); -double invpoissondistribution(ae_int_t k, double y, ae_state *_state); -double psi(double x, ae_state *_state); -double studenttdistribution(ae_int_t k, double t, ae_state *_state); -double invstudenttdistribution(ae_int_t k, double p, ae_state *_state); -void sinecosineintegrals(double x, - double* si, - double* ci, - ae_state *_state); -void hyperbolicsinecosineintegrals(double x, - double* shi, - double* chi, - ae_state *_state); - -} -#endif - diff --git a/src/inc/alglib/statistics.cpp b/src/inc/alglib/statistics.cpp deleted file mode 100644 index 4f0ef4e..0000000 --- a/src/inc/alglib/statistics.cpp +++ /dev/null @@ -1,19718 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#include "stdafx.h" -#include "statistics.h" - -// disable some irrelevant warnings -#if (AE_COMPILER==AE_MSVC) -#pragma warning(disable:4100) -#pragma warning(disable:4127) -#pragma warning(disable:4702) -#pragma warning(disable:4996) -#endif -using namespace std; - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Calculation of the distribution moments: mean, variance, skewness, kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -OUTPUT PARAMETERS - Mean - mean. - Variance- variance. - Skewness- skewness (if variance<>0; zero otherwise). - Kurtosis- kurtosis (if variance<>0; zero otherwise). - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemoments(const real_1d_array &x, const ae_int_t n, double &mean, double &variance, double &skewness, double &kurtosis) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::samplemoments(const_cast(x.c_ptr()), n, &mean, &variance, &skewness, &kurtosis, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the distribution moments: mean, variance, skewness, kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -OUTPUT PARAMETERS - Mean - mean. - Variance- variance. - Skewness- skewness (if variance<>0; zero otherwise). - Kurtosis- kurtosis (if variance<>0; zero otherwise). - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemoments(const real_1d_array &x, double &mean, double &variance, double &skewness, double &kurtosis) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::samplemoments(const_cast(x.c_ptr()), n, &mean, &variance, &skewness, &kurtosis, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the mean. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Mean' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplemean(const real_1d_array &x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::samplemean(const_cast(x.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the mean. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Mean' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplemean(const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::samplemean(const_cast(x.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the variance. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Variance' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplevariance(const real_1d_array &x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::samplevariance(const_cast(x.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the variance. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Variance' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplevariance(const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::samplevariance(const_cast(x.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the skewness. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Skewness' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double sampleskewness(const real_1d_array &x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::sampleskewness(const_cast(x.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the skewness. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Skewness' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double sampleskewness(const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::sampleskewness(const_cast(x.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Kurtosis' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplekurtosis(const real_1d_array &x, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::samplekurtosis(const_cast(x.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Calculation of the kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Kurtosis' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplekurtosis(const real_1d_array &x) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::samplekurtosis(const_cast(x.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -ADev - -Input parameters: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - ADev- ADev - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void sampleadev(const real_1d_array &x, const ae_int_t n, double &adev) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sampleadev(const_cast(x.c_ptr()), n, &adev, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -ADev - -Input parameters: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - ADev- ADev - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void sampleadev(const real_1d_array &x, double &adev) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::sampleadev(const_cast(x.c_ptr()), n, &adev, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Median calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - Median - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemedian(const real_1d_array &x, const ae_int_t n, double &median) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::samplemedian(const_cast(x.c_ptr()), n, &median, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Median calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - Median - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemedian(const real_1d_array &x, double &median) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::samplemedian(const_cast(x.c_ptr()), n, &median, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Percentile calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - P - percentile (0<=P<=1) - -Output parameters: - V - percentile - - -- ALGLIB -- - Copyright 01.03.2008 by Bochkanov Sergey -*************************************************************************/ -void samplepercentile(const real_1d_array &x, const ae_int_t n, const double p, double &v) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::samplepercentile(const_cast(x.c_ptr()), n, p, &v, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Percentile calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - P - percentile (0<=P<=1) - -Output parameters: - V - percentile - - -- ALGLIB -- - Copyright 01.03.2008 by Bochkanov Sergey -*************************************************************************/ -void samplepercentile(const real_1d_array &x, const double p, double &v) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::samplepercentile(const_cast(x.c_ptr()), n, p, &v, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -2-sample covariance - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - covariance (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double cov2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cov2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -2-sample covariance - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - covariance (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double cov2(const real_1d_array &x, const real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'cov2': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::cov2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson product-moment correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Pearson product-moment correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double pearsoncorr2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::pearsoncorr2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson product-moment correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Pearson product-moment correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double pearsoncorr2(const real_1d_array &x, const real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'pearsoncorr2': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::pearsoncorr2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Spearman's rank correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double spearmancorr2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spearmancorr2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Spearman's rank correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double spearmancorr2(const real_1d_array &x, const real_1d_array &y) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - if( (x.length()!=y.length())) - throw ap_error("Error while calling 'spearmancorr2': looks like one of arguments has wrong size"); - n = x.length(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spearmancorr2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::covm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_covm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_covm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm(const real_2d_array &x, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = x.rows(); - m = x.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::covm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_covm(const real_2d_array &x, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = x.rows(); - m = x.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_covm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson product-moment correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pearsoncorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_pearsoncorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_pearsoncorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson product-moment correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm(const real_2d_array &x, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = x.rows(); - m = x.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pearsoncorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_pearsoncorrm(const real_2d_array &x, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = x.rows(); - m = x.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_pearsoncorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spearmancorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_spearmancorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_spearmancorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm(const real_2d_array &x, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = x.rows(); - m = x.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spearmancorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_spearmancorrm(const real_2d_array &x, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m; - - n = x.rows(); - m = x.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_spearmancorrm(const_cast(x.c_ptr()), n, m, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cross-covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::covm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_covm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_covm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Cross-covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m1; - ae_int_t m2; - if( (x.rows()!=y.rows())) - throw ap_error("Error while calling 'covm2': looks like one of arguments has wrong size"); - n = x.rows(); - m1 = x.cols(); - m2 = y.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::covm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_covm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m1; - ae_int_t m2; - if( (x.rows()!=y.rows())) - throw ap_error("Error while calling 'covm2': looks like one of arguments has wrong size"); - n = x.rows(); - m1 = x.cols(); - m2 = y.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_covm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson product-moment cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pearsoncorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_pearsoncorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson product-moment cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m1; - ae_int_t m2; - if( (x.rows()!=y.rows())) - throw ap_error("Error while calling 'pearsoncorrm2': looks like one of arguments has wrong size"); - n = x.rows(); - m1 = x.cols(); - m2 = y.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pearsoncorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m1; - ae_int_t m2; - if( (x.rows()!=y.rows())) - throw ap_error("Error while calling 'pearsoncorrm2': looks like one of arguments has wrong size"); - n = x.rows(); - m1 = x.cols(); - m2 = y.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_pearsoncorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spearmancorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_spearmancorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_spearmancorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m1; - ae_int_t m2; - if( (x.rows()!=y.rows())) - throw ap_error("Error while calling 'spearmancorrm2': looks like one of arguments has wrong size"); - n = x.rows(); - m1 = x.cols(); - m2 = y.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spearmancorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_spearmancorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t n; - ae_int_t m1; - ae_int_t m2; - if( (x.rows()!=y.rows())) - throw ap_error("Error while calling 'spearmancorrm2': looks like one of arguments has wrong size"); - n = x.rows(); - m1 = x.cols(); - m2 = y.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_spearmancorrm2(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, m1, m2, const_cast(c.c_ptr()), &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rankdata(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rankdata(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rankdata(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rankdata(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rankdata(real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t npoints; - ae_int_t nfeatures; - - npoints = xy.rows(); - nfeatures = xy.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rankdata(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rankdata(real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t npoints; - ae_int_t nfeatures; - - npoints = xy.rows(); - nfeatures = xy.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rankdata(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rankdatacentered(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rankdatacentered(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rankdatacentered(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rankdatacentered(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* - -*************************************************************************/ -void rankdatacentered(real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t npoints; - ae_int_t nfeatures; - - npoints = xy.rows(); - nfeatures = xy.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::rankdatacentered(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - - -void smp_rankdatacentered(real_2d_array &xy) -{ - alglib_impl::ae_state _alglib_env_state; - ae_int_t npoints; - ae_int_t nfeatures; - - npoints = xy.rows(); - nfeatures = xy.cols(); - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::_pexec_rankdatacentered(const_cast(xy.c_ptr()), npoints, nfeatures, &_alglib_env_state); - - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete function, we recommend to use PearsonCorr2(). - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double pearsoncorrelation(const real_1d_array &x, const real_1d_array &y, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::pearsoncorrelation(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Obsolete function, we recommend to use SpearmanCorr2(). - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double spearmanrankcorrelation(const real_1d_array &x, const real_1d_array &y, const ae_int_t n) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - double result = alglib_impl::spearmanrankcorrelation(const_cast(x.c_ptr()), const_cast(y.c_ptr()), n, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return *(reinterpret_cast(&result)); - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Pearson's correlation coefficient significance test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions having zero correlation or whether their -correlation is non-zero. - -The following tests are performed: - * two-tailed test (null hypothesis - X and Y have zero correlation) - * left-tailed test (null hypothesis - the correlation coefficient is - greater than or equal to 0) - * right-tailed test (null hypothesis - the correlation coefficient is - less than or equal to 0). - -Requirements: - * the number of elements in each sample is not less than 5 - * normality of distributions of X and Y. - -Input parameters: - R - Pearson's correlation coefficient for X and Y - N - number of elements in samples, N>=5. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrelationsignificance(const double r, const ae_int_t n, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::pearsoncorrelationsignificance(r, n, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Spearman's rank correlation coefficient significance test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions having zero correlation or whether their -correlation is non-zero. - -The following tests are performed: - * two-tailed test (null hypothesis - X and Y have zero correlation) - * left-tailed test (null hypothesis - the correlation coefficient is - greater than or equal to 0) - * right-tailed test (null hypothesis - the correlation coefficient is - less than or equal to 0). - -Requirements: - * the number of elements in each sample is not less than 5. - -The test is non-parametric and doesn't require distributions X and Y to be -normal. - -Input parameters: - R - Spearman's rank correlation coefficient for X and Y - N - number of elements in samples, N>=5. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void spearmanrankcorrelationsignificance(const double r, const ae_int_t n, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::spearmanrankcorrelationsignificance(r, n, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Jarque-Bera test - -This test checks hypotheses about the fact that a given sample X is a -sample of normal random variable. - -Requirements: - * the number of elements in the sample is not less than 5. - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. N>=5 - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -Accuracy of the approximation used (5<=N<=1951): - -p-value relative error (5<=N<=1951) -[1, 0.1] < 1% -[0.1, 0.01] < 2% -[0.01, 0.001] < 6% -[0.001, 0] wasn't measured - -For N>1951 accuracy wasn't measured but it shouldn't be sharply different -from table values. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void jarqueberatest(const real_1d_array &x, const ae_int_t n, double &p) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::jarqueberatest(const_cast(x.c_ptr()), n, &p, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Mann-Whitney U-test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions of the same shape and same median or whether -their medians are different. - -The following tests are performed: - * two-tailed test (null hypothesis - the medians are equal) - * left-tailed test (null hypothesis - the median of the first sample - is greater than or equal to the median of the second sample) - * right-tailed test (null hypothesis - the median of the first sample - is less than or equal to the median of the second sample). - -Requirements: - * the samples are independent - * X and Y are continuous distributions (or discrete distributions well- - approximating continuous distributions) - * distributions of X and Y have the same shape. The only possible - difference is their position (i.e. the value of the median) - * the number of elements in each sample is not less than 5 - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - -The test is non-parametric and doesn't require distributions to be normal. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. N>=5 - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of the sample. M>=5 - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -To calculate p-values, special approximation is used. This method lets us -calculate p-values with satisfactory accuracy in interval [0.0001, 1]. -There is no approximation outside the [0.0001, 1] interval. Therefore, if -the significance level outlies this interval, the test returns 0.0001. - -Relative precision of approximation of p-value: - -N M Max.err. Rms.err. -5..10 N..10 1.4e-02 6.0e-04 -5..10 N..100 2.2e-02 5.3e-06 -10..15 N..15 1.0e-02 3.2e-04 -10..15 N..100 1.0e-02 2.2e-05 -15..100 N..100 6.1e-03 2.7e-06 - -For N,M>100 accuracy checks weren't put into practice, but taking into -account characteristics of asymptotic approximation used, precision should -not be sharply different from the values for interval [5, 100]. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void mannwhitneyutest(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::mannwhitneyutest(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Sign test - -This test checks three hypotheses about the median of the given sample. -The following tests are performed: - * two-tailed test (null hypothesis - the median is equal to the given - value) - * left-tailed test (null hypothesis - the median is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the median is less than or - equal to the given value) - -Requirements: - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - -The test is non-parametric and doesn't require distribution X to be normal - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. - Median - assumed median value. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -While calculating p-values high-precision binomial distribution -approximation is used, so significance levels have about 15 exact digits. - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void onesamplesigntest(const real_1d_array &x, const ae_int_t n, const double median, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::onesamplesigntest(const_cast(x.c_ptr()), n, median, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -One-sample t-test - -This test checks three hypotheses about the mean of the given sample. The -following tests are performed: - * two-tailed test (null hypothesis - the mean is equal to the given - value) - * left-tailed test (null hypothesis - the mean is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the mean is less than or equal - to the given value). - -The test is based on the assumption that a given sample has a normal -distribution and an unknown dispersion. If the distribution sharply -differs from normal, the test will work incorrectly. - -INPUT PARAMETERS: - X - sample. Array whose index goes from 0 to N-1. - N - size of sample, N>=0 - Mean - assumed value of the mean. - -OUTPUT PARAMETERS: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0, all p-values are set to 1.0 - * when variance of X[] is exactly zero, p-values are set - to 1.0 or 0.0, depending on difference between sample mean and - value of mean being tested. - - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void studentttest1(const real_1d_array &x, const ae_int_t n, const double mean, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::studentttest1(const_cast(x.c_ptr()), n, mean, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Two-sample pooled test - -This test checks three hypotheses about the mean of the given samples. The -following tests are performed: - * two-tailed test (null hypothesis - the means are equal) - * left-tailed test (null hypothesis - the mean of the first sample is - greater than or equal to the mean of the second sample) - * right-tailed test (null hypothesis - the mean of the first sample is - less than or equal to the mean of the second sample). - -Test is based on the following assumptions: - * given samples have normal distributions - * dispersions are equal - * samples are independent. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of sample. - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of sample. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0 or M=0, all p-values are set to 1.0 - * when both samples has exactly zero variance, p-values are set - to 1.0 or 0.0, depending on difference between means. - - -- ALGLIB -- - Copyright 18.09.2006 by Bochkanov Sergey -*************************************************************************/ -void studentttest2(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::studentttest2(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Two-sample unpooled test - -This test checks three hypotheses about the mean of the given samples. The -following tests are performed: - * two-tailed test (null hypothesis - the means are equal) - * left-tailed test (null hypothesis - the mean of the first sample is - greater than or equal to the mean of the second sample) - * right-tailed test (null hypothesis - the mean of the first sample is - less than or equal to the mean of the second sample). - -Test is based on the following assumptions: - * given samples have normal distributions - * samples are independent. -Equality of variances is NOT required. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of the sample. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0 or M=0, all p-values are set to 1.0 - * when both samples has zero variance, p-values are set - to 1.0 or 0.0, depending on difference between means. - * when only one sample has zero variance, test reduces to 1-sample - version. - - -- ALGLIB -- - Copyright 18.09.2006 by Bochkanov Sergey -*************************************************************************/ -void unequalvariancettest(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::unequalvariancettest(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Two-sample F-test - -This test checks three hypotheses about dispersions of the given samples. -The following tests are performed: - * two-tailed test (null hypothesis - the dispersions are equal) - * left-tailed test (null hypothesis - the dispersion of the first - sample is greater than or equal to the dispersion of the second - sample). - * right-tailed test (null hypothesis - the dispersion of the first - sample is less than or equal to the dispersion of the second sample) - -The test is based on the following assumptions: - * the given samples have normal distributions - * the samples are independent. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - sample size. - Y - sample 2. Array whose index goes from 0 to M-1. - M - sample size. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 19.09.2006 by Bochkanov Sergey -*************************************************************************/ -void ftest(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::ftest(const_cast(x.c_ptr()), n, const_cast(y.c_ptr()), m, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -One-sample chi-square test - -This test checks three hypotheses about the dispersion of the given sample -The following tests are performed: - * two-tailed test (null hypothesis - the dispersion equals the given - number) - * left-tailed test (null hypothesis - the dispersion is greater than - or equal to the given number) - * right-tailed test (null hypothesis - dispersion is less than or - equal to the given number). - -Test is based on the following assumptions: - * the given sample has a normal distribution. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. - Variance - dispersion value to compare with. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 19.09.2006 by Bochkanov Sergey -*************************************************************************/ -void onesamplevariancetest(const real_1d_array &x, const ae_int_t n, const double variance, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::onesamplevariancetest(const_cast(x.c_ptr()), n, variance, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} - -/************************************************************************* -Wilcoxon signed-rank test - -This test checks three hypotheses about the median of the given sample. -The following tests are performed: - * two-tailed test (null hypothesis - the median is equal to the given - value) - * left-tailed test (null hypothesis - the median is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the median is less than or - equal to the given value) - -Requirements: - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - * the distribution should be continuous and symmetric relative to its - median. - * number of distinct values in the X array should be greater than 4 - -The test is non-parametric and doesn't require distribution X to be normal - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. - Median - assumed median value. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -To calculate p-values, special approximation is used. This method lets us -calculate p-values with two decimal places in interval [0.0001, 1]. - -"Two decimal places" does not sound very impressive, but in practice the -relative error of less than 1% is enough to make a decision. - -There is no approximation outside the [0.0001, 1] interval. Therefore, if -the significance level outlies this interval, the test returns 0.0001. - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void wilcoxonsignedranktest(const real_1d_array &x, const ae_int_t n, const double e, double &bothtails, double &lefttail, double &righttail) -{ - alglib_impl::ae_state _alglib_env_state; - alglib_impl::ae_state_init(&_alglib_env_state); - try - { - alglib_impl::wilcoxonsignedranktest(const_cast(x.c_ptr()), n, e, &bothtails, &lefttail, &righttail, &_alglib_env_state); - alglib_impl::ae_state_clear(&_alglib_env_state); - return; - } - catch(alglib_impl::ae_error_type) - { - throw ap_error(_alglib_env_state.error_msg); - } -} -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS IMPLEMENTATION OF COMPUTATIONAL CORE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -static void basestat_rankdatarec(/* Real */ ae_matrix* xy, - ae_int_t i0, - ae_int_t i1, - ae_int_t nfeatures, - ae_bool iscentered, - ae_shared_pool* pool, - ae_int_t basecasecost, - ae_state *_state); -static void basestat_rankdatabasecase(/* Real */ ae_matrix* xy, - ae_int_t i0, - ae_int_t i1, - ae_int_t nfeatures, - ae_bool iscentered, - apbuffers* buf0, - apbuffers* buf1, - ae_state *_state); - - -static double correlationtests_spearmantail5(double s, ae_state *_state); -static double correlationtests_spearmantail6(double s, ae_state *_state); -static double correlationtests_spearmantail7(double s, ae_state *_state); -static double correlationtests_spearmantail8(double s, ae_state *_state); -static double correlationtests_spearmantail9(double s, ae_state *_state); -static double correlationtests_spearmantail(double t, - ae_int_t n, - ae_state *_state); - - -static void jarquebera_jarqueberastatistic(/* Real */ ae_vector* x, - ae_int_t n, - double* s, - ae_state *_state); -static double jarquebera_jarqueberaapprox(ae_int_t n, - double s, - ae_state *_state); -static double jarquebera_jbtbl5(double s, ae_state *_state); -static double jarquebera_jbtbl6(double s, ae_state *_state); -static double jarquebera_jbtbl7(double s, ae_state *_state); -static double jarquebera_jbtbl8(double s, ae_state *_state); -static double jarquebera_jbtbl9(double s, ae_state *_state); -static double jarquebera_jbtbl10(double s, ae_state *_state); -static double jarquebera_jbtbl11(double s, ae_state *_state); -static double jarquebera_jbtbl12(double s, ae_state *_state); -static double jarquebera_jbtbl13(double s, ae_state *_state); -static double jarquebera_jbtbl14(double s, ae_state *_state); -static double jarquebera_jbtbl15(double s, ae_state *_state); -static double jarquebera_jbtbl16(double s, ae_state *_state); -static double jarquebera_jbtbl17(double s, ae_state *_state); -static double jarquebera_jbtbl18(double s, ae_state *_state); -static double jarquebera_jbtbl19(double s, ae_state *_state); -static double jarquebera_jbtbl20(double s, ae_state *_state); -static double jarquebera_jbtbl30(double s, ae_state *_state); -static double jarquebera_jbtbl50(double s, ae_state *_state); -static double jarquebera_jbtbl65(double s, ae_state *_state); -static double jarquebera_jbtbl100(double s, ae_state *_state); -static double jarquebera_jbtbl130(double s, ae_state *_state); -static double jarquebera_jbtbl200(double s, ae_state *_state); -static double jarquebera_jbtbl301(double s, ae_state *_state); -static double jarquebera_jbtbl501(double s, ae_state *_state); -static double jarquebera_jbtbl701(double s, ae_state *_state); -static double jarquebera_jbtbl1401(double s, ae_state *_state); -static void jarquebera_jbcheb(double x, - double c, - double* tj, - double* tj1, - double* r, - ae_state *_state); - - -static void mannwhitneyu_ucheb(double x, - double c, - double* tj, - double* tj1, - double* r, - ae_state *_state); -static double mannwhitneyu_uninterpolate(double p1, - double p2, - double p3, - ae_int_t n, - ae_state *_state); -static double mannwhitneyu_usigma000(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma075(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma150(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma225(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma300(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma333(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma367(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_usigma400(ae_int_t n1, - ae_int_t n2, - ae_state *_state); -static double mannwhitneyu_utbln5n5(double s, ae_state *_state); -static double mannwhitneyu_utbln5n6(double s, ae_state *_state); -static double mannwhitneyu_utbln5n7(double s, ae_state *_state); -static double mannwhitneyu_utbln5n8(double s, ae_state *_state); -static double mannwhitneyu_utbln5n9(double s, ae_state *_state); -static double mannwhitneyu_utbln5n10(double s, ae_state *_state); -static double mannwhitneyu_utbln5n11(double s, ae_state *_state); -static double mannwhitneyu_utbln5n12(double s, ae_state *_state); -static double mannwhitneyu_utbln5n13(double s, ae_state *_state); -static double mannwhitneyu_utbln5n14(double s, ae_state *_state); -static double mannwhitneyu_utbln5n15(double s, ae_state *_state); -static double mannwhitneyu_utbln5n16(double s, ae_state *_state); -static double mannwhitneyu_utbln5n17(double s, ae_state *_state); -static double mannwhitneyu_utbln5n18(double s, ae_state *_state); -static double mannwhitneyu_utbln5n19(double s, ae_state *_state); -static double mannwhitneyu_utbln5n20(double s, ae_state *_state); -static double mannwhitneyu_utbln5n21(double s, ae_state *_state); -static double mannwhitneyu_utbln5n22(double s, ae_state *_state); -static double mannwhitneyu_utbln5n23(double s, ae_state *_state); -static double mannwhitneyu_utbln5n24(double s, ae_state *_state); -static double mannwhitneyu_utbln5n25(double s, ae_state *_state); -static double mannwhitneyu_utbln5n26(double s, ae_state *_state); -static double mannwhitneyu_utbln5n27(double s, ae_state *_state); -static double mannwhitneyu_utbln5n28(double s, ae_state *_state); -static double mannwhitneyu_utbln5n29(double s, ae_state *_state); -static double mannwhitneyu_utbln5n30(double s, ae_state *_state); -static double mannwhitneyu_utbln5n100(double s, ae_state *_state); -static double mannwhitneyu_utbln6n6(double s, ae_state *_state); -static double mannwhitneyu_utbln6n7(double s, ae_state *_state); -static double mannwhitneyu_utbln6n8(double s, ae_state *_state); -static double mannwhitneyu_utbln6n9(double s, ae_state *_state); -static double mannwhitneyu_utbln6n10(double s, ae_state *_state); -static double mannwhitneyu_utbln6n11(double s, ae_state *_state); -static double mannwhitneyu_utbln6n12(double s, ae_state *_state); -static double mannwhitneyu_utbln6n13(double s, ae_state *_state); -static double mannwhitneyu_utbln6n14(double s, ae_state *_state); -static double mannwhitneyu_utbln6n15(double s, ae_state *_state); -static double mannwhitneyu_utbln6n30(double s, ae_state *_state); -static double mannwhitneyu_utbln6n100(double s, ae_state *_state); -static double mannwhitneyu_utbln7n7(double s, ae_state *_state); -static double mannwhitneyu_utbln7n8(double s, ae_state *_state); -static double mannwhitneyu_utbln7n9(double s, ae_state *_state); -static double mannwhitneyu_utbln7n10(double s, ae_state *_state); -static double mannwhitneyu_utbln7n11(double s, ae_state *_state); -static double mannwhitneyu_utbln7n12(double s, ae_state *_state); -static double mannwhitneyu_utbln7n13(double s, ae_state *_state); -static double mannwhitneyu_utbln7n14(double s, ae_state *_state); -static double mannwhitneyu_utbln7n15(double s, ae_state *_state); -static double mannwhitneyu_utbln7n30(double s, ae_state *_state); -static double mannwhitneyu_utbln7n100(double s, ae_state *_state); -static double mannwhitneyu_utbln8n8(double s, ae_state *_state); -static double mannwhitneyu_utbln8n9(double s, ae_state *_state); -static double mannwhitneyu_utbln8n10(double s, ae_state *_state); -static double mannwhitneyu_utbln8n11(double s, ae_state *_state); -static double mannwhitneyu_utbln8n12(double s, ae_state *_state); -static double mannwhitneyu_utbln8n13(double s, ae_state *_state); -static double mannwhitneyu_utbln8n14(double s, ae_state *_state); -static double mannwhitneyu_utbln8n15(double s, ae_state *_state); -static double mannwhitneyu_utbln8n30(double s, ae_state *_state); -static double mannwhitneyu_utbln8n100(double s, ae_state *_state); -static double mannwhitneyu_utbln9n9(double s, ae_state *_state); -static double mannwhitneyu_utbln9n10(double s, ae_state *_state); -static double mannwhitneyu_utbln9n11(double s, ae_state *_state); -static double mannwhitneyu_utbln9n12(double s, ae_state *_state); -static double mannwhitneyu_utbln9n13(double s, ae_state *_state); -static double mannwhitneyu_utbln9n14(double s, ae_state *_state); -static double mannwhitneyu_utbln9n15(double s, ae_state *_state); -static double mannwhitneyu_utbln9n30(double s, ae_state *_state); -static double mannwhitneyu_utbln9n100(double s, ae_state *_state); -static double mannwhitneyu_utbln10n10(double s, ae_state *_state); -static double mannwhitneyu_utbln10n11(double s, ae_state *_state); -static double mannwhitneyu_utbln10n12(double s, ae_state *_state); -static double mannwhitneyu_utbln10n13(double s, ae_state *_state); -static double mannwhitneyu_utbln10n14(double s, ae_state *_state); -static double mannwhitneyu_utbln10n15(double s, ae_state *_state); -static double mannwhitneyu_utbln10n30(double s, ae_state *_state); -static double mannwhitneyu_utbln10n100(double s, ae_state *_state); -static double mannwhitneyu_utbln11n11(double s, ae_state *_state); -static double mannwhitneyu_utbln11n12(double s, ae_state *_state); -static double mannwhitneyu_utbln11n13(double s, ae_state *_state); -static double mannwhitneyu_utbln11n14(double s, ae_state *_state); -static double mannwhitneyu_utbln11n15(double s, ae_state *_state); -static double mannwhitneyu_utbln11n30(double s, ae_state *_state); -static double mannwhitneyu_utbln11n100(double s, ae_state *_state); -static double mannwhitneyu_utbln12n12(double s, ae_state *_state); -static double mannwhitneyu_utbln12n13(double s, ae_state *_state); -static double mannwhitneyu_utbln12n14(double s, ae_state *_state); -static double mannwhitneyu_utbln12n15(double s, ae_state *_state); -static double mannwhitneyu_utbln12n30(double s, ae_state *_state); -static double mannwhitneyu_utbln12n100(double s, ae_state *_state); -static double mannwhitneyu_utbln13n13(double s, ae_state *_state); -static double mannwhitneyu_utbln13n14(double s, ae_state *_state); -static double mannwhitneyu_utbln13n15(double s, ae_state *_state); -static double mannwhitneyu_utbln13n30(double s, ae_state *_state); -static double mannwhitneyu_utbln13n100(double s, ae_state *_state); -static double mannwhitneyu_utbln14n14(double s, ae_state *_state); -static double mannwhitneyu_utbln14n15(double s, ae_state *_state); -static double mannwhitneyu_utbln14n30(double s, ae_state *_state); -static double mannwhitneyu_utbln14n100(double s, ae_state *_state); -static double mannwhitneyu_usigma(double s, - ae_int_t n1, - ae_int_t n2, - ae_state *_state); - - - - - - - - -static void wsr_wcheb(double x, - double c, - double* tj, - double* tj1, - double* r, - ae_state *_state); -static double wsr_w5(double s, ae_state *_state); -static double wsr_w6(double s, ae_state *_state); -static double wsr_w7(double s, ae_state *_state); -static double wsr_w8(double s, ae_state *_state); -static double wsr_w9(double s, ae_state *_state); -static double wsr_w10(double s, ae_state *_state); -static double wsr_w11(double s, ae_state *_state); -static double wsr_w12(double s, ae_state *_state); -static double wsr_w13(double s, ae_state *_state); -static double wsr_w14(double s, ae_state *_state); -static double wsr_w15(double s, ae_state *_state); -static double wsr_w16(double s, ae_state *_state); -static double wsr_w17(double s, ae_state *_state); -static double wsr_w18(double s, ae_state *_state); -static double wsr_w19(double s, ae_state *_state); -static double wsr_w20(double s, ae_state *_state); -static double wsr_w21(double s, ae_state *_state); -static double wsr_w22(double s, ae_state *_state); -static double wsr_w23(double s, ae_state *_state); -static double wsr_w24(double s, ae_state *_state); -static double wsr_w25(double s, ae_state *_state); -static double wsr_w26(double s, ae_state *_state); -static double wsr_w27(double s, ae_state *_state); -static double wsr_w28(double s, ae_state *_state); -static double wsr_w29(double s, ae_state *_state); -static double wsr_w30(double s, ae_state *_state); -static double wsr_w40(double s, ae_state *_state); -static double wsr_w60(double s, ae_state *_state); -static double wsr_w120(double s, ae_state *_state); -static double wsr_w200(double s, ae_state *_state); -static double wsr_wsigma(double s, ae_int_t n, ae_state *_state); - - - - - -/************************************************************************* -Calculation of the distribution moments: mean, variance, skewness, kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -OUTPUT PARAMETERS - Mean - mean. - Variance- variance. - Skewness- skewness (if variance<>0; zero otherwise). - Kurtosis- kurtosis (if variance<>0; zero otherwise). - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemoments(/* Real */ ae_vector* x, - ae_int_t n, - double* mean, - double* variance, - double* skewness, - double* kurtosis, - ae_state *_state) -{ - ae_int_t i; - double v; - double v1; - double v2; - double stddev; - - *mean = 0; - *variance = 0; - *skewness = 0; - *kurtosis = 0; - - ae_assert(n>=0, "SampleMoments: N<0", _state); - ae_assert(x->cnt>=n, "SampleMoments: Length(X)ptr.p_double[i]; - } - *mean = *mean/n; - - /* - * Variance (using corrected two-pass algorithm) - */ - if( n!=1 ) - { - v1 = 0; - for(i=0; i<=n-1; i++) - { - v1 = v1+ae_sqr(x->ptr.p_double[i]-(*mean), _state); - } - v2 = 0; - for(i=0; i<=n-1; i++) - { - v2 = v2+(x->ptr.p_double[i]-(*mean)); - } - v2 = ae_sqr(v2, _state)/n; - *variance = (v1-v2)/(n-1); - if( ae_fp_less(*variance,0) ) - { - *variance = 0; - } - stddev = ae_sqrt(*variance, _state); - } - - /* - * Skewness and kurtosis - */ - if( ae_fp_neq(stddev,0) ) - { - for(i=0; i<=n-1; i++) - { - v = (x->ptr.p_double[i]-(*mean))/stddev; - v2 = ae_sqr(v, _state); - *skewness = *skewness+v2*v; - *kurtosis = *kurtosis+ae_sqr(v2, _state); - } - *skewness = *skewness/n; - *kurtosis = *kurtosis/n-3; - } -} - - -/************************************************************************* -Calculation of the mean. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Mean' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplemean(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double mean; - double tmp0; - double tmp1; - double tmp2; - double result; - - - samplemoments(x, n, &mean, &tmp0, &tmp1, &tmp2, _state); - result = mean; - return result; -} - - -/************************************************************************* -Calculation of the variance. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Variance' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplevariance(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double variance; - double tmp0; - double tmp1; - double tmp2; - double result; - - - samplemoments(x, n, &tmp0, &variance, &tmp1, &tmp2, _state); - result = variance; - return result; -} - - -/************************************************************************* -Calculation of the skewness. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Skewness' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double sampleskewness(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double skewness; - double tmp0; - double tmp1; - double tmp2; - double result; - - - samplemoments(x, n, &tmp0, &tmp1, &skewness, &tmp2, _state); - result = skewness; - return result; -} - - -/************************************************************************* -Calculation of the kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Kurtosis' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplekurtosis(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state) -{ - double kurtosis; - double tmp0; - double tmp1; - double tmp2; - double result; - - - samplemoments(x, n, &tmp0, &tmp1, &tmp2, &kurtosis, _state); - result = kurtosis; - return result; -} - - -/************************************************************************* -ADev - -Input parameters: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - ADev- ADev - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void sampleadev(/* Real */ ae_vector* x, - ae_int_t n, - double* adev, - ae_state *_state) -{ - ae_int_t i; - double mean; - - *adev = 0; - - ae_assert(n>=0, "SampleADev: N<0", _state); - ae_assert(x->cnt>=n, "SampleADev: Length(X)ptr.p_double[i]; - } - mean = mean/n; - - /* - * ADev - */ - for(i=0; i<=n-1; i++) - { - *adev = *adev+ae_fabs(x->ptr.p_double[i]-mean, _state); - } - *adev = *adev/n; -} - - -/************************************************************************* -Median calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - Median - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemedian(/* Real */ ae_vector* x, - ae_int_t n, - double* median, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_int_t i; - ae_int_t ir; - ae_int_t j; - ae_int_t l; - ae_int_t midp; - ae_int_t k; - double a; - double tval; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - *median = 0; - - ae_assert(n>=0, "SampleMedian: N<0", _state); - ae_assert(x->cnt>=n, "SampleMedian: Length(X)ptr.p_double[0]; - ae_frame_leave(_state); - return; - } - if( n==2 ) - { - *median = 0.5*(x->ptr.p_double[0]+x->ptr.p_double[1]); - ae_frame_leave(_state); - return; - } - - /* - * Common case, N>=3. - * Choose X[(N-1)/2] - */ - l = 0; - ir = n-1; - k = (n-1)/2; - for(;;) - { - if( ir<=l+1 ) - { - - /* - * 1 or 2 elements in partition - */ - if( ir==l+1&&ae_fp_less(x->ptr.p_double[ir],x->ptr.p_double[l]) ) - { - tval = x->ptr.p_double[l]; - x->ptr.p_double[l] = x->ptr.p_double[ir]; - x->ptr.p_double[ir] = tval; - } - break; - } - else - { - midp = (l+ir)/2; - tval = x->ptr.p_double[midp]; - x->ptr.p_double[midp] = x->ptr.p_double[l+1]; - x->ptr.p_double[l+1] = tval; - if( ae_fp_greater(x->ptr.p_double[l],x->ptr.p_double[ir]) ) - { - tval = x->ptr.p_double[l]; - x->ptr.p_double[l] = x->ptr.p_double[ir]; - x->ptr.p_double[ir] = tval; - } - if( ae_fp_greater(x->ptr.p_double[l+1],x->ptr.p_double[ir]) ) - { - tval = x->ptr.p_double[l+1]; - x->ptr.p_double[l+1] = x->ptr.p_double[ir]; - x->ptr.p_double[ir] = tval; - } - if( ae_fp_greater(x->ptr.p_double[l],x->ptr.p_double[l+1]) ) - { - tval = x->ptr.p_double[l]; - x->ptr.p_double[l] = x->ptr.p_double[l+1]; - x->ptr.p_double[l+1] = tval; - } - i = l+1; - j = ir; - a = x->ptr.p_double[l+1]; - for(;;) - { - do - { - i = i+1; - } - while(ae_fp_less(x->ptr.p_double[i],a)); - do - { - j = j-1; - } - while(ae_fp_greater(x->ptr.p_double[j],a)); - if( jptr.p_double[i]; - x->ptr.p_double[i] = x->ptr.p_double[j]; - x->ptr.p_double[j] = tval; - } - x->ptr.p_double[l+1] = x->ptr.p_double[j]; - x->ptr.p_double[j] = a; - if( j>=k ) - { - ir = j-1; - } - if( j<=k ) - { - l = i; - } - } - } - - /* - * If N is odd, return result - */ - if( n%2==1 ) - { - *median = x->ptr.p_double[k]; - ae_frame_leave(_state); - return; - } - a = x->ptr.p_double[n-1]; - for(i=k+1; i<=n-1; i++) - { - if( ae_fp_less(x->ptr.p_double[i],a) ) - { - a = x->ptr.p_double[i]; - } - } - *median = 0.5*(x->ptr.p_double[k]+a); - ae_frame_leave(_state); -} - - -/************************************************************************* -Percentile calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - P - percentile (0<=P<=1) - -Output parameters: - V - percentile - - -- ALGLIB -- - Copyright 01.03.2008 by Bochkanov Sergey -*************************************************************************/ -void samplepercentile(/* Real */ ae_vector* x, - ae_int_t n, - double p, - double* v, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_int_t i1; - double t; - ae_vector rbuf; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - *v = 0; - ae_vector_init(&rbuf, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=0, "SamplePercentile: N<0", _state); - ae_assert(x->cnt>=n, "SamplePercentile: Length(X)ptr.p_double[0]; - ae_frame_leave(_state); - return; - } - if( ae_fp_eq(p,1) ) - { - *v = x->ptr.p_double[n-1]; - ae_frame_leave(_state); - return; - } - t = p*(n-1); - i1 = ae_ifloor(t, _state); - t = t-ae_ifloor(t, _state); - *v = x->ptr.p_double[i1]*(1-t)+x->ptr.p_double[i1+1]*t; - ae_frame_leave(_state); -} - - -/************************************************************************* -2-sample covariance - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - covariance (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double cov2(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - double xmean; - double ymean; - double v; - double x0; - double y0; - double s; - ae_bool samex; - ae_bool samey; - double result; - - - ae_assert(n>=0, "Cov2: N<0", _state); - ae_assert(x->cnt>=n, "Cov2: Length(X)cnt>=n, "Cov2: Length(Y)ptr.p_double[0]; - y0 = y->ptr.p_double[0]; - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - s = x->ptr.p_double[i]; - samex = samex&&ae_fp_eq(s,x0); - xmean = xmean+s*v; - s = y->ptr.p_double[i]; - samey = samey&&ae_fp_eq(s,y0); - ymean = ymean+s*v; - } - if( samex||samey ) - { - result = 0; - return result; - } - - /* - * covariance - */ - v = (double)1/(double)(n-1); - result = 0; - for(i=0; i<=n-1; i++) - { - result = result+v*(x->ptr.p_double[i]-xmean)*(y->ptr.p_double[i]-ymean); - } - return result; -} - - -/************************************************************************* -Pearson product-moment correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Pearson product-moment correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double pearsoncorr2(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state) -{ - ae_int_t i; - double xmean; - double ymean; - double v; - double x0; - double y0; - double s; - ae_bool samex; - ae_bool samey; - double xv; - double yv; - double t1; - double t2; - double result; - - - ae_assert(n>=0, "PearsonCorr2: N<0", _state); - ae_assert(x->cnt>=n, "PearsonCorr2: Length(X)cnt>=n, "PearsonCorr2: Length(Y)ptr.p_double[0]; - y0 = y->ptr.p_double[0]; - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - s = x->ptr.p_double[i]; - samex = samex&&ae_fp_eq(s,x0); - xmean = xmean+s*v; - s = y->ptr.p_double[i]; - samey = samey&&ae_fp_eq(s,y0); - ymean = ymean+s*v; - } - if( samex||samey ) - { - result = 0; - return result; - } - - /* - * numerator and denominator - */ - s = 0; - xv = 0; - yv = 0; - for(i=0; i<=n-1; i++) - { - t1 = x->ptr.p_double[i]-xmean; - t2 = y->ptr.p_double[i]-ymean; - xv = xv+ae_sqr(t1, _state); - yv = yv+ae_sqr(t2, _state); - s = s+t1*t2; - } - if( ae_fp_eq(xv,0)||ae_fp_eq(yv,0) ) - { - result = 0; - } - else - { - result = s/(ae_sqrt(xv, _state)*ae_sqrt(yv, _state)); - } - return result; -} - - -/************************************************************************* -Spearman's rank correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Spearman's rank correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double spearmancorr2(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_vector _y; - apbuffers buf; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_vector_init_copy(&_y, y, _state, ae_true); - y = &_y; - _apbuffers_init(&buf, _state, ae_true); - - ae_assert(n>=0, "SpearmanCorr2: N<0", _state); - ae_assert(x->cnt>=n, "SpearmanCorr2: Length(X)cnt>=n, "SpearmanCorr2: Length(Y)=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _x; - ae_int_t i; - ae_int_t j; - double v; - ae_vector t; - ae_vector x0; - ae_vector same; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_matrix_clear(c); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&same, 0, DT_BOOL, _state, ae_true); - - ae_assert(n>=0, "CovM: N<0", _state); - ae_assert(m>=1, "CovM: M<1", _state); - ae_assert(x->rows>=n, "CovM: Rows(X)cols>=m||n==0, "CovM: Cols(X)ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Calculate means, - * check for constant columns - */ - ae_vector_set_length(&t, m, _state); - ae_vector_set_length(&x0, m, _state); - ae_vector_set_length(&same, m, _state); - ae_matrix_set_length(c, m, m, _state); - for(i=0; i<=m-1; i++) - { - t.ptr.p_double[i] = 0; - same.ptr.p_bool[i] = ae_true; - } - ae_v_move(&x0.ptr.p_double[0], 1, &x->ptr.pp_double[0][0], 1, ae_v_len(0,m-1)); - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - ae_v_addd(&t.ptr.p_double[0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m-1), v); - for(j=0; j<=m-1; j++) - { - same.ptr.p_bool[j] = same.ptr.p_bool[j]&&ae_fp_eq(x->ptr.pp_double[i][j],x0.ptr.p_double[j]); - } - } - - /* - * * center variables; - * * if we have constant columns, these columns are - * artificially zeroed (they must be zero in exact arithmetics, - * but unfortunately floating point ops are not exact). - * * calculate upper half of symmetric covariance matrix - */ - for(i=0; i<=n-1; i++) - { - ae_v_sub(&x->ptr.pp_double[i][0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,m-1)); - for(j=0; j<=m-1; j++) - { - if( same.ptr.p_bool[j] ) - { - x->ptr.pp_double[i][j] = 0; - } - } - } - rmatrixsyrk(m, n, (double)1/(double)(n-1), x, 0, 0, 1, 0.0, c, 0, 0, ae_true, _state); - rmatrixenforcesymmetricity(c, m, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_covm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, ae_state *_state) -{ - covm(x,n,m,c, _state); -} - - -/************************************************************************* -Pearson product-moment correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector t; - ae_int_t i; - ae_int_t j; - double v; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(c); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=0, "PearsonCorrM: N<0", _state); - ae_assert(m>=1, "PearsonCorrM: M<1", _state); - ae_assert(x->rows>=n, "PearsonCorrM: Rows(X)cols>=m||n==0, "PearsonCorrM: Cols(X)ptr.pp_double[i][i],0) ) - { - t.ptr.p_double[i] = 1/ae_sqrt(c->ptr.pp_double[i][i], _state); - } - else - { - t.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=m-1; i++) - { - v = t.ptr.p_double[i]; - for(j=0; j<=m-1; j++) - { - c->ptr.pp_double[i][j] = c->ptr.pp_double[i][j]*v*t.ptr.p_double[j]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_pearsoncorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, ae_state *_state) -{ - pearsoncorrm(x,n,m,c, _state); -} - - -/************************************************************************* -Spearman's rank correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - apbuffers buf; - ae_matrix xc; - ae_vector t; - double v; - double vv; - double x0; - ae_bool b; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(c); - _apbuffers_init(&buf, _state, ae_true); - ae_matrix_init(&xc, 0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - - ae_assert(n>=0, "SpearmanCorrM: N<0", _state); - ae_assert(m>=1, "SpearmanCorrM: M<1", _state); - ae_assert(x->rows>=n, "SpearmanCorrM: Rows(X)cols>=m||n==0, "SpearmanCorrM: Cols(X)ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Allocate - */ - ae_vector_set_length(&t, ae_maxint(n, m, _state), _state); - ae_matrix_set_length(c, m, m, _state); - - /* - * Replace data with ranks - */ - ae_matrix_set_length(&xc, m, n, _state); - rmatrixtranspose(n, m, x, 0, 0, &xc, 0, 0, _state); - rankdata(&xc, m, n, _state); - - /* - * 1. Calculate means, check for constant columns - * 2. Center variables, constant columns are - * artificialy zeroed (they must be zero in exact arithmetics, - * but unfortunately floating point is not exact). - */ - for(i=0; i<=m-1; i++) - { - - /* - * Calculate: - * * V - mean value of I-th variable - * * B - True in case all variable values are same - */ - v = 0; - b = ae_true; - x0 = xc.ptr.pp_double[i][0]; - for(j=0; j<=n-1; j++) - { - vv = xc.ptr.pp_double[i][j]; - v = v+vv; - b = b&&ae_fp_eq(vv,x0); - } - v = v/n; - - /* - * Center/zero I-th variable - */ - if( b ) - { - - /* - * Zero - */ - for(j=0; j<=n-1; j++) - { - xc.ptr.pp_double[i][j] = 0.0; - } - } - else - { - - /* - * Center - */ - for(j=0; j<=n-1; j++) - { - xc.ptr.pp_double[i][j] = xc.ptr.pp_double[i][j]-v; - } - } - } - - /* - * Calculate upper half of symmetric covariance matrix - */ - rmatrixsyrk(m, n, (double)1/(double)(n-1), &xc, 0, 0, 0, 0.0, c, 0, 0, ae_true, _state); - - /* - * Calculate Pearson coefficients (upper triangle) - */ - for(i=0; i<=m-1; i++) - { - if( ae_fp_greater(c->ptr.pp_double[i][i],0) ) - { - t.ptr.p_double[i] = 1/ae_sqrt(c->ptr.pp_double[i][i], _state); - } - else - { - t.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=m-1; i++) - { - v = t.ptr.p_double[i]; - for(j=i; j<=m-1; j++) - { - c->ptr.pp_double[i][j] = c->ptr.pp_double[i][j]*v*t.ptr.p_double[j]; - } - } - - /* - * force symmetricity - */ - rmatrixenforcesymmetricity(c, m, ae_true, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_spearmancorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, ae_state *_state) -{ - spearmancorrm(x,n,m,c, _state); -} - - -/************************************************************************* -Cross-covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _x; - ae_matrix _y; - ae_int_t i; - ae_int_t j; - double v; - ae_vector t; - ae_vector x0; - ae_vector y0; - ae_vector samex; - ae_vector samey; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_matrix_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_matrix_clear(c); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&samex, 0, DT_BOOL, _state, ae_true); - ae_vector_init(&samey, 0, DT_BOOL, _state, ae_true); - - ae_assert(n>=0, "CovM2: N<0", _state); - ae_assert(m1>=1, "CovM2: M1<1", _state); - ae_assert(m2>=1, "CovM2: M2<1", _state); - ae_assert(x->rows>=n, "CovM2: Rows(X)cols>=m1||n==0, "CovM2: Cols(X)rows>=n, "CovM2: Rows(Y)cols>=m2||n==0, "CovM2: Cols(Y)ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Allocate - */ - ae_vector_set_length(&t, ae_maxint(m1, m2, _state), _state); - ae_vector_set_length(&x0, m1, _state); - ae_vector_set_length(&y0, m2, _state); - ae_vector_set_length(&samex, m1, _state); - ae_vector_set_length(&samey, m2, _state); - ae_matrix_set_length(c, m1, m2, _state); - - /* - * * calculate means of X - * * center X - * * if we have constant columns, these columns are - * artificially zeroed (they must be zero in exact arithmetics, - * but unfortunately floating point ops are not exact). - */ - for(i=0; i<=m1-1; i++) - { - t.ptr.p_double[i] = 0; - samex.ptr.p_bool[i] = ae_true; - } - ae_v_move(&x0.ptr.p_double[0], 1, &x->ptr.pp_double[0][0], 1, ae_v_len(0,m1-1)); - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - ae_v_addd(&t.ptr.p_double[0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m1-1), v); - for(j=0; j<=m1-1; j++) - { - samex.ptr.p_bool[j] = samex.ptr.p_bool[j]&&ae_fp_eq(x->ptr.pp_double[i][j],x0.ptr.p_double[j]); - } - } - for(i=0; i<=n-1; i++) - { - ae_v_sub(&x->ptr.pp_double[i][0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,m1-1)); - for(j=0; j<=m1-1; j++) - { - if( samex.ptr.p_bool[j] ) - { - x->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * Repeat same steps for Y - */ - for(i=0; i<=m2-1; i++) - { - t.ptr.p_double[i] = 0; - samey.ptr.p_bool[i] = ae_true; - } - ae_v_move(&y0.ptr.p_double[0], 1, &y->ptr.pp_double[0][0], 1, ae_v_len(0,m2-1)); - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - ae_v_addd(&t.ptr.p_double[0], 1, &y->ptr.pp_double[i][0], 1, ae_v_len(0,m2-1), v); - for(j=0; j<=m2-1; j++) - { - samey.ptr.p_bool[j] = samey.ptr.p_bool[j]&&ae_fp_eq(y->ptr.pp_double[i][j],y0.ptr.p_double[j]); - } - } - for(i=0; i<=n-1; i++) - { - ae_v_sub(&y->ptr.pp_double[i][0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,m2-1)); - for(j=0; j<=m2-1; j++) - { - if( samey.ptr.p_bool[j] ) - { - y->ptr.pp_double[i][j] = 0; - } - } - } - - /* - * calculate cross-covariance matrix - */ - rmatrixgemm(m1, m2, n, (double)1/(double)(n-1), x, 0, 0, 1, y, 0, 0, 0, 0.0, c, 0, 0, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_covm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, ae_state *_state) -{ - covm2(x,y,n,m1,m2,c, _state); -} - - -/************************************************************************* -Pearson product-moment cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_matrix _x; - ae_matrix _y; - ae_int_t i; - ae_int_t j; - double v; - ae_vector t; - ae_vector x0; - ae_vector y0; - ae_vector sx; - ae_vector sy; - ae_vector samex; - ae_vector samey; - - ae_frame_make(_state, &_frame_block); - ae_matrix_init_copy(&_x, x, _state, ae_true); - x = &_x; - ae_matrix_init_copy(&_y, y, _state, ae_true); - y = &_y; - ae_matrix_clear(c); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&x0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&y0, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sy, 0, DT_REAL, _state, ae_true); - ae_vector_init(&samex, 0, DT_BOOL, _state, ae_true); - ae_vector_init(&samey, 0, DT_BOOL, _state, ae_true); - - ae_assert(n>=0, "PearsonCorrM2: N<0", _state); - ae_assert(m1>=1, "PearsonCorrM2: M1<1", _state); - ae_assert(m2>=1, "PearsonCorrM2: M2<1", _state); - ae_assert(x->rows>=n, "PearsonCorrM2: Rows(X)cols>=m1||n==0, "PearsonCorrM2: Cols(X)rows>=n, "PearsonCorrM2: Rows(Y)cols>=m2||n==0, "PearsonCorrM2: Cols(Y)ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Allocate - */ - ae_vector_set_length(&t, ae_maxint(m1, m2, _state), _state); - ae_vector_set_length(&x0, m1, _state); - ae_vector_set_length(&y0, m2, _state); - ae_vector_set_length(&sx, m1, _state); - ae_vector_set_length(&sy, m2, _state); - ae_vector_set_length(&samex, m1, _state); - ae_vector_set_length(&samey, m2, _state); - ae_matrix_set_length(c, m1, m2, _state); - - /* - * * calculate means of X - * * center X - * * if we have constant columns, these columns are - * artificially zeroed (they must be zero in exact arithmetics, - * but unfortunately floating point ops are not exact). - * * calculate column variances - */ - for(i=0; i<=m1-1; i++) - { - t.ptr.p_double[i] = 0; - samex.ptr.p_bool[i] = ae_true; - sx.ptr.p_double[i] = 0; - } - ae_v_move(&x0.ptr.p_double[0], 1, &x->ptr.pp_double[0][0], 1, ae_v_len(0,m1-1)); - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - ae_v_addd(&t.ptr.p_double[0], 1, &x->ptr.pp_double[i][0], 1, ae_v_len(0,m1-1), v); - for(j=0; j<=m1-1; j++) - { - samex.ptr.p_bool[j] = samex.ptr.p_bool[j]&&ae_fp_eq(x->ptr.pp_double[i][j],x0.ptr.p_double[j]); - } - } - for(i=0; i<=n-1; i++) - { - ae_v_sub(&x->ptr.pp_double[i][0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,m1-1)); - for(j=0; j<=m1-1; j++) - { - if( samex.ptr.p_bool[j] ) - { - x->ptr.pp_double[i][j] = 0; - } - sx.ptr.p_double[j] = sx.ptr.p_double[j]+x->ptr.pp_double[i][j]*x->ptr.pp_double[i][j]; - } - } - for(j=0; j<=m1-1; j++) - { - sx.ptr.p_double[j] = ae_sqrt(sx.ptr.p_double[j]/(n-1), _state); - } - - /* - * Repeat same steps for Y - */ - for(i=0; i<=m2-1; i++) - { - t.ptr.p_double[i] = 0; - samey.ptr.p_bool[i] = ae_true; - sy.ptr.p_double[i] = 0; - } - ae_v_move(&y0.ptr.p_double[0], 1, &y->ptr.pp_double[0][0], 1, ae_v_len(0,m2-1)); - v = (double)1/(double)n; - for(i=0; i<=n-1; i++) - { - ae_v_addd(&t.ptr.p_double[0], 1, &y->ptr.pp_double[i][0], 1, ae_v_len(0,m2-1), v); - for(j=0; j<=m2-1; j++) - { - samey.ptr.p_bool[j] = samey.ptr.p_bool[j]&&ae_fp_eq(y->ptr.pp_double[i][j],y0.ptr.p_double[j]); - } - } - for(i=0; i<=n-1; i++) - { - ae_v_sub(&y->ptr.pp_double[i][0], 1, &t.ptr.p_double[0], 1, ae_v_len(0,m2-1)); - for(j=0; j<=m2-1; j++) - { - if( samey.ptr.p_bool[j] ) - { - y->ptr.pp_double[i][j] = 0; - } - sy.ptr.p_double[j] = sy.ptr.p_double[j]+y->ptr.pp_double[i][j]*y->ptr.pp_double[i][j]; - } - } - for(j=0; j<=m2-1; j++) - { - sy.ptr.p_double[j] = ae_sqrt(sy.ptr.p_double[j]/(n-1), _state); - } - - /* - * calculate cross-covariance matrix - */ - rmatrixgemm(m1, m2, n, (double)1/(double)(n-1), x, 0, 0, 1, y, 0, 0, 0, 0.0, c, 0, 0, _state); - - /* - * Divide by standard deviations - */ - for(i=0; i<=m1-1; i++) - { - if( ae_fp_neq(sx.ptr.p_double[i],0) ) - { - sx.ptr.p_double[i] = 1/sx.ptr.p_double[i]; - } - else - { - sx.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=m2-1; i++) - { - if( ae_fp_neq(sy.ptr.p_double[i],0) ) - { - sy.ptr.p_double[i] = 1/sy.ptr.p_double[i]; - } - else - { - sy.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=m1-1; i++) - { - v = sx.ptr.p_double[i]; - for(j=0; j<=m2-1; j++) - { - c->ptr.pp_double[i][j] = c->ptr.pp_double[i][j]*v*sy.ptr.p_double[j]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_pearsoncorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, ae_state *_state) -{ - pearsoncorrm2(x,y,n,m1,m2,c, _state); -} - - -/************************************************************************* -Spearman's rank cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - double v; - double v2; - double vv; - ae_bool b; - ae_vector t; - double x0; - double y0; - ae_vector sx; - ae_vector sy; - ae_matrix xc; - ae_matrix yc; - apbuffers buf; - - ae_frame_make(_state, &_frame_block); - ae_matrix_clear(c); - ae_vector_init(&t, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&sy, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&xc, 0, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&yc, 0, 0, DT_REAL, _state, ae_true); - _apbuffers_init(&buf, _state, ae_true); - - ae_assert(n>=0, "SpearmanCorrM2: N<0", _state); - ae_assert(m1>=1, "SpearmanCorrM2: M1<1", _state); - ae_assert(m2>=1, "SpearmanCorrM2: M2<1", _state); - ae_assert(x->rows>=n, "SpearmanCorrM2: Rows(X)cols>=m1||n==0, "SpearmanCorrM2: Cols(X)rows>=n, "SpearmanCorrM2: Rows(Y)cols>=m2||n==0, "SpearmanCorrM2: Cols(Y)ptr.pp_double[i][j] = 0; - } - } - ae_frame_leave(_state); - return; - } - - /* - * Allocate - */ - ae_vector_set_length(&t, ae_maxint(ae_maxint(m1, m2, _state), n, _state), _state); - ae_vector_set_length(&sx, m1, _state); - ae_vector_set_length(&sy, m2, _state); - ae_matrix_set_length(c, m1, m2, _state); - - /* - * Replace data with ranks - */ - ae_matrix_set_length(&xc, m1, n, _state); - ae_matrix_set_length(&yc, m2, n, _state); - rmatrixtranspose(n, m1, x, 0, 0, &xc, 0, 0, _state); - rmatrixtranspose(n, m2, y, 0, 0, &yc, 0, 0, _state); - rankdata(&xc, m1, n, _state); - rankdata(&yc, m2, n, _state); - - /* - * 1. Calculate means, variances, check for constant columns - * 2. Center variables, constant columns are - * artificialy zeroed (they must be zero in exact arithmetics, - * but unfortunately floating point is not exact). - * - * Description of variables: - * * V - mean value of I-th variable - * * V2- variance - * * VV-temporary - * * B - True in case all variable values are same - */ - for(i=0; i<=m1-1; i++) - { - v = 0; - v2 = 0.0; - b = ae_true; - x0 = xc.ptr.pp_double[i][0]; - for(j=0; j<=n-1; j++) - { - vv = xc.ptr.pp_double[i][j]; - v = v+vv; - b = b&&ae_fp_eq(vv,x0); - } - v = v/n; - if( b ) - { - for(j=0; j<=n-1; j++) - { - xc.ptr.pp_double[i][j] = 0.0; - } - } - else - { - for(j=0; j<=n-1; j++) - { - vv = xc.ptr.pp_double[i][j]; - xc.ptr.pp_double[i][j] = vv-v; - v2 = v2+(vv-v)*(vv-v); - } - } - sx.ptr.p_double[i] = ae_sqrt(v2/(n-1), _state); - } - for(i=0; i<=m2-1; i++) - { - v = 0; - v2 = 0.0; - b = ae_true; - y0 = yc.ptr.pp_double[i][0]; - for(j=0; j<=n-1; j++) - { - vv = yc.ptr.pp_double[i][j]; - v = v+vv; - b = b&&ae_fp_eq(vv,y0); - } - v = v/n; - if( b ) - { - for(j=0; j<=n-1; j++) - { - yc.ptr.pp_double[i][j] = 0.0; - } - } - else - { - for(j=0; j<=n-1; j++) - { - vv = yc.ptr.pp_double[i][j]; - yc.ptr.pp_double[i][j] = vv-v; - v2 = v2+(vv-v)*(vv-v); - } - } - sy.ptr.p_double[i] = ae_sqrt(v2/(n-1), _state); - } - - /* - * calculate cross-covariance matrix - */ - rmatrixgemm(m1, m2, n, (double)1/(double)(n-1), &xc, 0, 0, 0, &yc, 0, 0, 1, 0.0, c, 0, 0, _state); - - /* - * Divide by standard deviations - */ - for(i=0; i<=m1-1; i++) - { - if( ae_fp_neq(sx.ptr.p_double[i],0) ) - { - sx.ptr.p_double[i] = 1/sx.ptr.p_double[i]; - } - else - { - sx.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=m2-1; i++) - { - if( ae_fp_neq(sy.ptr.p_double[i],0) ) - { - sy.ptr.p_double[i] = 1/sy.ptr.p_double[i]; - } - else - { - sy.ptr.p_double[i] = 0.0; - } - } - for(i=0; i<=m1-1; i++) - { - v = sx.ptr.p_double[i]; - for(j=0; j<=m2-1; j++) - { - c->ptr.pp_double[i][j] = c->ptr.pp_double[i][j]*v*sy.ptr.p_double[j]; - } - } - ae_frame_leave(_state); -} - - -/************************************************************************* -Single-threaded stub. HPC ALGLIB replaces it by multithreaded code. -*************************************************************************/ -void _pexec_spearmancorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, ae_state *_state) -{ - spearmancorrm2(x,y,n,m1,m2,c, _state); -} - - -void rankdata(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_state *_state) -{ - ae_frame _frame_block; - apbuffers buf0; - apbuffers buf1; - ae_int_t basecasecost; - ae_shared_pool pool; - - ae_frame_make(_state, &_frame_block); - _apbuffers_init(&buf0, _state, ae_true); - _apbuffers_init(&buf1, _state, ae_true); - ae_shared_pool_init(&pool, _state, ae_true); - - ae_assert(npoints>=0, "RankData: NPoints<0", _state); - ae_assert(nfeatures>=1, "RankData: NFeatures<1", _state); - ae_assert(xy->rows>=npoints, "RankData: Rows(XY)cols>=nfeatures||npoints==0, "RankData: Cols(XY)=0, "RankData: NPoints<0", _state); - ae_assert(nfeatures>=1, "RankData: NFeatures<1", _state); - ae_assert(xy->rows>=npoints, "RankData: Rows(XY)cols>=nfeatures||npoints==0, "RankData: Cols(XY)=i0, "RankDataRec: internal error", _state); - - /* - * Recursively split problem, if it is too large - */ - problemcost = inttoreal(i1-i0, _state)*inttoreal(nfeatures, _state)*log2(nfeatures, _state); - if( i1-i0>=2&&ae_fp_greater(problemcost,basecasecost) ) - { - im = (i1+i0)/2; - basestat_rankdatarec(xy, i0, im, nfeatures, iscentered, pool, basecasecost, _state); - basestat_rankdatarec(xy, im, i1, nfeatures, iscentered, pool, basecasecost, _state); - ae_frame_leave(_state); - return; - } - - /* - * Retrieve buffers from pool, call serial code, return buffers to pool - */ - ae_shared_pool_retrieve(pool, &_buf0, _state); - ae_shared_pool_retrieve(pool, &_buf1, _state); - basestat_rankdatabasecase(xy, i0, i1, nfeatures, iscentered, buf0, buf1, _state); - ae_shared_pool_recycle(pool, &_buf0, _state); - ae_shared_pool_recycle(pool, &_buf1, _state); - ae_frame_leave(_state); -} - - -static void basestat_rankdatabasecase(/* Real */ ae_matrix* xy, - ae_int_t i0, - ae_int_t i1, - ae_int_t nfeatures, - ae_bool iscentered, - apbuffers* buf0, - apbuffers* buf1, - ae_state *_state) -{ - ae_int_t i; - - - ae_assert(i1>=i0, "RankDataBasecase: internal error", _state); - if( buf1->ra0.cntra0, nfeatures, _state); - } - for(i=i0; i<=i1-1; i++) - { - ae_v_move(&buf1->ra0.ptr.p_double[0], 1, &xy->ptr.pp_double[i][0], 1, ae_v_len(0,nfeatures-1)); - rankx(&buf1->ra0, nfeatures, iscentered, buf0, _state); - ae_v_move(&xy->ptr.pp_double[i][0], 1, &buf1->ra0.ptr.p_double[0], 1, ae_v_len(0,nfeatures-1)); - } -} - - - - -/************************************************************************* -Pearson's correlation coefficient significance test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions having zero correlation or whether their -correlation is non-zero. - -The following tests are performed: - * two-tailed test (null hypothesis - X and Y have zero correlation) - * left-tailed test (null hypothesis - the correlation coefficient is - greater than or equal to 0) - * right-tailed test (null hypothesis - the correlation coefficient is - less than or equal to 0). - -Requirements: - * the number of elements in each sample is not less than 5 - * normality of distributions of X and Y. - -Input parameters: - R - Pearson's correlation coefficient for X and Y - N - number of elements in samples, N>=5. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrelationsignificance(double r, - ae_int_t n, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - double t; - double p; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - - /* - * Some special cases - */ - if( ae_fp_greater_eq(r,1) ) - { - *bothtails = 0.0; - *lefttail = 1.0; - *righttail = 0.0; - return; - } - if( ae_fp_less_eq(r,-1) ) - { - *bothtails = 0.0; - *lefttail = 0.0; - *righttail = 1.0; - return; - } - if( n<5 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * General case - */ - t = r*ae_sqrt((n-2)/(1-ae_sqr(r, _state)), _state); - p = studenttdistribution(n-2, t, _state); - *bothtails = 2*ae_minreal(p, 1-p, _state); - *lefttail = p; - *righttail = 1-p; -} - - -/************************************************************************* -Spearman's rank correlation coefficient significance test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions having zero correlation or whether their -correlation is non-zero. - -The following tests are performed: - * two-tailed test (null hypothesis - X and Y have zero correlation) - * left-tailed test (null hypothesis - the correlation coefficient is - greater than or equal to 0) - * right-tailed test (null hypothesis - the correlation coefficient is - less than or equal to 0). - -Requirements: - * the number of elements in each sample is not less than 5. - -The test is non-parametric and doesn't require distributions X and Y to be -normal. - -Input parameters: - R - Spearman's rank correlation coefficient for X and Y - N - number of elements in samples, N>=5. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void spearmanrankcorrelationsignificance(double r, - ae_int_t n, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - double t; - double p; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - - /* - * Special case - */ - if( n<5 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * General case - */ - if( ae_fp_greater_eq(r,1) ) - { - t = 1.0E10; - } - else - { - if( ae_fp_less_eq(r,-1) ) - { - t = -1.0E10; - } - else - { - t = r*ae_sqrt((n-2)/(1-ae_sqr(r, _state)), _state); - } - } - if( ae_fp_less(t,0) ) - { - p = correlationtests_spearmantail(t, n, _state); - *bothtails = 2*p; - *lefttail = p; - *righttail = 1-p; - } - else - { - p = correlationtests_spearmantail(-t, n, _state); - *bothtails = 2*p; - *lefttail = 1-p; - *righttail = p; - } -} - - -/************************************************************************* -Tail(S, 5) -*************************************************************************/ -static double correlationtests_spearmantail5(double s, ae_state *_state) -{ - double result; - - - if( ae_fp_less(s,0.000e+00) ) - { - result = studenttdistribution(3, -s, _state); - return result; - } - if( ae_fp_greater_eq(s,3.580e+00) ) - { - result = 8.304e-03; - return result; - } - if( ae_fp_greater_eq(s,2.322e+00) ) - { - result = 4.163e-02; - return result; - } - if( ae_fp_greater_eq(s,1.704e+00) ) - { - result = 6.641e-02; - return result; - } - if( ae_fp_greater_eq(s,1.303e+00) ) - { - result = 1.164e-01; - return result; - } - if( ae_fp_greater_eq(s,1.003e+00) ) - { - result = 1.748e-01; - return result; - } - if( ae_fp_greater_eq(s,7.584e-01) ) - { - result = 2.249e-01; - return result; - } - if( ae_fp_greater_eq(s,5.468e-01) ) - { - result = 2.581e-01; - return result; - } - if( ae_fp_greater_eq(s,3.555e-01) ) - { - result = 3.413e-01; - return result; - } - if( ae_fp_greater_eq(s,1.759e-01) ) - { - result = 3.911e-01; - return result; - } - if( ae_fp_greater_eq(s,1.741e-03) ) - { - result = 4.747e-01; - return result; - } - if( ae_fp_greater_eq(s,0.000e+00) ) - { - result = 5.248e-01; - return result; - } - result = 0; - return result; -} - - -/************************************************************************* -Tail(S, 6) -*************************************************************************/ -static double correlationtests_spearmantail6(double s, ae_state *_state) -{ - double result; - - - if( ae_fp_less(s,1.001e+00) ) - { - result = studenttdistribution(4, -s, _state); - return result; - } - if( ae_fp_greater_eq(s,5.663e+00) ) - { - result = 1.366e-03; - return result; - } - if( ae_fp_greater_eq(s,3.834e+00) ) - { - result = 8.350e-03; - return result; - } - if( ae_fp_greater_eq(s,2.968e+00) ) - { - result = 1.668e-02; - return result; - } - if( ae_fp_greater_eq(s,2.430e+00) ) - { - result = 2.921e-02; - return result; - } - if( ae_fp_greater_eq(s,2.045e+00) ) - { - result = 5.144e-02; - return result; - } - if( ae_fp_greater_eq(s,1.747e+00) ) - { - result = 6.797e-02; - return result; - } - if( ae_fp_greater_eq(s,1.502e+00) ) - { - result = 8.752e-02; - return result; - } - if( ae_fp_greater_eq(s,1.295e+00) ) - { - result = 1.210e-01; - return result; - } - if( ae_fp_greater_eq(s,1.113e+00) ) - { - result = 1.487e-01; - return result; - } - if( ae_fp_greater_eq(s,1.001e+00) ) - { - result = 1.780e-01; - return result; - } - result = 0; - return result; -} - - -/************************************************************************* -Tail(S, 7) -*************************************************************************/ -static double correlationtests_spearmantail7(double s, ae_state *_state) -{ - double result; - - - if( ae_fp_less(s,1.001e+00) ) - { - result = studenttdistribution(5, -s, _state); - return result; - } - if( ae_fp_greater_eq(s,8.159e+00) ) - { - result = 2.081e-04; - return result; - } - if( ae_fp_greater_eq(s,5.620e+00) ) - { - result = 1.393e-03; - return result; - } - if( ae_fp_greater_eq(s,4.445e+00) ) - { - result = 3.398e-03; - return result; - } - if( ae_fp_greater_eq(s,3.728e+00) ) - { - result = 6.187e-03; - return result; - } - if( ae_fp_greater_eq(s,3.226e+00) ) - { - result = 1.200e-02; - return result; - } - if( ae_fp_greater_eq(s,2.844e+00) ) - { - result = 1.712e-02; - return result; - } - if( ae_fp_greater_eq(s,2.539e+00) ) - { - result = 2.408e-02; - return result; - } - if( ae_fp_greater_eq(s,2.285e+00) ) - { - result = 3.320e-02; - return result; - } - if( ae_fp_greater_eq(s,2.068e+00) ) - { - result = 4.406e-02; - return result; - } - if( ae_fp_greater_eq(s,1.879e+00) ) - { - result = 5.478e-02; - return result; - } - if( ae_fp_greater_eq(s,1.710e+00) ) - { - result = 6.946e-02; - return result; - } - if( ae_fp_greater_eq(s,1.559e+00) ) - { - result = 8.331e-02; - return result; - } - if( ae_fp_greater_eq(s,1.420e+00) ) - { - result = 1.001e-01; - return result; - } - if( ae_fp_greater_eq(s,1.292e+00) ) - { - result = 1.180e-01; - return result; - } - if( ae_fp_greater_eq(s,1.173e+00) ) - { - result = 1.335e-01; - return result; - } - if( ae_fp_greater_eq(s,1.062e+00) ) - { - result = 1.513e-01; - return result; - } - if( ae_fp_greater_eq(s,1.001e+00) ) - { - result = 1.770e-01; - return result; - } - result = 0; - return result; -} - - -/************************************************************************* -Tail(S, 8) -*************************************************************************/ -static double correlationtests_spearmantail8(double s, ae_state *_state) -{ - double result; - - - if( ae_fp_less(s,2.001e+00) ) - { - result = studenttdistribution(6, -s, _state); - return result; - } - if( ae_fp_greater_eq(s,1.103e+01) ) - { - result = 2.194e-05; - return result; - } - if( ae_fp_greater_eq(s,7.685e+00) ) - { - result = 2.008e-04; - return result; - } - if( ae_fp_greater_eq(s,6.143e+00) ) - { - result = 5.686e-04; - return result; - } - if( ae_fp_greater_eq(s,5.213e+00) ) - { - result = 1.138e-03; - return result; - } - if( ae_fp_greater_eq(s,4.567e+00) ) - { - result = 2.310e-03; - return result; - } - if( ae_fp_greater_eq(s,4.081e+00) ) - { - result = 3.634e-03; - return result; - } - if( ae_fp_greater_eq(s,3.697e+00) ) - { - result = 5.369e-03; - return result; - } - if( ae_fp_greater_eq(s,3.381e+00) ) - { - result = 7.708e-03; - return result; - } - if( ae_fp_greater_eq(s,3.114e+00) ) - { - result = 1.087e-02; - return result; - } - if( ae_fp_greater_eq(s,2.884e+00) ) - { - result = 1.397e-02; - return result; - } - if( ae_fp_greater_eq(s,2.682e+00) ) - { - result = 1.838e-02; - return result; - } - if( ae_fp_greater_eq(s,2.502e+00) ) - { - result = 2.288e-02; - return result; - } - if( ae_fp_greater_eq(s,2.340e+00) ) - { - result = 2.883e-02; - return result; - } - if( ae_fp_greater_eq(s,2.192e+00) ) - { - result = 3.469e-02; - return result; - } - if( ae_fp_greater_eq(s,2.057e+00) ) - { - result = 4.144e-02; - return result; - } - if( ae_fp_greater_eq(s,2.001e+00) ) - { - result = 4.804e-02; - return result; - } - result = 0; - return result; -} - - -/************************************************************************* -Tail(S, 9) -*************************************************************************/ -static double correlationtests_spearmantail9(double s, ae_state *_state) -{ - double result; - - - if( ae_fp_less(s,2.001e+00) ) - { - result = studenttdistribution(7, -s, _state); - return result; - } - if( ae_fp_greater_eq(s,9.989e+00) ) - { - result = 2.306e-05; - return result; - } - if( ae_fp_greater_eq(s,8.069e+00) ) - { - result = 8.167e-05; - return result; - } - if( ae_fp_greater_eq(s,6.890e+00) ) - { - result = 1.744e-04; - return result; - } - if( ae_fp_greater_eq(s,6.077e+00) ) - { - result = 3.625e-04; - return result; - } - if( ae_fp_greater_eq(s,5.469e+00) ) - { - result = 6.450e-04; - return result; - } - if( ae_fp_greater_eq(s,4.991e+00) ) - { - result = 1.001e-03; - return result; - } - if( ae_fp_greater_eq(s,4.600e+00) ) - { - result = 1.514e-03; - return result; - } - if( ae_fp_greater_eq(s,4.272e+00) ) - { - result = 2.213e-03; - return result; - } - if( ae_fp_greater_eq(s,3.991e+00) ) - { - result = 2.990e-03; - return result; - } - if( ae_fp_greater_eq(s,3.746e+00) ) - { - result = 4.101e-03; - return result; - } - if( ae_fp_greater_eq(s,3.530e+00) ) - { - result = 5.355e-03; - return result; - } - if( ae_fp_greater_eq(s,3.336e+00) ) - { - result = 6.887e-03; - return result; - } - if( ae_fp_greater_eq(s,3.161e+00) ) - { - result = 8.598e-03; - return result; - } - if( ae_fp_greater_eq(s,3.002e+00) ) - { - result = 1.065e-02; - return result; - } - if( ae_fp_greater_eq(s,2.855e+00) ) - { - result = 1.268e-02; - return result; - } - if( ae_fp_greater_eq(s,2.720e+00) ) - { - result = 1.552e-02; - return result; - } - if( ae_fp_greater_eq(s,2.595e+00) ) - { - result = 1.836e-02; - return result; - } - if( ae_fp_greater_eq(s,2.477e+00) ) - { - result = 2.158e-02; - return result; - } - if( ae_fp_greater_eq(s,2.368e+00) ) - { - result = 2.512e-02; - return result; - } - if( ae_fp_greater_eq(s,2.264e+00) ) - { - result = 2.942e-02; - return result; - } - if( ae_fp_greater_eq(s,2.166e+00) ) - { - result = 3.325e-02; - return result; - } - if( ae_fp_greater_eq(s,2.073e+00) ) - { - result = 3.800e-02; - return result; - } - if( ae_fp_greater_eq(s,2.001e+00) ) - { - result = 4.285e-02; - return result; - } - result = 0; - return result; -} - - -/************************************************************************* -Tail(T,N), accepts T<0 -*************************************************************************/ -static double correlationtests_spearmantail(double t, - ae_int_t n, - ae_state *_state) -{ - double result; - - - if( n==5 ) - { - result = correlationtests_spearmantail5(-t, _state); - return result; - } - if( n==6 ) - { - result = correlationtests_spearmantail6(-t, _state); - return result; - } - if( n==7 ) - { - result = correlationtests_spearmantail7(-t, _state); - return result; - } - if( n==8 ) - { - result = correlationtests_spearmantail8(-t, _state); - return result; - } - if( n==9 ) - { - result = correlationtests_spearmantail9(-t, _state); - return result; - } - result = studenttdistribution(n-2, t, _state); - return result; -} - - - - -/************************************************************************* -Jarque-Bera test - -This test checks hypotheses about the fact that a given sample X is a -sample of normal random variable. - -Requirements: - * the number of elements in the sample is not less than 5. - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. N>=5 - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -Accuracy of the approximation used (5<=N<=1951): - -p-value relative error (5<=N<=1951) -[1, 0.1] < 1% -[0.1, 0.01] < 2% -[0.01, 0.001] < 6% -[0.001, 0] wasn't measured - -For N>1951 accuracy wasn't measured but it shouldn't be sharply different -from table values. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void jarqueberatest(/* Real */ ae_vector* x, - ae_int_t n, - double* p, - ae_state *_state) -{ - double s; - - *p = 0; - - - /* - * N is too small - */ - if( n<5 ) - { - *p = 1.0; - return; - } - - /* - * N is large enough - */ - jarquebera_jarqueberastatistic(x, n, &s, _state); - *p = jarquebera_jarqueberaapprox(n, s, _state); -} - - -static void jarquebera_jarqueberastatistic(/* Real */ ae_vector* x, - ae_int_t n, - double* s, - ae_state *_state) -{ - ae_int_t i; - double v; - double v1; - double v2; - double stddev; - double mean; - double variance; - double skewness; - double kurtosis; - - *s = 0; - - mean = 0; - variance = 0; - skewness = 0; - kurtosis = 0; - stddev = 0; - ae_assert(n>1, "Assertion failed", _state); - - /* - * Mean - */ - for(i=0; i<=n-1; i++) - { - mean = mean+x->ptr.p_double[i]; - } - mean = mean/n; - - /* - * Variance (using corrected two-pass algorithm) - */ - if( n!=1 ) - { - v1 = 0; - for(i=0; i<=n-1; i++) - { - v1 = v1+ae_sqr(x->ptr.p_double[i]-mean, _state); - } - v2 = 0; - for(i=0; i<=n-1; i++) - { - v2 = v2+(x->ptr.p_double[i]-mean); - } - v2 = ae_sqr(v2, _state)/n; - variance = (v1-v2)/(n-1); - if( ae_fp_less(variance,0) ) - { - variance = 0; - } - stddev = ae_sqrt(variance, _state); - } - - /* - * Skewness and kurtosis - */ - if( ae_fp_neq(stddev,0) ) - { - for(i=0; i<=n-1; i++) - { - v = (x->ptr.p_double[i]-mean)/stddev; - v2 = ae_sqr(v, _state); - skewness = skewness+v2*v; - kurtosis = kurtosis+ae_sqr(v2, _state); - } - skewness = skewness/n; - kurtosis = kurtosis/n-3; - } - - /* - * Statistic - */ - *s = (double)n/(double)6*(ae_sqr(skewness, _state)+ae_sqr(kurtosis, _state)/4); -} - - -static double jarquebera_jarqueberaapprox(ae_int_t n, - double s, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector vx; - ae_vector vy; - ae_matrix ctbl; - double t1; - double t2; - double t3; - double t; - double f1; - double f2; - double f3; - double f12; - double f23; - double x; - double result; - - ae_frame_make(_state, &_frame_block); - ae_vector_init(&vx, 0, DT_REAL, _state, ae_true); - ae_vector_init(&vy, 0, DT_REAL, _state, ae_true); - ae_matrix_init(&ctbl, 0, 0, DT_REAL, _state, ae_true); - - result = 1; - x = s; - if( n<5 ) - { - ae_frame_leave(_state); - return result; - } - - /* - * N = 5..20 are tabulated - */ - if( n>=5&&n<=20 ) - { - if( n==5 ) - { - result = ae_exp(jarquebera_jbtbl5(x, _state), _state); - } - if( n==6 ) - { - result = ae_exp(jarquebera_jbtbl6(x, _state), _state); - } - if( n==7 ) - { - result = ae_exp(jarquebera_jbtbl7(x, _state), _state); - } - if( n==8 ) - { - result = ae_exp(jarquebera_jbtbl8(x, _state), _state); - } - if( n==9 ) - { - result = ae_exp(jarquebera_jbtbl9(x, _state), _state); - } - if( n==10 ) - { - result = ae_exp(jarquebera_jbtbl10(x, _state), _state); - } - if( n==11 ) - { - result = ae_exp(jarquebera_jbtbl11(x, _state), _state); - } - if( n==12 ) - { - result = ae_exp(jarquebera_jbtbl12(x, _state), _state); - } - if( n==13 ) - { - result = ae_exp(jarquebera_jbtbl13(x, _state), _state); - } - if( n==14 ) - { - result = ae_exp(jarquebera_jbtbl14(x, _state), _state); - } - if( n==15 ) - { - result = ae_exp(jarquebera_jbtbl15(x, _state), _state); - } - if( n==16 ) - { - result = ae_exp(jarquebera_jbtbl16(x, _state), _state); - } - if( n==17 ) - { - result = ae_exp(jarquebera_jbtbl17(x, _state), _state); - } - if( n==18 ) - { - result = ae_exp(jarquebera_jbtbl18(x, _state), _state); - } - if( n==19 ) - { - result = ae_exp(jarquebera_jbtbl19(x, _state), _state); - } - if( n==20 ) - { - result = ae_exp(jarquebera_jbtbl20(x, _state), _state); - } - ae_frame_leave(_state); - return result; - } - - /* - * N = 20, 30, 50 are tabulated. - * In-between values are interpolated - * using interpolating polynomial of the second degree. - */ - if( n>20&&n<=50 ) - { - t1 = -1.0/20.0; - t2 = -1.0/30.0; - t3 = -1.0/50.0; - t = -1.0/n; - f1 = jarquebera_jbtbl20(x, _state); - f2 = jarquebera_jbtbl30(x, _state); - f3 = jarquebera_jbtbl50(x, _state); - f12 = ((t-t2)*f1+(t1-t)*f2)/(t1-t2); - f23 = ((t-t3)*f2+(t2-t)*f3)/(t2-t3); - result = ((t-t3)*f12+(t1-t)*f23)/(t1-t3); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - result = ae_exp(result, _state); - ae_frame_leave(_state); - return result; - } - - /* - * N = 50, 65, 100 are tabulated. - * In-between values are interpolated - * using interpolating polynomial of the second degree. - */ - if( n>50&&n<=100 ) - { - t1 = -1.0/50.0; - t2 = -1.0/65.0; - t3 = -1.0/100.0; - t = -1.0/n; - f1 = jarquebera_jbtbl50(x, _state); - f2 = jarquebera_jbtbl65(x, _state); - f3 = jarquebera_jbtbl100(x, _state); - f12 = ((t-t2)*f1+(t1-t)*f2)/(t1-t2); - f23 = ((t-t3)*f2+(t2-t)*f3)/(t2-t3); - result = ((t-t3)*f12+(t1-t)*f23)/(t1-t3); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - result = ae_exp(result, _state); - ae_frame_leave(_state); - return result; - } - - /* - * N = 100, 130, 200 are tabulated. - * In-between values are interpolated - * using interpolating polynomial of the second degree. - */ - if( n>100&&n<=200 ) - { - t1 = -1.0/100.0; - t2 = -1.0/130.0; - t3 = -1.0/200.0; - t = -1.0/n; - f1 = jarquebera_jbtbl100(x, _state); - f2 = jarquebera_jbtbl130(x, _state); - f3 = jarquebera_jbtbl200(x, _state); - f12 = ((t-t2)*f1+(t1-t)*f2)/(t1-t2); - f23 = ((t-t3)*f2+(t2-t)*f3)/(t2-t3); - result = ((t-t3)*f12+(t1-t)*f23)/(t1-t3); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - result = ae_exp(result, _state); - ae_frame_leave(_state); - return result; - } - - /* - * N = 200, 301, 501 are tabulated. - * In-between values are interpolated - * using interpolating polynomial of the second degree. - */ - if( n>200&&n<=501 ) - { - t1 = -1.0/200.0; - t2 = -1.0/301.0; - t3 = -1.0/501.0; - t = -1.0/n; - f1 = jarquebera_jbtbl200(x, _state); - f2 = jarquebera_jbtbl301(x, _state); - f3 = jarquebera_jbtbl501(x, _state); - f12 = ((t-t2)*f1+(t1-t)*f2)/(t1-t2); - f23 = ((t-t3)*f2+(t2-t)*f3)/(t2-t3); - result = ((t-t3)*f12+(t1-t)*f23)/(t1-t3); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - result = ae_exp(result, _state); - ae_frame_leave(_state); - return result; - } - - /* - * N = 501, 701, 1401 are tabulated. - * In-between values are interpolated - * using interpolating polynomial of the second degree. - */ - if( n>501&&n<=1401 ) - { - t1 = -1.0/501.0; - t2 = -1.0/701.0; - t3 = -1.0/1401.0; - t = -1.0/n; - f1 = jarquebera_jbtbl501(x, _state); - f2 = jarquebera_jbtbl701(x, _state); - f3 = jarquebera_jbtbl1401(x, _state); - f12 = ((t-t2)*f1+(t1-t)*f2)/(t1-t2); - f23 = ((t-t3)*f2+(t2-t)*f3)/(t2-t3); - result = ((t-t3)*f12+(t1-t)*f23)/(t1-t3); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - result = ae_exp(result, _state); - ae_frame_leave(_state); - return result; - } - - /* - * Asymptotic expansion - */ - if( n>1401 ) - { - result = -0.5*x+(jarquebera_jbtbl1401(x, _state)+0.5*x)*ae_sqrt((double)1401/(double)n, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - result = ae_exp(result, _state); - ae_frame_leave(_state); - return result; - } - ae_frame_leave(_state); - return result; -} - - -static double jarquebera_jbtbl5(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,0.4000) ) - { - x = 2*(s-0.000000)/0.400000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.097885e-20, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.854501e-20, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.756616e-20, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,1.1000) ) - { - x = 2*(s-0.400000)/0.700000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.324545e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.075941e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.772272e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.175686e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.576162e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.126861e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.434425e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.790359e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.809178e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.479704e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.717040e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.294170e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.880632e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.023344e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.601531e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.920403e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -5.188419e+02*(s-1.100000e+00)-4.767297e+00; - return result; -} - - -static double jarquebera_jbtbl6(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,0.2500) ) - { - x = 2*(s-0.000000)/0.250000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.274707e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.700471e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.425764e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,1.3000) ) - { - x = 2*(s-0.250000)/1.050000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.339000e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.011104e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.168177e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.085666e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.738606e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.022876e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.462402e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.908270e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.230772e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.006996e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.410222e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.893768e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.114564e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,1.8500) ) - { - x = 2*(s-1.300000)/0.550000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.794311e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.578700e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.394664e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.928290e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.813273e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.076063e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.835380e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.013013e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.058903e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.856915e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.710887e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.770029e+02*(s-1.850000e+00)-1.371015e+01; - return result; -} - - -static double jarquebera_jbtbl7(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.4000) ) - { - x = 2*(s-0.000000)/1.400000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.093681e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.695911e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.473192e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.203236e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.590379e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.291876e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.132007e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.411147e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.180067e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.487610e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.436561e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-1.400000)/1.600000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.947854e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.772675e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.707912e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.691171e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.132795e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.481310e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.867536e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.772327e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.033387e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.378277e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.497964e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.636814e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.581640e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,3.2000) ) - { - x = 2*(s-3.000000)/0.200000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -7.511008e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.140472e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.682053e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.568561e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.933930e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.140472e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.895025e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.140472e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.933930e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.568561e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.682053e+00, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.824116e+03*(s-3.200000e+00)-1.440330e+01; - return result; -} - - -static double jarquebera_jbtbl8(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.3000) ) - { - x = 2*(s-0.000000)/1.300000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -7.199015e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.095921e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.736828e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.047438e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.484320e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.937923e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.810470e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.139780e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.708443e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,2.0000) ) - { - x = 2*(s-1.300000)/0.700000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -3.378966e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.802461e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.547593e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.241042e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.203274e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.201990e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.125597e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.584426e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.546069e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,5.0000) ) - { - x = 2*(s-2.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.828366e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.137533e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.016671e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.745637e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.189801e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.621610e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.741122e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.516368e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.552085e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.787029e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.359774e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -5.087028e+00*(s-5.000000e+00)-1.071300e+01; - return result; -} - - -static double jarquebera_jbtbl9(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.3000) ) - { - x = 2*(s-0.000000)/1.300000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.279320e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.277151e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.669339e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.086149e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.333816e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.871249e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.007048e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.482245e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.355615e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,2.0000) ) - { - x = 2*(s-1.300000)/0.700000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.981430e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.972248e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.747737e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.808530e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.888305e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.001302e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.378767e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.108510e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.915372e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,7.0000) ) - { - x = 2*(s-2.000000)/5.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.387463e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.845231e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.809956e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.543461e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.880397e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.160074e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.356527e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.394428e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.619892e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.758763e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.790977e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.020952e+00*(s-7.000000e+00)-9.516623e+00; - return result; -} - - -static double jarquebera_jbtbl10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.2000) ) - { - x = 2*(s-0.000000)/1.200000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.590993e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.562730e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.353934e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.069933e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.849151e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.931406e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.636295e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.178340e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.917749e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,2.0000) ) - { - x = 2*(s-1.200000)/0.800000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.537658e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.962401e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.838715e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.055792e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.580316e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.781701e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.770362e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.838983e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.999052e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,7.0000) ) - { - x = 2*(s-2.000000)/5.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.337524e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.877029e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.734650e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.249254e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.320250e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.432266e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -8.711035e-01*(s-7.000000e+00)-7.212811e+00; - return result; -} - - -static double jarquebera_jbtbl11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.2000) ) - { - x = 2*(s-0.000000)/1.200000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.339517e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.051558e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.000992e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.022547e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.808401e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.592870e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.575081e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.086173e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.089011e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,2.2500) ) - { - x = 2*(s-1.200000)/1.050000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.523221e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.068388e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.179661e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.555524e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.238964e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.364320e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.895771e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.762774e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.201340e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,8.0000) ) - { - x = 2*(s-2.250000)/5.750000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.212179e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.684579e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.299519e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.606261e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.310869e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.320115e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -5.715445e-01*(s-8.000000e+00)-6.845834e+00; - return result; -} - - -static double jarquebera_jbtbl12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.0000) ) - { - x = 2*(s-0.000000)/1.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.736742e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.657836e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.047209e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.319599e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.545631e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.280445e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.815679e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.213519e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.256838e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-1.000000)/2.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.573947e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.515287e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.611880e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.271311e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.495815e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.141186e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.180886e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.388211e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.890761e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.233175e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.946156e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,12.0000) ) - { - x = 2*(s-3.000000)/9.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.947819e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.034157e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.878986e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.078603e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.990977e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.866215e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.897866e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.512252e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.073743e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.022621e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.501343e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.877243e-01*(s-1.200000e+01)-7.936839e+00; - return result; -} - - -static double jarquebera_jbtbl13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.0000) ) - { - x = 2*(s-0.000000)/1.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.713276e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.557541e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.459092e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.044145e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.546132e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.002374e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.349456e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.025669e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.590242e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-1.000000)/2.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.454383e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.467539e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.270774e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.075763e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.611647e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.990785e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.109212e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.135031e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.915919e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.522390e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.144701e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,13.0000) ) - { - x = 2*(s-3.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.736127e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.920809e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.175858e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.002049e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.158966e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.157781e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.762172e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.780347e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.193310e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.442421e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.547756e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.799944e-01*(s-1.300000e+01)-7.566269e+00; - return result; -} - - -static double jarquebera_jbtbl14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,1.0000) ) - { - x = 2*(s-0.000000)/1.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.698527e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.479081e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.640733e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.466899e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.469485e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.150009e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.965975e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.710210e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.327808e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-1.000000)/2.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -2.350359e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.421365e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.960468e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.149167e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.361109e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.976022e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.082700e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.563328e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.453123e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.917559e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.151067e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-3.000000)/12.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.746892e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.010441e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.566146e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.129690e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.929724e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.524227e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.192933e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.254730e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.620685e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.289618e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.112350e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.590621e-01*(s-1.500000e+01)-7.632238e+00; - return result; -} - - -static double jarquebera_jbtbl15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,2.0000) ) - { - x = 2*(s-0.000000)/2.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.043660e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.361653e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.009497e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.951784e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.377903e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.003253e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.271309e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,5.0000) ) - { - x = 2*(s-2.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -3.582778e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.349578e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.476514e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.717385e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.222591e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.635124e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.815993e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,17.0000) ) - { - x = 2*(s-5.000000)/12.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.115476e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.655936e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.404310e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.663794e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.868618e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.381447e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.444801e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.581503e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.468696e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.728509e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.206470e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.927937e-01*(s-1.700000e+01)-7.700983e+00; - return result; -} - - -static double jarquebera_jbtbl16(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,2.0000) ) - { - x = 2*(s-0.000000)/2.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.002570e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.298141e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.832803e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.877026e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.539436e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.439658e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.756911e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,5.0000) ) - { - x = 2*(s-2.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -3.486198e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.242944e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.020002e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.130531e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.512373e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.054876e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.556839e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,20.0000) ) - { - x = 2*(s-5.000000)/15.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.241608e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.832655e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.340545e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.361143e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.283219e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.484549e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.805968e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.057243e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.454439e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.177513e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.819209e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.391580e-01*(s-2.000000e+01)-7.963205e+00; - return result; -} - - -static double jarquebera_jbtbl17(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-0.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.566973e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.810330e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.840039e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.337294e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.383549e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.556515e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.656965e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.404569e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.447867e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,6.0000) ) - { - x = 2*(s-3.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -3.905684e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.222920e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.146667e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.809176e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.057028e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.211838e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.099683e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.161105e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.225465e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,24.0000) ) - { - x = 2*(s-6.000000)/18.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.594282e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.917838e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.455980e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.999589e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.604263e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.484445e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.819937e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.930390e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.771761e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.232581e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.029083e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.127771e-01*(s-2.400000e+01)-8.400197e+00; - return result; -} - - -static double jarquebera_jbtbl18(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-0.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.526802e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.762373e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.598890e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.189437e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.971721e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.823067e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.064501e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.014932e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.953513e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,6.0000) ) - { - x = 2*(s-3.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -3.818669e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.070918e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.277196e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.879817e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.887357e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.638451e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.502800e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.165796e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.034960e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,20.0000) ) - { - x = 2*(s-6.000000)/14.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.010656e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.496296e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.002227e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.338250e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.137036e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.586202e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.736384e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.332251e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.877982e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.160963e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.547247e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.684623e-01*(s-2.000000e+01)-7.428883e+00; - return result; -} - - -static double jarquebera_jbtbl19(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,3.0000) ) - { - x = 2*(s-0.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.490213e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.719633e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.459123e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.034878e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.113868e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.030922e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.054022e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.525623e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.277360e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,6.0000) ) - { - x = 2*(s-3.000000)/3.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -3.744750e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.977749e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.223716e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.363889e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.711774e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.557257e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.254794e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.034207e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.498107e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,20.0000) ) - { - x = 2*(s-6.000000)/14.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.872768e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.430689e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.136575e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.726627e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.421110e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.581510e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.559520e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.838208e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.428839e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.170682e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.006647e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.539373e-01*(s-2.000000e+01)-7.206941e+00; - return result; -} - - -static double jarquebera_jbtbl20(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.854794e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.948947e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.632184e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.139397e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.006237e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.810031e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.573620e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.951242e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.274092e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.464196e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.882139e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.575144e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.822804e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.061348e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.908404e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.978353e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.030989e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.327151e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.346404e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.840051e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.578551e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.813886e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.905973e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.358489e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.450795e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.941157e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.432418e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.070537e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.375654e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.367378e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.890859e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.679782e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -7.015854e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.487737e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.244254e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.318007e-01*(s-2.500000e+01)-7.742185e+00; - return result; -} - - -static double jarquebera_jbtbl30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.630822e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.724298e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.872756e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.658268e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.573597e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.994157e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.994825e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.394303e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.785029e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.990264e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.037838e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.755546e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.774473e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.821395e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.392603e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.353313e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.539322e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.197018e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.396848e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.804293e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.867928e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.768758e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.211792e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.925799e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.046235e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.536469e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.489642e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.263462e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.177316e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.590637e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.028212e-01*(s-2.500000e+01)-6.855288e+00; - return result; -} - - -static double jarquebera_jbtbl50(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.436279e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.519711e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.148699e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.001204e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.207620e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.034778e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.220322e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.033260e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.588280e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.851653e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.287733e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.234645e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.189127e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.429738e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.058822e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 9.086776e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.445783e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.311671e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.261298e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.496987e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.605249e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.162282e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.921095e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.888603e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.080113e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -9.313116e-02*(s-2.500000e+01)-6.479154e+00; - return result; -} - - -static double jarquebera_jbtbl65(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.360024e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.434631e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.514580e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 7.332038e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.158197e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.121233e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.051056e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.148601e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.214233e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.487977e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.424720e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.116715e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.043152e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.718149e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.313701e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.097305e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.181031e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.256975e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.858951e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.895179e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.933237e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -9.443768e-02*(s-2.500000e+01)-6.419137e+00; - return result; -} - - -static double jarquebera_jbtbl100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.257021e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.313418e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.628931e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.264287e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.518487e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.499826e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.836044e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.056508e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.279690e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.665746e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.290012e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.487632e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.704465e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.211669e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.866099e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.399767e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.498208e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.080097e-01*(s-2.500000e+01)-6.481094e+00; - return result; -} - - -static double jarquebera_jbtbl130(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.207999e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.253864e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.618032e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.112729e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.210546e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.732602e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.410527e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.026324e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.331990e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.779129e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.674749e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.669077e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.679136e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 8.833221e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -5.893951e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.475304e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.116734e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.045722e-01*(s-2.500000e+01)-6.510314e+00; - return result; -} - - -static double jarquebera_jbtbl200(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.146155e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.177398e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.297970e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.869745e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.717288e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.982108e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.427636e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.034235e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.455006e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.942996e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.973795e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.418812e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.156778e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.896705e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.086071e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.152176e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.725393e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.132404e-01*(s-2.500000e+01)-6.764034e+00; - return result; -} - - -static double jarquebera_jbtbl301(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.104290e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.125800e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.595847e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.219666e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.502210e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.414543e-05, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.754115e-05, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.065955e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.582060e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.004472e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -4.709092e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.105779e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.197391e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.386780e-04, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.311384e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.918763e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.626584e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.293626e-01*(s-2.500000e+01)-7.066995e+00; - return result; -} - - -static double jarquebera_jbtbl501(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.067426e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.079765e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -5.463005e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 6.875659e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.127574e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.740694e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.044502e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.746714e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 3.810594e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.197111e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.628194e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -8.846221e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.386405e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.418332e-01*(s-2.500000e+01)-7.468952e+00; - return result; -} - - -static double jarquebera_jbtbl701(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.050999e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.059769e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -3.922680e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 4.847054e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.192182e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.860007e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.963942e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.838711e-02, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.893112e-04, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.159788e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -6.917851e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -9.817020e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.383727e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -1.532706e-01*(s-2.500000e+01)-7.845715e+00; - return result; -} - - -static double jarquebera_jbtbl1401(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - if( ae_fp_less_eq(s,4.0000) ) - { - x = 2*(s-0.000000)/4.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -1.026266e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.030061e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.259222e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 2.536254e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,15.0000) ) - { - x = 2*(s-4.000000)/11.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -4.329849e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -2.095443e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 1.759363e-01, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -7.751359e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -6.124368e-03, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.793114e-03, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - if( ae_fp_less_eq(s,25.0000) ) - { - x = 2*(s-15.000000)/10.000000-1; - tj = 1; - tj1 = x; - jarquebera_jbcheb(x, -7.544330e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, -1.225382e+00, &tj, &tj1, &result, _state); - jarquebera_jbcheb(x, 5.392349e-02, &tj, &tj1, &result, _state); - if( ae_fp_greater(result,0) ) - { - result = 0; - } - return result; - } - result = -2.019375e-01*(s-2.500000e+01)-8.715788e+00; - return result; -} - - -static void jarquebera_jbcheb(double x, - double c, - double* tj, - double* tj1, - double* r, - ae_state *_state) -{ - double t; - - - *r = *r+c*(*tj); - t = 2*x*(*tj1)-(*tj); - *tj = *tj1; - *tj1 = t; -} - - - - -/************************************************************************* -Mann-Whitney U-test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions of the same shape and same median or whether -their medians are different. - -The following tests are performed: - * two-tailed test (null hypothesis - the medians are equal) - * left-tailed test (null hypothesis - the median of the first sample - is greater than or equal to the median of the second sample) - * right-tailed test (null hypothesis - the median of the first sample - is less than or equal to the median of the second sample). - -Requirements: - * the samples are independent - * X and Y are continuous distributions (or discrete distributions well- - approximating continuous distributions) - * distributions of X and Y have the same shape. The only possible - difference is their position (i.e. the value of the median) - * the number of elements in each sample is not less than 5 - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - -The test is non-parametric and doesn't require distributions to be normal. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. N>=5 - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of the sample. M>=5 - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -To calculate p-values, special approximation is used. This method lets us -calculate p-values with satisfactory accuracy in interval [0.0001, 1]. -There is no approximation outside the [0.0001, 1] interval. Therefore, if -the significance level outlies this interval, the test returns 0.0001. - -Relative precision of approximation of p-value: - -N M Max.err. Rms.err. -5..10 N..10 1.4e-02 6.0e-04 -5..10 N..100 2.2e-02 5.3e-06 -10..15 N..15 1.0e-02 3.2e-04 -10..15 N..100 1.0e-02 2.2e-05 -15..100 N..100 6.1e-03 2.7e-06 - -For N,M>100 accuracy checks weren't put into practice, but taking into -account characteristics of asymptotic approximation used, precision should -not be sharply different from the values for interval [5, 100]. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void mannwhitneyutest(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_frame _frame_block; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t t; - double tmp; - ae_int_t tmpi; - ae_int_t ns; - ae_vector r; - ae_vector c; - double u; - double p; - double mp; - double s; - double sigma; - double mu; - ae_int_t tiecount; - ae_vector tiesize; - - ae_frame_make(_state, &_frame_block); - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - ae_vector_init(&r, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c, 0, DT_INT, _state, ae_true); - ae_vector_init(&tiesize, 0, DT_INT, _state, ae_true); - - - /* - * Prepare - */ - if( n<=4||m<=4 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - ae_frame_leave(_state); - return; - } - ns = n+m; - ae_vector_set_length(&r, ns-1+1, _state); - ae_vector_set_length(&c, ns-1+1, _state); - for(i=0; i<=n-1; i++) - { - r.ptr.p_double[i] = x->ptr.p_double[i]; - c.ptr.p_int[i] = 0; - } - for(i=0; i<=m-1; i++) - { - r.ptr.p_double[n+i] = y->ptr.p_double[i]; - c.ptr.p_int[n+i] = 1; - } - - /* - * sort {R, C} - */ - if( ns!=1 ) - { - i = 2; - do - { - t = i; - while(t!=1) - { - k = t/2; - if( ae_fp_greater_eq(r.ptr.p_double[k-1],r.ptr.p_double[t-1]) ) - { - t = 1; - } - else - { - tmp = r.ptr.p_double[k-1]; - r.ptr.p_double[k-1] = r.ptr.p_double[t-1]; - r.ptr.p_double[t-1] = tmp; - tmpi = c.ptr.p_int[k-1]; - c.ptr.p_int[k-1] = c.ptr.p_int[t-1]; - c.ptr.p_int[t-1] = tmpi; - t = k; - } - } - i = i+1; - } - while(i<=ns); - i = ns-1; - do - { - tmp = r.ptr.p_double[i]; - r.ptr.p_double[i] = r.ptr.p_double[0]; - r.ptr.p_double[0] = tmp; - tmpi = c.ptr.p_int[i]; - c.ptr.p_int[i] = c.ptr.p_int[0]; - c.ptr.p_int[0] = tmpi; - t = 1; - while(t!=0) - { - k = 2*t; - if( k>i ) - { - t = 0; - } - else - { - if( k=1); - } - - /* - * compute tied ranks - */ - i = 0; - tiecount = 0; - ae_vector_set_length(&tiesize, ns-1+1, _state); - while(i<=ns-1) - { - j = i+1; - while(j<=ns-1) - { - if( ae_fp_neq(r.ptr.p_double[j],r.ptr.p_double[i]) ) - { - break; - } - j = j+1; - } - for(k=i; k<=j-1; k++) - { - r.ptr.p_double[k] = 1+(double)(i+j-1)/(double)2; - } - tiesize.ptr.p_int[tiecount] = j-i; - tiecount = tiecount+1; - i = j; - } - - /* - * Compute U - */ - u = 0; - for(i=0; i<=ns-1; i++) - { - if( c.ptr.p_int[i]==0 ) - { - u = u+r.ptr.p_double[i]; - } - } - u = n*m+n*(n+1)/2-u; - - /* - * Result - */ - mu = (double)(n*m)/(double)2; - tmp = ns*(ae_sqr(ns, _state)-1)/12; - for(i=0; i<=tiecount-1; i++) - { - tmp = tmp-tiesize.ptr.p_int[i]*(ae_sqr(tiesize.ptr.p_int[i], _state)-1)/12; - } - sigma = ae_sqrt((double)(m*n)/(double)ns/(ns-1)*tmp, _state); - s = (u-mu)/sigma; - if( ae_fp_less_eq(s,0) ) - { - p = ae_exp(mannwhitneyu_usigma(-(u-mu)/sigma, n, m, _state), _state); - mp = 1-ae_exp(mannwhitneyu_usigma(-(u-1-mu)/sigma, n, m, _state), _state); - } - else - { - mp = ae_exp(mannwhitneyu_usigma((u-mu)/sigma, n, m, _state), _state); - p = 1-ae_exp(mannwhitneyu_usigma((u+1-mu)/sigma, n, m, _state), _state); - } - *bothtails = ae_maxreal(2*ae_minreal(p, mp, _state), 1.0E-4, _state); - *lefttail = ae_maxreal(mp, 1.0E-4, _state); - *righttail = ae_maxreal(p, 1.0E-4, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Sequential Chebyshev interpolation. -*************************************************************************/ -static void mannwhitneyu_ucheb(double x, - double c, - double* tj, - double* tj1, - double* r, - ae_state *_state) -{ - double t; - - - *r = *r+c*(*tj); - t = 2*x*(*tj1)-(*tj); - *tj = *tj1; - *tj1 = t; -} - - -/************************************************************************* -Three-point polynomial interpolation. -*************************************************************************/ -static double mannwhitneyu_uninterpolate(double p1, - double p2, - double p3, - ae_int_t n, - ae_state *_state) -{ - double t1; - double t2; - double t3; - double t; - double p12; - double p23; - double result; - - - t1 = 1.0/15.0; - t2 = 1.0/30.0; - t3 = 1.0/100.0; - t = 1.0/n; - p12 = ((t-t2)*p1+(t1-t)*p2)/(t1-t2); - p23 = ((t-t3)*p2+(t2-t)*p3)/(t2-t3); - result = ((t-t3)*p12+(t1-t)*p23)/(t1-t3); - return result; -} - - -/************************************************************************* -Tail(0, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma000(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-6.76984e-01, -6.83700e-01, -6.89873e-01, n2, _state); - p2 = mannwhitneyu_uninterpolate(-6.83700e-01, -6.87311e-01, -6.90957e-01, n2, _state); - p3 = mannwhitneyu_uninterpolate(-6.89873e-01, -6.90957e-01, -6.92175e-01, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(0.75, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma075(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-1.44500e+00, -1.45906e+00, -1.47063e+00, n2, _state); - p2 = mannwhitneyu_uninterpolate(-1.45906e+00, -1.46856e+00, -1.47644e+00, n2, _state); - p3 = mannwhitneyu_uninterpolate(-1.47063e+00, -1.47644e+00, -1.48100e+00, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(1.5, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma150(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-2.65380e+00, -2.67352e+00, -2.69011e+00, n2, _state); - p2 = mannwhitneyu_uninterpolate(-2.67352e+00, -2.68591e+00, -2.69659e+00, n2, _state); - p3 = mannwhitneyu_uninterpolate(-2.69011e+00, -2.69659e+00, -2.70192e+00, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(2.25, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma225(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-4.41465e+00, -4.42260e+00, -4.43702e+00, n2, _state); - p2 = mannwhitneyu_uninterpolate(-4.42260e+00, -4.41639e+00, -4.41928e+00, n2, _state); - p3 = mannwhitneyu_uninterpolate(-4.43702e+00, -4.41928e+00, -4.41030e+00, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(3.0, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma300(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-6.89839e+00, -6.83477e+00, -6.82340e+00, n2, _state); - p2 = mannwhitneyu_uninterpolate(-6.83477e+00, -6.74559e+00, -6.71117e+00, n2, _state); - p3 = mannwhitneyu_uninterpolate(-6.82340e+00, -6.71117e+00, -6.64929e+00, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(3.33, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma333(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-8.31272e+00, -8.17096e+00, -8.13125e+00, n2, _state); - p2 = mannwhitneyu_uninterpolate(-8.17096e+00, -8.00156e+00, -7.93245e+00, n2, _state); - p3 = mannwhitneyu_uninterpolate(-8.13125e+00, -7.93245e+00, -7.82502e+00, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(3.66, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma367(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-9.98837e+00, -9.70844e+00, -9.62087e+00, n2, _state); - p2 = mannwhitneyu_uninterpolate(-9.70844e+00, -9.41156e+00, -9.28998e+00, n2, _state); - p3 = mannwhitneyu_uninterpolate(-9.62087e+00, -9.28998e+00, -9.11686e+00, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(4.0, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma400(ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double p1; - double p2; - double p3; - double result; - - - p1 = mannwhitneyu_uninterpolate(-1.20250e+01, -1.14911e+01, -1.13231e+01, n2, _state); - p2 = mannwhitneyu_uninterpolate(-1.14911e+01, -1.09927e+01, -1.07937e+01, n2, _state); - p3 = mannwhitneyu_uninterpolate(-1.13231e+01, -1.07937e+01, -1.05285e+01, n2, _state); - result = mannwhitneyu_uninterpolate(p1, p2, p3, n1, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 5) -*************************************************************************/ -static double mannwhitneyu_utbln5n5(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/2.611165e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -2.596264e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.412086e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.858542e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.614282e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.372686e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.524731e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.435331e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.284665e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.184141e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.298360e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 7.447272e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.938769e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.276205e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.138481e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.684625e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.558104e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 6) -*************************************************************************/ -static double mannwhitneyu_utbln5n6(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/2.738613e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -2.810459e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.684429e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.712858e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.009324e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.644391e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.034173e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.953498e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.279293e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.563485e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.971952e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.506309e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.541406e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.283205e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.016347e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.221626e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.286752e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 7) -*************************************************************************/ -static double mannwhitneyu_utbln5n7(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/2.841993e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -2.994677e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.923264e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.506190e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.054280e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.794587e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.726290e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.534180e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.517845e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.904428e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.882443e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.482988e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.114875e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.515082e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.996056e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.293581e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.349444e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 8) -*************************************************************************/ -static double mannwhitneyu_utbln5n8(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/2.927700e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.155727e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.135078e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.247203e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.309697e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.993725e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.567219e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.383704e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.002188e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.487322e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.443899e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.688270e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.600339e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.874948e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.811593e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.072353e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.659457e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 9) -*************************************************************************/ -static double mannwhitneyu_utbln5n9(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.298162e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.325016e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.939852e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.563029e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.222652e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.195200e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.445665e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.204792e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.775217e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.527781e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.221948e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.242968e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.607959e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.771285e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.694026e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.481190e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 10) -*************************************************************************/ -static double mannwhitneyu_utbln5n10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.061862e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.425360e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.496710e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.587658e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.812005e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.427637e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.515702e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.406867e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.796295e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.237591e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.654249e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.181165e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.011665e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.417927e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.534880e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.791255e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.871512e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 11) -*************************************************************************/ -static double mannwhitneyu_utbln5n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.115427e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.539959e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.652998e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.196503e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.054363e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.618848e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.109411e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.786668e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.215648e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.484220e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.935991e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.396191e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.894177e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.206979e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.519055e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.210326e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.189679e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 12) -*************************************************************************/ -static double mannwhitneyu_utbln5n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.162278e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.644007e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.796173e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.771177e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.290043e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.794686e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.702110e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.185959e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.416259e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.592056e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.201530e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.754365e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.978945e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.012032e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.304579e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.100378e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.728269e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 13) -*************************************************************************/ -static double mannwhitneyu_utbln5n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.203616e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.739120e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.928117e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.031605e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.519403e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.962648e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.292183e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.809293e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.465156e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.456278e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.446055e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.109490e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.218256e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.941479e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.058603e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.824402e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.830947e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 14) -*************************************************************************/ -static double mannwhitneyu_utbln5n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.240370e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.826559e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.050370e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.083408e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.743164e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.012030e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.884686e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.059656e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.327521e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.134026e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.584201e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.440618e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.524133e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.990007e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.887334e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.534977e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.705395e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 15) -*************************************************************************/ -static double mannwhitneyu_utbln5n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.851572e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.082033e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.095983e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.814595e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.073148e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.420213e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.517175e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.344180e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.371393e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.711443e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.228569e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.683483e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.267112e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.156044e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.131316e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.301023e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 16) -*************************************************************************/ -static double mannwhitneyu_utbln5n16(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.852210e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.077482e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.091186e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.797282e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.084994e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.667054e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.843909e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.456732e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.039830e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.723508e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.940608e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.478285e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.649144e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.237703e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.707410e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.874293e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 17) -*************************************************************************/ -static double mannwhitneyu_utbln5n17(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.851752e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.071259e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.084700e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.758898e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.073846e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.684838e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.964936e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.782442e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.956362e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.984727e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.196936e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.558262e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.690746e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.364855e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.401006e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.546748e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 18) -*************************************************************************/ -static double mannwhitneyu_utbln5n18(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.850840e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.064799e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.077651e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.712659e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.049217e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.571333e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.929809e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.752044e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.949464e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.896101e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.614460e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.384357e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.489113e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.445725e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.945636e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.424653e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 19) -*************************************************************************/ -static double mannwhitneyu_utbln5n19(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.850027e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.059159e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.071106e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.669960e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.022780e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.442555e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.851335e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.433865e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.514465e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.332989e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.606099e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.341945e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.402164e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.039761e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.512831e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.284427e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 20) -*************************************************************************/ -static double mannwhitneyu_utbln5n20(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.849651e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.054729e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.065747e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.636243e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.003234e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.372789e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.831551e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.763090e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.830626e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.122384e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.108328e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.557983e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.945666e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.965696e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.493236e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.162591e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 21) -*************************************************************************/ -static double mannwhitneyu_utbln5n21(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.849649e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.051155e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.061430e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.608869e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.902788e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.346562e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.874709e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.682887e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.026206e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.534551e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.990575e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.713334e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.737011e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.304571e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.133110e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.123457e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 22) -*************************************************************************/ -static double mannwhitneyu_utbln5n22(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.849598e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.047605e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.057264e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.579513e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.749602e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.275137e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.881768e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.177374e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.981056e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.696290e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.886803e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.085378e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.675242e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.426367e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.039613e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.662378e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 23) -*************************************************************************/ -static double mannwhitneyu_utbln5n23(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.849269e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.043761e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.052735e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.544683e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.517503e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.112082e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.782070e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.549483e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.747329e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.694263e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.147141e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.526209e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.039173e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.235615e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.656546e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.014423e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 24) -*************************************************************************/ -static double mannwhitneyu_utbln5n24(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.848925e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.040178e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.048355e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.510198e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.261134e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.915864e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.627423e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.307345e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.732992e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.869652e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.494176e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.047533e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.178439e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.424171e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.829195e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.840810e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 25) -*************************************************************************/ -static double mannwhitneyu_utbln5n25(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.848937e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.037512e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.044866e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.483269e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.063682e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.767778e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.508540e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.332756e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.881511e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.124041e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.368456e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.930499e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.779630e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.029528e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.658678e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.289695e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 26) -*************************************************************************/ -static double mannwhitneyu_utbln5n26(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.849416e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.035915e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.042493e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.466021e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.956432e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.698914e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.465689e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.035254e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.674614e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.492734e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.014021e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.944953e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.255750e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.075841e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.989330e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.134862e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 27) -*************************************************************************/ -static double mannwhitneyu_utbln5n27(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.850070e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.034815e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.040650e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.453117e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.886426e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.661702e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.452346e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.002476e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.720126e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.001400e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.729826e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.740640e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.206333e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.366093e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.193471e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.804091e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 28) -*************************************************************************/ -static double mannwhitneyu_utbln5n28(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.850668e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.033786e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.038853e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.440281e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.806020e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.612883e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.420436e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.787982e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.535230e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.263121e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.849609e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.863967e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.391610e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.720294e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.952273e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.901413e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 29) -*************************************************************************/ -static double mannwhitneyu_utbln5n29(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.851217e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.032834e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.037113e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.427762e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.719146e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.557172e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.375498e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.452033e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.187516e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.916936e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.065533e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.067301e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.615824e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.432244e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.417795e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.710038e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 30) -*************************************************************************/ -static double mannwhitneyu_utbln5n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.851845e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.032148e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.035679e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.417758e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.655330e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.522132e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.352106e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.326911e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.064969e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.813321e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.683881e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.813346e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.627085e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.832107e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.519336e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.888530e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 5, 100) -*************************************************************************/ -static double mannwhitneyu_utbln5n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.250000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.877940e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.039324e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.022243e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.305825e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.960119e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.112000e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.138868e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.418164e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.174520e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.489617e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.878301e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.302233e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.054113e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.458862e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.186591e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.623412e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 6) -*************************************************************************/ -static double mannwhitneyu_utbln6n6(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/2.882307e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.054075e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.998804e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.681518e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.067578e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.709435e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.952661e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.641700e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.304572e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.336275e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.770385e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.401891e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.246148e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.442663e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.502866e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.105855e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.739371e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 7) -*************************************************************************/ -static double mannwhitneyu_utbln6n7(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.265287e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.274613e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.582352e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.334293e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.915502e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.108091e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.546701e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.298827e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.891501e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.313717e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.989501e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.914594e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.062372e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.158841e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.596443e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.185662e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 8) -*************************************************************************/ -static double mannwhitneyu_utbln6n8(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.098387e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.450954e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.520462e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.420299e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.604853e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.165840e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.008756e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.723402e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.843521e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.883405e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.720980e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.301709e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.948034e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.776243e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.623736e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.742068e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.796927e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 9) -*************************************************************************/ -static double mannwhitneyu_utbln6n9(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.181981e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.616113e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.741650e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.204487e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.873068e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.446794e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.632286e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.266481e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.280067e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.780687e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.480242e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.592200e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.581019e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.264231e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.347174e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.167535e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.092185e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 10) -*************************************************************************/ -static double mannwhitneyu_utbln6n10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.253957e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.764382e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.942366e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.939896e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.137812e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.720270e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.281070e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.901060e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.824937e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.802812e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.258132e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.233536e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.085530e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.212151e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.001329e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.226048e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.035298e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 11) -*************************************************************************/ -static double mannwhitneyu_utbln6n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.316625e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.898597e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.125710e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.063297e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.396852e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.990126e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.927977e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.726500e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.858745e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.654590e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.217736e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.989770e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.768493e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.924364e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.140215e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.647914e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.924802e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 12) -*************************************************************************/ -static double mannwhitneyu_utbln6n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.371709e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.020941e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.294250e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.128842e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.650389e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.248611e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.578510e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.162852e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.746982e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.454209e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.128042e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.936650e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.530794e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.665192e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.994144e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.662249e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.368541e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 13) -*************************************************************************/ -static double mannwhitneyu_utbln6n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.420526e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.133167e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.450016e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.191088e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.898220e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.050249e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.226901e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.471113e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.007470e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.049420e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.059074e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.881249e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.452780e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.441805e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.787493e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.483957e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.481590e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 14) -*************************************************************************/ -static double mannwhitneyu_utbln6n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.450000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.201268e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.542568e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.226965e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.046029e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.136657e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.786757e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.843748e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.588022e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.253029e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.667188e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.788330e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.474545e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.540494e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.951188e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.863323e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.220904e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 15) -*************************************************************************/ -static double mannwhitneyu_utbln6n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.450000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.195689e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.526567e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.213617e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.975035e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.118480e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.859142e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.083312e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.298720e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.766708e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.026356e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.093113e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.135168e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.136376e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.190870e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.435972e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.413129e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 30) -*************************************************************************/ -static double mannwhitneyu_utbln6n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.450000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.166269e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.427399e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.118239e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.360847e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.745885e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.025041e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.187179e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.432089e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.408451e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.388774e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.795560e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.304136e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.258516e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.180236e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.388679e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.836027e-06, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 6, 100) -*************************************************************************/ -static double mannwhitneyu_utbln6n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.450000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.181350e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.417919e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.094201e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.195883e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.818937e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.514202e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.125047e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.022148e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.284181e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.157766e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.023752e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.127985e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.221690e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.516179e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.501398e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.380220e-06, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 7) -*************************************************************************/ -static double mannwhitneyu_utbln7n7(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.130495e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.501264e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.584790e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.577311e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.617002e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.145186e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.023462e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.408251e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.626515e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.072492e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.722926e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.095445e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.842602e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.751427e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.008927e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.892431e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.772386e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 8) -*************************************************************************/ -static double mannwhitneyu_utbln7n8(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.240370e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.709965e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.862154e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.504541e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.900195e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.439995e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.678028e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.485540e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.437047e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.440092e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.114227e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.516569e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.829457e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.787550e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.761866e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.991911e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.533481e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 9) -*************************************************************************/ -static double mannwhitneyu_utbln7n9(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.334314e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.896550e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.112671e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.037277e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.181695e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.765190e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.360116e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.695960e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.780578e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.963843e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.616148e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.852104e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.390744e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.014041e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.888101e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.467474e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.004611e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 10) -*************************************************************************/ -static double mannwhitneyu_utbln7n10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.415650e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.064844e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.340749e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.118888e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.459730e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.097781e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.057688e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.097406e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.209262e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.065641e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.196677e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.313994e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.827157e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.822284e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.389090e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.340850e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.395172e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 11) -*************************************************************************/ -static double mannwhitneyu_utbln7n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.486817e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.217795e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.549783e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.195905e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.733093e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.428447e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.760093e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.431676e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.717152e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.032199e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.832423e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.905979e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.302799e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.464371e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.456211e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.736244e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.140712e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 12) -*************************************************************************/ -static double mannwhitneyu_utbln7n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.500000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.235822e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.564100e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.190813e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.686546e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.395083e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.967359e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.747096e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.304144e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.903198e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.134906e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.175035e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.266224e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.892931e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.604706e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.070459e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.427010e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 13) -*************************************************************************/ -static double mannwhitneyu_utbln7n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.500000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.222204e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.532300e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.164642e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.523768e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.531984e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.467857e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.483804e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.524136e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.077740e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.745218e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.602085e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.828831e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.994070e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.873879e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.341937e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.706444e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 14) -*************************************************************************/ -static double mannwhitneyu_utbln7n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.500000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.211763e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.507542e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.143640e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.395755e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.808020e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.044259e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.182308e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.057325e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.724255e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.303900e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.113148e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.102514e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.559442e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.634986e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.776476e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.054489e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 15) -*************************************************************************/ -static double mannwhitneyu_utbln7n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.500000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.204898e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.489960e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.129172e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.316741e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.506107e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.983676e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.258013e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.262515e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.984156e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.912108e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.974023e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.056195e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.090842e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.232620e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.816339e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.020421e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 30) -*************************************************************************/ -static double mannwhitneyu_utbln7n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.500000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.176536e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.398705e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.045481e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.821982e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.962304e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.698132e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.062667e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.282353e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.014836e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.035683e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.004137e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.801453e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.920705e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.518735e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.821501e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.801008e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 7, 100) -*************************************************************************/ -static double mannwhitneyu_utbln7n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.500000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.188337e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.386949e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.022834e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.686517e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.323516e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.399392e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.644333e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.617044e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.031396e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.792066e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.675457e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.673416e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.258552e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.174214e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.073644e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.349958e-06, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 8) -*************************************************************************/ -static double mannwhitneyu_utbln8n8(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.360672e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -3.940217e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.168913e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.051485e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.195325e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.775196e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.385506e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.244902e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.525632e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.771275e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.332874e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.079599e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.882551e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.407944e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.769844e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.062433e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.872535e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 9) -*************************************************************************/ -static double mannwhitneyu_utbln8n9(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.464102e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.147004e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.446939e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.146155e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.488561e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.144561e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.116917e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.205667e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.515661e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.618616e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.599011e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.457324e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.482917e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.488267e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.469823e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.957591e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.058326e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 10) -*************************************************************************/ -static double mannwhitneyu_utbln8n10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.554093e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.334282e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.700860e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.235253e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.778489e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.527324e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.862885e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.589781e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.507355e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.717526e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.215726e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.848696e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.918854e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.219614e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.753761e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.573688e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.602177e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 11) -*************************************************************************/ -static double mannwhitneyu_utbln8n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.421882e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.812457e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.266153e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.849344e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.971527e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.258944e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.944820e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.894685e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.031836e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.514330e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.351660e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.206748e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.492600e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.005338e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.780099e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.673599e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 12) -*************************************************************************/ -static double mannwhitneyu_utbln8n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.398211e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.762214e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.226296e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.603837e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.643223e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.502438e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.544574e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.647734e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.442259e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.011484e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.384758e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.998259e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.659985e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.331046e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.638478e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.056785e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 13) -*************************************************************************/ -static double mannwhitneyu_utbln8n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.380670e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.724511e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.195851e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.420511e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.609928e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.893999e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.115919e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.291410e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.339664e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.801548e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.534710e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.793250e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.806718e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.384624e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.120582e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.936453e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 14) -*************************************************************************/ -static double mannwhitneyu_utbln8n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.368494e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.697171e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.174440e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.300621e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.087393e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.685826e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.085254e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.525658e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.966647e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.453388e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.826066e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.501958e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.336297e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.251972e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.118456e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.415959e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 15) -*************************************************************************/ -static double mannwhitneyu_utbln8n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.358397e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.674485e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.155941e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.195780e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.544830e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.426183e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.309902e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.650956e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.068874e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.538544e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.192525e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.073905e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.079673e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.423572e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.579647e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.765904e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 30) -*************************************************************************/ -static double mannwhitneyu_utbln8n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.318823e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.567159e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.064864e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.688413e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.153712e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.309389e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.226861e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.523815e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.780987e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.166866e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.922431e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.466397e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.690036e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.008185e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.271903e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.534751e-06, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 8, 100) -*************************************************************************/ -static double mannwhitneyu_utbln8n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.600000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.324531e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.547071e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.038129e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.541549e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.525605e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.044992e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.085713e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.017871e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.459226e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.092064e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.024349e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 7.366347e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.385637e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.321722e-08, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.439286e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.058079e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 9) -*************************************************************************/ -static double mannwhitneyu_utbln9n9(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.576237e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.372857e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.750859e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.248233e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.792868e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.559372e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.894941e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.643256e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.091370e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.285034e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.112997e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.806229e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.150741e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.509825e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.891051e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.485013e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.343653e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 10) -*************************************************************************/ -static double mannwhitneyu_utbln9n10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.516726e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.939333e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.305046e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.935326e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.029141e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.420592e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.053140e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.065930e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.523581e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.544888e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.813741e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.510631e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.536057e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.833815e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.189692e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.615050e-03, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 11) -*************************************************************************/ -static double mannwhitneyu_utbln9n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.481308e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.867483e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.249072e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.591790e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.400128e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.341992e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.463680e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.487211e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.671196e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.343472e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.544146e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.802335e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.117084e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.217443e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.858766e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.193687e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 12) -*************************************************************************/ -static double mannwhitneyu_utbln9n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.456776e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.817037e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.209788e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.362108e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.171356e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.661557e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.026141e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.361908e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.093885e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.298389e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.663603e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.768522e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.579015e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.868677e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.440652e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.523037e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 13) -*************************************************************************/ -static double mannwhitneyu_utbln9n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.438840e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.779308e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.180614e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.196489e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.346621e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.234857e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.796211e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.575715e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.525647e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.964651e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.275235e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.299124e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.397416e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.295781e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.237619e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 7.269692e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 14) -*************************************************************************/ -static double mannwhitneyu_utbln9n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.425981e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.751545e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.159543e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.086570e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.917446e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.120112e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.175519e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.515473e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.727772e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.070629e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.677569e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.876953e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.233502e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.508182e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.120389e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.847212e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 15) -*************************************************************************/ -static double mannwhitneyu_utbln9n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.414952e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.727612e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.140634e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.981231e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.382635e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.853575e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.571051e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.567625e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.214197e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.448700e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.712669e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.015050e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.438610e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.301363e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.309386e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.164772e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 30) -*************************************************************************/ -static double mannwhitneyu_utbln9n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.370720e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.615712e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.050023e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.504775e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.318265e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.646826e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.741492e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.735360e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.966911e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.100738e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.348991e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.527687e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.917286e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.397466e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.360175e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.892252e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 9, 100) -*************************************************************************/ -static double mannwhitneyu_utbln9n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.372506e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.590966e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.021758e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.359849e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.755519e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.533166e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.936659e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.634913e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.730053e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.791845e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.030682e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.228663e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.631175e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.636749e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.404599e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.789872e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 10) -*************************************************************************/ -static double mannwhitneyu_utbln10n10(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.468831e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.844398e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.231728e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.486073e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.781321e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.971425e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.215371e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.828451e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.419872e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.430165e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.740363e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.049211e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.269371e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.211393e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.232314e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.016081e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 11) -*************************************************************************/ -static double mannwhitneyu_utbln10n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.437998e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.782296e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.184732e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.219585e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.457012e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.296008e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.481501e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.527940e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.953426e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.563840e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.574403e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.535775e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.338037e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.002654e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.852676e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.318132e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 12) -*************************************************************************/ -static double mannwhitneyu_utbln10n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.416082e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.737458e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.150952e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.036884e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.609030e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.908684e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.439666e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.162647e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.451601e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.148757e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.803981e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.731621e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.346903e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.013151e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.956148e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.438381e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 13) -*************************************************************************/ -static double mannwhitneyu_utbln10n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.399480e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.702863e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.124829e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.897428e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.979802e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.634368e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.180461e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.484926e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.864376e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.186576e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.886925e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.836828e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.074756e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.209547e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.883266e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.380143e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 14) -*************************************************************************/ -static double mannwhitneyu_utbln10n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.386924e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.676124e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.104740e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.793826e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.558886e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.492462e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.052903e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.917782e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.878696e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.576046e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.764551e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.288778e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.757658e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.299101e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.265197e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.384503e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 15) -*************************************************************************/ -static double mannwhitneyu_utbln10n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.376846e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.654247e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.088083e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.705945e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.169677e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.317213e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.264836e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.548024e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.633910e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.505621e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.658588e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.320254e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.175277e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.122317e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.675688e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.661363e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 30) -*************************************************************************/ -static double mannwhitneyu_utbln10n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.333977e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.548099e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.004444e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.291014e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.523674e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.828211e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.716917e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.894256e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.433371e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.522675e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.764192e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.140235e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.629230e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.541895e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.944946e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.726360e-06, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 10, 100) -*************************************************************************/ -static double mannwhitneyu_utbln10n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.650000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.334008e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.522316e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.769627e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.158110e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.053650e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.242235e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.173571e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.033661e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.824732e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.084420e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.610036e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.728155e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.217130e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.340966e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.001235e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.694052e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 11) -*************************************************************************/ -static double mannwhitneyu_utbln11n11(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.519760e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.880694e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.200698e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.174092e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.072304e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.054773e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.506613e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.813942e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.223644e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.417416e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.499166e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.194332e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 7.369096e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.968590e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.630532e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.061000e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 12) -*************************************************************************/ -static double mannwhitneyu_utbln11n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.495790e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.832622e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.165420e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.987306e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.265621e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.723537e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.347406e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.353464e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.613369e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.102522e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.237709e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.665652e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.626903e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.167518e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.564455e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.047320e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 13) -*************************************************************************/ -static double mannwhitneyu_utbln11n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.477880e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.796242e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.138769e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.851739e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.722104e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.548304e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.176683e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.817895e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.842451e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.935870e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.421777e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.238831e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.867026e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.458255e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.306259e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.961487e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 14) -*************************************************************************/ -static double mannwhitneyu_utbln11n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.463683e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.766969e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.117082e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.739574e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.238865e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.350306e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.425871e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.640172e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.660633e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.879883e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.349658e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.271795e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.304544e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.024201e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.816867e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.596787e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 15) -*************************************************************************/ -static double mannwhitneyu_utbln11n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.452526e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.743570e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.099705e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.650612e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.858285e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.187036e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.689241e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.294360e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.072623e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.278008e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.322382e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.131558e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.305669e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.825627e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.332689e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.120973e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 30) -*************************************************************************/ -static double mannwhitneyu_utbln11n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.402621e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.627440e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.011333e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.224126e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.232856e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.859347e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.377381e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.756709e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.033230e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.875472e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.608399e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.102943e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.740693e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.343139e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.196878e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.658062e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 11, 100) -*************************************************************************/ -static double mannwhitneyu_utbln11n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.398795e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.596486e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.814761e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.085187e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.766529e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.379425e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.986351e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.214705e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.360075e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.260869e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.033307e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.727087e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.393883e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.242989e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.111928e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.898823e-09, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 12, 12) -*************************************************************************/ -static double mannwhitneyu_utbln12n12(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.472616e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.786627e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.132099e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.817523e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.570179e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.479511e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.799492e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.565350e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.530139e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.380132e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.242761e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.576269e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.018771e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.933911e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.002799e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.022048e-06, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 12, 13) -*************************************************************************/ -static double mannwhitneyu_utbln12n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.454800e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.750794e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.105988e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.684754e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.011826e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.262579e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.044492e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.478741e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.322165e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.621104e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.068753e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.468396e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.056235e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.327375e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.914877e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.784191e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 12, 14) -*************************************************************************/ -static double mannwhitneyu_utbln12n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.440910e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.722404e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.085254e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.579439e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.563738e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.066730e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.129346e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.014531e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.129679e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.000909e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.996174e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.377924e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.936304e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.051098e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.025820e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 8.730585e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 12, 15) -*************************************************************************/ -static double mannwhitneyu_utbln12n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.430123e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.700008e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.068971e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.499725e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.250897e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.473145e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.680008e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.483350e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.766992e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.891081e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.015140e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.977756e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.707414e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.114786e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.238865e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.381445e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 12, 30) -*************************************************************************/ -static double mannwhitneyu_utbln12n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.380023e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.585782e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.838583e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.103394e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.834015e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.635212e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.948212e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.574169e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.747980e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.833672e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.722433e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.181038e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.206473e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.716003e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.476434e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.217700e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 12, 100) -*************************************************************************/ -static double mannwhitneyu_utbln12n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.700000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.374567e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.553481e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.541334e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.701907e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.414757e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.404103e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.234388e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.453762e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.311060e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.317501e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.713888e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.309583e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.019804e-08, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.224829e-09, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.349019e-08, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.893302e-08, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 13, 13) -*************************************************************************/ -static double mannwhitneyu_utbln13n13(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.541046e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.859047e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.130164e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.689719e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.950693e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.231455e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.976550e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.538455e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.245603e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.142647e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.831434e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.032483e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.488405e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.156927e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.949279e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.532700e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 13, 14) -*************************************************************************/ -static double mannwhitneyu_utbln13n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.525655e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.828341e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.108110e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.579552e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.488307e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.032328e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.988741e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.766394e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.388950e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.338179e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.133440e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.023518e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.110570e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.202332e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.056132e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.536323e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 13, 15) -*************************************************************************/ -static double mannwhitneyu_utbln13n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.513585e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.803952e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.090686e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.495310e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.160314e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.073124e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.480313e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.478239e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.140914e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.311541e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.677105e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.115464e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.578563e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.044604e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.888939e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 2.395644e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 13, 30) -*************************************************************************/ -static double mannwhitneyu_utbln13n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.455999e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.678434e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.995491e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.078100e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.705220e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.258739e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.671526e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.185458e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.507764e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.411446e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.044355e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.285765e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.345282e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.066940e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.962037e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.723644e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 13, 100) -*************************************************************************/ -static double mannwhitneyu_utbln13n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.446787e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.640804e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.671552e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.364990e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.274444e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.047440e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.161439e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.171729e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.562171e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.359762e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.275494e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.747635e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.700292e-08, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.565559e-09, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 5.005396e-09, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 3.335794e-09, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 14, 14) -*************************************************************************/ -static double mannwhitneyu_utbln14n14(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.510624e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.798584e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.087107e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.478532e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.098050e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.855986e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.409083e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.299536e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.176177e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.479417e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.812761e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -5.225872e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 4.516521e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 6.730551e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 9.237563e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.611820e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 14, 15) -*************************************************************************/ -static double mannwhitneyu_utbln14n15(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.498681e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.774668e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.070267e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.399348e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.807239e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.845763e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.071773e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.261698e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.011695e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.305946e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.879295e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.999439e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.904438e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.944986e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.373908e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.140794e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 14, 30) -*************************************************************************/ -static double mannwhitneyu_utbln14n30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.440378e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.649587e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.807829e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.989753e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.463646e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.586580e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -6.745917e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.635398e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.923172e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.446699e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.613892e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.214073e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.651683e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.272777e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.464988e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.109803e-07, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 14, 100) -*************************************************************************/ -static double mannwhitneyu_utbln14n100(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/3.750000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - mannwhitneyu_ucheb(x, -4.429701e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -4.610577e+00, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -9.482675e-01, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.605550e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.062151e-02, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.525154e-03, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.835983e-04, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -8.411440e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.744901e-05, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.318850e-06, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.692100e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -1.536270e-07, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -3.705888e-08, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -7.999599e-09, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, -2.908395e-09, &tj, &tj1, &result, _state); - mannwhitneyu_ucheb(x, 1.546923e-09, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, N1, N2) -*************************************************************************/ -static double mannwhitneyu_usigma(double s, - ae_int_t n1, - ae_int_t n2, - ae_state *_state) -{ - double f0; - double f1; - double f2; - double f3; - double f4; - double s0; - double s1; - double s2; - double s3; - double s4; - double result; - - - result = 0; - - /* - * N1=5, N2 = 5, 6, 7, ... - */ - if( ae_minint(n1, n2, _state)==5 ) - { - if( ae_maxint(n1, n2, _state)==5 ) - { - result = mannwhitneyu_utbln5n5(s, _state); - } - if( ae_maxint(n1, n2, _state)==6 ) - { - result = mannwhitneyu_utbln5n6(s, _state); - } - if( ae_maxint(n1, n2, _state)==7 ) - { - result = mannwhitneyu_utbln5n7(s, _state); - } - if( ae_maxint(n1, n2, _state)==8 ) - { - result = mannwhitneyu_utbln5n8(s, _state); - } - if( ae_maxint(n1, n2, _state)==9 ) - { - result = mannwhitneyu_utbln5n9(s, _state); - } - if( ae_maxint(n1, n2, _state)==10 ) - { - result = mannwhitneyu_utbln5n10(s, _state); - } - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln5n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln5n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln5n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln5n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln5n15(s, _state); - } - if( ae_maxint(n1, n2, _state)==16 ) - { - result = mannwhitneyu_utbln5n16(s, _state); - } - if( ae_maxint(n1, n2, _state)==17 ) - { - result = mannwhitneyu_utbln5n17(s, _state); - } - if( ae_maxint(n1, n2, _state)==18 ) - { - result = mannwhitneyu_utbln5n18(s, _state); - } - if( ae_maxint(n1, n2, _state)==19 ) - { - result = mannwhitneyu_utbln5n19(s, _state); - } - if( ae_maxint(n1, n2, _state)==20 ) - { - result = mannwhitneyu_utbln5n20(s, _state); - } - if( ae_maxint(n1, n2, _state)==21 ) - { - result = mannwhitneyu_utbln5n21(s, _state); - } - if( ae_maxint(n1, n2, _state)==22 ) - { - result = mannwhitneyu_utbln5n22(s, _state); - } - if( ae_maxint(n1, n2, _state)==23 ) - { - result = mannwhitneyu_utbln5n23(s, _state); - } - if( ae_maxint(n1, n2, _state)==24 ) - { - result = mannwhitneyu_utbln5n24(s, _state); - } - if( ae_maxint(n1, n2, _state)==25 ) - { - result = mannwhitneyu_utbln5n25(s, _state); - } - if( ae_maxint(n1, n2, _state)==26 ) - { - result = mannwhitneyu_utbln5n26(s, _state); - } - if( ae_maxint(n1, n2, _state)==27 ) - { - result = mannwhitneyu_utbln5n27(s, _state); - } - if( ae_maxint(n1, n2, _state)==28 ) - { - result = mannwhitneyu_utbln5n28(s, _state); - } - if( ae_maxint(n1, n2, _state)==29 ) - { - result = mannwhitneyu_utbln5n29(s, _state); - } - if( ae_maxint(n1, n2, _state)>29 ) - { - f0 = mannwhitneyu_utbln5n15(s, _state); - f1 = mannwhitneyu_utbln5n30(s, _state); - f2 = mannwhitneyu_utbln5n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=6, N2 = 6, 7, 8, ... - */ - if( ae_minint(n1, n2, _state)==6 ) - { - if( ae_maxint(n1, n2, _state)==6 ) - { - result = mannwhitneyu_utbln6n6(s, _state); - } - if( ae_maxint(n1, n2, _state)==7 ) - { - result = mannwhitneyu_utbln6n7(s, _state); - } - if( ae_maxint(n1, n2, _state)==8 ) - { - result = mannwhitneyu_utbln6n8(s, _state); - } - if( ae_maxint(n1, n2, _state)==9 ) - { - result = mannwhitneyu_utbln6n9(s, _state); - } - if( ae_maxint(n1, n2, _state)==10 ) - { - result = mannwhitneyu_utbln6n10(s, _state); - } - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln6n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln6n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln6n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln6n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln6n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln6n15(s, _state); - f1 = mannwhitneyu_utbln6n30(s, _state); - f2 = mannwhitneyu_utbln6n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=7, N2 = 7, 8, ... - */ - if( ae_minint(n1, n2, _state)==7 ) - { - if( ae_maxint(n1, n2, _state)==7 ) - { - result = mannwhitneyu_utbln7n7(s, _state); - } - if( ae_maxint(n1, n2, _state)==8 ) - { - result = mannwhitneyu_utbln7n8(s, _state); - } - if( ae_maxint(n1, n2, _state)==9 ) - { - result = mannwhitneyu_utbln7n9(s, _state); - } - if( ae_maxint(n1, n2, _state)==10 ) - { - result = mannwhitneyu_utbln7n10(s, _state); - } - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln7n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln7n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln7n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln7n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln7n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln7n15(s, _state); - f1 = mannwhitneyu_utbln7n30(s, _state); - f2 = mannwhitneyu_utbln7n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=8, N2 = 8, 9, 10, ... - */ - if( ae_minint(n1, n2, _state)==8 ) - { - if( ae_maxint(n1, n2, _state)==8 ) - { - result = mannwhitneyu_utbln8n8(s, _state); - } - if( ae_maxint(n1, n2, _state)==9 ) - { - result = mannwhitneyu_utbln8n9(s, _state); - } - if( ae_maxint(n1, n2, _state)==10 ) - { - result = mannwhitneyu_utbln8n10(s, _state); - } - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln8n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln8n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln8n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln8n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln8n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln8n15(s, _state); - f1 = mannwhitneyu_utbln8n30(s, _state); - f2 = mannwhitneyu_utbln8n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=9, N2 = 9, 10, ... - */ - if( ae_minint(n1, n2, _state)==9 ) - { - if( ae_maxint(n1, n2, _state)==9 ) - { - result = mannwhitneyu_utbln9n9(s, _state); - } - if( ae_maxint(n1, n2, _state)==10 ) - { - result = mannwhitneyu_utbln9n10(s, _state); - } - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln9n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln9n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln9n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln9n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln9n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln9n15(s, _state); - f1 = mannwhitneyu_utbln9n30(s, _state); - f2 = mannwhitneyu_utbln9n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=10, N2 = 10, 11, ... - */ - if( ae_minint(n1, n2, _state)==10 ) - { - if( ae_maxint(n1, n2, _state)==10 ) - { - result = mannwhitneyu_utbln10n10(s, _state); - } - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln10n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln10n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln10n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln10n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln10n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln10n15(s, _state); - f1 = mannwhitneyu_utbln10n30(s, _state); - f2 = mannwhitneyu_utbln10n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=11, N2 = 11, 12, ... - */ - if( ae_minint(n1, n2, _state)==11 ) - { - if( ae_maxint(n1, n2, _state)==11 ) - { - result = mannwhitneyu_utbln11n11(s, _state); - } - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln11n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln11n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln11n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln11n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln11n15(s, _state); - f1 = mannwhitneyu_utbln11n30(s, _state); - f2 = mannwhitneyu_utbln11n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=12, N2 = 12, 13, ... - */ - if( ae_minint(n1, n2, _state)==12 ) - { - if( ae_maxint(n1, n2, _state)==12 ) - { - result = mannwhitneyu_utbln12n12(s, _state); - } - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln12n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln12n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln12n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln12n15(s, _state); - f1 = mannwhitneyu_utbln12n30(s, _state); - f2 = mannwhitneyu_utbln12n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=13, N2 = 13, 14, ... - */ - if( ae_minint(n1, n2, _state)==13 ) - { - if( ae_maxint(n1, n2, _state)==13 ) - { - result = mannwhitneyu_utbln13n13(s, _state); - } - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln13n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln13n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln13n15(s, _state); - f1 = mannwhitneyu_utbln13n30(s, _state); - f2 = mannwhitneyu_utbln13n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1=14, N2 = 14, 15, ... - */ - if( ae_minint(n1, n2, _state)==14 ) - { - if( ae_maxint(n1, n2, _state)==14 ) - { - result = mannwhitneyu_utbln14n14(s, _state); - } - if( ae_maxint(n1, n2, _state)==15 ) - { - result = mannwhitneyu_utbln14n15(s, _state); - } - if( ae_maxint(n1, n2, _state)>15 ) - { - f0 = mannwhitneyu_utbln14n15(s, _state); - f1 = mannwhitneyu_utbln14n30(s, _state); - f2 = mannwhitneyu_utbln14n100(s, _state); - result = mannwhitneyu_uninterpolate(f0, f1, f2, ae_maxint(n1, n2, _state), _state); - } - return result; - } - - /* - * N1 >= 15, N2 >= 15 - */ - if( ae_fp_greater(s,4) ) - { - s = 4; - } - if( ae_fp_less(s,3) ) - { - s0 = 0.000000e+00; - f0 = mannwhitneyu_usigma000(n1, n2, _state); - s1 = 7.500000e-01; - f1 = mannwhitneyu_usigma075(n1, n2, _state); - s2 = 1.500000e+00; - f2 = mannwhitneyu_usigma150(n1, n2, _state); - s3 = 2.250000e+00; - f3 = mannwhitneyu_usigma225(n1, n2, _state); - s4 = 3.000000e+00; - f4 = mannwhitneyu_usigma300(n1, n2, _state); - f1 = ((s-s0)*f1-(s-s1)*f0)/(s1-s0); - f2 = ((s-s0)*f2-(s-s2)*f0)/(s2-s0); - f3 = ((s-s0)*f3-(s-s3)*f0)/(s3-s0); - f4 = ((s-s0)*f4-(s-s4)*f0)/(s4-s0); - f2 = ((s-s1)*f2-(s-s2)*f1)/(s2-s1); - f3 = ((s-s1)*f3-(s-s3)*f1)/(s3-s1); - f4 = ((s-s1)*f4-(s-s4)*f1)/(s4-s1); - f3 = ((s-s2)*f3-(s-s3)*f2)/(s3-s2); - f4 = ((s-s2)*f4-(s-s4)*f2)/(s4-s2); - f4 = ((s-s3)*f4-(s-s4)*f3)/(s4-s3); - result = f4; - } - else - { - s0 = 3.000000e+00; - f0 = mannwhitneyu_usigma300(n1, n2, _state); - s1 = 3.333333e+00; - f1 = mannwhitneyu_usigma333(n1, n2, _state); - s2 = 3.666667e+00; - f2 = mannwhitneyu_usigma367(n1, n2, _state); - s3 = 4.000000e+00; - f3 = mannwhitneyu_usigma400(n1, n2, _state); - f1 = ((s-s0)*f1-(s-s1)*f0)/(s1-s0); - f2 = ((s-s0)*f2-(s-s2)*f0)/(s2-s0); - f3 = ((s-s0)*f3-(s-s3)*f0)/(s3-s0); - f2 = ((s-s1)*f2-(s-s2)*f1)/(s2-s1); - f3 = ((s-s1)*f3-(s-s3)*f1)/(s3-s1); - f3 = ((s-s2)*f3-(s-s3)*f2)/(s3-s2); - result = f3; - } - return result; -} - - - - -/************************************************************************* -Sign test - -This test checks three hypotheses about the median of the given sample. -The following tests are performed: - * two-tailed test (null hypothesis - the median is equal to the given - value) - * left-tailed test (null hypothesis - the median is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the median is less than or - equal to the given value) - -Requirements: - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - -The test is non-parametric and doesn't require distribution X to be normal - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. - Median - assumed median value. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -While calculating p-values high-precision binomial distribution -approximation is used, so significance levels have about 15 exact digits. - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void onesamplesigntest(/* Real */ ae_vector* x, - ae_int_t n, - double median, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_int_t i; - ae_int_t gtcnt; - ae_int_t necnt; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - if( n<=1 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Calculate: - * GTCnt - count of x[i]>Median - * NECnt - count of x[i]<>Median - */ - gtcnt = 0; - necnt = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_greater(x->ptr.p_double[i],median) ) - { - gtcnt = gtcnt+1; - } - if( ae_fp_neq(x->ptr.p_double[i],median) ) - { - necnt = necnt+1; - } - } - if( necnt==0 ) - { - - /* - * all x[i] are equal to Median. - * So we can conclude that Median is a true median :) - */ - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - *bothtails = ae_minreal(2*binomialdistribution(ae_minint(gtcnt, necnt-gtcnt, _state), necnt, 0.5, _state), 1.0, _state); - *lefttail = binomialdistribution(gtcnt, necnt, 0.5, _state); - *righttail = binomialcdistribution(gtcnt-1, necnt, 0.5, _state); -} - - - - -/************************************************************************* -One-sample t-test - -This test checks three hypotheses about the mean of the given sample. The -following tests are performed: - * two-tailed test (null hypothesis - the mean is equal to the given - value) - * left-tailed test (null hypothesis - the mean is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the mean is less than or equal - to the given value). - -The test is based on the assumption that a given sample has a normal -distribution and an unknown dispersion. If the distribution sharply -differs from normal, the test will work incorrectly. - -INPUT PARAMETERS: - X - sample. Array whose index goes from 0 to N-1. - N - size of sample, N>=0 - Mean - assumed value of the mean. - -OUTPUT PARAMETERS: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0, all p-values are set to 1.0 - * when variance of X[] is exactly zero, p-values are set - to 1.0 or 0.0, depending on difference between sample mean and - value of mean being tested. - - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void studentttest1(/* Real */ ae_vector* x, - ae_int_t n, - double mean, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_int_t i; - double xmean; - double x0; - double v; - ae_bool samex; - double xvariance; - double xstddev; - double v1; - double v2; - double stat; - double s; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - if( n<=0 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Mean - */ - xmean = 0; - x0 = x->ptr.p_double[0]; - samex = ae_true; - for(i=0; i<=n-1; i++) - { - v = x->ptr.p_double[i]; - xmean = xmean+v; - samex = samex&&ae_fp_eq(v,x0); - } - if( samex ) - { - xmean = x0; - } - else - { - xmean = xmean/n; - } - - /* - * Variance (using corrected two-pass algorithm) - */ - xvariance = 0; - xstddev = 0; - if( n!=1&&!samex ) - { - v1 = 0; - for(i=0; i<=n-1; i++) - { - v1 = v1+ae_sqr(x->ptr.p_double[i]-xmean, _state); - } - v2 = 0; - for(i=0; i<=n-1; i++) - { - v2 = v2+(x->ptr.p_double[i]-xmean); - } - v2 = ae_sqr(v2, _state)/n; - xvariance = (v1-v2)/(n-1); - if( ae_fp_less(xvariance,0) ) - { - xvariance = 0; - } - xstddev = ae_sqrt(xvariance, _state); - } - if( ae_fp_eq(xstddev,0) ) - { - if( ae_fp_eq(xmean,mean) ) - { - *bothtails = 1.0; - } - else - { - *bothtails = 0.0; - } - if( ae_fp_greater_eq(xmean,mean) ) - { - *lefttail = 1.0; - } - else - { - *lefttail = 0.0; - } - if( ae_fp_less_eq(xmean,mean) ) - { - *righttail = 1.0; - } - else - { - *righttail = 0.0; - } - return; - } - - /* - * Statistic - */ - stat = (xmean-mean)/(xstddev/ae_sqrt(n, _state)); - s = studenttdistribution(n-1, stat, _state); - *bothtails = 2*ae_minreal(s, 1-s, _state); - *lefttail = s; - *righttail = 1-s; -} - - -/************************************************************************* -Two-sample pooled test - -This test checks three hypotheses about the mean of the given samples. The -following tests are performed: - * two-tailed test (null hypothesis - the means are equal) - * left-tailed test (null hypothesis - the mean of the first sample is - greater than or equal to the mean of the second sample) - * right-tailed test (null hypothesis - the mean of the first sample is - less than or equal to the mean of the second sample). - -Test is based on the following assumptions: - * given samples have normal distributions - * dispersions are equal - * samples are independent. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of sample. - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of sample. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0 or M=0, all p-values are set to 1.0 - * when both samples has exactly zero variance, p-values are set - to 1.0 or 0.0, depending on difference between means. - - -- ALGLIB -- - Copyright 18.09.2006 by Bochkanov Sergey -*************************************************************************/ -void studentttest2(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_int_t i; - ae_bool samex; - ae_bool samey; - double x0; - double y0; - double xmean; - double ymean; - double v; - double stat; - double s; - double p; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - if( n<=0||m<=0 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Mean - */ - xmean = 0; - x0 = x->ptr.p_double[0]; - samex = ae_true; - for(i=0; i<=n-1; i++) - { - v = x->ptr.p_double[i]; - xmean = xmean+v; - samex = samex&&ae_fp_eq(v,x0); - } - if( samex ) - { - xmean = x0; - } - else - { - xmean = xmean/n; - } - ymean = 0; - y0 = y->ptr.p_double[0]; - samey = ae_true; - for(i=0; i<=m-1; i++) - { - v = y->ptr.p_double[i]; - ymean = ymean+v; - samey = samey&&ae_fp_eq(v,y0); - } - if( samey ) - { - ymean = y0; - } - else - { - ymean = ymean/m; - } - - /* - * S - */ - s = 0; - if( n+m>2 ) - { - for(i=0; i<=n-1; i++) - { - s = s+ae_sqr(x->ptr.p_double[i]-xmean, _state); - } - for(i=0; i<=m-1; i++) - { - s = s+ae_sqr(y->ptr.p_double[i]-ymean, _state); - } - s = ae_sqrt(s*((double)1/(double)n+(double)1/(double)m)/(n+m-2), _state); - } - if( ae_fp_eq(s,0) ) - { - if( ae_fp_eq(xmean,ymean) ) - { - *bothtails = 1.0; - } - else - { - *bothtails = 0.0; - } - if( ae_fp_greater_eq(xmean,ymean) ) - { - *lefttail = 1.0; - } - else - { - *lefttail = 0.0; - } - if( ae_fp_less_eq(xmean,ymean) ) - { - *righttail = 1.0; - } - else - { - *righttail = 0.0; - } - return; - } - - /* - * Statistic - */ - stat = (xmean-ymean)/s; - p = studenttdistribution(n+m-2, stat, _state); - *bothtails = 2*ae_minreal(p, 1-p, _state); - *lefttail = p; - *righttail = 1-p; -} - - -/************************************************************************* -Two-sample unpooled test - -This test checks three hypotheses about the mean of the given samples. The -following tests are performed: - * two-tailed test (null hypothesis - the means are equal) - * left-tailed test (null hypothesis - the mean of the first sample is - greater than or equal to the mean of the second sample) - * right-tailed test (null hypothesis - the mean of the first sample is - less than or equal to the mean of the second sample). - -Test is based on the following assumptions: - * given samples have normal distributions - * samples are independent. -Equality of variances is NOT required. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of the sample. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0 or M=0, all p-values are set to 1.0 - * when both samples has zero variance, p-values are set - to 1.0 or 0.0, depending on difference between means. - * when only one sample has zero variance, test reduces to 1-sample - version. - - -- ALGLIB -- - Copyright 18.09.2006 by Bochkanov Sergey -*************************************************************************/ -void unequalvariancettest(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_int_t i; - ae_bool samex; - ae_bool samey; - double x0; - double y0; - double xmean; - double ymean; - double xvar; - double yvar; - double v; - double df; - double p; - double stat; - double c; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - if( n<=0||m<=0 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Mean - */ - xmean = 0; - x0 = x->ptr.p_double[0]; - samex = ae_true; - for(i=0; i<=n-1; i++) - { - v = x->ptr.p_double[i]; - xmean = xmean+v; - samex = samex&&ae_fp_eq(v,x0); - } - if( samex ) - { - xmean = x0; - } - else - { - xmean = xmean/n; - } - ymean = 0; - y0 = y->ptr.p_double[0]; - samey = ae_true; - for(i=0; i<=m-1; i++) - { - v = y->ptr.p_double[i]; - ymean = ymean+v; - samey = samey&&ae_fp_eq(v,y0); - } - if( samey ) - { - ymean = y0; - } - else - { - ymean = ymean/m; - } - - /* - * Variance (using corrected two-pass algorithm) - */ - xvar = 0; - if( n>=2&&!samex ) - { - for(i=0; i<=n-1; i++) - { - xvar = xvar+ae_sqr(x->ptr.p_double[i]-xmean, _state); - } - xvar = xvar/(n-1); - } - yvar = 0; - if( m>=2&&!samey ) - { - for(i=0; i<=m-1; i++) - { - yvar = yvar+ae_sqr(y->ptr.p_double[i]-ymean, _state); - } - yvar = yvar/(m-1); - } - - /* - * Handle different special cases - * (one or both variances are zero). - */ - if( ae_fp_eq(xvar,0)&&ae_fp_eq(yvar,0) ) - { - if( ae_fp_eq(xmean,ymean) ) - { - *bothtails = 1.0; - } - else - { - *bothtails = 0.0; - } - if( ae_fp_greater_eq(xmean,ymean) ) - { - *lefttail = 1.0; - } - else - { - *lefttail = 0.0; - } - if( ae_fp_less_eq(xmean,ymean) ) - { - *righttail = 1.0; - } - else - { - *righttail = 0.0; - } - return; - } - if( ae_fp_eq(xvar,0) ) - { - - /* - * X is constant, unpooled 2-sample test reduces to 1-sample test. - * - * NOTE: right-tail and left-tail must be passed to 1-sample - * t-test in reverse order because we reverse order of - * of samples. - */ - studentttest1(y, m, xmean, bothtails, righttail, lefttail, _state); - return; - } - if( ae_fp_eq(yvar,0) ) - { - - /* - * Y is constant, unpooled 2-sample test reduces to 1-sample test. - */ - studentttest1(x, n, ymean, bothtails, lefttail, righttail, _state); - return; - } - - /* - * Statistic - */ - stat = (xmean-ymean)/ae_sqrt(xvar/n+yvar/m, _state); - c = xvar/n/(xvar/n+yvar/m); - df = (n-1)*(m-1)/((m-1)*ae_sqr(c, _state)+(n-1)*ae_sqr(1-c, _state)); - if( ae_fp_greater(stat,0) ) - { - p = 1-0.5*incompletebeta(df/2, 0.5, df/(df+ae_sqr(stat, _state)), _state); - } - else - { - p = 0.5*incompletebeta(df/2, 0.5, df/(df+ae_sqr(stat, _state)), _state); - } - *bothtails = 2*ae_minreal(p, 1-p, _state); - *lefttail = p; - *righttail = 1-p; -} - - - - -/************************************************************************* -Two-sample F-test - -This test checks three hypotheses about dispersions of the given samples. -The following tests are performed: - * two-tailed test (null hypothesis - the dispersions are equal) - * left-tailed test (null hypothesis - the dispersion of the first - sample is greater than or equal to the dispersion of the second - sample). - * right-tailed test (null hypothesis - the dispersion of the first - sample is less than or equal to the dispersion of the second sample) - -The test is based on the following assumptions: - * the given samples have normal distributions - * the samples are independent. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - sample size. - Y - sample 2. Array whose index goes from 0 to M-1. - M - sample size. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 19.09.2006 by Bochkanov Sergey -*************************************************************************/ -void ftest(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_int_t i; - double xmean; - double ymean; - double xvar; - double yvar; - ae_int_t df1; - ae_int_t df2; - double stat; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - if( n<=2||m<=2 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Mean - */ - xmean = 0; - for(i=0; i<=n-1; i++) - { - xmean = xmean+x->ptr.p_double[i]; - } - xmean = xmean/n; - ymean = 0; - for(i=0; i<=m-1; i++) - { - ymean = ymean+y->ptr.p_double[i]; - } - ymean = ymean/m; - - /* - * Variance (using corrected two-pass algorithm) - */ - xvar = 0; - for(i=0; i<=n-1; i++) - { - xvar = xvar+ae_sqr(x->ptr.p_double[i]-xmean, _state); - } - xvar = xvar/(n-1); - yvar = 0; - for(i=0; i<=m-1; i++) - { - yvar = yvar+ae_sqr(y->ptr.p_double[i]-ymean, _state); - } - yvar = yvar/(m-1); - if( ae_fp_eq(xvar,0)||ae_fp_eq(yvar,0) ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Statistic - */ - df1 = n-1; - df2 = m-1; - stat = ae_minreal(xvar/yvar, yvar/xvar, _state); - *bothtails = 1-(fdistribution(df1, df2, 1/stat, _state)-fdistribution(df1, df2, stat, _state)); - *lefttail = fdistribution(df1, df2, xvar/yvar, _state); - *righttail = 1-(*lefttail); -} - - -/************************************************************************* -One-sample chi-square test - -This test checks three hypotheses about the dispersion of the given sample -The following tests are performed: - * two-tailed test (null hypothesis - the dispersion equals the given - number) - * left-tailed test (null hypothesis - the dispersion is greater than - or equal to the given number) - * right-tailed test (null hypothesis - dispersion is less than or - equal to the given number). - -Test is based on the following assumptions: - * the given sample has a normal distribution. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. - Variance - dispersion value to compare with. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 19.09.2006 by Bochkanov Sergey -*************************************************************************/ -void onesamplevariancetest(/* Real */ ae_vector* x, - ae_int_t n, - double variance, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_int_t i; - double xmean; - double xvar; - double s; - double stat; - - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - - if( n<=1 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Mean - */ - xmean = 0; - for(i=0; i<=n-1; i++) - { - xmean = xmean+x->ptr.p_double[i]; - } - xmean = xmean/n; - - /* - * Variance - */ - xvar = 0; - for(i=0; i<=n-1; i++) - { - xvar = xvar+ae_sqr(x->ptr.p_double[i]-xmean, _state); - } - xvar = xvar/(n-1); - if( ae_fp_eq(xvar,0) ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - return; - } - - /* - * Statistic - */ - stat = (n-1)*xvar/variance; - s = chisquaredistribution(n-1, stat, _state); - *bothtails = 2*ae_minreal(s, 1-s, _state); - *lefttail = s; - *righttail = 1-(*lefttail); -} - - - - -/************************************************************************* -Wilcoxon signed-rank test - -This test checks three hypotheses about the median of the given sample. -The following tests are performed: - * two-tailed test (null hypothesis - the median is equal to the given - value) - * left-tailed test (null hypothesis - the median is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the median is less than or - equal to the given value) - -Requirements: - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - * the distribution should be continuous and symmetric relative to its - median. - * number of distinct values in the X array should be greater than 4 - -The test is non-parametric and doesn't require distribution X to be normal - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. - Median - assumed median value. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -To calculate p-values, special approximation is used. This method lets us -calculate p-values with two decimal places in interval [0.0001, 1]. - -"Two decimal places" does not sound very impressive, but in practice the -relative error of less than 1% is enough to make a decision. - -There is no approximation outside the [0.0001, 1] interval. Therefore, if -the significance level outlies this interval, the test returns 0.0001. - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void wilcoxonsignedranktest(/* Real */ ae_vector* x, - ae_int_t n, - double e, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state) -{ - ae_frame _frame_block; - ae_vector _x; - ae_int_t i; - ae_int_t j; - ae_int_t k; - ae_int_t t; - double tmp; - ae_int_t tmpi; - ae_int_t ns; - ae_vector r; - ae_vector c; - double w; - double p; - double mp; - double s; - double sigma; - double mu; - - ae_frame_make(_state, &_frame_block); - ae_vector_init_copy(&_x, x, _state, ae_true); - x = &_x; - *bothtails = 0; - *lefttail = 0; - *righttail = 0; - ae_vector_init(&r, 0, DT_REAL, _state, ae_true); - ae_vector_init(&c, 0, DT_INT, _state, ae_true); - - - /* - * Prepare - */ - if( n<5 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - ae_frame_leave(_state); - return; - } - ns = 0; - for(i=0; i<=n-1; i++) - { - if( ae_fp_eq(x->ptr.p_double[i],e) ) - { - continue; - } - x->ptr.p_double[ns] = x->ptr.p_double[i]; - ns = ns+1; - } - if( ns<5 ) - { - *bothtails = 1.0; - *lefttail = 1.0; - *righttail = 1.0; - ae_frame_leave(_state); - return; - } - ae_vector_set_length(&r, ns-1+1, _state); - ae_vector_set_length(&c, ns-1+1, _state); - for(i=0; i<=ns-1; i++) - { - r.ptr.p_double[i] = ae_fabs(x->ptr.p_double[i]-e, _state); - c.ptr.p_int[i] = i; - } - - /* - * sort {R, C} - */ - if( ns!=1 ) - { - i = 2; - do - { - t = i; - while(t!=1) - { - k = t/2; - if( ae_fp_greater_eq(r.ptr.p_double[k-1],r.ptr.p_double[t-1]) ) - { - t = 1; - } - else - { - tmp = r.ptr.p_double[k-1]; - r.ptr.p_double[k-1] = r.ptr.p_double[t-1]; - r.ptr.p_double[t-1] = tmp; - tmpi = c.ptr.p_int[k-1]; - c.ptr.p_int[k-1] = c.ptr.p_int[t-1]; - c.ptr.p_int[t-1] = tmpi; - t = k; - } - } - i = i+1; - } - while(i<=ns); - i = ns-1; - do - { - tmp = r.ptr.p_double[i]; - r.ptr.p_double[i] = r.ptr.p_double[0]; - r.ptr.p_double[0] = tmp; - tmpi = c.ptr.p_int[i]; - c.ptr.p_int[i] = c.ptr.p_int[0]; - c.ptr.p_int[0] = tmpi; - t = 1; - while(t!=0) - { - k = 2*t; - if( k>i ) - { - t = 0; - } - else - { - if( k=1); - } - - /* - * compute tied ranks - */ - i = 0; - while(i<=ns-1) - { - j = i+1; - while(j<=ns-1) - { - if( ae_fp_neq(r.ptr.p_double[j],r.ptr.p_double[i]) ) - { - break; - } - j = j+1; - } - for(k=i; k<=j-1; k++) - { - r.ptr.p_double[k] = 1+(double)(i+j-1)/(double)2; - } - i = j; - } - - /* - * Compute W+ - */ - w = 0; - for(i=0; i<=ns-1; i++) - { - if( ae_fp_greater(x->ptr.p_double[c.ptr.p_int[i]],e) ) - { - w = w+r.ptr.p_double[i]; - } - } - - /* - * Result - */ - mu = (double)(ns*(ns+1))/(double)4; - sigma = ae_sqrt((double)(ns*(ns+1)*(2*ns+1))/(double)24, _state); - s = (w-mu)/sigma; - if( ae_fp_less_eq(s,0) ) - { - p = ae_exp(wsr_wsigma(-(w-mu)/sigma, ns, _state), _state); - mp = 1-ae_exp(wsr_wsigma(-(w-1-mu)/sigma, ns, _state), _state); - } - else - { - mp = ae_exp(wsr_wsigma((w-mu)/sigma, ns, _state), _state); - p = 1-ae_exp(wsr_wsigma((w+1-mu)/sigma, ns, _state), _state); - } - *bothtails = ae_maxreal(2*ae_minreal(p, mp, _state), 1.0E-4, _state); - *lefttail = ae_maxreal(p, 1.0E-4, _state); - *righttail = ae_maxreal(mp, 1.0E-4, _state); - ae_frame_leave(_state); -} - - -/************************************************************************* -Sequential Chebyshev interpolation. -*************************************************************************/ -static void wsr_wcheb(double x, - double c, - double* tj, - double* tj1, - double* r, - ae_state *_state) -{ - double t; - - - *r = *r+c*(*tj); - t = 2*x*(*tj1)-(*tj); - *tj = *tj1; - *tj1 = t; -} - - -/************************************************************************* -Tail(S, 5) -*************************************************************************/ -static double wsr_w5(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-3.708099e+00*s+7.500000e+00, _state); - if( w>=7 ) - { - r = -6.931e-01; - } - if( w==6 ) - { - r = -9.008e-01; - } - if( w==5 ) - { - r = -1.163e+00; - } - if( w==4 ) - { - r = -1.520e+00; - } - if( w==3 ) - { - r = -1.856e+00; - } - if( w==2 ) - { - r = -2.367e+00; - } - if( w==1 ) - { - r = -2.773e+00; - } - if( w<=0 ) - { - r = -3.466e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 6) -*************************************************************************/ -static double wsr_w6(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-4.769696e+00*s+1.050000e+01, _state); - if( w>=10 ) - { - r = -6.931e-01; - } - if( w==9 ) - { - r = -8.630e-01; - } - if( w==8 ) - { - r = -1.068e+00; - } - if( w==7 ) - { - r = -1.269e+00; - } - if( w==6 ) - { - r = -1.520e+00; - } - if( w==5 ) - { - r = -1.856e+00; - } - if( w==4 ) - { - r = -2.213e+00; - } - if( w==3 ) - { - r = -2.549e+00; - } - if( w==2 ) - { - r = -3.060e+00; - } - if( w==1 ) - { - r = -3.466e+00; - } - if( w<=0 ) - { - r = -4.159e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 7) -*************************************************************************/ -static double wsr_w7(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-5.916080e+00*s+1.400000e+01, _state); - if( w>=14 ) - { - r = -6.325e-01; - } - if( w==13 ) - { - r = -7.577e-01; - } - if( w==12 ) - { - r = -9.008e-01; - } - if( w==11 ) - { - r = -1.068e+00; - } - if( w==10 ) - { - r = -1.241e+00; - } - if( w==9 ) - { - r = -1.451e+00; - } - if( w==8 ) - { - r = -1.674e+00; - } - if( w==7 ) - { - r = -1.908e+00; - } - if( w==6 ) - { - r = -2.213e+00; - } - if( w==5 ) - { - r = -2.549e+00; - } - if( w==4 ) - { - r = -2.906e+00; - } - if( w==3 ) - { - r = -3.243e+00; - } - if( w==2 ) - { - r = -3.753e+00; - } - if( w==1 ) - { - r = -4.159e+00; - } - if( w<=0 ) - { - r = -4.852e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 8) -*************************************************************************/ -static double wsr_w8(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-7.141428e+00*s+1.800000e+01, _state); - if( w>=18 ) - { - r = -6.399e-01; - } - if( w==17 ) - { - r = -7.494e-01; - } - if( w==16 ) - { - r = -8.630e-01; - } - if( w==15 ) - { - r = -9.913e-01; - } - if( w==14 ) - { - r = -1.138e+00; - } - if( w==13 ) - { - r = -1.297e+00; - } - if( w==12 ) - { - r = -1.468e+00; - } - if( w==11 ) - { - r = -1.653e+00; - } - if( w==10 ) - { - r = -1.856e+00; - } - if( w==9 ) - { - r = -2.079e+00; - } - if( w==8 ) - { - r = -2.326e+00; - } - if( w==7 ) - { - r = -2.601e+00; - } - if( w==6 ) - { - r = -2.906e+00; - } - if( w==5 ) - { - r = -3.243e+00; - } - if( w==4 ) - { - r = -3.599e+00; - } - if( w==3 ) - { - r = -3.936e+00; - } - if( w==2 ) - { - r = -4.447e+00; - } - if( w==1 ) - { - r = -4.852e+00; - } - if( w<=0 ) - { - r = -5.545e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 9) -*************************************************************************/ -static double wsr_w9(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-8.440972e+00*s+2.250000e+01, _state); - if( w>=22 ) - { - r = -6.931e-01; - } - if( w==21 ) - { - r = -7.873e-01; - } - if( w==20 ) - { - r = -8.912e-01; - } - if( w==19 ) - { - r = -1.002e+00; - } - if( w==18 ) - { - r = -1.120e+00; - } - if( w==17 ) - { - r = -1.255e+00; - } - if( w==16 ) - { - r = -1.394e+00; - } - if( w==15 ) - { - r = -1.547e+00; - } - if( w==14 ) - { - r = -1.717e+00; - } - if( w==13 ) - { - r = -1.895e+00; - } - if( w==12 ) - { - r = -2.079e+00; - } - if( w==11 ) - { - r = -2.287e+00; - } - if( w==10 ) - { - r = -2.501e+00; - } - if( w==9 ) - { - r = -2.742e+00; - } - if( w==8 ) - { - r = -3.019e+00; - } - if( w==7 ) - { - r = -3.294e+00; - } - if( w==6 ) - { - r = -3.599e+00; - } - if( w==5 ) - { - r = -3.936e+00; - } - if( w==4 ) - { - r = -4.292e+00; - } - if( w==3 ) - { - r = -4.629e+00; - } - if( w==2 ) - { - r = -5.140e+00; - } - if( w==1 ) - { - r = -5.545e+00; - } - if( w<=0 ) - { - r = -6.238e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 10) -*************************************************************************/ -static double wsr_w10(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-9.810708e+00*s+2.750000e+01, _state); - if( w>=27 ) - { - r = -6.931e-01; - } - if( w==26 ) - { - r = -7.745e-01; - } - if( w==25 ) - { - r = -8.607e-01; - } - if( w==24 ) - { - r = -9.551e-01; - } - if( w==23 ) - { - r = -1.057e+00; - } - if( w==22 ) - { - r = -1.163e+00; - } - if( w==21 ) - { - r = -1.279e+00; - } - if( w==20 ) - { - r = -1.402e+00; - } - if( w==19 ) - { - r = -1.533e+00; - } - if( w==18 ) - { - r = -1.674e+00; - } - if( w==17 ) - { - r = -1.826e+00; - } - if( w==16 ) - { - r = -1.983e+00; - } - if( w==15 ) - { - r = -2.152e+00; - } - if( w==14 ) - { - r = -2.336e+00; - } - if( w==13 ) - { - r = -2.525e+00; - } - if( w==12 ) - { - r = -2.727e+00; - } - if( w==11 ) - { - r = -2.942e+00; - } - if( w==10 ) - { - r = -3.170e+00; - } - if( w==9 ) - { - r = -3.435e+00; - } - if( w==8 ) - { - r = -3.713e+00; - } - if( w==7 ) - { - r = -3.987e+00; - } - if( w==6 ) - { - r = -4.292e+00; - } - if( w==5 ) - { - r = -4.629e+00; - } - if( w==4 ) - { - r = -4.986e+00; - } - if( w==3 ) - { - r = -5.322e+00; - } - if( w==2 ) - { - r = -5.833e+00; - } - if( w==1 ) - { - r = -6.238e+00; - } - if( w<=0 ) - { - r = -6.931e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 11) -*************************************************************************/ -static double wsr_w11(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-1.124722e+01*s+3.300000e+01, _state); - if( w>=33 ) - { - r = -6.595e-01; - } - if( w==32 ) - { - r = -7.279e-01; - } - if( w==31 ) - { - r = -8.002e-01; - } - if( w==30 ) - { - r = -8.782e-01; - } - if( w==29 ) - { - r = -9.615e-01; - } - if( w==28 ) - { - r = -1.050e+00; - } - if( w==27 ) - { - r = -1.143e+00; - } - if( w==26 ) - { - r = -1.243e+00; - } - if( w==25 ) - { - r = -1.348e+00; - } - if( w==24 ) - { - r = -1.459e+00; - } - if( w==23 ) - { - r = -1.577e+00; - } - if( w==22 ) - { - r = -1.700e+00; - } - if( w==21 ) - { - r = -1.832e+00; - } - if( w==20 ) - { - r = -1.972e+00; - } - if( w==19 ) - { - r = -2.119e+00; - } - if( w==18 ) - { - r = -2.273e+00; - } - if( w==17 ) - { - r = -2.437e+00; - } - if( w==16 ) - { - r = -2.607e+00; - } - if( w==15 ) - { - r = -2.788e+00; - } - if( w==14 ) - { - r = -2.980e+00; - } - if( w==13 ) - { - r = -3.182e+00; - } - if( w==12 ) - { - r = -3.391e+00; - } - if( w==11 ) - { - r = -3.617e+00; - } - if( w==10 ) - { - r = -3.863e+00; - } - if( w==9 ) - { - r = -4.128e+00; - } - if( w==8 ) - { - r = -4.406e+00; - } - if( w==7 ) - { - r = -4.680e+00; - } - if( w==6 ) - { - r = -4.986e+00; - } - if( w==5 ) - { - r = -5.322e+00; - } - if( w==4 ) - { - r = -5.679e+00; - } - if( w==3 ) - { - r = -6.015e+00; - } - if( w==2 ) - { - r = -6.526e+00; - } - if( w==1 ) - { - r = -6.931e+00; - } - if( w<=0 ) - { - r = -7.625e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 12) -*************************************************************************/ -static double wsr_w12(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-1.274755e+01*s+3.900000e+01, _state); - if( w>=39 ) - { - r = -6.633e-01; - } - if( w==38 ) - { - r = -7.239e-01; - } - if( w==37 ) - { - r = -7.878e-01; - } - if( w==36 ) - { - r = -8.556e-01; - } - if( w==35 ) - { - r = -9.276e-01; - } - if( w==34 ) - { - r = -1.003e+00; - } - if( w==33 ) - { - r = -1.083e+00; - } - if( w==32 ) - { - r = -1.168e+00; - } - if( w==31 ) - { - r = -1.256e+00; - } - if( w==30 ) - { - r = -1.350e+00; - } - if( w==29 ) - { - r = -1.449e+00; - } - if( w==28 ) - { - r = -1.552e+00; - } - if( w==27 ) - { - r = -1.660e+00; - } - if( w==26 ) - { - r = -1.774e+00; - } - if( w==25 ) - { - r = -1.893e+00; - } - if( w==24 ) - { - r = -2.017e+00; - } - if( w==23 ) - { - r = -2.148e+00; - } - if( w==22 ) - { - r = -2.285e+00; - } - if( w==21 ) - { - r = -2.429e+00; - } - if( w==20 ) - { - r = -2.581e+00; - } - if( w==19 ) - { - r = -2.738e+00; - } - if( w==18 ) - { - r = -2.902e+00; - } - if( w==17 ) - { - r = -3.076e+00; - } - if( w==16 ) - { - r = -3.255e+00; - } - if( w==15 ) - { - r = -3.443e+00; - } - if( w==14 ) - { - r = -3.645e+00; - } - if( w==13 ) - { - r = -3.852e+00; - } - if( w==12 ) - { - r = -4.069e+00; - } - if( w==11 ) - { - r = -4.310e+00; - } - if( w==10 ) - { - r = -4.557e+00; - } - if( w==9 ) - { - r = -4.821e+00; - } - if( w==8 ) - { - r = -5.099e+00; - } - if( w==7 ) - { - r = -5.373e+00; - } - if( w==6 ) - { - r = -5.679e+00; - } - if( w==5 ) - { - r = -6.015e+00; - } - if( w==4 ) - { - r = -6.372e+00; - } - if( w==3 ) - { - r = -6.708e+00; - } - if( w==2 ) - { - r = -7.219e+00; - } - if( w==1 ) - { - r = -7.625e+00; - } - if( w<=0 ) - { - r = -8.318e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 13) -*************************************************************************/ -static double wsr_w13(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-1.430909e+01*s+4.550000e+01, _state); - if( w>=45 ) - { - r = -6.931e-01; - } - if( w==44 ) - { - r = -7.486e-01; - } - if( w==43 ) - { - r = -8.068e-01; - } - if( w==42 ) - { - r = -8.683e-01; - } - if( w==41 ) - { - r = -9.328e-01; - } - if( w==40 ) - { - r = -1.001e+00; - } - if( w==39 ) - { - r = -1.072e+00; - } - if( w==38 ) - { - r = -1.146e+00; - } - if( w==37 ) - { - r = -1.224e+00; - } - if( w==36 ) - { - r = -1.306e+00; - } - if( w==35 ) - { - r = -1.392e+00; - } - if( w==34 ) - { - r = -1.481e+00; - } - if( w==33 ) - { - r = -1.574e+00; - } - if( w==32 ) - { - r = -1.672e+00; - } - if( w==31 ) - { - r = -1.773e+00; - } - if( w==30 ) - { - r = -1.879e+00; - } - if( w==29 ) - { - r = -1.990e+00; - } - if( w==28 ) - { - r = -2.104e+00; - } - if( w==27 ) - { - r = -2.224e+00; - } - if( w==26 ) - { - r = -2.349e+00; - } - if( w==25 ) - { - r = -2.479e+00; - } - if( w==24 ) - { - r = -2.614e+00; - } - if( w==23 ) - { - r = -2.755e+00; - } - if( w==22 ) - { - r = -2.902e+00; - } - if( w==21 ) - { - r = -3.055e+00; - } - if( w==20 ) - { - r = -3.215e+00; - } - if( w==19 ) - { - r = -3.380e+00; - } - if( w==18 ) - { - r = -3.551e+00; - } - if( w==17 ) - { - r = -3.733e+00; - } - if( w==16 ) - { - r = -3.917e+00; - } - if( w==15 ) - { - r = -4.113e+00; - } - if( w==14 ) - { - r = -4.320e+00; - } - if( w==13 ) - { - r = -4.534e+00; - } - if( w==12 ) - { - r = -4.762e+00; - } - if( w==11 ) - { - r = -5.004e+00; - } - if( w==10 ) - { - r = -5.250e+00; - } - if( w==9 ) - { - r = -5.514e+00; - } - if( w==8 ) - { - r = -5.792e+00; - } - if( w==7 ) - { - r = -6.066e+00; - } - if( w==6 ) - { - r = -6.372e+00; - } - if( w==5 ) - { - r = -6.708e+00; - } - if( w==4 ) - { - r = -7.065e+00; - } - if( w==3 ) - { - r = -7.401e+00; - } - if( w==2 ) - { - r = -7.912e+00; - } - if( w==1 ) - { - r = -8.318e+00; - } - if( w<=0 ) - { - r = -9.011e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 14) -*************************************************************************/ -static double wsr_w14(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-1.592953e+01*s+5.250000e+01, _state); - if( w>=52 ) - { - r = -6.931e-01; - } - if( w==51 ) - { - r = -7.428e-01; - } - if( w==50 ) - { - r = -7.950e-01; - } - if( w==49 ) - { - r = -8.495e-01; - } - if( w==48 ) - { - r = -9.067e-01; - } - if( w==47 ) - { - r = -9.664e-01; - } - if( w==46 ) - { - r = -1.029e+00; - } - if( w==45 ) - { - r = -1.094e+00; - } - if( w==44 ) - { - r = -1.162e+00; - } - if( w==43 ) - { - r = -1.233e+00; - } - if( w==42 ) - { - r = -1.306e+00; - } - if( w==41 ) - { - r = -1.383e+00; - } - if( w==40 ) - { - r = -1.463e+00; - } - if( w==39 ) - { - r = -1.546e+00; - } - if( w==38 ) - { - r = -1.632e+00; - } - if( w==37 ) - { - r = -1.722e+00; - } - if( w==36 ) - { - r = -1.815e+00; - } - if( w==35 ) - { - r = -1.911e+00; - } - if( w==34 ) - { - r = -2.011e+00; - } - if( w==33 ) - { - r = -2.115e+00; - } - if( w==32 ) - { - r = -2.223e+00; - } - if( w==31 ) - { - r = -2.334e+00; - } - if( w==30 ) - { - r = -2.450e+00; - } - if( w==29 ) - { - r = -2.570e+00; - } - if( w==28 ) - { - r = -2.694e+00; - } - if( w==27 ) - { - r = -2.823e+00; - } - if( w==26 ) - { - r = -2.956e+00; - } - if( w==25 ) - { - r = -3.095e+00; - } - if( w==24 ) - { - r = -3.238e+00; - } - if( w==23 ) - { - r = -3.387e+00; - } - if( w==22 ) - { - r = -3.541e+00; - } - if( w==21 ) - { - r = -3.700e+00; - } - if( w==20 ) - { - r = -3.866e+00; - } - if( w==19 ) - { - r = -4.038e+00; - } - if( w==18 ) - { - r = -4.215e+00; - } - if( w==17 ) - { - r = -4.401e+00; - } - if( w==16 ) - { - r = -4.592e+00; - } - if( w==15 ) - { - r = -4.791e+00; - } - if( w==14 ) - { - r = -5.004e+00; - } - if( w==13 ) - { - r = -5.227e+00; - } - if( w==12 ) - { - r = -5.456e+00; - } - if( w==11 ) - { - r = -5.697e+00; - } - if( w==10 ) - { - r = -5.943e+00; - } - if( w==9 ) - { - r = -6.208e+00; - } - if( w==8 ) - { - r = -6.485e+00; - } - if( w==7 ) - { - r = -6.760e+00; - } - if( w==6 ) - { - r = -7.065e+00; - } - if( w==5 ) - { - r = -7.401e+00; - } - if( w==4 ) - { - r = -7.758e+00; - } - if( w==3 ) - { - r = -8.095e+00; - } - if( w==2 ) - { - r = -8.605e+00; - } - if( w==1 ) - { - r = -9.011e+00; - } - if( w<=0 ) - { - r = -9.704e+00; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 15) -*************************************************************************/ -static double wsr_w15(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-1.760682e+01*s+6.000000e+01, _state); - if( w>=60 ) - { - r = -6.714e-01; - } - if( w==59 ) - { - r = -7.154e-01; - } - if( w==58 ) - { - r = -7.613e-01; - } - if( w==57 ) - { - r = -8.093e-01; - } - if( w==56 ) - { - r = -8.593e-01; - } - if( w==55 ) - { - r = -9.114e-01; - } - if( w==54 ) - { - r = -9.656e-01; - } - if( w==53 ) - { - r = -1.022e+00; - } - if( w==52 ) - { - r = -1.081e+00; - } - if( w==51 ) - { - r = -1.142e+00; - } - if( w==50 ) - { - r = -1.205e+00; - } - if( w==49 ) - { - r = -1.270e+00; - } - if( w==48 ) - { - r = -1.339e+00; - } - if( w==47 ) - { - r = -1.409e+00; - } - if( w==46 ) - { - r = -1.482e+00; - } - if( w==45 ) - { - r = -1.558e+00; - } - if( w==44 ) - { - r = -1.636e+00; - } - if( w==43 ) - { - r = -1.717e+00; - } - if( w==42 ) - { - r = -1.801e+00; - } - if( w==41 ) - { - r = -1.888e+00; - } - if( w==40 ) - { - r = -1.977e+00; - } - if( w==39 ) - { - r = -2.070e+00; - } - if( w==38 ) - { - r = -2.166e+00; - } - if( w==37 ) - { - r = -2.265e+00; - } - if( w==36 ) - { - r = -2.366e+00; - } - if( w==35 ) - { - r = -2.472e+00; - } - if( w==34 ) - { - r = -2.581e+00; - } - if( w==33 ) - { - r = -2.693e+00; - } - if( w==32 ) - { - r = -2.809e+00; - } - if( w==31 ) - { - r = -2.928e+00; - } - if( w==30 ) - { - r = -3.051e+00; - } - if( w==29 ) - { - r = -3.179e+00; - } - if( w==28 ) - { - r = -3.310e+00; - } - if( w==27 ) - { - r = -3.446e+00; - } - if( w==26 ) - { - r = -3.587e+00; - } - if( w==25 ) - { - r = -3.732e+00; - } - if( w==24 ) - { - r = -3.881e+00; - } - if( w==23 ) - { - r = -4.036e+00; - } - if( w==22 ) - { - r = -4.195e+00; - } - if( w==21 ) - { - r = -4.359e+00; - } - if( w==20 ) - { - r = -4.531e+00; - } - if( w==19 ) - { - r = -4.707e+00; - } - if( w==18 ) - { - r = -4.888e+00; - } - if( w==17 ) - { - r = -5.079e+00; - } - if( w==16 ) - { - r = -5.273e+00; - } - if( w==15 ) - { - r = -5.477e+00; - } - if( w==14 ) - { - r = -5.697e+00; - } - if( w==13 ) - { - r = -5.920e+00; - } - if( w==12 ) - { - r = -6.149e+00; - } - if( w==11 ) - { - r = -6.390e+00; - } - if( w==10 ) - { - r = -6.636e+00; - } - if( w==9 ) - { - r = -6.901e+00; - } - if( w==8 ) - { - r = -7.178e+00; - } - if( w==7 ) - { - r = -7.453e+00; - } - if( w==6 ) - { - r = -7.758e+00; - } - if( w==5 ) - { - r = -8.095e+00; - } - if( w==4 ) - { - r = -8.451e+00; - } - if( w==3 ) - { - r = -8.788e+00; - } - if( w==2 ) - { - r = -9.299e+00; - } - if( w==1 ) - { - r = -9.704e+00; - } - if( w<=0 ) - { - r = -1.040e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 16) -*************************************************************************/ -static double wsr_w16(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-1.933908e+01*s+6.800000e+01, _state); - if( w>=68 ) - { - r = -6.733e-01; - } - if( w==67 ) - { - r = -7.134e-01; - } - if( w==66 ) - { - r = -7.551e-01; - } - if( w==65 ) - { - r = -7.986e-01; - } - if( w==64 ) - { - r = -8.437e-01; - } - if( w==63 ) - { - r = -8.905e-01; - } - if( w==62 ) - { - r = -9.391e-01; - } - if( w==61 ) - { - r = -9.895e-01; - } - if( w==60 ) - { - r = -1.042e+00; - } - if( w==59 ) - { - r = -1.096e+00; - } - if( w==58 ) - { - r = -1.152e+00; - } - if( w==57 ) - { - r = -1.210e+00; - } - if( w==56 ) - { - r = -1.270e+00; - } - if( w==55 ) - { - r = -1.331e+00; - } - if( w==54 ) - { - r = -1.395e+00; - } - if( w==53 ) - { - r = -1.462e+00; - } - if( w==52 ) - { - r = -1.530e+00; - } - if( w==51 ) - { - r = -1.600e+00; - } - if( w==50 ) - { - r = -1.673e+00; - } - if( w==49 ) - { - r = -1.748e+00; - } - if( w==48 ) - { - r = -1.825e+00; - } - if( w==47 ) - { - r = -1.904e+00; - } - if( w==46 ) - { - r = -1.986e+00; - } - if( w==45 ) - { - r = -2.071e+00; - } - if( w==44 ) - { - r = -2.158e+00; - } - if( w==43 ) - { - r = -2.247e+00; - } - if( w==42 ) - { - r = -2.339e+00; - } - if( w==41 ) - { - r = -2.434e+00; - } - if( w==40 ) - { - r = -2.532e+00; - } - if( w==39 ) - { - r = -2.632e+00; - } - if( w==38 ) - { - r = -2.735e+00; - } - if( w==37 ) - { - r = -2.842e+00; - } - if( w==36 ) - { - r = -2.951e+00; - } - if( w==35 ) - { - r = -3.064e+00; - } - if( w==34 ) - { - r = -3.179e+00; - } - if( w==33 ) - { - r = -3.298e+00; - } - if( w==32 ) - { - r = -3.420e+00; - } - if( w==31 ) - { - r = -3.546e+00; - } - if( w==30 ) - { - r = -3.676e+00; - } - if( w==29 ) - { - r = -3.810e+00; - } - if( w==28 ) - { - r = -3.947e+00; - } - if( w==27 ) - { - r = -4.088e+00; - } - if( w==26 ) - { - r = -4.234e+00; - } - if( w==25 ) - { - r = -4.383e+00; - } - if( w==24 ) - { - r = -4.538e+00; - } - if( w==23 ) - { - r = -4.697e+00; - } - if( w==22 ) - { - r = -4.860e+00; - } - if( w==21 ) - { - r = -5.029e+00; - } - if( w==20 ) - { - r = -5.204e+00; - } - if( w==19 ) - { - r = -5.383e+00; - } - if( w==18 ) - { - r = -5.569e+00; - } - if( w==17 ) - { - r = -5.762e+00; - } - if( w==16 ) - { - r = -5.960e+00; - } - if( w==15 ) - { - r = -6.170e+00; - } - if( w==14 ) - { - r = -6.390e+00; - } - if( w==13 ) - { - r = -6.613e+00; - } - if( w==12 ) - { - r = -6.842e+00; - } - if( w==11 ) - { - r = -7.083e+00; - } - if( w==10 ) - { - r = -7.329e+00; - } - if( w==9 ) - { - r = -7.594e+00; - } - if( w==8 ) - { - r = -7.871e+00; - } - if( w==7 ) - { - r = -8.146e+00; - } - if( w==6 ) - { - r = -8.451e+00; - } - if( w==5 ) - { - r = -8.788e+00; - } - if( w==4 ) - { - r = -9.144e+00; - } - if( w==3 ) - { - r = -9.481e+00; - } - if( w==2 ) - { - r = -9.992e+00; - } - if( w==1 ) - { - r = -1.040e+01; - } - if( w<=0 ) - { - r = -1.109e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 17) -*************************************************************************/ -static double wsr_w17(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-2.112463e+01*s+7.650000e+01, _state); - if( w>=76 ) - { - r = -6.931e-01; - } - if( w==75 ) - { - r = -7.306e-01; - } - if( w==74 ) - { - r = -7.695e-01; - } - if( w==73 ) - { - r = -8.097e-01; - } - if( w==72 ) - { - r = -8.514e-01; - } - if( w==71 ) - { - r = -8.946e-01; - } - if( w==70 ) - { - r = -9.392e-01; - } - if( w==69 ) - { - r = -9.853e-01; - } - if( w==68 ) - { - r = -1.033e+00; - } - if( w==67 ) - { - r = -1.082e+00; - } - if( w==66 ) - { - r = -1.133e+00; - } - if( w==65 ) - { - r = -1.185e+00; - } - if( w==64 ) - { - r = -1.240e+00; - } - if( w==63 ) - { - r = -1.295e+00; - } - if( w==62 ) - { - r = -1.353e+00; - } - if( w==61 ) - { - r = -1.412e+00; - } - if( w==60 ) - { - r = -1.473e+00; - } - if( w==59 ) - { - r = -1.536e+00; - } - if( w==58 ) - { - r = -1.600e+00; - } - if( w==57 ) - { - r = -1.666e+00; - } - if( w==56 ) - { - r = -1.735e+00; - } - if( w==55 ) - { - r = -1.805e+00; - } - if( w==54 ) - { - r = -1.877e+00; - } - if( w==53 ) - { - r = -1.951e+00; - } - if( w==52 ) - { - r = -2.028e+00; - } - if( w==51 ) - { - r = -2.106e+00; - } - if( w==50 ) - { - r = -2.186e+00; - } - if( w==49 ) - { - r = -2.269e+00; - } - if( w==48 ) - { - r = -2.353e+00; - } - if( w==47 ) - { - r = -2.440e+00; - } - if( w==46 ) - { - r = -2.530e+00; - } - if( w==45 ) - { - r = -2.621e+00; - } - if( w==44 ) - { - r = -2.715e+00; - } - if( w==43 ) - { - r = -2.812e+00; - } - if( w==42 ) - { - r = -2.911e+00; - } - if( w==41 ) - { - r = -3.012e+00; - } - if( w==40 ) - { - r = -3.116e+00; - } - if( w==39 ) - { - r = -3.223e+00; - } - if( w==38 ) - { - r = -3.332e+00; - } - if( w==37 ) - { - r = -3.445e+00; - } - if( w==36 ) - { - r = -3.560e+00; - } - if( w==35 ) - { - r = -3.678e+00; - } - if( w==34 ) - { - r = -3.799e+00; - } - if( w==33 ) - { - r = -3.924e+00; - } - if( w==32 ) - { - r = -4.052e+00; - } - if( w==31 ) - { - r = -4.183e+00; - } - if( w==30 ) - { - r = -4.317e+00; - } - if( w==29 ) - { - r = -4.456e+00; - } - if( w==28 ) - { - r = -4.597e+00; - } - if( w==27 ) - { - r = -4.743e+00; - } - if( w==26 ) - { - r = -4.893e+00; - } - if( w==25 ) - { - r = -5.047e+00; - } - if( w==24 ) - { - r = -5.204e+00; - } - if( w==23 ) - { - r = -5.367e+00; - } - if( w==22 ) - { - r = -5.534e+00; - } - if( w==21 ) - { - r = -5.706e+00; - } - if( w==20 ) - { - r = -5.884e+00; - } - if( w==19 ) - { - r = -6.066e+00; - } - if( w==18 ) - { - r = -6.254e+00; - } - if( w==17 ) - { - r = -6.451e+00; - } - if( w==16 ) - { - r = -6.654e+00; - } - if( w==15 ) - { - r = -6.864e+00; - } - if( w==14 ) - { - r = -7.083e+00; - } - if( w==13 ) - { - r = -7.306e+00; - } - if( w==12 ) - { - r = -7.535e+00; - } - if( w==11 ) - { - r = -7.776e+00; - } - if( w==10 ) - { - r = -8.022e+00; - } - if( w==9 ) - { - r = -8.287e+00; - } - if( w==8 ) - { - r = -8.565e+00; - } - if( w==7 ) - { - r = -8.839e+00; - } - if( w==6 ) - { - r = -9.144e+00; - } - if( w==5 ) - { - r = -9.481e+00; - } - if( w==4 ) - { - r = -9.838e+00; - } - if( w==3 ) - { - r = -1.017e+01; - } - if( w==2 ) - { - r = -1.068e+01; - } - if( w==1 ) - { - r = -1.109e+01; - } - if( w<=0 ) - { - r = -1.178e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 18) -*************************************************************************/ -static double wsr_w18(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-2.296193e+01*s+8.550000e+01, _state); - if( w>=85 ) - { - r = -6.931e-01; - } - if( w==84 ) - { - r = -7.276e-01; - } - if( w==83 ) - { - r = -7.633e-01; - } - if( w==82 ) - { - r = -8.001e-01; - } - if( w==81 ) - { - r = -8.381e-01; - } - if( w==80 ) - { - r = -8.774e-01; - } - if( w==79 ) - { - r = -9.179e-01; - } - if( w==78 ) - { - r = -9.597e-01; - } - if( w==77 ) - { - r = -1.003e+00; - } - if( w==76 ) - { - r = -1.047e+00; - } - if( w==75 ) - { - r = -1.093e+00; - } - if( w==74 ) - { - r = -1.140e+00; - } - if( w==73 ) - { - r = -1.188e+00; - } - if( w==72 ) - { - r = -1.238e+00; - } - if( w==71 ) - { - r = -1.289e+00; - } - if( w==70 ) - { - r = -1.342e+00; - } - if( w==69 ) - { - r = -1.396e+00; - } - if( w==68 ) - { - r = -1.452e+00; - } - if( w==67 ) - { - r = -1.509e+00; - } - if( w==66 ) - { - r = -1.568e+00; - } - if( w==65 ) - { - r = -1.628e+00; - } - if( w==64 ) - { - r = -1.690e+00; - } - if( w==63 ) - { - r = -1.753e+00; - } - if( w==62 ) - { - r = -1.818e+00; - } - if( w==61 ) - { - r = -1.885e+00; - } - if( w==60 ) - { - r = -1.953e+00; - } - if( w==59 ) - { - r = -2.023e+00; - } - if( w==58 ) - { - r = -2.095e+00; - } - if( w==57 ) - { - r = -2.168e+00; - } - if( w==56 ) - { - r = -2.244e+00; - } - if( w==55 ) - { - r = -2.321e+00; - } - if( w==54 ) - { - r = -2.400e+00; - } - if( w==53 ) - { - r = -2.481e+00; - } - if( w==52 ) - { - r = -2.564e+00; - } - if( w==51 ) - { - r = -2.648e+00; - } - if( w==50 ) - { - r = -2.735e+00; - } - if( w==49 ) - { - r = -2.824e+00; - } - if( w==48 ) - { - r = -2.915e+00; - } - if( w==47 ) - { - r = -3.008e+00; - } - if( w==46 ) - { - r = -3.104e+00; - } - if( w==45 ) - { - r = -3.201e+00; - } - if( w==44 ) - { - r = -3.301e+00; - } - if( w==43 ) - { - r = -3.403e+00; - } - if( w==42 ) - { - r = -3.508e+00; - } - if( w==41 ) - { - r = -3.615e+00; - } - if( w==40 ) - { - r = -3.724e+00; - } - if( w==39 ) - { - r = -3.836e+00; - } - if( w==38 ) - { - r = -3.950e+00; - } - if( w==37 ) - { - r = -4.068e+00; - } - if( w==36 ) - { - r = -4.188e+00; - } - if( w==35 ) - { - r = -4.311e+00; - } - if( w==34 ) - { - r = -4.437e+00; - } - if( w==33 ) - { - r = -4.565e+00; - } - if( w==32 ) - { - r = -4.698e+00; - } - if( w==31 ) - { - r = -4.833e+00; - } - if( w==30 ) - { - r = -4.971e+00; - } - if( w==29 ) - { - r = -5.113e+00; - } - if( w==28 ) - { - r = -5.258e+00; - } - if( w==27 ) - { - r = -5.408e+00; - } - if( w==26 ) - { - r = -5.561e+00; - } - if( w==25 ) - { - r = -5.717e+00; - } - if( w==24 ) - { - r = -5.878e+00; - } - if( w==23 ) - { - r = -6.044e+00; - } - if( w==22 ) - { - r = -6.213e+00; - } - if( w==21 ) - { - r = -6.388e+00; - } - if( w==20 ) - { - r = -6.569e+00; - } - if( w==19 ) - { - r = -6.753e+00; - } - if( w==18 ) - { - r = -6.943e+00; - } - if( w==17 ) - { - r = -7.144e+00; - } - if( w==16 ) - { - r = -7.347e+00; - } - if( w==15 ) - { - r = -7.557e+00; - } - if( w==14 ) - { - r = -7.776e+00; - } - if( w==13 ) - { - r = -7.999e+00; - } - if( w==12 ) - { - r = -8.228e+00; - } - if( w==11 ) - { - r = -8.469e+00; - } - if( w==10 ) - { - r = -8.715e+00; - } - if( w==9 ) - { - r = -8.980e+00; - } - if( w==8 ) - { - r = -9.258e+00; - } - if( w==7 ) - { - r = -9.532e+00; - } - if( w==6 ) - { - r = -9.838e+00; - } - if( w==5 ) - { - r = -1.017e+01; - } - if( w==4 ) - { - r = -1.053e+01; - } - if( w==3 ) - { - r = -1.087e+01; - } - if( w==2 ) - { - r = -1.138e+01; - } - if( w==1 ) - { - r = -1.178e+01; - } - if( w<=0 ) - { - r = -1.248e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 19) -*************************************************************************/ -static double wsr_w19(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-2.484955e+01*s+9.500000e+01, _state); - if( w>=95 ) - { - r = -6.776e-01; - } - if( w==94 ) - { - r = -7.089e-01; - } - if( w==93 ) - { - r = -7.413e-01; - } - if( w==92 ) - { - r = -7.747e-01; - } - if( w==91 ) - { - r = -8.090e-01; - } - if( w==90 ) - { - r = -8.445e-01; - } - if( w==89 ) - { - r = -8.809e-01; - } - if( w==88 ) - { - r = -9.185e-01; - } - if( w==87 ) - { - r = -9.571e-01; - } - if( w==86 ) - { - r = -9.968e-01; - } - if( w==85 ) - { - r = -1.038e+00; - } - if( w==84 ) - { - r = -1.080e+00; - } - if( w==83 ) - { - r = -1.123e+00; - } - if( w==82 ) - { - r = -1.167e+00; - } - if( w==81 ) - { - r = -1.213e+00; - } - if( w==80 ) - { - r = -1.259e+00; - } - if( w==79 ) - { - r = -1.307e+00; - } - if( w==78 ) - { - r = -1.356e+00; - } - if( w==77 ) - { - r = -1.407e+00; - } - if( w==76 ) - { - r = -1.458e+00; - } - if( w==75 ) - { - r = -1.511e+00; - } - if( w==74 ) - { - r = -1.565e+00; - } - if( w==73 ) - { - r = -1.621e+00; - } - if( w==72 ) - { - r = -1.678e+00; - } - if( w==71 ) - { - r = -1.736e+00; - } - if( w==70 ) - { - r = -1.796e+00; - } - if( w==69 ) - { - r = -1.857e+00; - } - if( w==68 ) - { - r = -1.919e+00; - } - if( w==67 ) - { - r = -1.983e+00; - } - if( w==66 ) - { - r = -2.048e+00; - } - if( w==65 ) - { - r = -2.115e+00; - } - if( w==64 ) - { - r = -2.183e+00; - } - if( w==63 ) - { - r = -2.253e+00; - } - if( w==62 ) - { - r = -2.325e+00; - } - if( w==61 ) - { - r = -2.398e+00; - } - if( w==60 ) - { - r = -2.472e+00; - } - if( w==59 ) - { - r = -2.548e+00; - } - if( w==58 ) - { - r = -2.626e+00; - } - if( w==57 ) - { - r = -2.706e+00; - } - if( w==56 ) - { - r = -2.787e+00; - } - if( w==55 ) - { - r = -2.870e+00; - } - if( w==54 ) - { - r = -2.955e+00; - } - if( w==53 ) - { - r = -3.042e+00; - } - if( w==52 ) - { - r = -3.130e+00; - } - if( w==51 ) - { - r = -3.220e+00; - } - if( w==50 ) - { - r = -3.313e+00; - } - if( w==49 ) - { - r = -3.407e+00; - } - if( w==48 ) - { - r = -3.503e+00; - } - if( w==47 ) - { - r = -3.601e+00; - } - if( w==46 ) - { - r = -3.702e+00; - } - if( w==45 ) - { - r = -3.804e+00; - } - if( w==44 ) - { - r = -3.909e+00; - } - if( w==43 ) - { - r = -4.015e+00; - } - if( w==42 ) - { - r = -4.125e+00; - } - if( w==41 ) - { - r = -4.236e+00; - } - if( w==40 ) - { - r = -4.350e+00; - } - if( w==39 ) - { - r = -4.466e+00; - } - if( w==38 ) - { - r = -4.585e+00; - } - if( w==37 ) - { - r = -4.706e+00; - } - if( w==36 ) - { - r = -4.830e+00; - } - if( w==35 ) - { - r = -4.957e+00; - } - if( w==34 ) - { - r = -5.086e+00; - } - if( w==33 ) - { - r = -5.219e+00; - } - if( w==32 ) - { - r = -5.355e+00; - } - if( w==31 ) - { - r = -5.493e+00; - } - if( w==30 ) - { - r = -5.634e+00; - } - if( w==29 ) - { - r = -5.780e+00; - } - if( w==28 ) - { - r = -5.928e+00; - } - if( w==27 ) - { - r = -6.080e+00; - } - if( w==26 ) - { - r = -6.235e+00; - } - if( w==25 ) - { - r = -6.394e+00; - } - if( w==24 ) - { - r = -6.558e+00; - } - if( w==23 ) - { - r = -6.726e+00; - } - if( w==22 ) - { - r = -6.897e+00; - } - if( w==21 ) - { - r = -7.074e+00; - } - if( w==20 ) - { - r = -7.256e+00; - } - if( w==19 ) - { - r = -7.443e+00; - } - if( w==18 ) - { - r = -7.636e+00; - } - if( w==17 ) - { - r = -7.837e+00; - } - if( w==16 ) - { - r = -8.040e+00; - } - if( w==15 ) - { - r = -8.250e+00; - } - if( w==14 ) - { - r = -8.469e+00; - } - if( w==13 ) - { - r = -8.692e+00; - } - if( w==12 ) - { - r = -8.921e+00; - } - if( w==11 ) - { - r = -9.162e+00; - } - if( w==10 ) - { - r = -9.409e+00; - } - if( w==9 ) - { - r = -9.673e+00; - } - if( w==8 ) - { - r = -9.951e+00; - } - if( w==7 ) - { - r = -1.023e+01; - } - if( w==6 ) - { - r = -1.053e+01; - } - if( w==5 ) - { - r = -1.087e+01; - } - if( w==4 ) - { - r = -1.122e+01; - } - if( w==3 ) - { - r = -1.156e+01; - } - if( w==2 ) - { - r = -1.207e+01; - } - if( w==1 ) - { - r = -1.248e+01; - } - if( w<=0 ) - { - r = -1.317e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 20) -*************************************************************************/ -static double wsr_w20(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-2.678619e+01*s+1.050000e+02, _state); - if( w>=105 ) - { - r = -6.787e-01; - } - if( w==104 ) - { - r = -7.078e-01; - } - if( w==103 ) - { - r = -7.378e-01; - } - if( w==102 ) - { - r = -7.686e-01; - } - if( w==101 ) - { - r = -8.004e-01; - } - if( w==100 ) - { - r = -8.330e-01; - } - if( w==99 ) - { - r = -8.665e-01; - } - if( w==98 ) - { - r = -9.010e-01; - } - if( w==97 ) - { - r = -9.363e-01; - } - if( w==96 ) - { - r = -9.726e-01; - } - if( w==95 ) - { - r = -1.010e+00; - } - if( w==94 ) - { - r = -1.048e+00; - } - if( w==93 ) - { - r = -1.087e+00; - } - if( w==92 ) - { - r = -1.128e+00; - } - if( w==91 ) - { - r = -1.169e+00; - } - if( w==90 ) - { - r = -1.211e+00; - } - if( w==89 ) - { - r = -1.254e+00; - } - if( w==88 ) - { - r = -1.299e+00; - } - if( w==87 ) - { - r = -1.344e+00; - } - if( w==86 ) - { - r = -1.390e+00; - } - if( w==85 ) - { - r = -1.438e+00; - } - if( w==84 ) - { - r = -1.486e+00; - } - if( w==83 ) - { - r = -1.536e+00; - } - if( w==82 ) - { - r = -1.587e+00; - } - if( w==81 ) - { - r = -1.639e+00; - } - if( w==80 ) - { - r = -1.692e+00; - } - if( w==79 ) - { - r = -1.746e+00; - } - if( w==78 ) - { - r = -1.802e+00; - } - if( w==77 ) - { - r = -1.859e+00; - } - if( w==76 ) - { - r = -1.916e+00; - } - if( w==75 ) - { - r = -1.976e+00; - } - if( w==74 ) - { - r = -2.036e+00; - } - if( w==73 ) - { - r = -2.098e+00; - } - if( w==72 ) - { - r = -2.161e+00; - } - if( w==71 ) - { - r = -2.225e+00; - } - if( w==70 ) - { - r = -2.290e+00; - } - if( w==69 ) - { - r = -2.357e+00; - } - if( w==68 ) - { - r = -2.426e+00; - } - if( w==67 ) - { - r = -2.495e+00; - } - if( w==66 ) - { - r = -2.566e+00; - } - if( w==65 ) - { - r = -2.639e+00; - } - if( w==64 ) - { - r = -2.713e+00; - } - if( w==63 ) - { - r = -2.788e+00; - } - if( w==62 ) - { - r = -2.865e+00; - } - if( w==61 ) - { - r = -2.943e+00; - } - if( w==60 ) - { - r = -3.023e+00; - } - if( w==59 ) - { - r = -3.104e+00; - } - if( w==58 ) - { - r = -3.187e+00; - } - if( w==57 ) - { - r = -3.272e+00; - } - if( w==56 ) - { - r = -3.358e+00; - } - if( w==55 ) - { - r = -3.446e+00; - } - if( w==54 ) - { - r = -3.536e+00; - } - if( w==53 ) - { - r = -3.627e+00; - } - if( w==52 ) - { - r = -3.721e+00; - } - if( w==51 ) - { - r = -3.815e+00; - } - if( w==50 ) - { - r = -3.912e+00; - } - if( w==49 ) - { - r = -4.011e+00; - } - if( w==48 ) - { - r = -4.111e+00; - } - if( w==47 ) - { - r = -4.214e+00; - } - if( w==46 ) - { - r = -4.318e+00; - } - if( w==45 ) - { - r = -4.425e+00; - } - if( w==44 ) - { - r = -4.534e+00; - } - if( w==43 ) - { - r = -4.644e+00; - } - if( w==42 ) - { - r = -4.757e+00; - } - if( w==41 ) - { - r = -4.872e+00; - } - if( w==40 ) - { - r = -4.990e+00; - } - if( w==39 ) - { - r = -5.109e+00; - } - if( w==38 ) - { - r = -5.232e+00; - } - if( w==37 ) - { - r = -5.356e+00; - } - if( w==36 ) - { - r = -5.484e+00; - } - if( w==35 ) - { - r = -5.614e+00; - } - if( w==34 ) - { - r = -5.746e+00; - } - if( w==33 ) - { - r = -5.882e+00; - } - if( w==32 ) - { - r = -6.020e+00; - } - if( w==31 ) - { - r = -6.161e+00; - } - if( w==30 ) - { - r = -6.305e+00; - } - if( w==29 ) - { - r = -6.453e+00; - } - if( w==28 ) - { - r = -6.603e+00; - } - if( w==27 ) - { - r = -6.757e+00; - } - if( w==26 ) - { - r = -6.915e+00; - } - if( w==25 ) - { - r = -7.076e+00; - } - if( w==24 ) - { - r = -7.242e+00; - } - if( w==23 ) - { - r = -7.411e+00; - } - if( w==22 ) - { - r = -7.584e+00; - } - if( w==21 ) - { - r = -7.763e+00; - } - if( w==20 ) - { - r = -7.947e+00; - } - if( w==19 ) - { - r = -8.136e+00; - } - if( w==18 ) - { - r = -8.330e+00; - } - if( w==17 ) - { - r = -8.530e+00; - } - if( w==16 ) - { - r = -8.733e+00; - } - if( w==15 ) - { - r = -8.943e+00; - } - if( w==14 ) - { - r = -9.162e+00; - } - if( w==13 ) - { - r = -9.386e+00; - } - if( w==12 ) - { - r = -9.614e+00; - } - if( w==11 ) - { - r = -9.856e+00; - } - if( w==10 ) - { - r = -1.010e+01; - } - if( w==9 ) - { - r = -1.037e+01; - } - if( w==8 ) - { - r = -1.064e+01; - } - if( w==7 ) - { - r = -1.092e+01; - } - if( w==6 ) - { - r = -1.122e+01; - } - if( w==5 ) - { - r = -1.156e+01; - } - if( w==4 ) - { - r = -1.192e+01; - } - if( w==3 ) - { - r = -1.225e+01; - } - if( w==2 ) - { - r = -1.276e+01; - } - if( w==1 ) - { - r = -1.317e+01; - } - if( w<=0 ) - { - r = -1.386e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 21) -*************************************************************************/ -static double wsr_w21(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-2.877064e+01*s+1.155000e+02, _state); - if( w>=115 ) - { - r = -6.931e-01; - } - if( w==114 ) - { - r = -7.207e-01; - } - if( w==113 ) - { - r = -7.489e-01; - } - if( w==112 ) - { - r = -7.779e-01; - } - if( w==111 ) - { - r = -8.077e-01; - } - if( w==110 ) - { - r = -8.383e-01; - } - if( w==109 ) - { - r = -8.697e-01; - } - if( w==108 ) - { - r = -9.018e-01; - } - if( w==107 ) - { - r = -9.348e-01; - } - if( w==106 ) - { - r = -9.685e-01; - } - if( w==105 ) - { - r = -1.003e+00; - } - if( w==104 ) - { - r = -1.039e+00; - } - if( w==103 ) - { - r = -1.075e+00; - } - if( w==102 ) - { - r = -1.112e+00; - } - if( w==101 ) - { - r = -1.150e+00; - } - if( w==100 ) - { - r = -1.189e+00; - } - if( w==99 ) - { - r = -1.229e+00; - } - if( w==98 ) - { - r = -1.269e+00; - } - if( w==97 ) - { - r = -1.311e+00; - } - if( w==96 ) - { - r = -1.353e+00; - } - if( w==95 ) - { - r = -1.397e+00; - } - if( w==94 ) - { - r = -1.441e+00; - } - if( w==93 ) - { - r = -1.486e+00; - } - if( w==92 ) - { - r = -1.533e+00; - } - if( w==91 ) - { - r = -1.580e+00; - } - if( w==90 ) - { - r = -1.628e+00; - } - if( w==89 ) - { - r = -1.677e+00; - } - if( w==88 ) - { - r = -1.728e+00; - } - if( w==87 ) - { - r = -1.779e+00; - } - if( w==86 ) - { - r = -1.831e+00; - } - if( w==85 ) - { - r = -1.884e+00; - } - if( w==84 ) - { - r = -1.939e+00; - } - if( w==83 ) - { - r = -1.994e+00; - } - if( w==82 ) - { - r = -2.051e+00; - } - if( w==81 ) - { - r = -2.108e+00; - } - if( w==80 ) - { - r = -2.167e+00; - } - if( w==79 ) - { - r = -2.227e+00; - } - if( w==78 ) - { - r = -2.288e+00; - } - if( w==77 ) - { - r = -2.350e+00; - } - if( w==76 ) - { - r = -2.414e+00; - } - if( w==75 ) - { - r = -2.478e+00; - } - if( w==74 ) - { - r = -2.544e+00; - } - if( w==73 ) - { - r = -2.611e+00; - } - if( w==72 ) - { - r = -2.679e+00; - } - if( w==71 ) - { - r = -2.748e+00; - } - if( w==70 ) - { - r = -2.819e+00; - } - if( w==69 ) - { - r = -2.891e+00; - } - if( w==68 ) - { - r = -2.964e+00; - } - if( w==67 ) - { - r = -3.039e+00; - } - if( w==66 ) - { - r = -3.115e+00; - } - if( w==65 ) - { - r = -3.192e+00; - } - if( w==64 ) - { - r = -3.270e+00; - } - if( w==63 ) - { - r = -3.350e+00; - } - if( w==62 ) - { - r = -3.432e+00; - } - if( w==61 ) - { - r = -3.515e+00; - } - if( w==60 ) - { - r = -3.599e+00; - } - if( w==59 ) - { - r = -3.685e+00; - } - if( w==58 ) - { - r = -3.772e+00; - } - if( w==57 ) - { - r = -3.861e+00; - } - if( w==56 ) - { - r = -3.952e+00; - } - if( w==55 ) - { - r = -4.044e+00; - } - if( w==54 ) - { - r = -4.138e+00; - } - if( w==53 ) - { - r = -4.233e+00; - } - if( w==52 ) - { - r = -4.330e+00; - } - if( w==51 ) - { - r = -4.429e+00; - } - if( w==50 ) - { - r = -4.530e+00; - } - if( w==49 ) - { - r = -4.632e+00; - } - if( w==48 ) - { - r = -4.736e+00; - } - if( w==47 ) - { - r = -4.842e+00; - } - if( w==46 ) - { - r = -4.950e+00; - } - if( w==45 ) - { - r = -5.060e+00; - } - if( w==44 ) - { - r = -5.172e+00; - } - if( w==43 ) - { - r = -5.286e+00; - } - if( w==42 ) - { - r = -5.402e+00; - } - if( w==41 ) - { - r = -5.520e+00; - } - if( w==40 ) - { - r = -5.641e+00; - } - if( w==39 ) - { - r = -5.763e+00; - } - if( w==38 ) - { - r = -5.889e+00; - } - if( w==37 ) - { - r = -6.016e+00; - } - if( w==36 ) - { - r = -6.146e+00; - } - if( w==35 ) - { - r = -6.278e+00; - } - if( w==34 ) - { - r = -6.413e+00; - } - if( w==33 ) - { - r = -6.551e+00; - } - if( w==32 ) - { - r = -6.692e+00; - } - if( w==31 ) - { - r = -6.835e+00; - } - if( w==30 ) - { - r = -6.981e+00; - } - if( w==29 ) - { - r = -7.131e+00; - } - if( w==28 ) - { - r = -7.283e+00; - } - if( w==27 ) - { - r = -7.439e+00; - } - if( w==26 ) - { - r = -7.599e+00; - } - if( w==25 ) - { - r = -7.762e+00; - } - if( w==24 ) - { - r = -7.928e+00; - } - if( w==23 ) - { - r = -8.099e+00; - } - if( w==22 ) - { - r = -8.274e+00; - } - if( w==21 ) - { - r = -8.454e+00; - } - if( w==20 ) - { - r = -8.640e+00; - } - if( w==19 ) - { - r = -8.829e+00; - } - if( w==18 ) - { - r = -9.023e+00; - } - if( w==17 ) - { - r = -9.223e+00; - } - if( w==16 ) - { - r = -9.426e+00; - } - if( w==15 ) - { - r = -9.636e+00; - } - if( w==14 ) - { - r = -9.856e+00; - } - if( w==13 ) - { - r = -1.008e+01; - } - if( w==12 ) - { - r = -1.031e+01; - } - if( w==11 ) - { - r = -1.055e+01; - } - if( w==10 ) - { - r = -1.079e+01; - } - if( w==9 ) - { - r = -1.106e+01; - } - if( w==8 ) - { - r = -1.134e+01; - } - if( w==7 ) - { - r = -1.161e+01; - } - if( w==6 ) - { - r = -1.192e+01; - } - if( w==5 ) - { - r = -1.225e+01; - } - if( w==4 ) - { - r = -1.261e+01; - } - if( w==3 ) - { - r = -1.295e+01; - } - if( w==2 ) - { - r = -1.346e+01; - } - if( w==1 ) - { - r = -1.386e+01; - } - if( w<=0 ) - { - r = -1.456e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 22) -*************************************************************************/ -static double wsr_w22(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-3.080179e+01*s+1.265000e+02, _state); - if( w>=126 ) - { - r = -6.931e-01; - } - if( w==125 ) - { - r = -7.189e-01; - } - if( w==124 ) - { - r = -7.452e-01; - } - if( w==123 ) - { - r = -7.722e-01; - } - if( w==122 ) - { - r = -7.999e-01; - } - if( w==121 ) - { - r = -8.283e-01; - } - if( w==120 ) - { - r = -8.573e-01; - } - if( w==119 ) - { - r = -8.871e-01; - } - if( w==118 ) - { - r = -9.175e-01; - } - if( w==117 ) - { - r = -9.486e-01; - } - if( w==116 ) - { - r = -9.805e-01; - } - if( w==115 ) - { - r = -1.013e+00; - } - if( w==114 ) - { - r = -1.046e+00; - } - if( w==113 ) - { - r = -1.080e+00; - } - if( w==112 ) - { - r = -1.115e+00; - } - if( w==111 ) - { - r = -1.151e+00; - } - if( w==110 ) - { - r = -1.187e+00; - } - if( w==109 ) - { - r = -1.224e+00; - } - if( w==108 ) - { - r = -1.262e+00; - } - if( w==107 ) - { - r = -1.301e+00; - } - if( w==106 ) - { - r = -1.340e+00; - } - if( w==105 ) - { - r = -1.381e+00; - } - if( w==104 ) - { - r = -1.422e+00; - } - if( w==103 ) - { - r = -1.464e+00; - } - if( w==102 ) - { - r = -1.506e+00; - } - if( w==101 ) - { - r = -1.550e+00; - } - if( w==100 ) - { - r = -1.594e+00; - } - if( w==99 ) - { - r = -1.640e+00; - } - if( w==98 ) - { - r = -1.686e+00; - } - if( w==97 ) - { - r = -1.733e+00; - } - if( w==96 ) - { - r = -1.781e+00; - } - if( w==95 ) - { - r = -1.830e+00; - } - if( w==94 ) - { - r = -1.880e+00; - } - if( w==93 ) - { - r = -1.930e+00; - } - if( w==92 ) - { - r = -1.982e+00; - } - if( w==91 ) - { - r = -2.034e+00; - } - if( w==90 ) - { - r = -2.088e+00; - } - if( w==89 ) - { - r = -2.142e+00; - } - if( w==88 ) - { - r = -2.198e+00; - } - if( w==87 ) - { - r = -2.254e+00; - } - if( w==86 ) - { - r = -2.312e+00; - } - if( w==85 ) - { - r = -2.370e+00; - } - if( w==84 ) - { - r = -2.429e+00; - } - if( w==83 ) - { - r = -2.490e+00; - } - if( w==82 ) - { - r = -2.551e+00; - } - if( w==81 ) - { - r = -2.614e+00; - } - if( w==80 ) - { - r = -2.677e+00; - } - if( w==79 ) - { - r = -2.742e+00; - } - if( w==78 ) - { - r = -2.808e+00; - } - if( w==77 ) - { - r = -2.875e+00; - } - if( w==76 ) - { - r = -2.943e+00; - } - if( w==75 ) - { - r = -3.012e+00; - } - if( w==74 ) - { - r = -3.082e+00; - } - if( w==73 ) - { - r = -3.153e+00; - } - if( w==72 ) - { - r = -3.226e+00; - } - if( w==71 ) - { - r = -3.300e+00; - } - if( w==70 ) - { - r = -3.375e+00; - } - if( w==69 ) - { - r = -3.451e+00; - } - if( w==68 ) - { - r = -3.529e+00; - } - if( w==67 ) - { - r = -3.607e+00; - } - if( w==66 ) - { - r = -3.687e+00; - } - if( w==65 ) - { - r = -3.769e+00; - } - if( w==64 ) - { - r = -3.851e+00; - } - if( w==63 ) - { - r = -3.935e+00; - } - if( w==62 ) - { - r = -4.021e+00; - } - if( w==61 ) - { - r = -4.108e+00; - } - if( w==60 ) - { - r = -4.196e+00; - } - if( w==59 ) - { - r = -4.285e+00; - } - if( w==58 ) - { - r = -4.376e+00; - } - if( w==57 ) - { - r = -4.469e+00; - } - if( w==56 ) - { - r = -4.563e+00; - } - if( w==55 ) - { - r = -4.659e+00; - } - if( w==54 ) - { - r = -4.756e+00; - } - if( w==53 ) - { - r = -4.855e+00; - } - if( w==52 ) - { - r = -4.955e+00; - } - if( w==51 ) - { - r = -5.057e+00; - } - if( w==50 ) - { - r = -5.161e+00; - } - if( w==49 ) - { - r = -5.266e+00; - } - if( w==48 ) - { - r = -5.374e+00; - } - if( w==47 ) - { - r = -5.483e+00; - } - if( w==46 ) - { - r = -5.594e+00; - } - if( w==45 ) - { - r = -5.706e+00; - } - if( w==44 ) - { - r = -5.821e+00; - } - if( w==43 ) - { - r = -5.938e+00; - } - if( w==42 ) - { - r = -6.057e+00; - } - if( w==41 ) - { - r = -6.177e+00; - } - if( w==40 ) - { - r = -6.300e+00; - } - if( w==39 ) - { - r = -6.426e+00; - } - if( w==38 ) - { - r = -6.553e+00; - } - if( w==37 ) - { - r = -6.683e+00; - } - if( w==36 ) - { - r = -6.815e+00; - } - if( w==35 ) - { - r = -6.949e+00; - } - if( w==34 ) - { - r = -7.086e+00; - } - if( w==33 ) - { - r = -7.226e+00; - } - if( w==32 ) - { - r = -7.368e+00; - } - if( w==31 ) - { - r = -7.513e+00; - } - if( w==30 ) - { - r = -7.661e+00; - } - if( w==29 ) - { - r = -7.813e+00; - } - if( w==28 ) - { - r = -7.966e+00; - } - if( w==27 ) - { - r = -8.124e+00; - } - if( w==26 ) - { - r = -8.285e+00; - } - if( w==25 ) - { - r = -8.449e+00; - } - if( w==24 ) - { - r = -8.617e+00; - } - if( w==23 ) - { - r = -8.789e+00; - } - if( w==22 ) - { - r = -8.965e+00; - } - if( w==21 ) - { - r = -9.147e+00; - } - if( w==20 ) - { - r = -9.333e+00; - } - if( w==19 ) - { - r = -9.522e+00; - } - if( w==18 ) - { - r = -9.716e+00; - } - if( w==17 ) - { - r = -9.917e+00; - } - if( w==16 ) - { - r = -1.012e+01; - } - if( w==15 ) - { - r = -1.033e+01; - } - if( w==14 ) - { - r = -1.055e+01; - } - if( w==13 ) - { - r = -1.077e+01; - } - if( w==12 ) - { - r = -1.100e+01; - } - if( w==11 ) - { - r = -1.124e+01; - } - if( w==10 ) - { - r = -1.149e+01; - } - if( w==9 ) - { - r = -1.175e+01; - } - if( w==8 ) - { - r = -1.203e+01; - } - if( w==7 ) - { - r = -1.230e+01; - } - if( w==6 ) - { - r = -1.261e+01; - } - if( w==5 ) - { - r = -1.295e+01; - } - if( w==4 ) - { - r = -1.330e+01; - } - if( w==3 ) - { - r = -1.364e+01; - } - if( w==2 ) - { - r = -1.415e+01; - } - if( w==1 ) - { - r = -1.456e+01; - } - if( w<=0 ) - { - r = -1.525e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 23) -*************************************************************************/ -static double wsr_w23(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-3.287856e+01*s+1.380000e+02, _state); - if( w>=138 ) - { - r = -6.813e-01; - } - if( w==137 ) - { - r = -7.051e-01; - } - if( w==136 ) - { - r = -7.295e-01; - } - if( w==135 ) - { - r = -7.544e-01; - } - if( w==134 ) - { - r = -7.800e-01; - } - if( w==133 ) - { - r = -8.061e-01; - } - if( w==132 ) - { - r = -8.328e-01; - } - if( w==131 ) - { - r = -8.601e-01; - } - if( w==130 ) - { - r = -8.880e-01; - } - if( w==129 ) - { - r = -9.166e-01; - } - if( w==128 ) - { - r = -9.457e-01; - } - if( w==127 ) - { - r = -9.755e-01; - } - if( w==126 ) - { - r = -1.006e+00; - } - if( w==125 ) - { - r = -1.037e+00; - } - if( w==124 ) - { - r = -1.069e+00; - } - if( w==123 ) - { - r = -1.101e+00; - } - if( w==122 ) - { - r = -1.134e+00; - } - if( w==121 ) - { - r = -1.168e+00; - } - if( w==120 ) - { - r = -1.202e+00; - } - if( w==119 ) - { - r = -1.237e+00; - } - if( w==118 ) - { - r = -1.273e+00; - } - if( w==117 ) - { - r = -1.309e+00; - } - if( w==116 ) - { - r = -1.347e+00; - } - if( w==115 ) - { - r = -1.384e+00; - } - if( w==114 ) - { - r = -1.423e+00; - } - if( w==113 ) - { - r = -1.462e+00; - } - if( w==112 ) - { - r = -1.502e+00; - } - if( w==111 ) - { - r = -1.543e+00; - } - if( w==110 ) - { - r = -1.585e+00; - } - if( w==109 ) - { - r = -1.627e+00; - } - if( w==108 ) - { - r = -1.670e+00; - } - if( w==107 ) - { - r = -1.714e+00; - } - if( w==106 ) - { - r = -1.758e+00; - } - if( w==105 ) - { - r = -1.804e+00; - } - if( w==104 ) - { - r = -1.850e+00; - } - if( w==103 ) - { - r = -1.897e+00; - } - if( w==102 ) - { - r = -1.944e+00; - } - if( w==101 ) - { - r = -1.993e+00; - } - if( w==100 ) - { - r = -2.042e+00; - } - if( w==99 ) - { - r = -2.093e+00; - } - if( w==98 ) - { - r = -2.144e+00; - } - if( w==97 ) - { - r = -2.195e+00; - } - if( w==96 ) - { - r = -2.248e+00; - } - if( w==95 ) - { - r = -2.302e+00; - } - if( w==94 ) - { - r = -2.356e+00; - } - if( w==93 ) - { - r = -2.412e+00; - } - if( w==92 ) - { - r = -2.468e+00; - } - if( w==91 ) - { - r = -2.525e+00; - } - if( w==90 ) - { - r = -2.583e+00; - } - if( w==89 ) - { - r = -2.642e+00; - } - if( w==88 ) - { - r = -2.702e+00; - } - if( w==87 ) - { - r = -2.763e+00; - } - if( w==86 ) - { - r = -2.825e+00; - } - if( w==85 ) - { - r = -2.888e+00; - } - if( w==84 ) - { - r = -2.951e+00; - } - if( w==83 ) - { - r = -3.016e+00; - } - if( w==82 ) - { - r = -3.082e+00; - } - if( w==81 ) - { - r = -3.149e+00; - } - if( w==80 ) - { - r = -3.216e+00; - } - if( w==79 ) - { - r = -3.285e+00; - } - if( w==78 ) - { - r = -3.355e+00; - } - if( w==77 ) - { - r = -3.426e+00; - } - if( w==76 ) - { - r = -3.498e+00; - } - if( w==75 ) - { - r = -3.571e+00; - } - if( w==74 ) - { - r = -3.645e+00; - } - if( w==73 ) - { - r = -3.721e+00; - } - if( w==72 ) - { - r = -3.797e+00; - } - if( w==71 ) - { - r = -3.875e+00; - } - if( w==70 ) - { - r = -3.953e+00; - } - if( w==69 ) - { - r = -4.033e+00; - } - if( w==68 ) - { - r = -4.114e+00; - } - if( w==67 ) - { - r = -4.197e+00; - } - if( w==66 ) - { - r = -4.280e+00; - } - if( w==65 ) - { - r = -4.365e+00; - } - if( w==64 ) - { - r = -4.451e+00; - } - if( w==63 ) - { - r = -4.539e+00; - } - if( w==62 ) - { - r = -4.628e+00; - } - if( w==61 ) - { - r = -4.718e+00; - } - if( w==60 ) - { - r = -4.809e+00; - } - if( w==59 ) - { - r = -4.902e+00; - } - if( w==58 ) - { - r = -4.996e+00; - } - if( w==57 ) - { - r = -5.092e+00; - } - if( w==56 ) - { - r = -5.189e+00; - } - if( w==55 ) - { - r = -5.287e+00; - } - if( w==54 ) - { - r = -5.388e+00; - } - if( w==53 ) - { - r = -5.489e+00; - } - if( w==52 ) - { - r = -5.592e+00; - } - if( w==51 ) - { - r = -5.697e+00; - } - if( w==50 ) - { - r = -5.804e+00; - } - if( w==49 ) - { - r = -5.912e+00; - } - if( w==48 ) - { - r = -6.022e+00; - } - if( w==47 ) - { - r = -6.133e+00; - } - if( w==46 ) - { - r = -6.247e+00; - } - if( w==45 ) - { - r = -6.362e+00; - } - if( w==44 ) - { - r = -6.479e+00; - } - if( w==43 ) - { - r = -6.598e+00; - } - if( w==42 ) - { - r = -6.719e+00; - } - if( w==41 ) - { - r = -6.842e+00; - } - if( w==40 ) - { - r = -6.967e+00; - } - if( w==39 ) - { - r = -7.094e+00; - } - if( w==38 ) - { - r = -7.224e+00; - } - if( w==37 ) - { - r = -7.355e+00; - } - if( w==36 ) - { - r = -7.489e+00; - } - if( w==35 ) - { - r = -7.625e+00; - } - if( w==34 ) - { - r = -7.764e+00; - } - if( w==33 ) - { - r = -7.905e+00; - } - if( w==32 ) - { - r = -8.049e+00; - } - if( w==31 ) - { - r = -8.196e+00; - } - if( w==30 ) - { - r = -8.345e+00; - } - if( w==29 ) - { - r = -8.498e+00; - } - if( w==28 ) - { - r = -8.653e+00; - } - if( w==27 ) - { - r = -8.811e+00; - } - if( w==26 ) - { - r = -8.974e+00; - } - if( w==25 ) - { - r = -9.139e+00; - } - if( w==24 ) - { - r = -9.308e+00; - } - if( w==23 ) - { - r = -9.481e+00; - } - if( w==22 ) - { - r = -9.658e+00; - } - if( w==21 ) - { - r = -9.840e+00; - } - if( w==20 ) - { - r = -1.003e+01; - } - if( w==19 ) - { - r = -1.022e+01; - } - if( w==18 ) - { - r = -1.041e+01; - } - if( w==17 ) - { - r = -1.061e+01; - } - if( w==16 ) - { - r = -1.081e+01; - } - if( w==15 ) - { - r = -1.102e+01; - } - if( w==14 ) - { - r = -1.124e+01; - } - if( w==13 ) - { - r = -1.147e+01; - } - if( w==12 ) - { - r = -1.169e+01; - } - if( w==11 ) - { - r = -1.194e+01; - } - if( w==10 ) - { - r = -1.218e+01; - } - if( w==9 ) - { - r = -1.245e+01; - } - if( w==8 ) - { - r = -1.272e+01; - } - if( w==7 ) - { - r = -1.300e+01; - } - if( w==6 ) - { - r = -1.330e+01; - } - if( w==5 ) - { - r = -1.364e+01; - } - if( w==4 ) - { - r = -1.400e+01; - } - if( w==3 ) - { - r = -1.433e+01; - } - if( w==2 ) - { - r = -1.484e+01; - } - if( w==1 ) - { - r = -1.525e+01; - } - if( w<=0 ) - { - r = -1.594e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 24) -*************************************************************************/ -static double wsr_w24(double s, ae_state *_state) -{ - ae_int_t w; - double r; - double result; - - - r = 0; - w = ae_round(-3.500000e+01*s+1.500000e+02, _state); - if( w>=150 ) - { - r = -6.820e-01; - } - if( w==149 ) - { - r = -7.044e-01; - } - if( w==148 ) - { - r = -7.273e-01; - } - if( w==147 ) - { - r = -7.507e-01; - } - if( w==146 ) - { - r = -7.746e-01; - } - if( w==145 ) - { - r = -7.990e-01; - } - if( w==144 ) - { - r = -8.239e-01; - } - if( w==143 ) - { - r = -8.494e-01; - } - if( w==142 ) - { - r = -8.754e-01; - } - if( w==141 ) - { - r = -9.020e-01; - } - if( w==140 ) - { - r = -9.291e-01; - } - if( w==139 ) - { - r = -9.567e-01; - } - if( w==138 ) - { - r = -9.849e-01; - } - if( w==137 ) - { - r = -1.014e+00; - } - if( w==136 ) - { - r = -1.043e+00; - } - if( w==135 ) - { - r = -1.073e+00; - } - if( w==134 ) - { - r = -1.103e+00; - } - if( w==133 ) - { - r = -1.135e+00; - } - if( w==132 ) - { - r = -1.166e+00; - } - if( w==131 ) - { - r = -1.198e+00; - } - if( w==130 ) - { - r = -1.231e+00; - } - if( w==129 ) - { - r = -1.265e+00; - } - if( w==128 ) - { - r = -1.299e+00; - } - if( w==127 ) - { - r = -1.334e+00; - } - if( w==126 ) - { - r = -1.369e+00; - } - if( w==125 ) - { - r = -1.405e+00; - } - if( w==124 ) - { - r = -1.441e+00; - } - if( w==123 ) - { - r = -1.479e+00; - } - if( w==122 ) - { - r = -1.517e+00; - } - if( w==121 ) - { - r = -1.555e+00; - } - if( w==120 ) - { - r = -1.594e+00; - } - if( w==119 ) - { - r = -1.634e+00; - } - if( w==118 ) - { - r = -1.675e+00; - } - if( w==117 ) - { - r = -1.716e+00; - } - if( w==116 ) - { - r = -1.758e+00; - } - if( w==115 ) - { - r = -1.800e+00; - } - if( w==114 ) - { - r = -1.844e+00; - } - if( w==113 ) - { - r = -1.888e+00; - } - if( w==112 ) - { - r = -1.932e+00; - } - if( w==111 ) - { - r = -1.978e+00; - } - if( w==110 ) - { - r = -2.024e+00; - } - if( w==109 ) - { - r = -2.070e+00; - } - if( w==108 ) - { - r = -2.118e+00; - } - if( w==107 ) - { - r = -2.166e+00; - } - if( w==106 ) - { - r = -2.215e+00; - } - if( w==105 ) - { - r = -2.265e+00; - } - if( w==104 ) - { - r = -2.316e+00; - } - if( w==103 ) - { - r = -2.367e+00; - } - if( w==102 ) - { - r = -2.419e+00; - } - if( w==101 ) - { - r = -2.472e+00; - } - if( w==100 ) - { - r = -2.526e+00; - } - if( w==99 ) - { - r = -2.580e+00; - } - if( w==98 ) - { - r = -2.636e+00; - } - if( w==97 ) - { - r = -2.692e+00; - } - if( w==96 ) - { - r = -2.749e+00; - } - if( w==95 ) - { - r = -2.806e+00; - } - if( w==94 ) - { - r = -2.865e+00; - } - if( w==93 ) - { - r = -2.925e+00; - } - if( w==92 ) - { - r = -2.985e+00; - } - if( w==91 ) - { - r = -3.046e+00; - } - if( w==90 ) - { - r = -3.108e+00; - } - if( w==89 ) - { - r = -3.171e+00; - } - if( w==88 ) - { - r = -3.235e+00; - } - if( w==87 ) - { - r = -3.300e+00; - } - if( w==86 ) - { - r = -3.365e+00; - } - if( w==85 ) - { - r = -3.432e+00; - } - if( w==84 ) - { - r = -3.499e+00; - } - if( w==83 ) - { - r = -3.568e+00; - } - if( w==82 ) - { - r = -3.637e+00; - } - if( w==81 ) - { - r = -3.708e+00; - } - if( w==80 ) - { - r = -3.779e+00; - } - if( w==79 ) - { - r = -3.852e+00; - } - if( w==78 ) - { - r = -3.925e+00; - } - if( w==77 ) - { - r = -4.000e+00; - } - if( w==76 ) - { - r = -4.075e+00; - } - if( w==75 ) - { - r = -4.151e+00; - } - if( w==74 ) - { - r = -4.229e+00; - } - if( w==73 ) - { - r = -4.308e+00; - } - if( w==72 ) - { - r = -4.387e+00; - } - if( w==71 ) - { - r = -4.468e+00; - } - if( w==70 ) - { - r = -4.550e+00; - } - if( w==69 ) - { - r = -4.633e+00; - } - if( w==68 ) - { - r = -4.718e+00; - } - if( w==67 ) - { - r = -4.803e+00; - } - if( w==66 ) - { - r = -4.890e+00; - } - if( w==65 ) - { - r = -4.978e+00; - } - if( w==64 ) - { - r = -5.067e+00; - } - if( w==63 ) - { - r = -5.157e+00; - } - if( w==62 ) - { - r = -5.249e+00; - } - if( w==61 ) - { - r = -5.342e+00; - } - if( w==60 ) - { - r = -5.436e+00; - } - if( w==59 ) - { - r = -5.531e+00; - } - if( w==58 ) - { - r = -5.628e+00; - } - if( w==57 ) - { - r = -5.727e+00; - } - if( w==56 ) - { - r = -5.826e+00; - } - if( w==55 ) - { - r = -5.927e+00; - } - if( w==54 ) - { - r = -6.030e+00; - } - if( w==53 ) - { - r = -6.134e+00; - } - if( w==52 ) - { - r = -6.240e+00; - } - if( w==51 ) - { - r = -6.347e+00; - } - if( w==50 ) - { - r = -6.456e+00; - } - if( w==49 ) - { - r = -6.566e+00; - } - if( w==48 ) - { - r = -6.678e+00; - } - if( w==47 ) - { - r = -6.792e+00; - } - if( w==46 ) - { - r = -6.907e+00; - } - if( w==45 ) - { - r = -7.025e+00; - } - if( w==44 ) - { - r = -7.144e+00; - } - if( w==43 ) - { - r = -7.265e+00; - } - if( w==42 ) - { - r = -7.387e+00; - } - if( w==41 ) - { - r = -7.512e+00; - } - if( w==40 ) - { - r = -7.639e+00; - } - if( w==39 ) - { - r = -7.768e+00; - } - if( w==38 ) - { - r = -7.899e+00; - } - if( w==37 ) - { - r = -8.032e+00; - } - if( w==36 ) - { - r = -8.167e+00; - } - if( w==35 ) - { - r = -8.305e+00; - } - if( w==34 ) - { - r = -8.445e+00; - } - if( w==33 ) - { - r = -8.588e+00; - } - if( w==32 ) - { - r = -8.733e+00; - } - if( w==31 ) - { - r = -8.881e+00; - } - if( w==30 ) - { - r = -9.031e+00; - } - if( w==29 ) - { - r = -9.185e+00; - } - if( w==28 ) - { - r = -9.341e+00; - } - if( w==27 ) - { - r = -9.501e+00; - } - if( w==26 ) - { - r = -9.664e+00; - } - if( w==25 ) - { - r = -9.830e+00; - } - if( w==24 ) - { - r = -1.000e+01; - } - if( w==23 ) - { - r = -1.017e+01; - } - if( w==22 ) - { - r = -1.035e+01; - } - if( w==21 ) - { - r = -1.053e+01; - } - if( w==20 ) - { - r = -1.072e+01; - } - if( w==19 ) - { - r = -1.091e+01; - } - if( w==18 ) - { - r = -1.110e+01; - } - if( w==17 ) - { - r = -1.130e+01; - } - if( w==16 ) - { - r = -1.151e+01; - } - if( w==15 ) - { - r = -1.172e+01; - } - if( w==14 ) - { - r = -1.194e+01; - } - if( w==13 ) - { - r = -1.216e+01; - } - if( w==12 ) - { - r = -1.239e+01; - } - if( w==11 ) - { - r = -1.263e+01; - } - if( w==10 ) - { - r = -1.287e+01; - } - if( w==9 ) - { - r = -1.314e+01; - } - if( w==8 ) - { - r = -1.342e+01; - } - if( w==7 ) - { - r = -1.369e+01; - } - if( w==6 ) - { - r = -1.400e+01; - } - if( w==5 ) - { - r = -1.433e+01; - } - if( w==4 ) - { - r = -1.469e+01; - } - if( w==3 ) - { - r = -1.503e+01; - } - if( w==2 ) - { - r = -1.554e+01; - } - if( w==1 ) - { - r = -1.594e+01; - } - if( w<=0 ) - { - r = -1.664e+01; - } - result = r; - return result; -} - - -/************************************************************************* -Tail(S, 25) -*************************************************************************/ -static double wsr_w25(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -5.150509e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.695528e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.437637e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.611906e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -7.625722e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.579892e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.086876e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.906543e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.354881e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, 1.007195e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -8.437327e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 26) -*************************************************************************/ -static double wsr_w26(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -5.117622e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.635159e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.395167e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.382823e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -6.531987e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.060112e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -8.203697e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.516523e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.431364e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, 6.384553e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -3.238369e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 27) -*************************************************************************/ -static double wsr_w27(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -5.089731e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.584248e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.359966e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.203696e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.753344e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.761891e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -7.096897e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.419108e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.581214e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, 3.033766e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.901441e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 28) -*************************************************************************/ -static double wsr_w28(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -5.065046e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.539163e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.328939e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.046376e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.061515e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.469271e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.711578e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -8.389153e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.250575e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, 4.047245e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.128555e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 29) -*************************************************************************/ -static double wsr_w29(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -5.043413e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.499756e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.302137e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.915129e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.516329e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.260064e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.817269e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.478130e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.111668e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, 4.093451e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.135860e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 30) -*************************************************************************/ -static double wsr_w30(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -5.024071e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.464515e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.278342e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.800030e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.046294e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.076162e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -3.968677e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.911679e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -8.619185e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, 5.125362e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -3.984370e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 40) -*************************************************************************/ -static double wsr_w40(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -4.904809e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.248327e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.136698e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.170982e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.824427e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -3.888648e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.344929e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, 2.790407e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.619858e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, 3.359121e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.883026e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 60) -*************************************************************************/ -static double wsr_w60(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -4.809656e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.077191e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.029402e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -7.507931e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, -6.506226e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.391278e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.263635e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, 2.302271e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.384348e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, 1.865587e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.622355e-04, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 120) -*************************************************************************/ -static double wsr_w120(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -4.729426e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.934426e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -9.433231e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.492504e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, 1.673948e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, -6.077014e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -7.215768e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, 9.086734e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, -8.447980e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, 6.705028e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.828507e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S, 200) -*************************************************************************/ -static double wsr_w200(double s, ae_state *_state) -{ - double x; - double tj; - double tj1; - double result; - - - result = 0; - x = ae_minreal(2*(s-0.000000e+00)/4.000000e+00-1, 1.0, _state); - tj = 1; - tj1 = x; - wsr_wcheb(x, -4.700240e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.883080e+00, &tj, &tj1, &result, _state); - wsr_wcheb(x, -9.132168e-01, &tj, &tj1, &result, _state); - wsr_wcheb(x, -3.512684e-02, &tj, &tj1, &result, _state); - wsr_wcheb(x, 1.726342e-03, &tj, &tj1, &result, _state); - wsr_wcheb(x, -5.189796e-04, &tj, &tj1, &result, _state); - wsr_wcheb(x, -1.628659e-06, &tj, &tj1, &result, _state); - wsr_wcheb(x, 4.261786e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, -4.002498e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, 3.146287e-05, &tj, &tj1, &result, _state); - wsr_wcheb(x, -2.727576e-05, &tj, &tj1, &result, _state); - return result; -} - - -/************************************************************************* -Tail(S,N), S>=0 -*************************************************************************/ -static double wsr_wsigma(double s, ae_int_t n, ae_state *_state) -{ - double f0; - double f1; - double f2; - double f3; - double f4; - double x0; - double x1; - double x2; - double x3; - double x4; - double x; - double result; - - - result = 0; - if( n==5 ) - { - result = wsr_w5(s, _state); - } - if( n==6 ) - { - result = wsr_w6(s, _state); - } - if( n==7 ) - { - result = wsr_w7(s, _state); - } - if( n==8 ) - { - result = wsr_w8(s, _state); - } - if( n==9 ) - { - result = wsr_w9(s, _state); - } - if( n==10 ) - { - result = wsr_w10(s, _state); - } - if( n==11 ) - { - result = wsr_w11(s, _state); - } - if( n==12 ) - { - result = wsr_w12(s, _state); - } - if( n==13 ) - { - result = wsr_w13(s, _state); - } - if( n==14 ) - { - result = wsr_w14(s, _state); - } - if( n==15 ) - { - result = wsr_w15(s, _state); - } - if( n==16 ) - { - result = wsr_w16(s, _state); - } - if( n==17 ) - { - result = wsr_w17(s, _state); - } - if( n==18 ) - { - result = wsr_w18(s, _state); - } - if( n==19 ) - { - result = wsr_w19(s, _state); - } - if( n==20 ) - { - result = wsr_w20(s, _state); - } - if( n==21 ) - { - result = wsr_w21(s, _state); - } - if( n==22 ) - { - result = wsr_w22(s, _state); - } - if( n==23 ) - { - result = wsr_w23(s, _state); - } - if( n==24 ) - { - result = wsr_w24(s, _state); - } - if( n==25 ) - { - result = wsr_w25(s, _state); - } - if( n==26 ) - { - result = wsr_w26(s, _state); - } - if( n==27 ) - { - result = wsr_w27(s, _state); - } - if( n==28 ) - { - result = wsr_w28(s, _state); - } - if( n==29 ) - { - result = wsr_w29(s, _state); - } - if( n==30 ) - { - result = wsr_w30(s, _state); - } - if( n>30 ) - { - x = 1.0/n; - x0 = 1.0/30; - f0 = wsr_w30(s, _state); - x1 = 1.0/40; - f1 = wsr_w40(s, _state); - x2 = 1.0/60; - f2 = wsr_w60(s, _state); - x3 = 1.0/120; - f3 = wsr_w120(s, _state); - x4 = 1.0/200; - f4 = wsr_w200(s, _state); - f1 = ((x-x0)*f1-(x-x1)*f0)/(x1-x0); - f2 = ((x-x0)*f2-(x-x2)*f0)/(x2-x0); - f3 = ((x-x0)*f3-(x-x3)*f0)/(x3-x0); - f4 = ((x-x0)*f4-(x-x4)*f0)/(x4-x0); - f2 = ((x-x1)*f2-(x-x2)*f1)/(x2-x1); - f3 = ((x-x1)*f3-(x-x3)*f1)/(x3-x1); - f4 = ((x-x1)*f4-(x-x4)*f1)/(x4-x1); - f3 = ((x-x2)*f3-(x-x3)*f2)/(x3-x2); - f4 = ((x-x2)*f4-(x-x4)*f2)/(x4-x2); - f4 = ((x-x3)*f4-(x-x4)*f3)/(x4-x3); - result = f4; - } - return result; -} - - - -} - diff --git a/src/inc/alglib/statistics.h b/src/inc/alglib/statistics.h deleted file mode 100644 index d324946..0000000 --- a/src/inc/alglib/statistics.h +++ /dev/null @@ -1,1305 +0,0 @@ -/************************************************************************* -Copyright (c) Sergey Bochkanov (ALGLIB project). - ->>> SOURCE LICENSE >>> -This program is free software; you can redistribute it and/or modify -it under the terms of the GNU General Public License as published by -the Free Software Foundation (www.fsf.org); either version 2 of the -License, or (at your option) any later version. - -This program is distributed in the hope that it will be useful, -but WITHOUT ANY WARRANTY; without even the implied warranty of -MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -GNU General Public License for more details. - -A copy of the GNU General Public License is available at -http://www.fsf.org/licensing/licenses ->>> END OF LICENSE >>> -*************************************************************************/ -#ifndef _statistics_pkg_h -#define _statistics_pkg_h -#include "ap.h" -#include "alglibinternal.h" -#include "linalg.h" -#include "specialfunctions.h" - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (DATATYPES) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ - -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS C++ INTERFACE -// -///////////////////////////////////////////////////////////////////////// -namespace alglib -{ - - -/************************************************************************* -Calculation of the distribution moments: mean, variance, skewness, kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -OUTPUT PARAMETERS - Mean - mean. - Variance- variance. - Skewness- skewness (if variance<>0; zero otherwise). - Kurtosis- kurtosis (if variance<>0; zero otherwise). - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemoments(const real_1d_array &x, const ae_int_t n, double &mean, double &variance, double &skewness, double &kurtosis); -void samplemoments(const real_1d_array &x, double &mean, double &variance, double &skewness, double &kurtosis); - - -/************************************************************************* -Calculation of the mean. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Mean' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplemean(const real_1d_array &x, const ae_int_t n); -double samplemean(const real_1d_array &x); - - -/************************************************************************* -Calculation of the variance. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Variance' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplevariance(const real_1d_array &x, const ae_int_t n); -double samplevariance(const real_1d_array &x); - - -/************************************************************************* -Calculation of the skewness. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Skewness' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double sampleskewness(const real_1d_array &x, const ae_int_t n); -double sampleskewness(const real_1d_array &x); - - -/************************************************************************* -Calculation of the kurtosis. - -INPUT PARAMETERS: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -NOTE: - -This function return result which calculated by 'SampleMoments' function -and stored at 'Kurtosis' variable. - - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -double samplekurtosis(const real_1d_array &x, const ae_int_t n); -double samplekurtosis(const real_1d_array &x); - - -/************************************************************************* -ADev - -Input parameters: - X - sample - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - ADev- ADev - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void sampleadev(const real_1d_array &x, const ae_int_t n, double &adev); -void sampleadev(const real_1d_array &x, double &adev); - - -/************************************************************************* -Median calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - -Output parameters: - Median - - -- ALGLIB -- - Copyright 06.09.2006 by Bochkanov Sergey -*************************************************************************/ -void samplemedian(const real_1d_array &x, const ae_int_t n, double &median); -void samplemedian(const real_1d_array &x, double &median); - - -/************************************************************************* -Percentile calculation. - -Input parameters: - X - sample (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only leading N elements of X are processed - * if not given, automatically determined from size of X - P - percentile (0<=P<=1) - -Output parameters: - V - percentile - - -- ALGLIB -- - Copyright 01.03.2008 by Bochkanov Sergey -*************************************************************************/ -void samplepercentile(const real_1d_array &x, const ae_int_t n, const double p, double &v); -void samplepercentile(const real_1d_array &x, const double p, double &v); - - -/************************************************************************* -2-sample covariance - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - covariance (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double cov2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n); -double cov2(const real_1d_array &x, const real_1d_array &y); - - -/************************************************************************* -Pearson product-moment correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Pearson product-moment correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -double pearsoncorr2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n); -double pearsoncorr2(const real_1d_array &x, const real_1d_array &y); - - -/************************************************************************* -Spearman's rank correlation coefficient - -Input parameters: - X - sample 1 (array indexes: [0..N-1]) - Y - sample 2 (array indexes: [0..N-1]) - N - N>=0, sample size: - * if given, only N leading elements of X/Y are processed - * if not given, automatically determined from input sizes - -Result: - Spearman's rank correlation coefficient - (zero for N=0 or N=1) - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double spearmancorr2(const real_1d_array &x, const real_1d_array &y, const ae_int_t n); -double spearmancorr2(const real_1d_array &x, const real_1d_array &y); - - -/************************************************************************* -Covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c); -void smp_covm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c); -void covm(const real_2d_array &x, real_2d_array &c); -void smp_covm(const real_2d_array &x, real_2d_array &c); - - -/************************************************************************* -Pearson product-moment correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c); -void smp_pearsoncorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c); -void pearsoncorrm(const real_2d_array &x, real_2d_array &c); -void smp_pearsoncorrm(const real_2d_array &x, real_2d_array &c); - - -/************************************************************************* -Spearman's rank correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X are used - * if not given, automatically determined from input size - M - M>0, number of variables: - * if given, only leading M columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M,M], correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c); -void smp_spearmancorrm(const real_2d_array &x, const ae_int_t n, const ae_int_t m, real_2d_array &c); -void spearmancorrm(const real_2d_array &x, real_2d_array &c); -void smp_spearmancorrm(const real_2d_array &x, real_2d_array &c); - - -/************************************************************************* -Cross-covariance matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with covariance matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-covariance matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void covm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c); -void smp_covm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c); -void covm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c); -void smp_covm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c); - - -/************************************************************************* -Pearson product-moment cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c); -void smp_pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c); -void pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c); -void smp_pearsoncorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c); - - -/************************************************************************* -Spearman's rank cross-correlation matrix - -SMP EDITION OF ALGLIB: - - ! This function can utilize multicore capabilities of your system. In - ! order to do this you have to call version with "smp_" prefix, which - ! indicates that multicore code will be used. - ! - ! This note is given for users of SMP edition; if you use GPL edition, - ! or commercial edition of ALGLIB without SMP support, you still will - ! be able to call smp-version of this function, but all computations - ! will be done serially. - ! - ! We recommend you to carefully read ALGLIB Reference Manual, section - ! called 'SMP support', before using parallel version of this function. - ! - ! You should remember that starting/stopping worker thread always have - ! non-zero cost. Although multicore version is pretty efficient on - ! large problems, we do not recommend you to use it on small problems - - ! with correlation matrices smaller than 128*128. - -INPUT PARAMETERS: - X - array[N,M1], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - Y - array[N,M2], sample matrix: - * J-th column corresponds to J-th variable - * I-th row corresponds to I-th observation - N - N>=0, number of observations: - * if given, only leading N rows of X/Y are used - * if not given, automatically determined from input sizes - M1 - M1>0, number of variables in X: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - M2 - M2>0, number of variables in Y: - * if given, only leading M1 columns of X are used - * if not given, automatically determined from input size - -OUTPUT PARAMETERS: - C - array[M1,M2], cross-correlation matrix (zero if N=0 or N=1) - - -- ALGLIB -- - Copyright 28.10.2010 by Bochkanov Sergey -*************************************************************************/ -void spearmancorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c); -void smp_spearmancorrm2(const real_2d_array &x, const real_2d_array &y, const ae_int_t n, const ae_int_t m1, const ae_int_t m2, real_2d_array &c); -void spearmancorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c); -void smp_spearmancorrm2(const real_2d_array &x, const real_2d_array &y, real_2d_array &c); - - -/************************************************************************* - -*************************************************************************/ -void rankdata(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures); -void smp_rankdata(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures); -void rankdata(real_2d_array &xy); -void smp_rankdata(real_2d_array &xy); - - -/************************************************************************* - -*************************************************************************/ -void rankdatacentered(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures); -void smp_rankdatacentered(const real_2d_array &xy, const ae_int_t npoints, const ae_int_t nfeatures); -void rankdatacentered(real_2d_array &xy); -void smp_rankdatacentered(real_2d_array &xy); - - -/************************************************************************* -Obsolete function, we recommend to use PearsonCorr2(). - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double pearsoncorrelation(const real_1d_array &x, const real_1d_array &y, const ae_int_t n); - - -/************************************************************************* -Obsolete function, we recommend to use SpearmanCorr2(). - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -double spearmanrankcorrelation(const real_1d_array &x, const real_1d_array &y, const ae_int_t n); - -/************************************************************************* -Pearson's correlation coefficient significance test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions having zero correlation or whether their -correlation is non-zero. - -The following tests are performed: - * two-tailed test (null hypothesis - X and Y have zero correlation) - * left-tailed test (null hypothesis - the correlation coefficient is - greater than or equal to 0) - * right-tailed test (null hypothesis - the correlation coefficient is - less than or equal to 0). - -Requirements: - * the number of elements in each sample is not less than 5 - * normality of distributions of X and Y. - -Input parameters: - R - Pearson's correlation coefficient for X and Y - N - number of elements in samples, N>=5. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void pearsoncorrelationsignificance(const double r, const ae_int_t n, double &bothtails, double &lefttail, double &righttail); - - -/************************************************************************* -Spearman's rank correlation coefficient significance test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions having zero correlation or whether their -correlation is non-zero. - -The following tests are performed: - * two-tailed test (null hypothesis - X and Y have zero correlation) - * left-tailed test (null hypothesis - the correlation coefficient is - greater than or equal to 0) - * right-tailed test (null hypothesis - the correlation coefficient is - less than or equal to 0). - -Requirements: - * the number of elements in each sample is not less than 5. - -The test is non-parametric and doesn't require distributions X and Y to be -normal. - -Input parameters: - R - Spearman's rank correlation coefficient for X and Y - N - number of elements in samples, N>=5. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void spearmanrankcorrelationsignificance(const double r, const ae_int_t n, double &bothtails, double &lefttail, double &righttail); - -/************************************************************************* -Jarque-Bera test - -This test checks hypotheses about the fact that a given sample X is a -sample of normal random variable. - -Requirements: - * the number of elements in the sample is not less than 5. - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. N>=5 - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -Accuracy of the approximation used (5<=N<=1951): - -p-value relative error (5<=N<=1951) -[1, 0.1] < 1% -[0.1, 0.01] < 2% -[0.01, 0.001] < 6% -[0.001, 0] wasn't measured - -For N>1951 accuracy wasn't measured but it shouldn't be sharply different -from table values. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void jarqueberatest(const real_1d_array &x, const ae_int_t n, double &p); - -/************************************************************************* -Mann-Whitney U-test - -This test checks hypotheses about whether X and Y are samples of two -continuous distributions of the same shape and same median or whether -their medians are different. - -The following tests are performed: - * two-tailed test (null hypothesis - the medians are equal) - * left-tailed test (null hypothesis - the median of the first sample - is greater than or equal to the median of the second sample) - * right-tailed test (null hypothesis - the median of the first sample - is less than or equal to the median of the second sample). - -Requirements: - * the samples are independent - * X and Y are continuous distributions (or discrete distributions well- - approximating continuous distributions) - * distributions of X and Y have the same shape. The only possible - difference is their position (i.e. the value of the median) - * the number of elements in each sample is not less than 5 - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - -The test is non-parametric and doesn't require distributions to be normal. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. N>=5 - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of the sample. M>=5 - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -To calculate p-values, special approximation is used. This method lets us -calculate p-values with satisfactory accuracy in interval [0.0001, 1]. -There is no approximation outside the [0.0001, 1] interval. Therefore, if -the significance level outlies this interval, the test returns 0.0001. - -Relative precision of approximation of p-value: - -N M Max.err. Rms.err. -5..10 N..10 1.4e-02 6.0e-04 -5..10 N..100 2.2e-02 5.3e-06 -10..15 N..15 1.0e-02 3.2e-04 -10..15 N..100 1.0e-02 2.2e-05 -15..100 N..100 6.1e-03 2.7e-06 - -For N,M>100 accuracy checks weren't put into practice, but taking into -account characteristics of asymptotic approximation used, precision should -not be sharply different from the values for interval [5, 100]. - - -- ALGLIB -- - Copyright 09.04.2007 by Bochkanov Sergey -*************************************************************************/ -void mannwhitneyutest(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail); - -/************************************************************************* -Sign test - -This test checks three hypotheses about the median of the given sample. -The following tests are performed: - * two-tailed test (null hypothesis - the median is equal to the given - value) - * left-tailed test (null hypothesis - the median is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the median is less than or - equal to the given value) - -Requirements: - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - -The test is non-parametric and doesn't require distribution X to be normal - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. - Median - assumed median value. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -While calculating p-values high-precision binomial distribution -approximation is used, so significance levels have about 15 exact digits. - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void onesamplesigntest(const real_1d_array &x, const ae_int_t n, const double median, double &bothtails, double &lefttail, double &righttail); - -/************************************************************************* -One-sample t-test - -This test checks three hypotheses about the mean of the given sample. The -following tests are performed: - * two-tailed test (null hypothesis - the mean is equal to the given - value) - * left-tailed test (null hypothesis - the mean is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the mean is less than or equal - to the given value). - -The test is based on the assumption that a given sample has a normal -distribution and an unknown dispersion. If the distribution sharply -differs from normal, the test will work incorrectly. - -INPUT PARAMETERS: - X - sample. Array whose index goes from 0 to N-1. - N - size of sample, N>=0 - Mean - assumed value of the mean. - -OUTPUT PARAMETERS: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0, all p-values are set to 1.0 - * when variance of X[] is exactly zero, p-values are set - to 1.0 or 0.0, depending on difference between sample mean and - value of mean being tested. - - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void studentttest1(const real_1d_array &x, const ae_int_t n, const double mean, double &bothtails, double &lefttail, double &righttail); - - -/************************************************************************* -Two-sample pooled test - -This test checks three hypotheses about the mean of the given samples. The -following tests are performed: - * two-tailed test (null hypothesis - the means are equal) - * left-tailed test (null hypothesis - the mean of the first sample is - greater than or equal to the mean of the second sample) - * right-tailed test (null hypothesis - the mean of the first sample is - less than or equal to the mean of the second sample). - -Test is based on the following assumptions: - * given samples have normal distributions - * dispersions are equal - * samples are independent. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of sample. - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of sample. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0 or M=0, all p-values are set to 1.0 - * when both samples has exactly zero variance, p-values are set - to 1.0 or 0.0, depending on difference between means. - - -- ALGLIB -- - Copyright 18.09.2006 by Bochkanov Sergey -*************************************************************************/ -void studentttest2(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail); - - -/************************************************************************* -Two-sample unpooled test - -This test checks three hypotheses about the mean of the given samples. The -following tests are performed: - * two-tailed test (null hypothesis - the means are equal) - * left-tailed test (null hypothesis - the mean of the first sample is - greater than or equal to the mean of the second sample) - * right-tailed test (null hypothesis - the mean of the first sample is - less than or equal to the mean of the second sample). - -Test is based on the following assumptions: - * given samples have normal distributions - * samples are independent. -Equality of variances is NOT required. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. - Y - sample 2. Array whose index goes from 0 to M-1. - M - size of the sample. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -NOTE: this function correctly handles degenerate cases: - * when N=0 or M=0, all p-values are set to 1.0 - * when both samples has zero variance, p-values are set - to 1.0 or 0.0, depending on difference between means. - * when only one sample has zero variance, test reduces to 1-sample - version. - - -- ALGLIB -- - Copyright 18.09.2006 by Bochkanov Sergey -*************************************************************************/ -void unequalvariancettest(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail); - -/************************************************************************* -Two-sample F-test - -This test checks three hypotheses about dispersions of the given samples. -The following tests are performed: - * two-tailed test (null hypothesis - the dispersions are equal) - * left-tailed test (null hypothesis - the dispersion of the first - sample is greater than or equal to the dispersion of the second - sample). - * right-tailed test (null hypothesis - the dispersion of the first - sample is less than or equal to the dispersion of the second sample) - -The test is based on the following assumptions: - * the given samples have normal distributions - * the samples are independent. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - sample size. - Y - sample 2. Array whose index goes from 0 to M-1. - M - sample size. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 19.09.2006 by Bochkanov Sergey -*************************************************************************/ -void ftest(const real_1d_array &x, const ae_int_t n, const real_1d_array &y, const ae_int_t m, double &bothtails, double &lefttail, double &righttail); - - -/************************************************************************* -One-sample chi-square test - -This test checks three hypotheses about the dispersion of the given sample -The following tests are performed: - * two-tailed test (null hypothesis - the dispersion equals the given - number) - * left-tailed test (null hypothesis - the dispersion is greater than - or equal to the given number) - * right-tailed test (null hypothesis - dispersion is less than or - equal to the given number). - -Test is based on the following assumptions: - * the given sample has a normal distribution. - -Input parameters: - X - sample 1. Array whose index goes from 0 to N-1. - N - size of the sample. - Variance - dispersion value to compare with. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - - -- ALGLIB -- - Copyright 19.09.2006 by Bochkanov Sergey -*************************************************************************/ -void onesamplevariancetest(const real_1d_array &x, const ae_int_t n, const double variance, double &bothtails, double &lefttail, double &righttail); - -/************************************************************************* -Wilcoxon signed-rank test - -This test checks three hypotheses about the median of the given sample. -The following tests are performed: - * two-tailed test (null hypothesis - the median is equal to the given - value) - * left-tailed test (null hypothesis - the median is greater than or - equal to the given value) - * right-tailed test (null hypothesis - the median is less than or - equal to the given value) - -Requirements: - * the scale of measurement should be ordinal, interval or ratio (i.e. - the test could not be applied to nominal variables). - * the distribution should be continuous and symmetric relative to its - median. - * number of distinct values in the X array should be greater than 4 - -The test is non-parametric and doesn't require distribution X to be normal - -Input parameters: - X - sample. Array whose index goes from 0 to N-1. - N - size of the sample. - Median - assumed median value. - -Output parameters: - BothTails - p-value for two-tailed test. - If BothTails is less than the given significance level - the null hypothesis is rejected. - LeftTail - p-value for left-tailed test. - If LeftTail is less than the given significance level, - the null hypothesis is rejected. - RightTail - p-value for right-tailed test. - If RightTail is less than the given significance level - the null hypothesis is rejected. - -To calculate p-values, special approximation is used. This method lets us -calculate p-values with two decimal places in interval [0.0001, 1]. - -"Two decimal places" does not sound very impressive, but in practice the -relative error of less than 1% is enough to make a decision. - -There is no approximation outside the [0.0001, 1] interval. Therefore, if -the significance level outlies this interval, the test returns 0.0001. - - -- ALGLIB -- - Copyright 08.09.2006 by Bochkanov Sergey -*************************************************************************/ -void wilcoxonsignedranktest(const real_1d_array &x, const ae_int_t n, const double e, double &bothtails, double &lefttail, double &righttail); -} - -///////////////////////////////////////////////////////////////////////// -// -// THIS SECTION CONTAINS COMPUTATIONAL CORE DECLARATIONS (FUNCTIONS) -// -///////////////////////////////////////////////////////////////////////// -namespace alglib_impl -{ -void samplemoments(/* Real */ ae_vector* x, - ae_int_t n, - double* mean, - double* variance, - double* skewness, - double* kurtosis, - ae_state *_state); -double samplemean(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -double samplevariance(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -double sampleskewness(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -double samplekurtosis(/* Real */ ae_vector* x, - ae_int_t n, - ae_state *_state); -void sampleadev(/* Real */ ae_vector* x, - ae_int_t n, - double* adev, - ae_state *_state); -void samplemedian(/* Real */ ae_vector* x, - ae_int_t n, - double* median, - ae_state *_state); -void samplepercentile(/* Real */ ae_vector* x, - ae_int_t n, - double p, - double* v, - ae_state *_state); -double cov2(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state); -double pearsoncorr2(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state); -double spearmancorr2(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state); -void covm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, - ae_state *_state); -void _pexec_covm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, ae_state *_state); -void pearsoncorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, - ae_state *_state); -void _pexec_pearsoncorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, ae_state *_state); -void spearmancorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, - ae_state *_state); -void _pexec_spearmancorrm(/* Real */ ae_matrix* x, - ae_int_t n, - ae_int_t m, - /* Real */ ae_matrix* c, ae_state *_state); -void covm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, - ae_state *_state); -void _pexec_covm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, ae_state *_state); -void pearsoncorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, - ae_state *_state); -void _pexec_pearsoncorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, ae_state *_state); -void spearmancorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, - ae_state *_state); -void _pexec_spearmancorrm2(/* Real */ ae_matrix* x, - /* Real */ ae_matrix* y, - ae_int_t n, - ae_int_t m1, - ae_int_t m2, - /* Real */ ae_matrix* c, ae_state *_state); -void rankdata(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_state *_state); -void _pexec_rankdata(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, ae_state *_state); -void rankdatacentered(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, - ae_state *_state); -void _pexec_rankdatacentered(/* Real */ ae_matrix* xy, - ae_int_t npoints, - ae_int_t nfeatures, ae_state *_state); -double pearsoncorrelation(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state); -double spearmanrankcorrelation(/* Real */ ae_vector* x, - /* Real */ ae_vector* y, - ae_int_t n, - ae_state *_state); -void pearsoncorrelationsignificance(double r, - ae_int_t n, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void spearmanrankcorrelationsignificance(double r, - ae_int_t n, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void jarqueberatest(/* Real */ ae_vector* x, - ae_int_t n, - double* p, - ae_state *_state); -void mannwhitneyutest(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void onesamplesigntest(/* Real */ ae_vector* x, - ae_int_t n, - double median, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void studentttest1(/* Real */ ae_vector* x, - ae_int_t n, - double mean, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void studentttest2(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void unequalvariancettest(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void ftest(/* Real */ ae_vector* x, - ae_int_t n, - /* Real */ ae_vector* y, - ae_int_t m, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void onesamplevariancetest(/* Real */ ae_vector* x, - ae_int_t n, - double variance, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); -void wilcoxonsignedranktest(/* Real */ ae_vector* x, - ae_int_t n, - double e, - double* bothtails, - double* lefttail, - double* righttail, - ae_state *_state); - -} -#endif - diff --git a/src/inc/alglib/stdafx.h b/src/inc/alglib/stdafx.h deleted file mode 100644 index 99a8091..0000000 --- a/src/inc/alglib/stdafx.h +++ /dev/null @@ -1,2 +0,0 @@ - -